Earth Observation for Sustainable Development
Urban Development Project
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 685761.
ESA Ref: AO/1-8346/15/I-NB
Doc. No.: City Operations Report
Issue/Rev.: 3.0
Date: 24.04.2018
EO4SD-Urban Project: Arusha City Report
Lead: Partners: Financed by:
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EO4SD-Urban Arusha City Operations Report Page I
Consortium Partners
No. Name Short Name Country
1 GAF AG GAF Germany
2 Système d'Information à Référence Spatiale SAS SIRS France
3 GISAT S.R.O. GISAT Czech Republic
4 Egis SA EGIS France
5 Deutsche Luft- und Raumfahrt e. V DLR Germany
6 Netherlands Geomatics & Earth Observation B.V. NEO The Netherlands
7 JOANNEUM Research Forschungsgesellschaft mbH JR Austria
8 GISBOX SRL GISBOX Romania
Disclaimer:
The contents of this document are the copyright of GAF AG and Partners. It is released by GAF AG on
the condition that it will not be copied in whole, in section or otherwise reproduced (whether by
photographic, reprographic or any other method) and that the contents thereof shall not be divulged to
any other person other than of the addressed (save to the other authorised officers of their organisation
having a need to know such contents, for the purpose of which disclosure is made by GAF AG) without
prior consent of GAF AG.
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Summary
This document contains information related to the provision of geo-spatial products from the European
Space Agency (ESA) supported project “Earth Observation for Sustainable Development” Urban Applications (EO4SD-Urban) to the World Bank Urban Planning Study for Tanzania programme for
the City of Arusha.
Affiliation/Function Name Date
Prepared GAF AG J. Freitas Santos, D.
Angelova,
18/04/2018
Reviewed GAF AG A. Broszeit 19/04/2018
Approved GAF AG, Project Coordinator T. Haeusler 24/04/2018
The document is accepted under the assumption that all verification activities were carried out correctly
and any discrepancies are documented properly.
Distribution
Affiliation Name Copies
ESA Z. Bartalis electronic copy
World Bank Chyi-Yun Huang electronic copy
Document Status Sheet
Issue Date Details
1.0 18/08/2017 First Document Issue
2.0 15/11/2017 Second Document Issue
3.0 24/04/2018 Third Document Issue
Document Change Record
# Date Request Location Details
1 15/11/2017 Ch. 2.6, 2.7,
2.8, 3.5, 3.6,
4.4, 4.5, 4.6
and Annexe 2
Three products were added to the Service
Operations Report: 1. Urban Green Areas 2.
Planned and Unplanned Settlement Areas 3.
Population Distribution and Density
2 15/11/2017 Annexe 1 Description of processing methods included
for: Transport Network, Urban Green Areas,
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Planned and Unplanned Settlement Areas,
Population Distribution and Density.
3 24/04/2018 Ch. 2.1, 3.3.1,
4.2, 4.3 and
4.4
After a User’s request, the spatial datasets for
the LULC, Transport Network and Green
Areas products were adjusted. These updates
implied changes in the following Sections of
the City Operations Report.
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Executive Summary
The European Space Agency (ESA) has been working closely together with the International Finance
Institutes (IFIs) and their client countries to demonstrate the benefits of Earth Observation (EO) in the
IFI development programmes. Earth Observation for Sustainable Development (EO4SD) is a new ESA
initiative, which aims to achieve an increase in the uptake of satellite based information in the regional
and global IFI programmes. The overall aim of the EO4SD Urban project is to integrate the application
of satellite data for urban development programmes being implemented by the IFIs or Multi-Lateral
Development Banks (MDBs) with the developing countries. The overall goal will be achieved via
implementation of the following main objectives:
To provide a service portfolio of Baseline and Derived urban-related geo-spatial products
To provide the geo-spatial products and services on a geographical regional basis
To ensure that the products and services are user-driven
This Report describes the generation and the provision of EO-based information products to the World
Bank (WB) supported Technical Assistance project “Urban Planning Study-Impact & Effectiveness
of Urban Planning on City Spatial Development” for Tanzania and the counterpart City authorities
in Arusha. The Report provides a Service Description by referring to the user driven service
requirements and the associated product list with the detailed product specifications. The following
products were requested:
Urban Land Use/ Land Cover
Urban Extent
Urban Green Areas
Extent and Type of Informal Settlements
Population Distribution and Density
Transport Infrastructure - Road Network
The current Version of this Report contains the description of the generation and delivery of the Land
Use/Land Cover (LU/LC) and the LU/LC Change between two time periods of 2005 and 2015 as well
as the Transport Infrastructure product. The Derived products of Green Areas, Informal Settlements and
Population Density product are also included in this Report. Furthermore, it focuses on the
improvements of the LULC, Transport infrastructure and Green Areas products which have been done
based on the User Utility Review.
This City Operations Report for Arusha systematically reviews the main production steps involved and
importantly highlights the Quality Control (QC) mechanisms involved; the steps of QC and the
assessment of quality is provided in related QC forms in the Annexe of this Report. There is also the
provision of standard analytical work undertaken with the products which can be further included as
inputs into further urban development assessments, modelling and reports.
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Table of Contents
1 GENERAL BACKGROUND OF EO4SD-URBAN ................................................................... 1 2 SERVICE DESCRIPTION........................................................................................................... 1
2.1 STAKEHOLDERS AND REQUIREMENTS ..................................................................................... 1
2.2 SERVICE AREA SPECIFICATION ............................................................................................... 2
2.3 PRODUCT LIST AND PRODUCT SPECIFICATIONS ...................................................................... 3
2.4 LAND USE/LAND COVER NOMENCLATURE ............................................................................. 3
2.5 TRANSPORT INFRASTRUCTURE - ROAD NETWORK ................................................................. 6
2.6 URBAN GREEN AREAS ............................................................................................................. 6
2.7 PLANNED AND UNPLANNED SETTLEMENT AREAS .................................................................. 7
2.8 POPULATION DISTRIBUTION AND DENSITY ............................................................................. 7
2.9 TERMS OF ACCESS ................................................................................................................... 8
3 SERVICE OPERATIONS ............................................................................................................ 9
3.1 SOURCE DATA ......................................................................................................................... 9
3.2 PROCESSING METHODS ......................................................................................................... 10
3.3 ACCURACY ASSESSMENT OF MAP PRODUCTS ...................................................................... 10
3.3.1 The Applied Sampling Design ........................................................................................................ 10
3.3.2 The Applied Response Design ........................................................................................................ 12
3.3.3 The Applied Analysis Design .......................................................................................................... 13
3.4 ACCURACY ASSESSMENT OF TRANSPORT NETWORK ........................................................... 14
3.5 ACCURACY ASSESSMENT OF URBAN GREEN AREAS ............................................................ 16
3.6 ACCURACY ASSESSMENT OF PLANNED AND UNPLANNED SETTLEMENTS ........................... 17
3.7 QUALITY CONTROL/ASSURANCE .......................................................................................... 19
3.8 METADATA ............................................................................................................................ 20
4 ANALYSIS OF MAPPING RESULTS ..................................................................................... 22
4.1 URBAN EXTENT – DEVELOPMENTS 2000, 2005, 2010 AND 2015 .......................................... 22
4.2 LAND COVER LAND USE 2005 AND 2015 .............................................................................. 24
4.2.1 Spatial Distribution of Main LU/LC Change Categories ................................................................ 26
4.2.2 Changes of Agricultural Areas ........................................................................................................ 28
4.3 TRANSPORT NETWORK .......................................................................................................... 30
4.4 URBAN GREEN AREAS ........................................................................................................... 30
4.5 PLANNED AND UNPLANNED SETTLEMENT AREAS ................................................................ 32
4.6 POPULATION DISTRIBUTION AND DENSITY ........................................................................... 36
4.7 CONCLUDING POINTS ............................................................................................................ 38
5 REFERENCES ............................................................................................................................ 39
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Annexes
Annex 1: Processing Methods for EO4SD-Urban Products
Annex 2: Filled Quality Control Sheets
List of Figures
Figure 1: Illustration of Core and Peri-Urban Areas of Mapping for Arusha. ................................... 2
Figure 2: Mapping result of the city of Arusha of the year 2015 overlaid with randomly distributed
sample points used for accuracy assessment..................................................................... 13
Figure 3: Example of the applied sampling design to generate randomly distributed point for the
Accuracy Assessment of the road network. ...................................................................... 14
Figure 4: Secondary sampling grid to generate the sampling points at spatial intersection of roads
and grid cells. Roads are represented as white lines, grid as black lines and final sampling
point as black dots. ............................................................................................................ 15
Figure 5: Result of the Urban Green Area mapping in Arusha (change product) with sampling points
used for product validation. .............................................................................................. 16
Figure 6: Result of the Informal Settlement Area mapping in Arusha (change product) with sampling
points used for product validation. .................................................................................... 18
Figure 7: Quality Control process for EO4SD-Urban product generation. At each intermediate
processing step output properties are compared against pre-defined requirements. ......... 19
Figure 8: Urban Extent developments in the epochs 2000 to 2005, 2005 to 2010 and 2010 to 2015 in
Arusha and surrounding region. ........................................................................................ 23
Figure 9: Urban Extent developments in the epochs 2000 to 2005, 2005 to 2010 and 2010 to 2015 in
Arusha within the Core Urban Area. ................................................................................ 23
Figure 10: Left: Overview of the Service Area. Right: Detailed Land Cover Land Use 2015 in Arusha.
.......................................................................................................................................... 24
Figure 11: Detailed Land Cover Land Use 2005 structure: Presented as Overall, Core Urban and in
Peri-Urban in % (left) and km2 (right). ............................................................................ 25
Figure 12: Detailed Land Cover Land Use 2015 structure: Presented as Overall, Core Urban and in
Peri-Urban in % (left) and km2 (right). ............................................................................. 25
Figure 13: Land Cover Land Use Change Types - Spatial Distribution. ........................................... 27
Figure 14: Land Cover Land Use Change Types 2000-2015 - overall, in Core Urban and in Peri-Urban
Zone in % (left) and km² (right) in Arusha. ...................................................................... 27
Figure 15: Spatial distribution of changes from Agricultural Areas to other Classes between 2005 and
2015. ................................................................................................................................. 28
Figure 16: Changes of Agricultural Areas into other LU classes between 2005 and 2015; Presented as
Overall, Core Urban and in Peri-Urban Zone in % (left) and km2 (right). ....................... 29
Figure 17: Transport Network of Arusha in 2005 and 2015. ............................................................. 30
Figure 18: Map overview of Urban Green Areas in Arusha. Green area loss, gain and stable green
areas can be identified. The map is delivered as separate product of high resolution for
printing at paper size DIN A0, which is 84.1 cm x 118.9 cm. .......................................... 31
Figure 19: Percentage of urban green areas within the core area of Arusha. The pie chart illustrates the
status and change of urban green areas in-between 2005 and 2015. ................................ 31
Figure 20: Bar charts for both points in time presenting the total area of urban greenery versus non-
green areas. ....................................................................................................................... 32
Figure 21: Planned settlement areas in Arusha in 2015. .................................................................... 33
Figure 22: Unplanned settlement areas in Arusha in 2015. ................................................................ 33
Figure 23: Map overview of changes in planned and unplanned settlement areas in Arusha during the
years 2005 and 2015. ........................................................................................................ 34
Figure 24: Percentage of planned and unplanned areas within the core area of Arusha. The pie chart
illustrates the status and change of planned and unplanned areas in-between 2005 and 2015.
…………………………………………………...…………………………………….35
Figure 25: Bar charts for both points in time presenting the total area of planned and unplanned
settlement areas. ................................................................................................................ 35
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Figure 26: Overview Map of Population Distribution Change in Arusha (2005 – 2015). ................. 36
Figure 27: Population Distribution Change within the Core Urban Districts of Arusha between 2005
and 2015. ........................................................................................................................... 37
Figure 28: Changes in Population Distribution, in relation to soil sealing degree in Arusha between
2005 and 2015. .................................................................................................................. 37
List of Tables
Table 1: LU/LC Nomenclature for 2005 and 2015............................................................................ 4 Table 2: Nomenclature used for the mapping and identification of Urban Green Areas. ................. 7
Table 3: Nomenclature used for the mapping and identification of Planned and Unplanned
Settlement Areas. ................................................................................................................. 7
Table 4: Nomenclature used for the mapping and identification of Population Distribution and
Density. ................................................................................................................................ 8 Table 5: Number of sampling points for the EO4SD-Urban mapping classes after applied sampling
design with information on overall land cover by class. ................................................... 12 Table 6: Validation result of the complemented Transport Network in Arusha, which is based on
OSM data. .......................................................................................................................... 15 Table 7: Calculation of the minimum number of samples according Goodchild et al. (1994). ....... 16
Table 8: Results of the Accuracy Assessment of Urban Green Areas in Arusha, 2005. ................. 17 Table 9: Results of the Accuracy Assessment of Urban Green Areas in Arusha, 2015. ................. 17 Table 10: Calculation of the minimum number of samples according Goodchild et al. (1994). ....... 18
Table 11: Results of the Accuracy Assessment of Informal Settlement Areas in Arusha, 2015. ...... 19 Table 12: Detailed information on area and percentage of total area for each class for 2005 and 2015
as well as the changes. ....................................................................................................... 26 Table 13: Overall LU/LC Statistics. .................................................................................................. 28 Table 14: Statistics of changes of Agricultural areas. ....................................................................... 29
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List of Abbreviations
CDS City Development Strategy
CS Client States
DEM Digital Elevation Model
DLR German Space Agency
EEA European Environmental Agency
EGIS Consulting Company for Environmental Impact Assessment and Urban Planning, France
EO Earth Observation
ESA European Space Agency
EU European Union
GAF GAF AG, Geospatial Service Provider, Germany
GIS Geographic Information System
GISAT Geospatial Service Provider, Czech Republic
GISBOX Romanian company with activities of Photogrammetry and GIS
GUF Global Urban Footprint
HR High Resolution
HRL High Resolution Layer
IFI International Financing Institute
INSPIRE Infrastructure for Spatial Information in the European Community
ISO/TC 211 Standardization of Digital Geographic Information
JR JOANNEUM Research, Austria
LC / LU Land Cover / Land Use
LULCC Land Use and Land Cover Change
MMU Minimum Mapping Unit
NDVI Normalized Difference Vegetation Index
NEO Geospatial Service Provider, The Netherlands
QA Quality Assurance
QC Quality Control
QM Quality Management
SP Service Provider
VHR Very High Resolution
WB World Bank
WBG World Bank Group
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1 General Background of EO4SD-Urban
Since 2008 the European Space Agency (ESA) has worked closely together with the International
Finance Institutes (IFIs) and their client countries to harness the benefits of Earth Observation (EO) in
their operations and resources management. Earth Observation for Sustainable Development (EO4SD)
is a new ESA initiative, which aims to achieve an increase in the uptake of satellite based information
in the regional and global IFI programmes. The EO4SD-Urban project initiated in May 2016 (with a
duration of 3 years) has the overall aim to integrate the application of satellite data for urban
development programmes being implemented by the IFIs with the developing countries. The overall
goal will be achieved via implementation of the following main objectives:
To provide the services on a regional basis (i.e. large geographical areas); in the context of the
current proposal with a focus on S. Asia, SE Asia and Africa, for at least 35-40 cities.
To ensure that the products and services are user-driven; i.e. priority products and services to
be agreed on with the MDBs in relation to their regional programs and furthermore to implement
the project with a strong stakeholder engagement especially in context with the validation of the
products/services on their utility.
To provide a service portfolio of Baseline and Derived urban-related geo-spatial products that
have clear technical specifications, and are produced on an operational manner that are
stringently quality controlled and validated by the user community.
To provide a technology transfer component in the project via capacity building exercises in the
different regions in close co-operation with the MDB programmes.
This Report supports the fulfilment of the third objective which requires the provision of geo-spatial
Baseline and Derived geo-spatial products to various stakeholders in the IFIs and counterpart City
authorities. The Report provides a Service Description, and then in Chapter 3 systematically reviews the
main production steps involved and importantly highlights whenever there are Quality control (QC)
mechanisms involved with the related QC forms in the Annexe of this Report. The description of the
processes is kept intentionally at a top leave and avoiding technical details as the Report is considered
mainly for non-technical IFI staff and experts and City authorities. Finally Chapter 4 presents the
standard analytical work undertaken with the products which can be inputs into further urban
development assessments, modelling and reports.
2 Service Description
The following Section summarises the service as it has been realised for the city of Arusha, Tanzania
within the EO4SD-Urban Project and as it had been delivered to the Task Team Leader (TTL) of the
World Bank programme Urban Planning Study for Tanzania in August 2017.
2.1 Stakeholders and Requirements
The EO4SD-Urban products described in this Report were provided to the World Bank (WB) Technical
Assistance project “Urban Planning Study-Impact & Effectiveness of Urban Planning on City
Spatial Development” for Tanzania. The Urban Planning Study is linked to the larger WB project
‘Tanzania Strategic Cities Project’ (TSCP). The TSCP supports the seven secondary cities: Tanga,
Arusha, Mwanza, Kigoma, Dodoma, Mbeya and Mtwara, in addition to the Capital Development
Authority (CDA) in Dodoma, the national capital. These cities are of strategic importance as they are
within the top ten most populous cities in Tanzania with high population growth rates. In the context of
the current EO4SD Urban project, three of the overall seven cities - Arusha, Kigoma, Dodoma - could
be provided with the geo-spatial datasets. The higher objective of the Urban Planning Study, as stated
in its Draft Concept Note (World Bank, 2016a) is, “to enhance the urban development and inform policies and development strategies of cities in Tanzania through gaining further insights on urban
planning system and development processes and the effectiveness of master and detailed urban
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plans.” It aims to increase the appreciation of practitioners and local authorities on the importance of urban plans for guiding urban growth and mitigating the potential problems and higher costs of
retrofitting unplanned development. In this context, the Bank is collaborating with the Consortium to
investigate the spatial development characteristics of Tanzanian cities with and without urban plans, and
attempt to assess the impact and effectiveness of such urban plans. This specific Version of the Report
focusses on providing the new results from the improved LULC, Transport infrastructure and Green
areas products.
2.2 Service Area Specification
The Areas of Interest (AOI) for mapping the Urban Area and the Peri-Urban Areas for Arusha was
depicted in a power point slide, and sent to the Users for verification. The boundaries depicted were
based on the municipality and administration boundaries of the cities. These boundaries were obtained
from the GADM database of Global Administrative Areas (http://www.gadm.org/).
As the city administrative boundary data availability is different for each country/city, the AoIs for the
Urban and Peri-Urban Area were in some cases adjusted to areas which could provide the best examples
of the geo-spatial products that Users may require. The adjustments are based on population distribution
data from LandScan (http://web.ornl.gov/sci/landscan/) and on visual interpretation of built-up areas as
evidenced on Google Earth. LandScan is the finest resolution global population distribution data (~1km
spatial resolution) available and represents an ambient population.
Figure 1: Illustration of Core and Peri-Urban Areas of Mapping for Arusha.
The Core region has an area of 211 km2 and the Peri-Urban has an area of 414 km2, for a total service
or mapping area of 625 km2.
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2.3 Product List and Product Specifications
During the discussions related to the AoIs the potential geo-spatial products that could be provided for
the Cities were also reviewed with the WB Team and users. It was noted that the Baseline Land
Use/Land Cover (LU/LC) products (for the Core and Peri-Urban areas) were a standard product that
would be provided for all Cities as it is required for the derived products. In the case of Arusha, the full
list of products for both the Core and Peri-Urban areas are as follows:
Urban Land Use/ Land Cover
Urban Extent
Urban Green Areas
Extent and Type of Informal Settlements
Population Distribution and Density
Transport Infrastructure - Road Network
For each of these products two time slots were used to provide historic and recent information; thus for
Arusha it was 2005 and 2015. Due to EO data availability the time epochs varied with a plus/minus year
or two around these 2 time slots. The current Report will focus on the provision of the Baseline LU/LC
products and the Transport Infrastructure for Arusha.
2.4 Land Use/Land Cover Nomenclature
A pre-cursor to starting production was the establishment with the stakeholders on the relevant Land
Use/Land Cover (LU/LC) nomenclature as well as class definitions. The approach taken was to use a
standard remote sensing based LU/LC nomenclature and then adapt it to the user’s LU requirements.
Thus the remote-sensing based LU/LC classes in the urban context can be grouped into 5 Level 1 classes,
which are Artificial Areas, Natural/ Semi Natural, Agricultural, Wetland, Water Bodies. These classes
can then be sub-divided into several different more detailed classes such that the dis-aggregation can be
down to Level 2-4. This hierarchical classification system is often used in operational Urban mapping
programmes and is the basis for example of the European Commission’s Urban Atlas programme which
provides pan-European comparable LU/LC data for with regular updates. A depiction of the way the
levels and classes are structured is presented as follows:
Level I Artificial surfaces
- Level II Urban Fabric
Level III
Continuous Urban Fabric (Sealing Layer-S.L. > 80%)
Discontinuous Urban Fabric (S.L. 10% - 80%)
Discontinuous Dense Urban Fabric (S.L. 50% - 80%)
Discontinuous Medium Density Urban Fabric (S.L. 30% - 50%)
Discontinuous Low Density Urban Fabric (S.L. 10% - 30%)
Discontinuous Very Low Density Urban Fabric (S.L. < 10%)
- Level II
Industrial, commercial, public, military, private and transport units
Level III
Industrial, commercial, public, military and private units zoning data
Road and rail network and associated land (Open Street Map or In-country data needed)
- Level IV Fast transit roads and associated land
(Reference: European Union, 2011)
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It should be noted that in the current project, the Level 1 classes were used as the basis for classification
of the Peri-Urban areas using the High Resolution (HR) data such as Landsat or Sentinel. However, for
the Core Urban areas using the Very High Resolution (VHR) data it was possible to go down to Level
III and IV. The different Levels, classes and sub-classes from the remote sensing based Urban
classification, were adapted to the User requirements based on existing Master Plans for cities and/or
direct advise from the User on critical classes required. The final LU/LC nomenclature had to be
endorsed by the user before productions started.
In order to link the Urban Atlas classes described in the previous Section with the Tanzanian cities, the
Consortium used the documentation provided by the Cities for their Master Plans, and assessed which
LU/LC classes could be mapped with remote sensing and linked to the Urban Atlas nomenclature. The
merging of LU/LC classes was provided to the WB Team and users for review and endorsement. For
Arusha, the Consortium referred to the Arusha Master Plan 2015-2035 (2016), Ministry of Lands,
Housing and Human Development, Tanzania in order to assess which classes could be mapped with
remote sensing. See Table 1 for final LU nomenclature used for the different epochs.
Table 1: LU/LC Nomenclature for 2005 and 2015.
2005 2015
Level I Level II Level III Level IV Level I Level II Level III Level IV
1000
Artificial
Surfaces
1100
Residential
1110 Very Low
Density
1000
Artificial
Surfaces
1100
Residential
1110 Very
Low Density
1120 Low
Density
1120 Low
Density
1130 Medium
Density
1130 Medium
Density
1140 High
Density
1140 High
Density
1150 Very High
Density
1150 Very
High Density
1200
Industrial,
Commercia
l, Public,
Military,
Private and
Transport
Units
1210 Industrial,
Commercial,
Public, Military
and Private
Units
- 1200
Industrial,
Commercial,
Public,
Military,
Private and
Transport
Units
1210
Industrial,
Commercial,
Public,
Military and
Private Units
1211 Commercial
- 1212 Industry
- 1213 University
- 1214 Schools
- 1215 Government
- 1216 Military
- 1217 Hospitals
- 1218
Public Buildings
1220 Roads 1221
Arterial
1220
Roads
1221 Arterial
1222
Collector
1222 Collector
1230 Railway 1230 Railway
1240 Airport 1240 Airport
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1250 Port 1250 Port
1300 Mine,
Dump and
Constructio
n Sites
1310 Mineral
Extraction and
Dump Sites
1300 Mine,
Dump and
Construction
Sites
1310 Mineral
Extraction
and Dump
Sites
1320
Construction
Sites
1320
Construction
Sites
1330 Land
without Current
Use
1330
Land without
Current Use
1400 Urban
Open
Spaces
1410 Urban
Parks
1400
Urban Open
Spaces
1410
Urban Parks
1420 Recreation
Facilities (Sport
Facilities,
Stadiums, Golf
Courses, etc.)
1420
Recreation
Facilities
(Sport
Facilities,
Stadiums,
Golf Courses,
etc.)
1430
Cemeteries
1430
Cemeteries
2000.
Agricultu
ral Area
2000
Agricultu
ral Area
3000
Natural
and
Semi-
natural
Areas
3100 Forest
and Shrub
Lands
3000
Natural
and
Semi-
natural
Areas
3100
Forest and
Shrub Lands
3200
Natural
Areas
(Savannah,
Grassland)
3200
Natural Areas
(Savannah,
Grassland)
4000
Wetlands
4000
Wetlands
5000
Water
5000
Water
It is important to note that the possibility to classify at Level IV was highly dependent on the availability
of reliable Reference datasets from the City or sources such as Google Earth. This aspect is further
discussed in Chapter 3.
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2.5 Transport Infrastructure - Road Network
The road hierarchy used in the classification is based on the international road classification standards;
this is for example defined by the European Commission (https://ec.europa.eu/transport/road_safety/-
specialist/knowledge/road/designing_for_road_function/road_classification_en).
Roads are divided into three groups-Arterial or through traffic flow routes (in our case Arterial Roads),
distributor road (in our case Collector Roads), and access roads (in our case Local Roads). The three
road types are defined as follows:
Arterial Roads:
Roads with a flow function allow efficient throughput of (long distance) motorized traffic. All
motorways and express roads as well as some urban ring roads have a flow function. The number of
access and exit points is limited. (https://ec.europa.eu/transport/road_safety/specialist/knowledge/-
road/designing_for_road_function/road_classification_en)
Collector Roads:
Roads with an area distributor function allow entering and leaving residential areas, recreational areas,
industrial zones, and rural settlements with scattered destinations. Junctions are for traffic exchange
(allowing changes in direction etc.); road sections between junctions should facilitate traffic in flowing.
(https://ec.europa.eu/transport/road_safety/specialist/knowledge/road/designing_for_road_function/roa
d_classification_en)
Local Roads:
Roads with an access function allow actual access to properties alongside a road or street. Both junctions
and the road sections between them are for traffic exchange. (https://ec.europa.eu/transport/-
road_safety/specialist/knowledge/road/designing_for_road_function/road_classification_en).
Arterial roads and collector roads were the main focus of the classification. These types of roads were
identified for the entire AoI. Within the geospatial dataset the road features can be identified within the
attribute table. A value of 1 is assigned to the arterial roads and a value of 2 to the collector lines.
Spatial Accuracy:
The Collector roads and Arterial roads are integrated within the LULC mapping by applying a buffer
around the road centre lines of 12.0 m for the arterial roads and 7.5m for collector roads. The 7.5m are
set as the maximum allowable difference of the mapped centre line in comparison to the location in the
VHR imagery.
2.6 Urban Green Areas
Urban Green Areas were separated within the Land Use/Land Cover product only in the land use classes
1411 Urban Parks (Parks, Gardens) and 1412 Cemeteries. However, all other urbanised land use classes,
like 1100 Residential, 1200 Industrial, Commercial, Public, Military, Private and Transport Units, and
1300 Mine, Dump and Construction Sites can also be partially vegetated. In order to get an overall
synopsis of all Green Areas within the urbanised classed, i.e. all sub-classes under 1000 Artificial
Surfaces, a further land cover classification of the VHR data was performed by applying a semi-
automated classification algorithm. Forested areas, Grassland and Agricultural areas which are part of
the LULC product were not further considered in this Urban Green Areas land cover. The applied
nomenclature and class coding is given in Table 2.
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Table 2: Nomenclature used for the mapping and identification of Urban Green Areas.
Single date
Code 0 Non-green area
Code 1 Urban green area
Code 255 Non-urban areas. All areas that do not fall in the Level I class of the Land Use
Land Cover product (See Table 1).
Change product
Code 0 Non-green area. No vegetated surfaces occurring on “Artificial Surfaces”, Level I, at both points in time.
Code 1 Permanent urban green area. Vegetated surfaces in 2005 and 2015.
Code 2 Loss of urban green area. Vegetated areas in 2005, which changed to non-
vegetated areas in 2015.
Code 3 New urban green area. Non-vegetated surfaces in 2005 with vegetation cover in
2015.
Code 255 Non-Urban Areas. All Areas that do not fall in the Level I class of the Land Use
Land Cover product.
2.7 Planned and Unplanned Settlement Areas
For the classification of Planned and Unplanned Settlements only the Core Urban area was assessed.
Furthermore the LU classes that were targeted were only the residential areas, including the LULC
classes “1100 Residential” and “1211 Commercial Area.” It has to be noted that within the Commercial
Area class only those polygons with a residential component, like in the CBD were classified as planned
or unplanned settlements. The applied nomenclature is given in Table 3.
Table 3: Nomenclature used for the mapping and identification of Planned and Unplanned Settlement
Areas.
Single date
Planned settlements
Unplanned settlements
Change product
No change in planned settlement area
No change in unplanned settlement area
Expansion of planned settlement area
Expansion of unplanned settlement area
Decrease of planned settlement area
Decrease of unplanned settlement area
Unplanned to planned settlement area
2.8 Population Distribution and Density
The Population Distribution and Density product is calculated only for the residential areas (LULC class
11) within the Core Urban Districts of Arusha. The current product is derived from globally available
WorldPop data from 2015 with spatial resolution of 100m. Additionally, the population residuals are
adjusted, using interpolation of population counts from the official population census data. The historic
product is derived entirely based on interpolation of the official population census data. The applied
nomenclature is given in Table 4.
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Table 4: Nomenclature used for the mapping and identification of Population Distribution and Density.
Single date
Population Density between 0 – 150 inhabitants/ km2
Population Density between 151 – 300 inhabitants/ km2
Population Density between 301 – 600 inhabitants/ km2
Population Density between 601 – 1500 inhabitants/ km2
Population Density between 1501 – 3000 inhabitants/ km2
Population Density between 3001 – 5000 inhabitants/ km2
Population Density between 5001 – 9000 inhabitants/ km2
Population Density between 9001 – 18000 inhabitants/ km2
Population Density between 18001 - 41000 inhabitants/ km2
Change product
Unchanged Population Distribution
Up to -100% decrease
Up to 200% increase
201% - 400% increase
401% - 600% increase
601% - 800% increase
801% - 1000% increase
More than 1000% increase
2.9 Terms of Access
The Dissemination of the digital data and the Report was undertaken via FTP.
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3 Service Operations
The following Sections present all steps of the service operations including the necessary input data, the
processing methods, the accuracy assessment and the Quality Control procedures. Methods are
presented in a top-level and standardised manner for all the EO4SD-Urban City Reports.
3.1 Source Data
This Section presents the remote sensing and ancillary datasets that were used. Different types of data
from several data providers have been acquired. A complete list of source data as well as a quality
assessment is provided in Annex 2.
A summary of the main data used is provided in the following Sections.
High Resolution Optical EO Data
The major data sources for the peri-urban current and historic mapping of urban LULUC, urban extent
and imperviousness were Landsat and Sentinel-2 data which were accessible and downloadable free of
charge.
Aster: as a source of historical data 1 scenes in total of Advanced Spaceborne Thermal Emission
and Reflection Radiometer Satellite (Aster) has been acquired available for the year 2006 that
is covering the whole area of interest. All image bands have been used as baseline data in the
production process. The ortho-rectification was achieved by an image to image registration
using the Global Landcover-Ortho image product. The NASA accuracy specification for the
Global Landcover-Ortho product was 50m RMSE, everywhere in the world.
Sentinel-2: the most recent data coverage comprises one Sentinel-2 data set. The data was
downloaded and processed at Level 1C.
Very High Resolution Optical EO Data
The VHR data for the core urban area mapping had to be acquired and purchased through commercial
EO Data Providers such as Airbus Defence and European Space Imaging.
It has to be noted that under the current collaboration project the VHR EO data had to be purchased
under mono-license agreements between GAF AG and the EO Data Providers. If EO data would have
to be distributed to other stakeholders then further licences for multiple users would have to be
purchased.
The following VHR sensor data have been acquired to cover the entire AoI:
WorldView-2 and GeoEye-1: o 6 scenes for 2016
o 2 scenes for 2015
Quickbird-2: o 6 scenes for 2005
Detailed lists of the used EO data as well as their quality is documented in the attached Quality Control
Sheets in Annex 2.
Ancillary Data
Data Provided by User:
o ARU_Landmarks.shp: Information on 70 objects that were used to assist the mapping
procedure. Mainly located around the city centre.
o Arusha_river.shp: Spatial location of rivers. Data was integrated in the LULC mapping
by using the spatial location of that data set. The polygons for the water class are based
on the VHR imagery.
o ARU_roads.shp: Similar to OSM data. A slight geometric shift in the data was
identified. Data was used to verify the Open Street Map (OSM) data, which was used
as basis for the road classes.
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o tanzania_health_facility_registry6322wgs84.shp: 6323 Points of Interest (POIs) on
Health Centres, Hospitals and Dispensaries. The point based information was overlaid
on the LULC mapping, but no information was available on spatial precision and
completeness.
o updated_schools_november_2014_Dodoma.shp: Location of 12723 schools in
Tanzania. Data was used for map overlay as provided by User. No information was
available on spatial precision and completeness.
Open Street Map (OSM) data: OSM data is freely available and generated by volunteers across
the globe. The so called crowd sourced data is not always complete, but has for the most parts
of the world valuable spatial information. Data was downloaded to complement the Transport
Network layer and further enhanced. The spatial location of the OSM based streets was used a
geospatial reference.
Detailed lists of the used ancillary data as well as their quality is documented in the attached Quality
Control Sheets in Annex 2.
3.2 Processing Methods
Data processing starts at an initial stage with quality checks and verification of all incoming data. This
assessment is performed in order to guarantee the correctness of data before geometric or radiometric
pre-processing is continued. These checks follow defined procedures in order to detect anomalies,
artefacts and inconsistencies. Furthermore all image and statistical data were visualised and interpreted
by operators.
The main techniques and standards used for data analysis, processing and modelling for each product
are described in Annex 1.
3.3 Accuracy Assessment of Map Products
Data and maps derived from remote sensing contain - like any other map - uncertainties which can be
caused by many factors. The components, which might have an influence on the quality of the maps
derived from EO include quality and suitability of satellite data, interoperability of different sensors,
radiometric and geometric processing, cartographic and thematic standards, and image interpretation
procedures, post-processing of the map products and finally the availability and quality of reference
data. However, the accuracy of map products has a major impact on secondary products and its utility
and therefore an accuracy assessment was considered as a critical component of the entire production
and products delivery process. The main goal of the thematic accuracy assessment was to guarantee the
quality of the mapping products with reference to the accuracy thresholds set by the user requirements.
The applied accuracy assessments were based on the use of reference data, and applying statistical
sampling to deduce estimates of error in the classifications. In order to provide an efficient, reliable and
robust method to implement an accuracy assessment, there are three major components that had to be
defined: the sampling design, which determines the spatial location of the reference data, the response
design that describes how the reference data is obtained and an analyses design that defines the accuracy
estimates. These steps were undertaken in a harmonised manner for the validation of all the geo-spatial
products.
3.3.1 The Applied Sampling Design
The sampling design specifies the sample size, sample allocation and the reference assessment units
(i.e. pixels or image blocks). Generally, different sampling schemes can be used in collecting accuracy
assessment data including: simple random sampling, systematic sampling, stratified random sampling,
cluster sampling, and stratified systematic unaligned sampling. In the current project a single stage
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stratified random sampling based on the method described by Olofson et al (20131) was applied which
used the map product as the basis for stratification. This ensured that all classes even very minor ones
were included in the sample.
However, in complex LU/LC products with many classes, this usually results in a large number of
strata (one stratum per LU/LC classes), of which some classes cover only very small areas (e.g. sport
fields, cemeteries) and not being adequately represented in the sampling. In order to achieve a
representative sampling for the statistical analyses of the mapping accuracy it was decided to extend
the single stage stratified random sampling. At the first stage the number of required samples was
allocated within each of the Level I strata (see Table 2). In the second stage all Level III classes that
were not covered by the first sampling, were grouped into one new stratum. Within that stratum the
same number of samples was randomly allocated as the Level I strata received. To avoid a clustering
of point samples within classes and to minimise the effect of spatial autocorrelation a minimum
distance in between the sample points was set to be 150 m. The final sample size for each class can be
considered to be as close as possible to the proportion of the area covered by each stratum considering
that the target was to determine the overall accuracy of the entire map.
The total sample size per stratum was determined by the expected standard error and the estimated
error rate based on the following formula which assumes a simple random sampling (i.e. the
stratification is not considered):
n = 𝑃∗𝐸𝑧 ²
n = number of samples per strata / map class
p = expected accuracy
q = 1 – p
E = Level of acceptable (allowable) sample error
Z = z-value (the given level of significance)
Hence, with an expected accuracy of p = 0.85, a 95% confidence level and an acceptable sampling
error of 5%, the minimum sample size is 196. A 10% oversampling was applied to compensate for
stratification inefficiencies and potentially inadequate samples (e.g. in case of cloudy or shady
reference data). For each Level I strata 215 samples have been randomly allocated. Afterwards, within
all classes of Level III (see Table 5) that did not received samples in the first run, additionally 215
samples were randomly drawn across all these classes.
1 Olofsson, P., Foody, G. M., Stehman, S. V., & Woodcock, C. E. (2013). Making better use of accuracy data in
land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation.
Remote Sensing of Environment, 129, 122–131. doi:10.1016/j.rse.2012.10.031
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Table 5: Number of sampling points for the EO4SD-Urban mapping classes after applied sampling
design with information on overall land cover by class.
Class Name Class
ID
No. of
Sampling
Points
Km²
coverage
Residential 1100 178 148.2
Commercial 1210 16 11.5
Roads 1220 150 5.8
Airport 1240 1 0.5
Mining 1310 66 0.9
Construction 1320 48 3.4
Land without current use 1330 7 4.2
Urban Parks 1410 12 0.2
Recreational Facilities 1420 1 0.9
Cemetery 1430 3 0.1
Agriculture 2000 215 359.2
Forest 3100 182 59
Natural Areas 3200 33 29.2
Water 5000 29 1.8
Total -- 941 624.8
3.3.2 The Applied Response Design
The response design determines the reference information for comparing the map labels to the reference
labels. Collecting reference data on the ground by means of intensive fieldwork is both costly and time
consuming and in most projects not feasible. The most cost effective reference data sources are VHR
satellite data with 0.5 m to 1 m spatial resolution. Czaplewski (2003)2 indicated that visual interpretation
of EO data is acceptable if the spatial resolution of EO data is sufficiently better compared to the
thematic classification system. However, if there are no EO data with better spatial resolution available,
the assessment results need to be checked against the imagery used in the production process.
The calculated number of necessary sampling points for each mapping category was randomly
distributed among the strata and overlaid to the VHR data of each epoch. The following Figure is
showing the mapping result with the overlaid sample points.
2 Czaplewski, R. L. (2003). Chapter 5: accuracy assessment of maps of forest condition: statistical design and
methodological considerations, pp. 115–140. In Michael A.Wulder, & Steven E. Franklin (Eds.), Remote
sensing of forest environments: concepts and case studies. Boston: Kluwer Academic Publishers (515 pp.).
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Figure 2: Mapping result of the city of Arusha of the year 2015 overlaid with randomly distributed sample
points used for accuracy assessment.
In this way a reference information could be extracted for each sample point by visual interpretation of
the VHR data for all mapped classes. The size of the area to be observed had to be related to the
Minimum Mapping Unit (MMU) of the map product to be assessed. The reference information of each
sampling point was compared with the mapping results and the numbers of correctly and not-correctly
classified observations were recorded for each class. From this information the specific error matrices
and statistics were computed (see next Section).
3.3.3 The Applied Analysis Design
Each class usually has errors of both omission and commission, and in most situations, these errors for
a class are not equal. In order to calculate these errors as well as the uncertainties (confidence intervals)
for the area of each class a statistically sound accuracy assessment was implemented.
The confusion matrix is a common and effective way to represent quantitative errors in a categorical
map, especially for maps derived from remote sensing data. The matrices for each assessment epoch
were generated by comparing the “reference” information of the samples with their corresponding classes on the map. The Reference represented the “truth”, while the Map provided the data obtained
from the map result. Thematic accuracy for each class and overall accuracy is then presented in error
matrices (see Tables below). Unequal sampling intensity resulting from the random sampling approach
was accounted for by applying a weight factor (p) to each sample unit based on the ratio between the
number of samples and the size of the stratum considered3:
3 Selkowitz, D. J., & Stehman, S. V. (2011). Thematic accuracy of the National Land Cover Database (NLCD)
2001 land cover for Alaska. Remote Sensing of Environment, 115(6), 1401–1407. doi:10.1016/j.rse.2011.01.020.
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�̂� = ( 1𝑀) ∑ 1𝜋 ℎ∗𝑥∈ ,
Where i and j are the columns and rows in the matrix, M is the total number of possible units (population)
and π is the sampling intensity for a given sample unit u in stratum h.
Overall accuracy and User and producer accuracy were computed for all thematic classes and 95%
confidence intervals were calculated for each accuracy metric.
The standard error of the error rate was calculated as follows: 𝜎ℎ = √ ℎ − ℎ𝑛ℎ where nh is the sample
size for stratum h and ph is the expected error rate. The standard error was calculated for each stratum
and an overall standard error was calculated based on the following formula: 𝜎 = √∑ 𝑤ℎ . 𝜎ℎ
In which 𝑤ℎ is the proportion of the total area covered by each stratum. The 95% Confidence Interval
(CI) is +/- 1.96*𝜎.
The confusion matrices are provided within the Annex 2 and showing the mapping error for each
relevant class. For each class the number of samples which are correctly and not correctly classified are
listed, which allows the calculation of the user and producer accuracies for each class as well as the
confidence interval at 95% confidence levels based on the formulae above.
The Land Use/Land Cover product for Arusha has an overall mapping accuracy of 88.8% with a
CI ranging from 86.8% to 90.9% at a 95% CI. The specific class accuracies are given in Annex 2.
3.4 Accuracy Assessment of Transport Network
The road network was partially integrated in the LULC map by selecting first and second level roads.
These are the Arterial Roads and the Collector Lines. For the accuracy assessment of the Road network
it should be noted that the sampling design, response design and analyses design are different from the
one used for validating the LULC maps. The Accuracy Assessment of the Transport Network is related
to the geospatial precision of the collected and digitised centerlines of the roads.
Sampling Methodology
A systematic random sampling was applied to define the primary and secondy sampling units. Over the
entire AoI a regular grid of 450m by 450m was created. Based on these grid cells a random selection of
2% sample cells were selected. An example is given in Figure 3 with the road network in grey, the grid
cells in black and the randomly selcted cells in green.
Figure 3: Example of the applied sampling design to generate randomly distributed point for the
Accuracy Assessment of the road network.
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Within the randomly selected cells another grid of 150m distance was created (see Figure 4). All
intersection between the created road layer and the 150m grid were extracted as points. At all points the
road locations were visually checked and if any, the differences between spatial location on VHR
imagery and spatial location of the digitised lines recorded.
Figure 4: Secondary sampling grid to generate the sampling points at spatial intersection of roads and
grid cells. Roads are represented as white lines, grid as black lines and final sampling point as
black dots.
Overall 303 sampling points were created and their differences recorded. The result is presented in Table
6 as histogram of deviations. For the entire sampling population a Mean Difference of 1.66 m and a
Standard Deviation of 2.51 m was calculated.
Table 6: Validation result of the complemented Transport Network in Arusha, which is based on OSM
data.
Distance in m Frequency
0.0 177
1.5 5
3.0 50
4.5 35
6.0 14
7.5 12
Above 7.5 meters 10
By setting a maximum allowable difference of 7.5 m the distances are separated into two classes. Correct
street locations and in-correct street locations. The statistical analysis for the two classes reveal an
overall accuracy of 96.7% was achieved.
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3.5 Accuracy Assessment of Urban Green Areas
The validation of the Green Area mapping results is done in a similar way as the validation for the Land
Use Land Cover product. The necessary amount of sampling points are calculated according to the
formula of Goodchild et al. (1994), which is given in Table 7.
Table 7: Calculation of the minimum number of samples according Goodchild et al. (1994).
Variables Values
p 0.85
q 0.15
E 0.05
z 1.96
196
n with 10% oversampling 215
with:
p = required accuracy of the data
q = 1-p
E = Level of acceptable (allowable) sample error
Z = value from table (for the given level of significance)
The calculated number of 215 sample points was randomly distributed among the entire map and
overlaid on the VHR data of each epoch. The following Figure shows the mapping result with the
overlaid sample points.
Figure 5: Result of the Urban Green Area mapping in Arusha (change product) with sampling points
used for product validation.
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At each sample point location the reference data was collected by visual interpretation of the VHR data.
The size of the area to be observed had to be related to the Minimum Mapping Unit (MMU) of the map
product to be assessed. Finally, visual interpreted land cover type was compared with the mapping
results and the numbers of correctly and not-correctly classified observations were recorded. From this
information the specific error matrices and statistics were computed.
The confusion matrices show the mapping error for each relevant class. For each class the number of
samples which are correctly and not correctly classified are listed in the Tables below. They allow the
calculation of the user and producer accuracies for each class as well as the confidence interval at 95%
confidence levels based on the formulae above. The results of the Accuracy Assessment are listed in
Table 8 and Table 9 below, for 2005 and 2015 respectively.
Table 8: Results of the Accuracy Assessment of Urban Green Areas in Arusha, 2005.
Overall Accuracy 94.9 %.
Urban Green 2005 Reference Data
Totals 0 - Non-Urban Green Area 1 - Urban Green Area
0 - Non-Urban Green Area 97 9 106
1 - Urban Green Area 7 102 109
Totals 104 111 215
Table 9: Results of the Accuracy Assessment of Urban Green Areas in Arusha, 2015.
Overall Accuracy 92.6 %.
Urban Green 2015 Reference Data
Totals 0 - Non-Urban Green Area 1 - Urban Green Area
0 - Non-Urban Green Area 118 1 119
1 - Urban Green Area 10 86 96
Totals 128 87 215
The confusion matrices are additionally provided within the Quality Control documentation in Annex 2
and showing the mapping error for each relevant class. For each class the number of samples which are
correctly and not correctly classified are listed, which allows the calculation of the user and producer
accuracies for each class as well as the confidence interval at 95% confidence levels.
3.6 Accuracy Assessment of Planned and Unplanned Settlements
The validation of the Planned and Unplanned Settlement mapping results is done in a similar way as the
validation for the Land Use Land Cover and the Urban Green Area product. The necessary amount of
sampling points are calculated according to the formula of Goodchild et al. (1994), which is given in
Table 10.
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Table 10: Calculation of the minimum number of samples according Goodchild et al. (1994).
Variables Values
p 0.85
q 0.15
E 0.05
z 1.96
196
n with 10% oversampling 215
with:
p = required accuracy of the data
q = 1-p
E = Level of acceptable (allowable) sample error
Z = value from table (for the given level of significance)
The calculated number of 215 sample points was randomly distributed among the entire map and
overlaid on the VHR data of each epoch. The following Figure shows the mapping result with the
overlaid sample points.
Figure 6: Result of the Informal Settlement Area mapping in Arusha (change product) with sampling
points used for product validation.
At each sample point location the reference data was collected by visual interpretation of the VHR data.
The size of the area to be observed had to be related to the Minimum Mapping Unit (MMU) of the map
product to be assessed. Finally, visual interpreted land cover type was compared with the mapping
results and the numbers of correctly and not-correctly classified observations were recorded. From this
information the specific error matrices and statistics were computed.
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The confusion matrices show the mapping error for each relevant class. For each class the number of
samples which are correctly and not correctly classified are listed in the Tables below. They allow the
calculation of the user and producer accuracies for each class as well as the confidence interval at 95%
confidence levels based on the formulae above. The results of the Accuracy Assessment is listed in
Table 11 for 2015.
Table 11: Results of the Accuracy Assessment of Informal Settlement Areas in Arusha, 2015.
Overall Accuracy 94.4 %.
Settlements 2015 Reference Data
Totals Planned Settlement Area Unplanned Settlement Area
Planned Settlement Area 97 10 107
Unplanned Settlement Area 2 105 107
Totals 99 115 214
The confusion matrices are additionally provided within the Quality Control documentation in Annex 2
and showing the mapping error for each relevant class. For each class the number of samples which are
correctly and not correctly classified are listed, which allows the calculation of the user and producer
accuracies for each class as well as the confidence interval at 95% confidence levels.
3.7 Quality Control/Assurance
A detailed Quality Control and Quality Assurance (QC/QA) system has been developed which records
and documents all quality relevant processes ranging from the agreed product requirements, the different
types of input data and their quality as well as the subsequent processing and accuracy assessment steps.
The main goal of the QC/QA procedures was the verification of the completeness, logical consistency,
geometric and thematic accuracy and that metadata are following ISO standards on geographic data
quality and INSPIRE data specifications. These assessments were recorded in Data Quality Sheets
which are provided in Annex 2. The QC/QA procedures were based on an assessment of a series of
relevant data elements and processing steps which are part of the categories listed below:
Product requirements;
Specifications of input data: EO data, in-situ data, ancillary data;
Data quality checks: EO data quality, in-situ data quality, ancillary data quality;
Geometric correction, geometric accuracy, data fusion (if applicable), data processing;
Thematic processing: classification, plausibility checks;
Accuracy: thematic accuracy, error matrices
Delivery checks: completeness, compliancy with requirements
After each intermediate processing step a QC/QA was performed to evaluate products appropriateness
for the subsequent processing (see Figure 7).
Figure 7: Quality Control process for EO4SD-Urban product generation. At each intermediate processing
step output properties are compared against pre-defined requirements.
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After the initial definition of the product specifications (output) necessary input data were defined and
acquired. Input data include all satellite data and reference data e.g. in-situ data, reference maps,
topographic data, relevant studies, existing standards and specifications, statistics. These input data were
the baseline for the subsequent processing and therefore all input data had to be checked for
completeness, accuracy and consistency. The evaluation of the quality of input data provides
confidence of their suitability for further use (e.g. comparison with actual data) in the subsequent
processing line. Data processing towards the end-product required multiple intermediate processing
steps. To guarantee a traceable and quality assured map production the QC/QA assessment was
performed and documented by personnel responsible for the Quality Control/Assurance. The results of
all relevant steps provided information of the acceptance status of a dataset/product.
The documentation is furthermore important to provide a comprehensive and transparent summary of
each production step and the changes made to the input data. With this information the user will be able
to evaluate the provided services and products. Especially the accuracy assessment of map products and
the related error matrices are highly important to rate the quality and compare map products from
different service providers.
The finalised QC/QA forms are attached in Annex 2.
3.8 Metadata
Metadata provides additional information about the delivered products to enable it to be better
understood. In the current project a harmonised approach to provide metadata in a standardised format
applicable to all products and end-users was adopted. Metadata are provided as XML files, compliant
to the ISO standard 19115 "Metadata" and ISO 19139 "XML Scheme Implementation". The metadata
files have been created and validated by the GIS/IP-operator for each map product with the Infrastructure
for Spatial Information in Europe (INSPIRE) Metadata Editor available at: http://inspire-
geoportal.ec.europa.eu/editor/.
The European Community enacted a Directive in 2007 for the creation of a common geo-data
infrastructure to provide a consistent metadata scheme for geospatial services and products that could
be used not only in Europe but globally. The geospatial infrastructure called INSPIRE was built in a
close relation to existing International Organization for Standardization (ISO) standards. These are ISO
191115, ISO 19119 and ISO 15836. The primary incentive of INSPIRE is to facilitate the use and
sharing of spatial information by providing key elements and guidelines for the creation of metadata for
geospatial products and services.
The INSPIRE Metadata provides a core set of metadata elements which are part of all the delivered geo-
spatial products to the users. Furthermore, the metadata elements provide elements that are necessary to
perform queries, store and relocate data in an efficient manner. The minimum required information is
specified in the Commission Regulation (EC) No 1205/2008 of 3 December 2008 and contains 10
elements:
Information on overall Product in terms of: Point of contact for product generation, date of
creation
Identification of Product: Resource title, Abstract (a short description of product) and Locator
Classification of Spatial Data
Keywords (that define the product)
Geographic information: Area Coverage of the Product
Temporal Reference: Temporal extent; date of publication; date of last revision; date of
creation
Quality and Validity: Lineage, spatial resolution
Conformity: degree of conformance to specifications
Data access constraints or Limitations
Responsible party: contact details and role of contact group/person
These elements (not exhaustive) constitute the core information that has to be provided to meet the
minimum requirements for Metadata compliancy. Each element and its sub-categories or elements have
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specific definitions; for example in the element “Quality” there is a component called “Lineage” which has a specific definition as follows: “a statement on process history and/or overall quality of the spatial data set. Where appropriate it may include a statement whether the data set has been validated or quality
assured, whether it is the official version (if multiple versions exist), and whether it has legal validity.
The value domain of this element is free text,” (INSPIRE Metadata Technical Guidelines, 2013). The detailed information on the Metadata elements and their definitions can be found in the “INSPIRE Metadata Implementing Rules: Technical Guidelines,” (2013). Each of the EO4SD-Urban products will
be accompanied by such a descriptive metadata file. It should be noted that the internal use of metadata
in these institutions might not be established at an operational level, but the file format (*.xml) and the
web accessibility of data viewers enable for the full utility of the metadata.
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4 Analysis of Mapping Results
This Chapter will present and assess all results which have been produced within the framework of the
current project, in the context of presentation of the Urban Extent product, the LU/LC products and the
Transport Infrastructure product. Furthermore the Sections that follow will provide the results of some
standard analytics undertaken with these products including the following:
Urban Extent – Developments from 2000, 2005, 2010 to 2015
Land Cover Land Use - Status and Trends between 2005 and 2015
Transport Infrastructure - Status and Change between 2005 and 2015
Urban Green Areas – Status and Change between 2005 and 2015
Planned and Unplanned Settlement Areas – Status and Change between 2005 and 2015
Population Distribution and Density – Status and Change between 2005 and 2015
It is envisaged that these analytics provide information on general trends and developments in the Core
and Peri-Urban areas which can then be further interpreted and used by Urban planners and the City
Authorities for city planning.
It should be noted that all digital data sets for these products are provided in concurrence with this City
Report with all the related metadata and Quality Control documentation
4.1 Urban Extent – Developments 2000, 2005, 2010 and 2015
The Urban Extent product in the EO4SD-Urban project is provided by the German Aerospace Centre
(DLR) and is provided for 4 points in time; the 2015 Global Urban Footprint (GUF) Plus product has
been produced jointly exploiting multi-temporal 30m Landsat-8 and ESA Sentinel-1 data with 10m
resolution acquired in 2014-2015. And for the years 2000, 2005 and 2010, the Urban Extent products
generated – given the unavailability of freely and easily accessible multi-temporal radar data at high
resolution – were based only on multi-temporal 30m Landsat-5 and Landsat-7 imagery, and scaled up
to 10m resolution.
As the Urban Extent 2015 product was based on the ESA Sentinel-1 dataset which is a Synthetic
Aperture Radar (SAR) in C band it should be noted that some structures which are flat in nature such as
airport runways were not classified; this is due to the fact that radar relies on backscatter which is more
prominent from vertical features. The GUF+ 2015 products will be validated and available as public
domain data from October 2017 onwards on the Urban Thematic Exploitation Platform (TEP) supported
by the DLR.
In the current project the Urban Extent product for Arusha was first used to assess historical
developments from 2000-2015. Further analysis by overlaying administrative boundaries can be
performed to assess urbanisation extent patterns based on administrative units.
Results:
The first result provided using the different Urban Extent products from 2000 to 2015 is illustrated in
Figure 6 which shows the Urban development in the Core and Peri-Urban areas as well as surrounding
regions of Arusha. The Urban Extent developments after 2000 can be examined by Urban Planners to
identify different patterns of growth such as “Edge Growth” or “Leapfrog Growth” depending on the
location of the developments.
From the depiction of urban extent developments between 2000 and 2015 in Figure 8 and Figure 9, one
can note that urban extent development occurred in Arusha in large blocks around existing residential
areas and also stretches in two parallel lines towards south. The urban development till 2005 stretches
towards east in very small fragmented patches. The same patterns appear in the next epoch 2005 to 2010
whereas additionally larger built-up areas occur in the western as well as in the eastern outskirts of
Arusha. In 2010, the urban extension further increased at the surrounding, but most of the extension
occurred in the very south of the core urban area and stretches in very small fragmented patters far to
the east.
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Figure 8: Urban Extent developments in the epochs 2000 to 2005, 2005 to 2010 and 2010 to 2015 in Arusha
and surrounding region.
Figure 9: Urban Extent developments in the epochs 2000 to 2005, 2005 to 2010 and 2010 to 2015 in Arusha
within the Core Urban Area.
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4.2 Land Cover Land Use 2005 and 2015
This Section will present the results of the LU/LC mapping for 2005 and 2015 as well the statistical
information on the changes between these two epochs. The LU/LC overview map for 2015 is depicted
in Figure 10 and a cartographic version of the map layout is provided as a pdf file in addition to the geo-
spatial product.
Figure 10: Left: Overview of the Service Area. Right: Detailed Land Cover Land Use 2015 in Arusha.
In the 2005 epoch the most dominant LU/LC classes occurring in the Overall area were: Agriculture
(61.28% of the total area), Residential (20.0 % of the total area) and Forest areas (9.06% of the total
area). By 2015 these classes remained as the most dominant with some slight changes. For instance, the
Residential and the Forest area classes increased by 3.73% and 0.38% (2.4 km2) respectively. Further
information on the class disaggregation and area coverage is presented in Figure 11 and Figure 12 for
the epochs 2005 and 2015 respectively.
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Figure 11: Detailed Land Cover Land Use 2005 structure: Presented as Overall, Core Urban and in Peri-
Urban in % (left) and km2 (right).
Similarly to the Overall area, Agriculture was the most dominant class in 2005 in both the Core and the
Peri-Urban areas with 56.48% and 63.72%, respectively. The main difference in these two areas is the
coverage and density of the Residential class, and the extent of the commercial and industrial areas. The
Residential areas in the Peri-Urban belong to the low density classes and are scattered over larger areas.
By 2015 (see Figure 12), several trends were observed in the class distribution and the area coverage.
For instance, the Agriculture class decreased by 3.79% for the period 2005 - 2015, by losing 23.68 km2
from its initial area of 382.90 km2 in 2005. The Residential class which represented 20% (124.89 km2)
from the Overall area in 2005, increased by 3.73% thus accounting for 148.21 km2 from the Overall area
by 2015. Detailed information on the area, percentage distribution and changes can be further observed
in Table 12.
Figure 12: Detailed Land Cover Land Use 2015 structure: Presented as Overall, Core Urban and in Peri-
Urban in % (left) and km2 (right).
The next Section will highlight the LU/LC change information between the two epochs in more detail.
Description of LULC Changes:
In addition to the overall LU/LC classification for the two epochs it is interesting to assess the different
trends between classes over the 10 year time period. The quantitative figures for each class (combined
Core and Peri-Urban) are first provided in Table 12 to get an overview.
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Table 12: Detailed information on area and percentage of total area for each class for 2005 and 2015 as
well as the changes.
LU/LC Classes 2015 2005 Change Change per Year
sqkm % of
total sqkm
% of
total sqkm % sqkm %
Residential 0-10 % 73.30 11.73% 83.63 13.38% -10.33 -1.65% -1.03 -0.17%
Residential 10-30 % 33.44 5.35% 23.34 3.74% 10.09 1.62% 1.01 0.16%
Residential 30-50 % 22.93 3.67% 9.14 1.46% 13.79 2.21% 1.38 0.22%
Residential 50-80 % 14.86 2.38% 7.38 1.18% 7.48 1.20% 0.75 0.12%
Residential 80-100 % 3.68 0.59% 1.40 0.22% 2.28 0.37% 0.23 0.04%
Industrial, Commercial, Public, Military 11.45 1.83% 9.55 1.53% 1.91 0.30% 0.19 0.03%
Arterial Line 1.37 0.22% 1.37 0.22% 0.00 0.00% 0.00 0.00%
Toll Line 4.46 0.71% 4.46 0.71% 0.00 0.00% 0.00 0.00%
Airport 0.50 0.08% 0.50 0.08% 0.00 0.00% 0.00 0.00%
Mining_QuarryAreas_DumpSites 0.89 0.14% 0.55 0.09% 0.34 0.05% 0.03 0.01%
Construction Site 3.38 0.54% 1.93 0.31% 1.45 0.23% 0.14 0.02%
Land without current use 4.16 0.67% 2.93 0.47% 1.23 0.20% 0.12 0.02%
Urban Parks 0.19 0.03% 0.18 0.03% 0.01 0.00% 0.00 0.00%
Recreation Facilities 0.90 0.14% 0.75 0.12% 0.15 0.02% 0.01 0.00%
Cemeteries 0.06 0.01% 0.04 0.01% 0.02 0.00% 0.00 0.00%
Agricultural Area 359.21 57.49% 382.90 61.28% -23.68 -3.79% -2.37 -0.38%
Forest 59.01 9.44% 56.61 9.06% 2.40 0.38% 0.24 0.04%
Natural areas (non-forested) 29.22 4.68% 36.33 5.82% -7.11 -1.14% -0.71 -0.11%
Bare Soil 0.00 0.00% 0.06 0.01% -0.06 -0.01% -0.01 0.00%
Wetlands 0.01 0.00% 0.01 0.00% 0.00 0.00% 0.00 0.00%
Water 1.78 0.28% 1.74 0.28% 0.04 0.01% 0.00 0.00%
Total 624.81 100.00% 624.81 100.00%
The area statistics of the LU/LC classes show a lot of change dynamics within the Residential density
classes. For example, it can be noted that there is an evident loss of area in the Very Low Density class
(0-10 %) with 1.65% and increase in all other density classes.
4.2.1 Spatial Distribution of Main LU/LC Change Categories
In order to better analyse the growth trend and the spatial distribution of changes meaningful
aggregations of the LU/LC classes in both epochs were used. The following categories were developed:
Urban Densification: Changes from lower Residential Density Class into a higher Residential
Density class;
Urban Residential Extension: all changes from Non-Urban Residential classes to a Residential
class;
Other Urban Land Use Extension: all changes from Non-Residential Urban classes to Other
Urban and Non-Urban classes.
Changes within Natural and Semi-Natural Areas: all changes in between the natural and semi-
natural classes (e.g. Forest into agriculture).
The overlay analysis of these aggregated categories of the epochs 2005 and 2015 is depicted in Figure
13.
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Figure 13: Land Cover Land Use Change Types - Spatial Distribution.
The spatial distribution of the change types as depicted in Figure 13 shows that the urban densification
in the 10 year period mainly happened in the city centre of Arusha and in the eastern Peri-Urban area
along the main roads. The Extension of the Residential areas (red colour in Figure 13) occurred mainly
in the south-western part of the Core Urban area. Small scattered areas in the eastern part of the Core
city experienced Residential extension as well.
The statistics of the Change categories are presented in Figure 14 and Table 13. The statistics in Figure
14 provide a quantitative aspects to the LU change classes; for example it’s interesting to note that Urban Densification was the most dominant class, covering 76.45% of the Peri-Urban area, whereas the
Residential Extension accounted for the most changes in the Core Urban area with 45% of all the
changes.
Figure 14: Land Cover Land Use Change Types 2000-2015 - overall, in Core Urban and in Peri-Urban
Zone in % (left) and km² (right) in Arusha.
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Regarding the Overall area of Arusha, the most dynamic changes which occurred between 2005 – 2015
were represented by the Urban Densification class, i.e. changes from lower to higher Residential Density
classes with 48.34 % from the total area as presented in Figure 14. The second largest block of changes
can be observed within the Urban Residential Extension, followed by Other Urban Land Use Extension.
A main difference between the Core Urban and the Peri-Urban areas in Arusha was that only 0.27%
from the Peri-Urban Zone was used for residential extension, whereas the land conversion in the Core
Urban Zone was mainly related to Urban Residential Extension with 45%. The quantitative data is
presented in Table 13.
Table 13: Overall LU/LC Statistics.
Change Classes Change Overall Change Core Urban Change Peri-Urban
sqkm % sqkm % sqkm %
Urban Densification 32.28 48.34% 17.32 36.69% 14.96 76.45%
Urban Residential Extension 21.38 32.02% 21.33 45.19% 0.05 0.27%
Other Urban Land Use
Extension 7.58 11.35% 5.06 10.71% 2.53 12.91%
Change within Natural and
Semi-Natural Areas 5.53 8.28% 3.50 7.41% 2.03 10.38%
Total 66.77 100.00% 47.20 100.00% 19.57 100.00%
4.2.2 Changes of Agricultural Areas
In order to analyse the relatively large loss of Agricultural areas as noted in the earlier part of Section
4.2, further change analysis was performed. The following change categories were developed for this
analysis:
Agriculture to Residential area
Agriculture to Industry, Commercial; Public or Military area
Agriculture to Plantations
The spatial distribution of these changes are displayed in Figure 15.
Figure 15: Spatial distribution of changes from Agricultural Areas to other Classes between 2005 and 2015.
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The conversion of agricultural areas into other LULC classes was mainly caused by the residential
extension and reforestation in Arusha. These two conversion types are shown in Figure 15 in blue and
green colours respectively. Especially in the south-western area of Arusha, large agricultural areas were
replaced by urban settlements.
Figure 15 and Figure 16 provide a quantitative overview of the changes of the agricultural areas into
other LU classes between 2005 and 2015 in Arusha. As it can be observed in Figure 15, most of the
agricultural areas subject to change were converted to residential areas (blue colour). A few larger areas
in the western part of Arusha were converted into industrial or commercial land use (red colour). Within
the core region most of this conversion type are scattered through-out the entire city (see Figure 15, red
colour). The third change class in Figure 15 depicts changes from agricultural to forested areas, which
only have a minor contribution to the LULC changes in the period between 2005 - 2015, but they are
the main change type in the Peri-Urban area. The percentage of each change type is presented in Figure
16.
Figure 16: Changes of Agricultural Areas into other LU classes between 2005 and 2015; Presented as
Overall, Core Urban and in Peri-Urban Zone in % (left) and km2 (right).
The statistics in Figure 16 and Table 14 show that the majority of Agricultural areas in the Core Urban
has been converted into Residential area in the 2005 – 2015 period. In the Peri-Urban Zone the
agricultural areas mainly changed to residential areas, followed by forested land.
Table 14: Statistics of changes of Agricultural areas.
Change Classes Change Overall Change Core Urban Change Peri-Urban
sqkm % sqkm % sqkm %
Agricultural Area to
Residential 19.68 88.24% 19.64 88.41% 0.04 46.12%
Agricultural Area to Industrial,
Commercial, Public or Military
Area
1.39 6.24% 1.38 6.23% 0.01 8.25%
Agricultural Area to Forest 1.23 5.52% 1.19 5.36% 0.04 45.62%
Total 22.30 100.00% 22.21 100.00% 0.09 100.00%
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4.3 Transport Network
The Transport Network was created for both points in time (2005 and 2015) using three road types. The
Arterial roads and Collector roads were integrated in the LULC map by applying a buffer of 12 m and
8 m for the Arterial and Collector roads, respectively. Local roads are only part of the vector data set,
which are provided to the user.
Figure 17 depicts the Transport Network for both points in time. The left figure presents the Transport
Network in 2005 and the right the Transport Network in 2015. The main changes of the Transport
Network occurred in terms of densification of Local roads in 2015 in the north-western part and in the
south-western part of the Core Urban area.
Figure 17: Transport Network of Arusha in 2005 and 2015.
4.4 Urban Green Areas
The location and extent of green areas are determined within the product of urban land use/ land cover
at Level I. Urban green areas refer to land within and on the edges of a city that is partly or completely
covered with grass, trees, shrubs, or other vegetation. The product delivered provides accurate
information (1 m resolution) on the spatial location and extent of green areas located within the Urban
Extent (Level I class: 1000) derived from the baseline LULC information product. Detecting and
monitoring urban green coverage needs very high resolution optical satellite images, which explains the
product generation over the Core Urban Area of Arusha only.
The overview map in Figure 18 gives an overview of the mapping result and the structure of the map,
which is designed for a print out at DIN A0 paper size (84.1 x 118.9 cm).
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Figure 18: Map overview of Urban Green Areas in Arusha. Green area loss, gain and stable green areas
can be identified. The map is delivered as separate product of high resolution for printing at
paper size DIN A0, which is 84.1 cm x 118.9 cm.
The spatial distribution of the change types as depicted in Figure 18 shows that changes are distributed
across the entire city centre area of Arusha. The surrounding areas are mostly covered by Agriculture
and/or Natural or semi-Natural areas. Overall 24.79% of the entire area was covered by vegetation in
2006 and 2016. The percentage of gain of green area (15.66%) is a little bit higher than the percentage
of loss of green area (14.34%) resulting in an increase of urban green areas in Arusha. About 45.20% of
the overall area was covered by artificial class (Level I class).
Figure 19: Percentage of urban green areas within the core area of Arusha. The pie chart illustrates the
status and change of urban green areas in-between 2005 and 2015.
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The changes within the urban green areas can be illustrated as an overall area coverage. Figure 20
represents the green area coverage in square kilometers in 2005 and 2015, compared to the amount of
the artificial areas class. Both classes demonstrated a relatively equal growth within the 10 year period.
Urban areas showed an increase of 17.65 km² (from 37.7 km² to 55.35 km²) and green areas an increase
of 8.27 km² (from 24.17 km² to 32.44 km²).
Figure 20: Bar charts for both points in time presenting the total area of urban greenery versus non-
green areas.
4.5 Planned and Unplanned Settlement Areas
The location and extent of planned and unplanned settlement areas are determined within the classes
“1100 Residential” and “1211 Commercial Areas” of the urban land use/ land cover product. Polygons
with the LU class “Commercial Areas” were only included for classification in this product, when they also contained residential buildings.
In an effort to use spatial patterns to depict the class of “planned settlements” (see Figure 21) there was
a focus on residential areas where the houses are oriented in the same direction, similar in size and shape
and have a well-defined road network, often in a grid pattern which provides almost direct access to
every house. For “unplanned settlements” (see Figure 22) houses are not oriented in the same direction
and streets, if existing are curved, small and appear to have been constructed in an ad hoc manner (no
distinct patterns). Also not every building can be reached by a road.
To further support the classification effort, the Arusha Master Plan 2015-2035, Volume 2: Technical
Supplement 1-6 (2016) page 82 was used to help in orienting the locations of planned and unplanned
settlement areas.
0
10
20
30
40
50
60
2005 2015
are
a i
n k
m²
urban (km2) green (km2)
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Figure 21: Planned settlement areas in Arusha in 2015.
Figure 22: Unplanned settlement areas in Arusha in 2015.
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Due to the complexity of identifying these classes, the detection and monitoring planned and unplanned
settlements needs VHR optical satellite images; thus the product was generated only over the Core
Urban Area of Arusha.
The overview map in Figure 23 shows the changes in planned and unplanned settlement areas in Arusha
between 2005 and 2015. The map is delivered as separate product of high resolution for printing at paper
size DIN A0, which is 84.1 cm x 118.9 cm. gives an overview of the mapping result and the structure
of the map, which is designed for a print out at DIN A0 paper size (84.1 x 118.9 cm).
Figure 23: Map overview of changes in planned and unplanned settlement areas in Arusha during the years
2005 and 2015.
Figure 24 shows the spatial distribution of planned and unplanned areas between the two years 2005
and 2015 in Arusha. Most of the city is covered by unplanned settlement areas, only the inner city as
well as a part stretching out to the south has planned settlements. The overview map also shows that
most of the new developed settlements are unplanned.
An analysis of the changes is depicted in Figure 24. The pie chart shows that more than half of the Core
Urban area has unplanned settlements (52.17%), and only 11.53% of the residential area is planned.
During the 10 years the unplanned settlement area further grew by 31.53%, while the planned settlement
area only grew by 3.96%.
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Figure 24: Percentage of planned and unplanned areas within the core area of Arusha. The pie chart
illustrates the status and change of planned and unplanned areas in-between 2005 and 2015.
The changes in the overall area coverage for both the planned and unplanned settlement areas are
illustrated in Figure 25; the bar chart represents the planned and unplanned settlement areas in square
kilometres in 2005 and 2015. Both unplanned and planned settlement increased in area within the 10
years, however the unplanned settlement area which was already more than half of the whole residential
area increased in area more than the planned settlement area. In 2005 the planned settlement area
covered 8.14km2, while the unplanned settlement area covered 36.59km2. In 2015, the planned
settlement area increased to 11.20km2, and the unplanned settlement to 57.52km2.
Figure 25: Bar charts for both points in time presenting the total area of planned and unplanned settlement
areas.
52.17%
11.53%
31.53%
3.96% 0.28% 0.32% 0.81%
No Change in Unplanned Settlement Area No Change in Planned Settlement Area
Expansion of Unplanned Settlement Area Expansion of Planned Settlement Area
Decrease of Unplanned Settlement Area Decrease of Planned Settlement Area
Unplanned to Planned Settlement Area
0
10
20
30
40
50
60
70
2005 2015
Are
a [
km2
]
Unplanned Settlement Area Planned Settlement Area
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4.6 Population Distribution and Density
The Population Distribution and Density Change product contains spatial explicit information about
population distribution within the Core Urban Districts of Arusha. The product is derived for all units
which belong to the residential class (LULC class 11), using five different data sources: 1) Land
Use/Land Cover Baseline Product; 2) WorldPop data (2015) with a spatial resolution of 100m; 3)
Official Population census data from 2002 and 2012; 4) Imperviousness/Soil Sealing layer (see Annex
1 for a full description of this product) and 5) Administrative boundaries. An important note is that the
product has been always developed for the period 2005 – 2015, due to data availability (the global
WorldPop dataset was only available only for the year 2015), and to meet the user requirements,
regarding time interval of 10 years between the historic and current state of the product.
The overview map in Figure 26 shows eight different change classes which were identified, based on a
frequency distribution analysis of the changes that occur: Unchanged Population Distribution; Up to –100% decrease; Up to 200% increase; 201% - 400% increase; 401% - 600% increase; 601% - 800%
increase; 801% - 1000% increase; More than 1000% increase. The Unchanged Population Distribution
class is defined as the residential units which experienced stable population distribution between 2005
and 2015, with values within the overall annual population growth rate (-4% to 4%). The map is
delivered as separate product of high resolution for printing at paper size DIN A0, which is 84.1 cm x
118.9 cm. It has to be noted that the GIS data set allows map printouts at any other scale which however
would require different map sheet layouts and cartographic designs.
Figure 26: Overview Map of Population Distribution Change in Arusha (2005 – 2015).
The spatial distribution of the change types as depicted in Figure 26 can be grouped in 3 main categories:
1) unchanged areas; 2) areas which experienced population decrease and 3) areas which increased in
terms of population distribution at a different degree. The overview map shows that most of the
residential units in Arusha experienced increase of up to 200% (depicted in light yellow colour). The
decrease in population is heterogeneously distributed across all Core Urban Districts, whereas the
highest degree of increase (More than 1000%) is observed mainly on the east, which is closely linked
to urban expansion and densification in this area.
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An analysis of the change types depicted in Figure 27, shows that 64.09% of the area was subject to
population increase of up to 200%, followed by decreasing population which accounted for 13.41% of
the total area. Only 1.5% remained unchanged (within the range of the annual growth rate of -4% to
4%).
Figure 27: Population Distribution Change within the Core Urban Districts of Arusha between 2005 and
2015.
Figure 28 provides further detail on the population distribution changes between 2005 and 2015, in
relation to build up areas which are based on the amount of soil sealing degree in each Core Urban
District.
Figure 28: Changes in Population Distribution, in relation to soil sealing degree in Arusha between 2005
and 2015.
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For instance, it is noted that in Sokoni I (the biggest district in terms of population) more than 25000
people moved to build up areas with a sealing degree of 50 – 80%. Similarly, a high proportion of the
residents in Elerai, Sombetini and Unga Ltd moved to areas with sealing degree of 50 – 80%. The
increase of inhabitants in most of the Core Urban Districts was proportionally distributed among the
different soil sealing classes. In contrast, the Very High Density class (80 – 100%) experienced decrease
in most of the districts. This trend can be driven by different factors, such as: rent price in central
districts, lack of land availability, and conversion of residential units to commercial, industrial, central
business districts in the city centre, etc. However, the information presented in Figure 28 cannot
explicitly define those driving forces. Therefore, further research is needed to identify and justify the
drivers behind such phenomena, thus helping urban planners and local officials in the decision – making
process.
4.7 Concluding Points
This Chapter 4 presented only a summary and overview of what is possible in term of analytics with the
geo-spatial datasets provided for Arusha in the current project. This Report is a living document and
will be complemented with further analysis during the project. Important would be to further analyse
the EO4SD Urban datasets with the City Master Plan for Arusha in order to enhance the latter for
planning purposes.
Earth Observation for Sustainable Doc. No.: City-Operations Report
Development – Urban Project Issue/Rev-No.: 3.0
EO4SD-Urban Arusha City Operations Report Page 39
5 References
Arusha Master Plan 2015-2035 (2016). Ministry of Lands, Housing and Human Development,
Tanzania.
Czaplewski, R. L. (2003). Chapter 5: Accuracy assessment of maps of forest condition: statistical design
and methodological considerations, pp. 115–140. In Michael A. Wulder, & Steven E. Franklin (Eds.),
Remote sensing of forest environments: concepts and case studies. Boston: Kluwer Academic Publishers
(515 pp.).
European Union (2011). Mapping Guide for a European Urban Atlas, Version 11.0
Goodchild, M., Chih-Chang, L. and Leung, Y. (1994): Visualizing fuzzy maps, pp. 158-67. In
Heamshaw, H.H. and Unwin, D.J. (Eds.), Visualization in geographical information systems.
Chichester: Wiley.
Olofsson, P., Foody, G. M., Stehman, S. V., & Woodcock, C. E. (2013). Making better use of accuracy
data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified
estimation. Remote Sensing of Environment, 129, 122–131. doi:10.1016/j.rse.2012.10.031
Selkowitz, D. J., & Stehman, S. V. (2011). Thematic accuracy of the National Land Cover Database
(NLCD) 2001 land cover for Alaska. Remote Sensing of Environment, 115(6), 1401–1407.
doi:10.1016/j.rse.2011.01.020.
Internet
Road classification, European Commission, 2017, https://ec.europa.eu/transport/road_safety/specialist-
/knowledge/road/designing_for_road_function/road_classification_en, last accessed 2017.08.17
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Annex 1 – Processing Methods for EO4SD-Urban Products
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Summary of Processing Methods
Urban and Peri-Urban Land Use/Land Cover and Change
The input includes Very High Spatial Resolution (VHR) imagery from different sensors acquired at
different time. The data is pre-processed to ensure a high level of geometric and radiometric quality
(ortho-rectification, radiometric calibration, pan-sharpening).
The complexity when dealing with VHR images comes from the internal variability of the information
for a single land-use. For instance, an urban area is represented by a high number of heterogeneous pixel
values hampering the use of automated pixel-based classification techniques.
For these VHR images, it is possible to identify textures (or pattern) inside an entity such as an
agricultural parcel or an urban lot. In other words, whereas pixel-based techniques focus on the local
information of each single pixel (including intensity / DN value), texture analysis provides global
information in a group of neighbouring pixels (including distribution of a group intensity / DN values
but also spatial arrangement of these values). Texture and spectral information are combined with a
segmentation algorithm in an Object Based Image Analysis (OBIA) approach to reach a high degree of
automation for most of the peri-urban rural classes. However, within urban land, land use information
is often difficult to obtain from the imagery alone and ancillary/in situ data needs to be used. The
heterogeneity and format of these data mean that another information extraction method based on
Computer Aided Photo-Interpretation techniques (CAPI) need to be used to fully characterise the LULC
classes in urban areas. Therefore, a mix of automated (OBIA) and CAPI are used to optimise the
cost/quality ratio for the production of the LULC/LUCC product. The output format is typically in vector
form which makes it easier for integration in a GIS and for subsequent analysis.
Level 4 of the nomenclature can be obtained based on additional information. These can be generated
by more detailed CAPI (e.g. identification of waste sites) or by an automated approach based on
derived/additional products. An example is illustration by categorising the density of the urban fabric
which is related to population density and can then subsequently used for disaggregating population
data.
Information on urban fabric density can be obtained through several manners with increasing level of
complexity. The Imperviousness Degree (IMD) or Soil Sealing (SL) layer (see separate product) can be
produced relatively easily based on the urban extent derived from the LULC product and a linear model
between imperviousness areas and vegetation vigour that can be obtained from Sentinel 2 or equivalent
NDVI time series. This additional layer can be used to identify continuous and discontinuous urban
fabric classes. Five urban fabric classes can be extracted based on a fully automated procedure:
Continuous urban fabric (IMD > 80%)
Discontinuous dense urban fabric: (IMD 50-80 %)
Discontinuous medium density urban fabric (IMD: 30-50 %)
Discontinuous low density urban fabric (IMD 10-30 %)
Discontinuous very low density urban fabric (IMD < 10 %)
Manual enhancement is the final post-processing step of the production framework. It will aim to
validate the detected classes and adjust classes’ polygon geometry if necessary to ensure that the correct
MMU is applied. Finally, a thorough completeness and logical consistency check is applied to ensure
the topological integrity and coherence of the product.
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Change detection: Four important aspects have to be considered to monitor land use/land cover change
effectively with remote sensing images: (1) detecting that changes have occurred, (2) identifying the
nature of the change, (3) characterising the areal extent of the change and (4) assessing the spatial pattern
of the change.
The change detection layer can be derived based on an image-to-image approach provided the same
sensor is used. An original and efficient image processing chain is promoted to compare two dates’ images and provide multi-labelled changes. The approach mainly relies on texture analysis, which has
the benefits to deal easily with heterogeneous data and VHR images. The applied change mapping
approach is based on spectral information of both dates’ images and more accurate than a map-to-map
comparison.
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Summary of Processing Methods
Urban Extent and Change
Reliably outlining urban areas is of high importance since an accurate characterization of the urban
extent is fundamental for accurately estimating, among others, the population distribution, the use of
resources (e.g., soil, energy, water, materials), infrastructure and transport needs, socioeconomic
development, human health and food security. Moreover, monitoring the change in the extent of urban
areas over time is of great support for properly modelling the spatial-temporal patterns of urbanisation
evolution and, thus, better estimating future trends and implementing suitable planning strategies.
The product is a binary mask outlining in the area of interest the urban areas (intended as built-up
structures) with respect to all other land-cover classes merged together into a single information class.
The urban class and the non-urban class are associated with value “255” and “0”, respectively. Regrouping of relevant LULC thematic classes can be used to depict urban extent precisely. Instead, if
a detailed LULC product is not available for the selected study region, then the information will be
derived by the following approach.
The product is generated at 30 m spatial resolution by properly exploiting Landsat-4/5/7/8 multi-
temporal imagery acquired over the peri-urban and urban area within a given time interval of interest in
which no relevant changes are expected to occur (typically a time period of 1-2 years allows to obtain
very accurate results). For all the considered scenes, cloud masking and, optionally, atmospheric
correction are performed. Next, a series of features specifically suitable for delineating urban areas are
derived for each image. These include both spectral indexes (e.g., the normalized different vegetation
index (NDVI), the atmospherically resistant vegetation index (ARVI), the normalized difference water
index (NDWI), etc.) and texture features (e.g., occurrence textures, co-occurrence texture, local
coefficient of variation, etc.). The core idea is then to compute per each pixel key temporal statistics for
all the extracted features, like temporal maximum, minimum, mean, variance, median, etc. This allows
compressing all the information contained in the different multi-temporal acquisitions, but at the same
time to easily and effectively characterize the underlying dynamics. It is worth noting that for different
pixels in the study area, different number of scenes might be available. However, in the hypothesis of a
sufficient minimum number of acquisitions for computing consistent statistics, this does not represent
an issue. Moreover, in this framework it is also possible to obtain spatially consistent datasets to be
employed for the desired analyses even when investigating large areas. Training data for the urban and
non-urban class are then extracted by employing a strategy based on the analysis of the DLR Global
Urban Footprint (GUF) layer (which varies depending whether the target period of interest refers to a
time interval before or after that which the GUF refers to). Afterwards, a Support Vector Machines
(SVM) classifier is employed where a Radial Basis Function (RBF) kernel is used.
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Summary of Processing Methods
Transport Network
The transportation network is mainly manually digitised and mapped on the basis of very high resolution
(VHR) optical satellite imagery. Requested features are obtained by integration of auxiliary data such
as OpenStreetMap (OSM) or local datasets as a starting point for the product generation. The revision
and update of the auxiliary data is realised by using up-to-date VHR data. The workflow can be specified
as follows:
Quality check of available ancillary data such as local data sets.
Processing of optical satellite data – dependent on satellite data product level (geometric,
atmospheric and radiometric corrections, enhancements – colour optimization, mosaicking,
tiling).
Identification, collection and integration of available ancillary data (e.g. Open Street Map)
Identification and adjustment of spatial inconsistencies. The OSM data is used as spatial
reference. Upon User request other data sets can be used.
Update of the network by visual photo-interpretation according road hierarchy (see description
below).
Update of attributes by photointerpretation.
Generalization, application of MMU (minimum allowable dangling length)
Quality control and accuracy assessment
o Statistical sampling of check points
o Independent evaluation of products (second interpreter, third party assessment)
Change detection
The road hierarchy used in the classification is based on the international road classification
standards. One definition is specified by the European Commission. Roads are divided into three
groups - arterial or through traffic flow routes (in our case Arterial Roads), distributor road (in our
case Collector Roads), and access roads (in our case Local Roads). The three road types are defined
as follows: Arterial Roads - roads with a flow function allow efficient throughput of (long distance)
motorized traffic. All motorways and express roads as well as some urban ring roads have a flow
function. The number of access and exit points is limited. Collector Roads - roads with an area
distributor function allow entering and leaving residential areas, recreational areas, industrial zones,
and rural settlements with scattered destinations. Local Roads - roads with an access function allow
actual access to properties alongside a road or street. Arterial roads and collector roads were the
main focus of the classification. These types of roads were identified for the entire Area of Interest.
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Summary of Processing Methods
Urban Green Areas
The location and extent of green areas are determined within the product of urban land use/ land cover
at Level I. Urban green areas refer to land within and on the edges of a city that is partly or completely
covered with grass, trees, shrubs, or other vegetation. This includes public parks, private gardens,
cemeteries, forested areas as well as trees, river alignments, hedges etc. The product delivered within
EO4SD-Urban project thus provides accurate information (1 m resolution) on the spatial location and
extent of the green areas located within the Urban Extent (Level I class: 1000) derived from the baseline
LULC information product.
Detecting and monitoring urban green coverage needs very high resolution optical satellite images,
which explains the product generation over the Core Urban Area of AOI only. The same images have
been logically used for generating the LULC information product. Consequently, the usual preliminary
quality check and pre-processing tasks were already implemented.
Urban Green Areas have been detected using automated non-supervised classification method. More
precisely, each single multispectral VHR scene has been classified by specifying the most appropriate
algorithm and class number. Then, pixel units from the classes considered as representing green areas
have been combined into 1 single class. From this operation results the required binary raster product.
At this stage, it only remains necessary to apply some post-processing steps:
Morphological filter is applied to fill small gaps within the green areas (caused by shadow)
Resampling of the data to the provided spatial resolution of 1m
Removing small pixel groups under the minimum mapping unit.
Integrating the information provided by the LULC product (e.g. class Urban Parks, Cemeteries).
Validation of Mapping results
Furthermore, using archive very high resolution images, current and historic extent of urban green areas
are compared to identify their temporal evolution – extent growth or reduction. Quality control and
accuracy assessment tasks are performed by means of visual interpretation considering also the LULC
dataset.
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Summary of Processing Methods
Planned and Unplanned Settlements
The location and extent of planned and unplanned settlement areas are determined within the classes
“1100 Residential” and “1211 Commercial Areas” of the urban land use/ land cover product. Polygons
with the LU class “Commercial Areas” were only included for classification in this product, when they also contained residential buildings.
Two distinct between the two types following rules were applied:
In planned settlement areas (see Figure 1 left side) houses are oriented in the same direction, are
similar in size and shape and show a good road network reaching every house.
In unplanned settlements (see Figure 1 right side) houses are not oriented in the same direction
and streets, if existing are curved and small. Not every building can be reached by a road.
Figure 1: Planned (left) and unplanned (right) settlement areas in Arusha in 2015.
To further improve the results, the City Urban Master plans were used to help in the distinction in
planned and unplanned settlement areas. Detecting and monitoring planned and unplanned settlements
need very high resolution (VHR) optical satellite images; therefore this product was only generated over
the Core Urban Area of Arusha only. As the same images VHR images were used for generating the
LULC information product, the usual preliminary quality check and pre-processing tasks were already
implemented. Planned and unplanned areas have been detected by visual interpretation of the actual and
historic VHR image by following the rules described above and with the help of the City Master Plans
of the three cities.
Quality control and accuracy assessment tasks are also performed by means of visual interpretation
considering also the LULC dataset.
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Summary of Processing Methods
Population Distribution and Density
This product aims at providing information on the spatial distribution of residents in a specified Area of
Interest (AOI). Additionally, it depicts: a) location of people; b) estimated population in a specific spatial
unit; and c) changes in population distribution throughout the years.
Data sources and types
To derive the Population Distribution and Density product, five main ancillary data types are used: 1)
Land Use/Land Cover Baseline Product; 2) WorldPop data; 3) Population census data; 4)
Imperviousness/Soil Sealing layer produced by DLR and 5) Administrative boundaries.
1) LULC product is used to extract the residential units within the Core Urban Districts of the city.
2) WorldPop data is a globally available dataset with spatial resolution of 100m, which provides
disaggregated population counts at a specific spatial unit (on a pixel level) for the year 2015.
Therefore, this dataset is used to interpolate the population counts for each residential unit in
the current state of the product, whereas the historic state is modelled, based on soil sealing
information and official population census data.
3) Population census data is further applied to improve the quality of the current state of the product
by adjusting population residuals, and to extrapolate the population size in the historic state of
the product.
4) Imperviousness/Soil Sealing layer has been produced by the German Space Agency (DLR). The
product provides for each pixel identified as urban the corresponding estimated Percentage
Impervious Surface (PIS). When used with the population data this PIS layer supports the
disaggregation of the population counts from district level to pixel level, using the mean sealing
degree for each urban fabric class.
5) Administrative boundaries (district level) are downloaded from the Global Administrative
Areas Website. Furthermore, they are clipped with the specified AOI and used to inform the
total number of inhabitants in each district, and to estimate the overall population density on a
district level.
Methodology
This section describes the general structure of the methodology which is used to compute Population
Distribution and Density for the specific study period. It covers the following two parts: 1) Interpolation
and projection of the population data; and 2) Spatial disaggregation and adjustment of the population
residuals.
1. Interpolation and projection of the population data
The first step from this methodological approach is to interpolate and to project the total number of
inhabitants per district for the specific reference year, based on official population census data. To
support this analysis, population census datasets from 2002 and 2012 are acquired from the Integrated
Public Use Microdata Series (IPUMS) and the United Republic of Tanzania, Ministry of East African
Cooperation, in order to estimate the population size in the Core Urban Districts of Arusha for 2005 and
2015, respectively (IPUMS; MEAC, 2013) . First, the overall annual population growth rate is calculated
based on the total population size, using the following formula as in Kindu et al. (2015): 𝑃 = 𝑃 𝑒 (1)
where: 𝑃0 is the total population in 2002, 𝑃 is the total population in 2012,
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Page 11
represents the number of years between the two periods,
is the average annual growth rate.
Next, the average annual population growth rate per district is derived, based on the available census
data (2002 – 2012). Thus, the population size for each district is projected and estimated for 2015, using
equation (1). Finally, the population size in each district in 2005 is interpolated, based on the average
annual population growth rate per district or the overall annual growth rate (when census data for the
initial reference year (2002) was not available).
The population census datasets are used as a supportive parameter in the computation of the current state
of the product, in order to re-distribute the population residuals, created from the interpolation of the
WoldPop data. In contrast, it is the main source for population disaggregation in the historic state, since
no historic WorldPop data is available.
2. Spatial disaggregation and adjustment of the population residuals
In order to improve the quality of the current state of the product (based on WorldPop data), and to
interpolate the historic, further disaggregation analysis is conducted, following the general approach of
Batista e Silva et al. (2013): 𝐾 = 𝑃𝑆∑ 𝑈𝑐∗𝑆𝑐 𝑐 (2)
where: 𝐾 represents the number of inhabitants per unit (%) of sealed surface in each district,
Ps accounts for the total population in the specific district, based on the official census data,
Uc is the number of urban pixels (in raster data), or the sum of class area within the district (in
a vector data),
Sc is the mean soil sealing degree of each urban fabric class c.
Next, the estimated number of residents for each urban fabric class c (𝑃𝑐 is derived, as in Batista e
Silva et al. (2013): 𝑃𝑐 = 𝑘 ∗ 𝑆𝑐 (3)
Finally, a dasymetric population technique is applied to disaggregate the number of residents per spatial
unit and to adjust the population residuals. This approach takes into consideration the residential
polygons from the LULC product, denoted as ‘target’ zone, and the number of inhabitants per district, based on the official census data as ‘source’ zone, using the following formula (Batista e Silva, et al. 2013, p.17): 𝑃 ’ = 𝑃 ∗ 𝐴𝑖∗𝑊𝑖∑𝑖 𝐴𝑖∗𝑊𝑖 (4)
where: 𝑃𝑖′ refers to the estimated population in the target zone i, 𝑃 is the known population in the ‘source’ zone s
Ai is the area of the ‘target’ zone polygons
Wi is the weighting parameter related to the population density of the ‘target’ zone. n corresponds to the number of transitional polygons within each source polygon.
Output product
The output product is delivered in a vector format, presenting number of inhabitants and population
density (inhabitants/sqkm) for each residential spatial unit from the LULC product.
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Page 12
Validation
The final product, which is developed by a disaggregated procedure such as the one herein described is
never less accurate than the original source data (JRC Technical Report, 2013). Further detail on the
quality and accuracy of the product is directly linked to the JRC Technical Report (2013), as follows:
‘By disaggregating numerical data from one coarse geometry to a finer geometry, we always gain detail
and approximate ground truth without the risk of deteriorating the source information. The degree to
which the disaggregation approximates reality, however, varies greatly, and it depends chiefly on: 1) the
quality of the ancillary data and 2) the appropriateness of the disaggregation algorithm and its
parameters.’
Reference
Batista e Silva, F., Gallego, J. & Lavalle, C. (2013). A high-resolution grid map for Europe. Journal of
Maps, 9(1), 16-28.
Batista e Silva, F. (DG JRC), Poelman, H. (DG Regional Policy), Martens, V. (DG Regional Policy),
Lavalle, C. (DG JRC). (2013). "Population Estimation for the Urban Atlas Polygons" Joint
Research Center (JRC) Technical Reports.
Kindu, M., Schneider, T., Teketay, D., & Knoke, T. (2015). Drivers of land use/land cover changes in
Munessa-Shashemene landscape of the south-central highlands of Ethiopia. Environmental
monitoring and assessment, 187(7), 452.
Minnesota Population Center. Integrated Public Use Microdata Series, International (IPUMS –
International): Version 6.5. Tanzania – Population and Housing Census 2002. Minneapolis:
University of Minnesota, 2017. http://doi.org/10.18128/D020.V6.5.
United Republic of Tanzania, Ministry of East African Cooperation (MEAC). (2013). 2012
POPULATION AND HOUSING CENSUS TANZANIA. Population Distribution by
Administrative Areas. Downloaded from: http://meac.go.tz/
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Annex 2 – Filled Quality Control Sheets
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Earth Observation for Sustainable Development - Urban
Quality Assurance and Quality Control Sheets These QA/QC Templates were prepared by GAF AG compliant with ISO 9001:2008 Quality Management System
standards and can only be used by Partners in the current EO4SD-Urban Project.
Project Title: EO4SD-Urban
Project Leader: GAF AG
Service Provider: GAF AG Editor: AB
Client: WB Date: 24.04.2018
Overview of QC-Sheets and Processing Steps Sheet used Sheet filled
in
Requirements
0.1 Requirements Yes Yes
Specifications of Input Data
1.1 List of EO Data Yes Yes
1.2 List of In-situ Data Yes Yes
1.3 List of Ancillary Data Yes Yes
Data Quality Checks
2.1 EO Data Quality Yes Yes
2.2 In-situ Data Quality Yes Yes
2.3 Ancillary Data Quality Yes Yes
Pre-Processing of EO Data
3.1 Geometric Correction Yes Yes
3.1.1 Data Fusion Yes Yes
3.2 Data Processing Yes Yes
Thematic Processing
4.1 Classification Yes Yes
4.2 Intermediate Quality Control of Land Use Data Yes Yes
Accuracy Assessment
5.1 Thematic Accuracy Yes Yes
5.2 Error Matrices Yes Yes
Delivery Checks / Delivery
6.1 Completeness Yes Yes
6.2 Compliancy Yes Yes
Glossary (index numbers in the QA/QC tables refer to the glossary at the end of this document)
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 2
Further QC-relevant Documents:
Comments / Characteristics:
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 3
0.1 Requirements
Product 1 (28) Urban Land Use/ Land Cover and Change
Abstract
Land Use/Land Cover (LU/LC) information product should contain spatial explicit information on different land use and land cover occurring in both the Core and Peri-Urban areas of the City of Arusha. The Core area has detailed LU/LC nomenclature that is either at Level 3 or 4 whereas the Peri-Urban area LU/LC nomenclature is at an aggregated Level 1 or 2. The input data for the Core area was the Very High Resolution data of WorldView/GeoEye (2015) and Quickbird (2005) and the input data for the Peri-Urban area was Aster (2005) and Sentinel-2 (2015). The LU/LC product is the Baseline Product from which various derived products (such as Green Areas and Informal Settlements) are produced.
Service / Product Specifications
Area Coverage
Country: Tanzania A) Wall-to-wall: yes
City: Arusha Selected Sites: 1
Area km² Core Urban: 211 B) Sampling based: n/a
Peri-Urban: 414
Time Period - Update Frequency
A) Baseline Year(s): B) Update Frequency
2005 and 2015/2016 2 points in time
Comments:
Geographic Reference System
UTM 37S
Mapping Classes and Definitions
Residential (Built-Up Areas)
Built-up areas and their associated land, such as gardens, parks, planted areas and non-surfaced public
areas and the infrastructure, if these areas are not suitable to be mapped separately with regard to the
minimum mapping unit size.
Very High: Average degree of soil sealing: 80 -100% Residential buildings, roads and other artificially
surfaced areas.
High: Average degree of soil sealing: > 50 - 80% Residential buildings, roads and other artificially surfaced
areas.
Medium: Average degree of soil sealing: > 30 - 50% Residential buildings, roads and other artificially
surfaced areas. The vegetated areas are predominant, but the land is not dedicated to forestry or
agriculture.
Low: Average degree of soil sealing: 10 - 30% Residential buildings, roads and other artificially surfaced
areas. The vegetated areas are predominant, but the land is not dedicated to forestry or agriculture.
Super Low: Average degree of soil sealing: <10 % Residential buildings, roads and other artificially
surfaced areas. The vegetated areas are predominant, but the land is not dedicated to forestry or
agriculture. Example: exclusive residential areas with large gardens.
Commercial* warehouses, CBD, shopping malls, markets, other commercial facilities
Industry* Factories, … and associated land
University* University and associated land
Schools* Schools and associated land including sport fields
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Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 4
Hospitals* Hospitals and associated land
Government* Governmental Buildings
Military* Military and associated land
Public Buildings*
“Big” public buildings like churches, bibliotheca, …
Arterial Line Highways, connecting the city with other cities
Collector Line Bigger Connecting roads within the city
Railway Railway facilities including stations, cargo stations and associated land.
Airport Administrative area of airports, mostly fenced. Included are all airport installations: runways, buildings and associated land.
Port Port and associated area
Mining Area and Dump Sites
Open pit extraction sites (sand, quarries) including water surface, if < MinMU, open-cast mines, inland salinas, oil and gas fields;
Construction sites
Spaces under construction or development, soil or bedrock excavations for construction purposes or other earthworks visible in the image.
Land without current use
Areas in the vicinity of artificial surfaces still waiting to be used or re-used. The area is obviously in a transitional position, “waiting to be used”. Waste land, removed former industry areas, (“brown fields”) gaps in between new construction areas or leftover land in the urban context (“green fields”). No actual agricultural or recreational use. No construction is visible, without maintenance, but no undisturbed fully natural or semi-natural vegetation (secondary ruderal vegetation).
Urban Parks
Public green areas for predominantly recreational use such as gardens, zoos, parks, castle parks. Suburban natural areas that have become and are managed as urban parks. Forests or green areas extending from the surroundings into urban areas are mapped as green urban areas when at least two sides are bordered by urban areas and structures, and traces of recreational use are visible.
Recreation facilities
All sports and leisure facilities including associated land, whether public or commercially managed: Golf courses, Sports fields (also outside the settlement area), Camp grounds, Riding grounds, Racecourses, Amusement parks, Swimming resorts etc., Glider or sports airports.
Cemeteries Cemeteries and associated area
Agricultural Area
Cultivated areas non-irrigated or permanently irrigated including rice fields: arable land (annual crops), permanent crops, complex or mixed cultivation, orchards; pasture and meadow under agricultural use, grazed or mechanically harvested.
Forest and Shrub lands
High woody vegetation in natural forests; transitional woodland; low vegetation cover with bushes and shrubs.
Natural Areas Natural area where there is little vegetation and does not serve as construction site.
Bare Soil Natural Area with no vegetation
Wetlands Areas flooded or liable to flooding during a large part of the year by fresh, brackish or standing water with specific vegetation coverage made of low shrub, semi-ligneous or herbaceous species; shallow water areas covered with reed.
Water Visible water areas like lakes, rivers, ponds (natural, artificial).
Comment: * These classes have only to be mapped in 2015. For the mapping of the historic land cover these classes were merged to the class Commercial, Public, Military and Private Units.
Cloud and Cloud Shadow Detection and Removal
Information for the entire AOI is required and therefore clouded areas have to replace by other Earth Observation (EO) data.
Spatial Resolution
n.a. (Product provided as Shapefile)
Minimum Mapping Unit (MMU)
Minimum Mapping Unit is 0.25 ha for the urban area, 0.5 ha for the peri-urban area.
Data Type & Format
Shapefile *.shp and GeoPDF
Bit Depth
n.a.
Class Coding
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Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 5
Class Code Class Name RGB Code
1100 Residential (Built-Up Areas) 255; 0; 0 (main class only)
1211 Commercial 197; 0; 255
1212 Industry 132; 0; 168
1213 University 194; 158; 215
1214 Schools 232; 190; 255
1215 Government 0; 255; 197
1216 Military 115; 115; 0
1217 Hospitals 115; 223; 255
1218 Public Buildings 192; 252; 234
1221 Arterial Line 78; 78; 78
1222 Collector Line 120; 120; 120
1230 Railway 52; 52; 52
1240 Airport 168; 0; 132
1250 Port 0; 168; 132
1310 Mining Area and Dump Sites 115; 76; 0
1320 Construction sites 255; 115; 223
1330 Land without current use 242; 242; 242
1410 Urban Parks 85; 255; 0
1420 Recreation facilities 255; 170; 0
1430 Cemeteries 230; 230; 0
2000 Agricultural Area 255; 235; 175
3100 Forest and Shrub lands 38; 115; 0
3200 Natural Areas 180; 215; 158
3300 Bare Soil 204; 204; 204
4000 Wetlands 76; 0; 115
5000 Water 0; 112; 255
Metadata
Provided as INSPIRE conformant *xml data set, covering at least the mandatory elements.
Service / Product Quality
Thematic Accuracy
Overall Accuracy: >80%
Positional Accuracy
RMSE < 15 m
Delivery Procedure
Service Provision
Online via FTP.
Delivery Date
End of April 2018
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 6
1.1 List of EO Data
Sensoren (8) Sentinel-2, Landsat 5, QB-2, PLX
Incoming Date
Acquisition Date
Proc. Level
Path / Row
AOI - City / Region / Country
Spatial Res. No. of Bands
Cloud Cover (Data Provider)
Projection / Spheroid (16)
Data Format (3)
Bit Depth (5)
Header / Metadata (2)
File Name [e.g yymmdd; tbd...]
Sentinel-2
1. S2A_OPER_MSI_L1C_TL_MTI__20160204T095011_A003235_T37MBS
23.03.2017
04.02.2016
1C 37/MBS
Arusha, TZA
10m 4 0.4%
WGS 84 / UTM zone 37S
.tiff 16 Bit
.xml
Aster
2. AST_L1T_00302052006075950_20150513015728_107850
13.15.2015
5.02.2006
L1T 168/ 064
Arusha, TZA
30m 9 0% WGS 84 / UTM zone 37
.hdf 8 bit & 16 bit
.xml
WV-2, GE-1
3.
16SEP17081149-M2AS_R1C1-056358854010_01_P001
31.03.2017
17.09.2016
LV2A R1C1 Arusha, TZA
MUL: 2m; PAN: 0.5m
4 0% UTM 37S .tiff 16 Bit
.xml
16SEP17081149-M2AS_R1C2-056358854010_01_P001
31.03.2017
17.09.2016
LV2A R1C2 Arusha, TZA
MUL: 2m; PAN: 0.5m
4 0% UTM 37S .tiff 16 Bit
.xml
16SEP17081149-M2AS_R1C3-056358854010_01_P001
31.03.2017
17.09.2016
LV2A R1C3 Arusha, TZA
MUL: 2m; PAN: 0.5m
4 0% UTM 37S .tiff 16 Bit
.xml
16SEP17081149-M2AS_R2C1-056358854010_01_P001
31.03.2017
17.09.2016
LV2A R2C1 Arusha, TZA
MUL: 2m; PAN: 0.5m
4 0% UTM 37S .tiff 16 Bit
.xml
16SEP17081149-M2AS_R2C2-056358854010_01_P001
31.03.2017
17.09.2016
LV2A R2C2 Arusha, TZA
MUL: 2m; PAN: 0.5m
4 0% UTM 37S .tiff 16 Bit
.xml
16SEP17081149-M2AS_R2C3-056358854010_01_P001
31.03.2017
17.09.2016
LV2A R2C3 Arusha, TZA
MUL: 2m; PAN: 0.5m
4 0% UTM 37S .tiff 16 Bit
.xml
4. 15DEC30080039-M2AS-056358854010_01_P002
31.03.2017
30.12.2015
LV2A Arusha, TZA
MUL: 2m; PAN: 0.5m
4 0% UTM 37S .tiff 16 Bit
.xml
5. 15JAN04081530-M2AS-056358854010_01_P003
31.03.2017
04.01.2015
LV2A Arusha, TZA
MUL: 2m; PAN: 0.5m
4 4.7e-02%
UTM 37S .tiff 16 Bit
.xml
Quickbird-2
6.
05DEC14082246-M2AS_R1C1-056358854020_01_P001
31.03.2017
05.12.2005
LV2A R1C1 Arusha, TZA
MUL: 2m; PAN: 0.6m
4 0% UTM 37S GeoTIFF
16 Bit
.imd,
.rpb,
.til, xml
05DEC14082246-M2AS_R1C2-056358854020_01_P001
31.03.2017
05.12.2005
LV2A R1C2 Arusha, TZA
MUL: 2m; PAN: 0.6m
4 0% UTM 37S GeoTIFF
16 Bit
.imd,
.rpb,
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Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 7
.til, xml
05DEC14082246-M2AS_R2C1-056358854020_01_P001
31.03.2017
05.12.2005
LV2A R2C1 Arusha, TZA
MUL: 2m; PAN: 0.6m
4 0% UTM 37S GeoTIFF
16 Bit
.imd,
.rpb,
.til, xml
05DEC14082246-M2AS_R2C2-056358854020_01_P001
31.03.2017
05.12.2005
LV2A R2C2 Arusha, TZA
MUL: 2m; PAN: 0.6m
4 0% UTM 37S GeoTIFF
16 Bit
.imd,
.rpb,
.til, xml
7.
05SEP15081836-M2AS_R1C1-056358854020_01_P002
31.03.2017
15.09.2005
LV2A R1C1 Arusha, TZA
MUL: 2m; PAN: 0.6m
4 0% UTM 37S GeoTIFF
16 Bit
.imd,
.rpb,
.til, xml
05SEP15081836-M2AS_R2C1-056358854020_01_P002
31.03.2017
15.09.2005
LV2A R2C1 Arusha, TZA
MUL: 2m; PAN: 0.6m
4 0% UTM 37S GeoTIFF
16 Bit
.imd,
.rpb,
.til, xml
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 8
1.2 List of In-situ Data
Dataset 1 (14)
Date of Receipt from Client
Metadata (2)
Reference Mapping Date / Date of Creation
Geographic Area/- City / Region / Country
Area Coverage
Data Type (4)
Data Format (3)
Projection / Spheroid (16)
Positional Accuracy (11)
No. of Classes
Thematic Accuracy
Availability of Class Definitions
No In-situ Data Used
Lineage(29):
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Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 9
1.3 List of Ancillary Data
Dataset 1 (14)
Schools Date of Receipt from Client
Metadata (2)
Reference Mapping Date / Date of Creation
Geographic Area/- City / Region / Country
Area Coverage
Data Type (4)
Data Format (3)
Projection / Spheroid (16)
Positional Accuracy (11)
No. of Classes
Thematic Accuracy
Availability of Class Definitions
updated_schools_november_2014.shp
12.04.2016 not
available 2014/n.a. Tanzania (country level) 100% Vector *.shp GCS_WGS_1984 unknown n.a. unknown No
Lineage(29): List of schools in Tanzania.
Source (30):: Received from MaryGrace Weber.
Dataset 2 (14)
Health Facilities
Date of Receipt from Client
Metadata (2)
Reference Mapping Date / Date of Creation
Geographic Area / City / Region / Country
Area Coverage
Data Type (4)
Data Format (3)
Projection / Spheroid (16)
Positional Accuracy (11)
No. of Classes
Thematic Accuracy
Class Definitions
tanzania_health_facility_registry6322wgs84.shp
12.04.2016 not
available n.a. / n.a. Tanzania (country level) 100% Vector *.shp GCS_WGS_1984 unknown n.a. unknown No
Lineage(29): List of Health Facilities.
Source (30):: Received from MaryGrace Weber.
Dataset 3 (14)
Landmarks Date of Receipt from Client
Metadata (2)
Reference Mapping Date / Date of Creation
Geographic Area/- City / Region / Country
Area Coverage
Data Type (4)
Data Format (3)
Projection / Spheroid (16)
Positional Accuracy (11)
No. of Classes
Thematic Accuracy
Availability of Class Definitions
ARU_Landmarks.shp 21.12.2016 not
available n.a./n.a.
Arusha (municipal level)
100% Vector *.shp unknown unknown n.a. unknown No
Lineage(29): List of different points of interest in the core urban region, like stadium, bus station, post office, …
Source (30):: Received from MaryGrace Weber.
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 10
Dataset 4 (14)
Roads Date of Receipt from Client
Metadata (2)
Reference Mapping Date / Date of Creation
Geographic Area/- City / Region / Country
Area Coverage
Data Type (4)
Data Format (3)
Projection / Spheroid (16)
Positional Accuracy (11)
No. of Classes
Thematic Accuracy
Availability of Class Definitions
ARU_Roads 21.12.2016 not
available n.a./n.a.
Arusha (municipal level)
100% Vector *.shp unknown unknown n.a. unknown No
Lineage(29): Road network
Source (30):: Received from MaryGrace Weber.
Dataset 5 (14)
OSM Data Date of Receipt from Client
Metadata (2)
Reference Mapping Date / Date of Creation
Geographic Area / City / Region / Country
Area Coverage
Data Type (4)
Data Format (3)
Projection / Spheroid (16)
Positional Accuracy (11)
No. of Classes
Thematic Accuracy
Class Definitions
OSM_Data_Arusha_Tanz
ania n.a.
not available
n.a. /n.a. Arusha (municipality boundary)
100% Vector *.shp WGS 84 unknown n.a. unknown n.a.
Lineage(29): OSM data were used as basis for the road network as well as for the classification. Railway and Rivers were also extracted and enhanced.
Source (30): https://www.openstreetmap.org/search?query=Arusha#map=11/-3.4363/36.7020
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Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 11
2.1 EO Data Quality
Sensoren(8) Sentinel-2, Landsat 5, QB-2, PLX
Ba
cku
p
Re
ad
ab
ility
(1
)
Ch
eck
He
ad
er
/
Me
tad
ata
(2)
Band Specifications
Pro
ject
ion
/ S
ph
ero
id (1
6)
Sce
ne
Lo
cati
on
Co
mp
lete
ne
ss o
f
Ad
dit
ion
al D
ata
Da
ta F
orm
at
(3)
Bit
De
pth
(5)
Clo
ud
Co
ver
(in
tern
al
che
ck)
Ra
dio
me
try
To
po
gra
ph
y
Dro
pp
ed
Lin
es
/ A
rte
fact
s (7
)
Acc
ep
tan
ce S
tatu
s
Comment
s File Name [e.g yymmdd; tbd...]
No
. o
f B
an
ds
Ba
nd
Re
gis
tra
tio
n
Sp
ect
ral/
Sp
ati
al
Re
solu
tio
n
Sentinel-2
1. S2A_OPER_MSI_L1C_TL_MTI__20160204T095011_A003235_T37MBS
Y - Q Y - V Q Y - V Q Y - V Q Y - V Q Y - Q Y - V Y - V Y - V Q Y - Q Y - Q
Y - V Q
Y - V Q
Y - V
N - V Q
Yes none
Aster
2. AST_L1T_00302052006075950_20150513015728_107850
Y - Q Y - V Q Y - V Q Y - V Q Y - V Q Y - Q Y - V Y - V Y - V Q Y - Q Y - Q
Y - V Q
Y - V Q
Y - V
N - V Q
Yes none
GeoEye-1/Worldview-2
3. 16SEP17081149-M2AS-056358854010_01_P001
Y - Q Y - V Q Y - V Q Y - V Q Y - V Q Y - Q Y - V Y - V Y - V Q Y - Q Y - Q
Y - V Q
Y - V Q
Y - V
N - V Q
Yes none
4. 15DEC30080039-M2AS-056358854010_01_P002
Y - Q Y - V Q Y - V Q Y - V Q Y - V Q Y - Q Y - V Y - V Y - V Q Y - Q Y - Q
Y - V Q
Y - V Q
Y - V
N - V Q
Yes none
5. 15JAN04081530-M2AS-056358854010_01_P003
Y - Q Y - V Q Y - V Q Y - V Q Y - V Q Y - Q Y - V Y - V Y - V Q Y - Q Y - Q
Y - V Q
Y - V Q
Y - V
N - V Q
Yes none
Quickbird-2
6. 05DEC14082246-M2AS-056358854020_01_P001
Y - Q Y - V Q Y - V Q Y - V Q Y - V Q Y - Q Y - V Y - V Y - V Q Y - Q Y - Q
Y - V Q
Y - V Q
Y - V
N - V Q
Yes none
7. 05SEP15081836-M2AS-056358854020_01_P002
Y - Q Y - V Q Y - V Q Y - V Q Y - V Q Y - Q Y - V Y - V Y - V Q Y - Q Y - Q
Y - V Q
Y - V Q
Y - V
N - V Q
Yes none
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 12
2.2 In-situ Data Quality
Dataset 1 (14)
Ba
cku
p
Re
ad
ab
ility
(1)
Ch
eck
He
ad
er
/
Me
tad
ata
(2)
Ext
en
t
Pro
ject
ion
/ S
ph
ero
id
(16
)
Sp
ati
al R
eso
luti
on
Da
ta F
orm
at
(3)
Bit
De
pth
(5)
Lo
cati
on
Co
mp
lete
ne
ss
Ge
om
. M
isa
lign
me
nt
Pla
usi
bili
ty
Dro
pp
ed
Lin
es
/ A
rte
fact
s (7
)
Acc
ep
tan
ce S
tatu
s
Comments
File Name [e.g yymmdd; tbd...]
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 13
2.3 Ancillary Data Quality
Dataset 1 (14) LU Map
Re
ad
ab
ility
(1)
Ch
eck
He
ad
er
/Me
tad
ata
(2)
Da
ta C
ove
rage
M
atc
he
s S
erv
ice
A
rea
(%
)
Pro
ject
ion
/ S
ph
ero
id
, E
PS
G(1
6)
Sp
ati
al R
eso
luti
on
a
nd
un
it (
e.g
. m
, k
m)
Da
ta F
orm
at
(3)
Bit
De
pth
(5)
Co
mp
lete
ne
ss
(Ve
cto
r: A
ttri
bu
te
Ta
ble
; R
ast
er:
Th
em
ati
c V
alu
es)
Ge
om
. M
isa
lign
me
nt
Dro
pp
ed
Lin
es
/
Art
efa
cts
(E
O d
ata
o
nly
)(7
)
Uti
lity
for
Cu
rre
nt
Pro
ject
File Name [e.g yymmdd; tbd...]
updated_schools_november_2014.shp
☒ Yes
☐ No
☐ Complete
(INSPIRE/ISO19119)
☐ Incomplete
☒ not available
☒ Yes
☐ No
☐ Unknown
If no: xxx%
☒ Correct
☐ Incorrect
☐ Unknown
EPSG: 32749
n.a. *.shp n.a. ☒ Complete
☐Incomplete
☐ No
☐ Yes
☒ Unknown
If yes: XX m
☐ No
☐ Yes
☒ n.a.
If yes: xxx %
☐ None
☒ Partial
☐ Full
Comments: Accuracy of the dataset is unknown. Some points show schools, others not.
Dataset 2,3,4 (14)
Re
ad
ab
ility
(1)
Ch
eck
He
ad
er
/Me
tad
ata
(2)
Da
ta C
ove
rage
M
atc
he
s S
erv
ice
Are
a
(%)
Pro
ject
ion
/ S
ph
ero
id
EP
SG
(16
)
Sp
ati
al R
eso
luti
on
a
nd
un
it (
e.g
. m
, k
m)
Da
ta F
orm
at
(3)
Bit
De
pth
(5)
Co
mp
lete
ne
ss
(Ve
cto
r: A
ttri
bu
te
Ta
ble
; R
ast
er:
Th
em
ati
c V
alu
es)
Ge
om
. M
isa
lign
me
nt
Dro
pp
ed
Lin
es
/
Art
efa
cts
(E
O d
ata
o
nly
)(7
)
Uti
lity
for
Cu
rre
nt
Pro
ject
File Name [e.g yymmdd; tbd...]
tanzania_health_facility_registry6322wgs84.shp
☒ Yes
☐ No
☐ Complete
(INSPIRE/ISO19119)
☐ Incomplete
☒ not available
☐ Yes
☐ No
☒ Unknown
If no: %
☒ Correct
☐ Incorrect
☐ Unknown
EPSG: 32749
n.a. *.shp n.a. ☒ Complete
☐Incomplete
☐ No
☐ Yes
☒ Unknown
If yes: XX m
☐ No
☐ Yes
☒ n.a.
If yes: xxx %
☐ None
☒ Partial
☐ Full
ARU_Landmarks.shp ☒ Yes
☐ No
☐ Complete
(INSPIRE/ISO19119)
☐ Incomplete
☒ not available
☐ Yes
☐ No
☒ Unknown
If no: %
☒ Correct
☐ Incorrect
☐ Unknown
EPSG: 32749
n.a. *.shp n.a. ☐ Complete
☒Incomplete
☐ No
☐ Yes
☒ Unknown
If yes: XX m
☐ No
☐ Yes
☒ n.a.
If yes: xxx %
☐ None
☒ Partial
☐ Full
ARU_Roads ☒ Yes
☐ No
☐ Complete
(INSPIRE/ISO19119)
☐ Incomplete
☒ not available
☐ Yes
☐ No
☒ Unknown
If no: %
☒ Correct
☐ Incorrect
☐ Unknown
EPSG: 32749
n.a. *.shp n.a. ☐ Complete
☒Incomplete
☐ No
☐ Yes
☒ Unknown
If yes: XX m
☐ No
☐ Yes
☒ n.a.
If yes: xxx %
☐ None
☒ Partial
☐ Full
OSM_Data_Arusha_Tanzania ☒ Yes
☐ No
☐ Complete
(INSPIRE/ISO19119)
☐ Incomplete
☒ not available
☐ Yes
☐ No
☒ Unknown
If no: %
☒ Correct
☐ Incorrect
☐ Unknown
EPSG: 32749
n.a. *.shp n.a. ☐ Complete
☒Incomplete
☐ No
☐ Yes
☒ Unknown
If yes: XX m
☐ No
☐ Yes
☒ n.a.
If yes: xxx %
☐ None
☒ Partial
☐ Full
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 14
Comments:
tanzania_health_facility_registry6322wgs84.shp:
Accuracy of the data is unknown. Some points show schools, others not. ARU_Landmarks.shp:
Accuracy of the data is unknown.
ARU_Roads.shp:
Accuracy of the data is unknown.
OSM_Data_Arusha_Tanzania:
Were used in addition to the Road layers received from the city of Semarang. They were visually checked and corrected
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 15
3.1 Geometric Correction
Sensoren (8) Sentinel-2, Landsat 5, QB-2, PLX
Pro
cess
ing D
ate
AO
I
Cit
y /
Re
gio
n /
Co
un
try
Pro
ject
ion
/ S
ph
ero
id (1
6)
No
. &
RM
S (
m)
of
GC
Ps
(17
)
No
. &
RM
S (
m)
of
TP
s (1
8)
No
. &
RM
S (
m)
of
CP
s (1
9)
Dig
ita
l E
leva
tio
n M
od
el
(DE
M)
Mo
de
l /
Alg
ori
thm
(13
)
Re
sam
plin
g
Me
tho
d (2
0)
Va
lida
tio
n R
ep
ort
s
Acc
ep
tan
ce S
tatu
s
Output File Name [e.g yymmdd; tbd...] N
o. File Name [e.g yymmdd; tbd...]
Sentinel-2
1. S2A_OPER_MSI_L1C_TL_MTI__20160204T095011_A003235_T37MBS
12.04.2017
Arusha TZA
UTM37S/ WGS84
N/A N/A N/A N/A N/A N/A N/A Yes
S2A_OPER_MSI_L1C_TL_MTI__20160204T095011_A003235_T37MB
S
Aster
2.
AST_L1T_00302052006075950_20150513015728_107850_SWIR_456789_utm37_toa
12.04.2017
Arusha TZA
UTM37S/ WGS84
N/A N/A N/A N/A N/A N/A N/A Yes
AST_L1T_00302052006075950_20150513015728_107850_SWIR_456789_utm37_toa
AST_L1T_00302052006075950_20150513015728_107850_VNIR123_utm37_toa
12.04.2017
Arusha TZA
UTM37S/ WGS84
N/A N/A N/A N/A N/A N/A N/A Yes
AST_L1T_00302052006075950_20150513015728_107850_VNIR123_utm37_toa
GeoEye-1/Worldview-2
3. 15DEC30080039-M2AS-056358854010_01_P002_toa_PSH
12.04.2017
Arusha TZA
UTM37S / WGS84
MGCPs 11;
RMSE x1,17; y
1,07
4; RMSE 0,61; y 0,67
N/A SRTM
30 0 order polynom
CC Yes Yes
o15DEC30080039-M2AS-056358854010_01_P002_toa_PSH
4. 16SEP17081149-M2AS-056358854010_01_P001_toa_PSH
12.04.2017
Arusha TZA
UTM37S / WGS84
MGCPs 11;
RMSE x1,17; y
1,07
4; RMSE 0,61; y 0,67
N/A SRTM 30
0 order polynom
CC Yes Yes
o16SEP17081149-M2AS-056358854010_01_P001_toa_PSH
5. 15JAN04081530-M2AS-056358854010_01_P003_toa_PSH
12.04.2017
Arusha TZA
UTM37S / WGS84
MGCPs 11;
RMSE
4; RMSE 0,61; y 0,67
N/A SRTM 30
0 order polynom
CC Yes Yes
o15JAN04081530-M2AS-056358854010_01_P003_toa_PSH
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 16
x1,17; y 1,07
Quickbird-2
6. 05DEC14082246-M2AS-056358854020_01_P001_toa_PSH
12.04.2017
Arusha TZA
UTM37S / WGS84
MGCPs 15;
RMSE x1,01; y0,79
N/A N/A SRTM 30
Affin, 1st order poynom
CC Yes Yes
o05DEC14082246-M2AS-056358854020_01_P001_toa_PSH
7. 05SEP15081836-M2AS-056358854020_01_P002_toa_PSH
12.04.2017
Arusha TZA
UTM37S / WGS84
MGCPs 15;
RMSE x1,01; y0,79
N/A N/A SRTM 30
Affin, 1st order poynom
CC Yes Yes
o05SEP15081836-M2AS-056358854020_01_P002_toa_PSH
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Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 17
3.1.1 Data Fusion
Dataset 4 (14) Input A Input B Output
Acc
ep
tan
ce S
tatu
s
Comments
Filename 16SEP17081149-M2AS-
056358854010_01_P001 16SEP17081149-P2AS-
056358854010_01_P001 16SEP17081149-M2AS-
056358854010_01_P001_toa_PSH
none
Sensor GeoEye-1 GeoEye-1 GeoEye-1
Method PCI / Pansharp2 PCI / Pansharp2 PCI / Pansharp2
Spatial Resolution / (MS/Pan)
2m 0,5m 0,5m 0,5m 0,5m MS
Band Combination BGR NIR PAN BGR NIR
Data Format (3) & Bit Depth (5)
*.tif 16 Bit u *.tif 16 Bit u *.pix 16 Bit u Yes
Dataset 5 (14) Input A Input B Output
Acc
ep
tan
ce S
tatu
s
Comments
Filename 15DEC30080039-M2AS-
056358854010_01_P002 15DEC30080039-P2AS-
056358854010_01_P002 15DEC30080039-M2AS-
056358854010_01_P002_toa_PSH
none
Sensor GeoEye-1 GeoEye-1 GeoEye-1
Method PCI / Pansharp2 PCI / Pansharp2 PCI / Pansharp2
Spatial Resolution / (MS/Pan)
2m 2m 0,5m Pan 0,5m MS
Band Combination BGR NIR PAN BGR NIR
Data Format (3) & Bit Depth (5)
*.tif 16 Bit u *.tif 16 Bit u *.pix 16 Bit u Yes
Dataset 6 (14) Input A Input B Output
Acc
ep
tan
ce S
tatu
s
Comments
Filename 15JAN04081530-M2AS-
056358854010_01_P003 15JAN04081529-P2AS-
056358854010_01_P003 15JAN04081530-M2AS-
056358854010_01_P003_toa_PSH
none
Sensor Worldview-2 Worldview-2 Worldview-2
Method PCI / Pansharp2 PCI / Pansharp2 PCI / Pansharp2
Spatial Resolution / (MS/Pan)
2m MS 0,5m Pan 0,5m MS
Band Combination BGR NIR PAN BGR NIR
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 18
Data Format (3) & Bit Depth (5)
*.tif 16 Bit u *.tif 16 Bit u *.pix 16 Bit u Yes
Dataset 7 (14) Input A Input B Output
Acc
ep
tan
ce S
tatu
s
Comments
Filename 05DEC14082246-M2AS-
056358854020_01_P001 05DEC14082245-P2AS-
056358854020_01_P001 05DEC14082246-M2AS-
056358854020_01_P001_toa_PSH
none
Sensor Quickbird-2 Quickbird-2 Quickbird-2
Method PCI / Pansharp2 PCI / Pansharp2 PCI / Pansharp2
Spatial Resolution / (MS/Pan)
2m MS 0,6m Pan 0,6m MS
Band Combination BGR NIR PAN BGR NIR
Data Format (3) & Bit Depth (5)
*.tif 16 Bit u *.tif 16 Bit u *.pix 16 Bit u Yes
Dataset 8 (14) Input A Input B Output
Acc
ep
tan
ce S
tatu
s
Comments
Filename 05SEP15081836-M2AS-
056358854020_01_P002 05SEP15081836-P2AS-
056358854020_01_P002 05SEP15081836-M2AS-
056358854020_01_P002_toa_PSH
none
Sensor Quickbird-2 Quickbird-2 Quickbird-2
Method PCI / Pansharp2 PCI / Pansharp2 PCI / Pansharp2
Spatial Resolution / (MS/Pan)
2m MS 0,6m Pan 0,6m MS
Band Combination BGR NIR PAN BGR NIR
Data Format (3) & Bit Depth (5)
*.tif 16 Bit u *.tif 16 Bit u *.pix 16 Bit u Yes
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 19
3.2 Data Processing
Sensoren (8)
Landsat 5, QB-2, PLX Atmospheric Correction
Radiometric Processing (15)
Topographic Normalisation
OTHER Calculation:
Acc
ep
tan
ce S
tatu
s
Ba
cku
p
Comment
No.
File Name [e.g yymmdd; tbd...] Processing
Date
pro
cess
ed
Software / Method
pro
cess
ed
Software / Method
pro
cess
ed
Software / Method
pro
cess
ed
Software / Method
Sentinel-2
S2A_OPER_MSI_L1C_TL_MTI__20160204T095011_A003235_T37MBS
10.04.2017 No N/A No N/A No N/A No N/A Yes Yes none
Aster
2. AST_L1T_00302052006075950_20150513015728_107850_VNIR123_utm37
12.04.2017 No N/A No N/A No N/A Yes GAFmap /
TOA Yes No none
GeoEye1/Worldview-2
3. 16SEP17081149-M2AS-056358854010_01_P001
12.04.2017 No N/A No N/A No N/A Yes GAFmap / TOA
Yes No none
4. 15DEC30080039-M2AS-056358854010_01_P002
12.04.2017 No N/A No N/A No N/A Yes GAFmap / TOA
Yes No none
5. 15JAN04081530-M2AS-056358854010_01_P003
12.04.2017 No N/A No N/A No N/A Yes GAFmap / TOA
Yes No none
Quickbird-2
6. 05DEC14082246-M2AS-056358854020_01_P001
12.04.2017 No N/A No N/A No N/A Yes GAFmap / TOA
Yes No none
7. 05SEP15081836-M2AS-056358854020_01_P002
12.04.2017 No N/A No N/A No N/A Yes GAFmap / TOA
Yes No none
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 20
4.1 Classification
Sensors (8) Sentinel-2, Aster, WorldView, GeoEye, QB
Processing Date
Cloud Masking Thematic
Classification Manual
Enhancement Mosaicking
Acc
ep
tan
ce S
tatu
s
Ba
cku
p
Comment No.
File Name [e.g yymmdd; tbd...]
pro
cess
ed
Software / Method
pro
cess
ed
Software / Method
pro
cess
ed
Software /
Method
pro
cess
ed
Software / Method
1. S2A_OPER_MSI_L1C_TL_MTI__20160204T095011_A003235_T37MBS
09.06.2017 no No clouds Yes
eCognition/semi-automatic approach
Yes
eCognition and ArcGIS 10 / Visual Interpretation
Yes
ArcGIS 10 / Mosaic Dataset with manual neatline selection
Yes Yes
2. AST_L1T_00302052006075950_20150513015728_107850_VNIR123_utm37
07.08.2017 no View clouds No NA No NA No
Only used to assist visual interpretatio
n
3.
16SEP17081149-M2AS-056358854010_01_P001 15DEC30080039-M2AS-056358854010_01_P002 15JAN04081530-M2AS-056358854010_01_P003
01.06.2017 no No clouds Yes
GAFMap and ArcMap/visual mapping
Yes
GAFMap and ArcMap/Visual interpretation
Yes
ArcGIS 10 / Mosaic Dataset with manual neatline selection
Yes Yes
4.
05DEC14082246-M2AS-056358854020_01_P001 05SEP15081836-M2AS-056358854020_01_P002
07.08.2017 no No clouds Yes
GAFMap and ArcMap/visual mapping
Yes
GAFMap and ArcMap/Visual interpretation
Yes
ArcGIS 10 / Mosaic Dataset with manual neatline selection
Yes Yes
Earth Observation for Sustainable
Development – Urban
Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 21
4.2 Intermediate Quality Control of LCLU Data
No.
x = ok (checked/performed) o = not performed
n/a = not applicable
Co
mp
lian
t
Each item was checked during production and if necessary the source of error was identified and
corrected.
Description of QC item Comment
1 Data in predefined coordinate system?
(e.g. UTM WGS84 system) x
2 Do all features have attributes? x
3
All feature attributes are valid (attribute
value range, flags, correct comments)?
1) Do all features have a valid class
name?
x
4 All feature attributes present and filled
in? x
5 Data coherence at the border of the
urban area checked? x
6 Positional Accuracy of the features
according to User Specifications? x
7 All feature geometries valid? x
8
Vertices of feature geometries adjusted?
(e.g. missing, duplicated/ very close
vertices)
x
9 Multipart features resolved (Ring loops)? x
10 No data gaps? (Clean Gaps only if there
are over ~200 gaps)
11 No data overlaps? (Clean Overlaps only if
there are over ~200 gaps) x
12
Feature geometries optimized (e.g.
spikes, cutbacks minimized) (Find acute
angles, remove angles < 15°)
x
13 Minimum Mapping Area checked? (Urban
0.25ha, peri-urban 0.5ha) x
14 Minimum Mapping Width checked? x
15 Data checked for unnecessary polygon
boundaries? (after final dissolve) x
16 Service Area completely covered by
requested data (no data gaps)? x
17 No data outside delivery AoI? x
18
Add two new columns with the code and
the name of the class.(Name them:
code_year and name_year of the actual
classification”
19
Feature attribution and feature relations
plausible? (Plausibility Check ) --> Change
product
x
20 Calculation of area [AREA_km2] adjusted
to final data? x
21 Re-calculation of [ID] (FID + 1)? x
22
LCLU product file naming and versioning
according to specifications?
(City_program_product_referenceDate_C
oordinateSystem)
x
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 22
5.1 Thematic Accuracy
No. Product Name
Processing Date
Area Coverage
No
. o
f C
lass
es
Re
fere
nce
Da
ta
Sa
mp
le S
ize
Sa
mp
ling U
nit
(21
)
Sa
mp
ling
De
sign
(22
)
Sa
mp
le E
xclu
sio
n
Cri
teri
a (2
3)
Th
em
ati
c A
ccu
racy
(2
4)
Acc
ep
tan
ce S
tatu
s
Comment
1.
Land Cover Map 2015
14.08.2017 Full AOI (urban and peri-urban)
Mapped. 22
classes
VHR data sources for urban area. Bing Maps by Microsoft and Google Imagery for Peri-urban area.
215 per strata (Level 1 classes)
Point Stratified random sampling None, all used 88.8% Y
Four classes did not occur: ‘1216 Military’; ‘1230 Railway’; ‘1250 Port’ and ‘3300 Bare Soil’
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 23
5.2 Error Matrices
Class Name (columns = Ground Truth; Rows = Mapped Class)
Re
sid
en
tia
l
Co
mm
erc
ial
Ro
ad
s
Air
po
rt
Min
ing
Co
nst
ruct
ion
La
nd
wit
ho
ut
curr
en
t u
se
Urb
an
Pa
rks
Re
cre
ati
on
al
Fa
cilit
ies
Ce
me
tery
Agri
cult
ure
Fo
rest
Na
tura
l A
rea
s
Wa
ter
User Accuracy and Confidence Interval at 95% Confidence
Level
Class ID 11 121 122 124 131 132 133 141 142 143 2 31 32 5
Totals
Residential 11 139 9 0 0 0 0 0 0 0 0 22 6 2 0 178 78.1 % ± 6.4 Commercial 121 1 13 0 0 2 0 0 0 16 81.3 % ± 22.3
Roads 122 0 0 147 0 0 0 0 0 0 0 0 3 0 0 150 98.0 % ± 2.6 Airport 124 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 100.0 % ± n.a. Mining 131 0 0 0 0 66 0 0 0 0 0 0 0 0 0 66 100.0 % ± 0.7
Construction 132 0 0 0 0 0 45 0 0 0 0 0 0 3 0 48 93.8 % ± 7.9 Land without current use 133 0 0 0 0 0 0 6 0 0 0 1 0 0 0 7 85.7 % ± 33.1
Urban Parks 141 0 1 0 0 0 0 0 11 0 0 0 0 0 0 12 91.7 % ± 19.8 Recreational Facilities 142 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 100.0 % ± n.a
Cemetery 143 0 0 0 0 0 0 0 0 0 3 0 0 0 0 3 100.0 % ± 16.7 Agriculture 2 1 0 0 0 0 0 0 0 0 0 192 18 4 0 215 89.3 % ± 4.4
Forest 31 7 0 0 0 0 0 0 2 1 0 11 154 7 0 182 84.6 % ± 5.5 Natural Areas 32 1 0 0 0 0 0 0 0 0 0 3 0 29 0 33 87.9 % ± 12.7
Water 5 0 0 0 0 0 0 0 0 0 0 0 0 0 29 29 100.0 % ± 1.7
Totals 149 23 147 1 66 45 6 13 2 3 231 181 45 29 941
Producer Accuracy and Confidence Interval at 95% Confidence Level
93
.3%
±
4.4
56
.5 %
±
22
.4
10
0.0
%
±0
.3
10
0.0
%
±n
.a.
10
0.0
%
± 0
.8
10
0,0
0%
±1
.1
10
0.0
% ±
8
.3
84
,62
%
± 2
3.5
50
,00
%
±n
.a
10
0,0
0%
±
n.a
83
,12
%
±5
.1
85
,08
%
±5
.5
64
,44
%
±1
5.1
10
0.0
%
±1
.7 Overall Accuracy: 88.8%
Confidence Interval 86.8% - 90.9%
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6.1 Completeness
INPUT DATA
No.
Item AOI Coverage [km²]
Area Coverage [km²]
Completeness of Coverage (25)
No. of Scenes Scenes used in Production
Completeness of Verification (27)
Metadata Comments
1. HR EO Data
Arusha
414 km² 100% 2 2 100% Yes none
2. VHR EO Data 211 km² 100% 5 5 100% Yes none
3. In-situ Data N/A N/A N/A N/A N/A N/A none
4. Ancillary Data N/A N/A N/A N/A N/A N/A none
PRODUCTS
No.
Product (28) AOI Coverage [km²]
Product Coverage [km²]
Completeness of Coverage (25)
Completeness of Classification (26)
Unclassifiable Area [%]
Completeness of Verification (27)
Metadata Comments
1. Urban and Peri Urban Areas 2005 and 2015
625 km² 625 km² 100% 100% 0% 100% Yes none
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6.2 Compliancy
Product 1 (28) EO4SD_Arusha_WB_LULC_2005_2015
Abstract
Land Use/Land Cover (LU/LC) information product contains spatial explicit information on different land use and land cover occurring in both the Core and Peri-Urban areas of the City of Arusha. The Core area has detailed LU/LC nomenclature that is either at Level 3 or 4 whereas the Peri-Urban area LU/LC nomenclature is at an aggregated Level 1 or 2. The input data for the Core area was the Very High Resolution data of WorldView/GeoEye (2015) and Quickbird (2005) and the input data for the Peri-Urban area was Aster (2005) and Sentinel-2 (2015). The LU/LC product is the Baseline Product from which various derived products (such as Green Areas and Informal Settlements) are produced.
Service / Product Specifications
Area Coverage
Requirements Achieved Specifications Compliancy Comments
Country 1: Tanzania Country 1: Tanzania Yes ---
Country 2: N/A Country 2: N/A --- ---
Country 3: N/A Country 3: N/A --- ---
Country 4: N/A Country 4: N/A --- ---
A) Wall-to-wall: Arusha city A) Wall-to-wall: Arusha city Yes --- Core (211 km²) and Peri-Urban
area (414 km²) Full coverage of AOIs, Core and Peri-Urban area
Area of Interest. 625 km², defined by the national user.
B) Sampling based: B) Sampling based: --- ---
N/A N/A
Time Period - Update Frequency
Requirements Achieved Specifications Compliancy Comments
Baseline Year(s): Baseline Year(s): Yes
See EO data acquisition dates. 2005 and 2015 (+/- 1 years) 2005 and 2015
B) Update Frequency B) Update Frequency Yes --
No update No update
Geographic Reference System
Requirements Achieved Specifications Compliancy Comments
EPSG:32737; WGS84 / UTM 37S
EPSG:32737; WGS84 / UTM 37S Yes --
Mapping Classes and Definitions
Requirements Achieved Specifications Compliancy
26 Main classes (see 0.1 Requirements sheet) All classes were mapped according to their definition.
Yes
Cloud and Shadow Detection and Removal
Requirements Achieved Specifications Compliancy Comments
Cloud and shadow covered areas shall be removed.
Full AoI coverage with spatial explicit LULC cover information
Yes ---
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Spatial Resolution
Requirements Achieved Specifications Compliancy Comments
Na Na Na Only applicable for raster
data
Minimum Mapping Unit (MMU)
Requirements Achieved Specifications Compliancy Comments
0.25 ha and 0.5 ha MMU of 0.25 ha and 0.5 ha Yes ---
Data Type
Requirements Achieved Specifications Compliancy Comments
Vector data yes Yes ---
GeoPDF None were set Na ---
Bit Depth
Requirements Achieved Specifications Compliancy Comments
None Na Na ---
Data Format
Requirements Achieved Specifications Compliancy Comments
*.shp Shapefile Shapefile Yes ---
(open cross-platform format)
Class Coding (Raster Data Only)
Requirements Achieved Specifications Compliancy
Na Na ---
Metadata
Requirements Achieved Specifications Compliancy Comments
Metadata ISO compliant INSPIRE compliant and attached to
product Yes ---
Service / Product Quality
Thematic Accuracy
Requirements Achieved Specifications Compliancy Comments
Overall Accuracy: >80% 88% Yes ---
Positional Accuracy
Requirements Achieved Specifications Compliancy Comments
RMSE < 15 m 15m Y ---
Delivery Procedure
Service Provision
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Requirements Achieved Specifications Compliancy Comments
online via FTP Uploaded to FTP Yes ---
Delivery Date
Requirements Achieved Specifications Compliancy Comments
End of August 2017 Third Delivery April 2018 Yes ---
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Glossary
Quality Checks
Prefix Suffix Explanation
Y (Yes) - V Visually checked
N (No) - Q Quantitatively/qualitatively checked
N/A - V Q Visually and quantitatively/qualitatively checked
- N/A Not applicable
Readability (1) Check readability of all required input data. Can the data be stored again?
Header / Metadata (2) Check Image Header Information and/or Metadata for completeness / distinctive features.
Data Format (3) For digital data, please give file format (e.g. *.tiff, *.shp).
Data Type (4) Please specify the type of the data (e.g. raster, vector or analogue).
Bit Depth (5) Please give pixel depth and sign of raster data (e.g. 8 bit unsigned integer).
Dynamic Range (6) Check dynamic range of all image bands. Visual check of dynamic range should be accompanied by histograms and statistics.
Dropped Lines & Artefacts (7) Check Image for dropped lines and other artefacts. If such occurs, please give description of extent and influence in the Comments section.
Sensor 1, 2, … (8) Please delete / add additional sensor sections as necessary.
Purpose (9)
The purpose and use of the given auxiliary or reference data should be stated:
ORHTOrectification, GEOmetric correction, REFerence, VERification source, POSitional ACCuracy assessment, THEmatic ACCuracy assessment.
Sampling Methodology (10)
Methodology of in situ or reference/auxiliary data sampling scheme should be outlined. In case of sample plots, also state how the plot positions have been determined (e.g. from GPS measurements, topographic maps, terrestrial triangulation, EO data, etc.) and how the sampling grid was established.
Positional Accuracy (11)
Positional accuracy of collected in situ data should be given. For reference/auxiliary data it MUST be given. If unknown, the data’s use must be explained. For DEM or other data with 3D information please specify both vertical and horizontal Positional Accuracy. For analogue data (e.g. maps) try to give approximate accuracy related to mapping scale.
Completeness (12) Data should be checked for spatial/temporal/content gaps.
Model / Algorithm (13)
Give the name of the software and its version. Specify the software module/algorithm used for: a) geometric correction, e.g., Polynomial and its degree, Rational Functions, Thin Plate Spline, etc. b) classification, e.g., ISODATA, Maximum Likelihood, Neural Networks, etc.
Dataset 1, 2, … (14) Please delete / add additional dataset sections as necessary.
Radiometric Processing (15) State whether (and which) radiometric processing was applied (e.g., contrast enhancement, histogram matching, bundle block adjustment, radiometric normalisation, filtering, etc.).
Projection; Spheroid / Ellipsoid (16)
Always specify completely, i.e. at minimum the Projection (+Zone, if applicable), Spheroid / Ellipsoid, Map Datum. Give additional information if necessary to unambiguously define the reference system.
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Ground Control Points (GCPs) (17)
Give the number, distribution and RMS of used Ground Control Points (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of GCPs should be attached as a snapshot, or described in the Comments section.
Tie Points (TPs) (18)
In case of mosaicking, give the number of used Tie Points and their total RMS (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of TPs should be attached as a snapshot, or described in the Comments section.
Check Points (CPs) (19)
The real measure of positional accuracy and the only measure which should be examined as to its Acceptable Range. Give the independent Check Points’ total RMS (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of CP's should be attached as a snapshot, or described in the Comments section.
Resampling Method (20) Specify, if / which resampling algorithm has been used (e.g. NN=Nearest Neighbour, BIL=Bilinear Interpolation, CC=Cubic Convolution).
Sampling unit (21) Specify sampling unit of Accuracy Assessment as POINT, FRAME, POLYGON and how it is treated (e.g. pixel center, polygon centre, etc.).
Sampling Design (22) SYST=Systematic, RAND=Random, STRAT=Stratified, SBCLASS=Stratified by class, SBAREA=Stratified by area
Sample exclusion criteria (23)
Describe which criteria you apply for sampling point selection resp. exclusion of certain points. For example, if the point is too close to a class boundary (less than 1 pixel), it is excluded. If the selected sample point is not representative of the class , it is excluded. For these reasons, it is recommended to oversample by 10% to compensate for sample point exclusion.
Thematic Accuracy (24)
Provide a detailed description of the Accuracy Assessment results in the form of error matrices showing commission and omission errors, user’s , producer’s and overall accuracies and other measures of Thematic Accuracy, as the confidence level (usually fixed at 95%) and the respective confidence interval, at least for the overall accuracy. If classification is done in a phased approach, e.g. if Forest Area and subsequently Forest Type are mapped, independent reports have to be produced.
Completeness of Coverage (25) State whether the coverage is limited to a subset, or portion of the final product.
Completeness of Classification (26)
State whether classification was constrained to a subset, or portion of the final product.
Completeness of Verification (27)
State whether verification applied to lineage, positional, or thematic accuracy is constrained to a subset, or portion of the final product.
Product (28) Please delete / add additional product sections as necessary.
Lineage (29) Definition (INSPIRE 2015): Lineage is “a statement on process history and/or overall quality of the spatial data set. Where appropriate it may include a statement whether the data set has been validated or quality assured, whether it is the official
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version (if multiple versions exist), and whether it has legal validity. The value domain of this element is free text.”
Source(30) Definition (INSPIRE, 2015): “This is the description of the organisation responsible for the establishment, management, maintenance or distribution of the resource. This description shall include: name of the organisation and contact email address.”
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Earth Observation for Sustainable Development - Urban
Quality Assurance and Quality Control Sheets These QA/QC Templates were prepared by GAF AG compliant with ISO 9001:2008 Quality Management System
standards and can only be used by Partners in the current EO4SD-Urban Project.
Project Title: EO4SD-Urban
Project Leader: GAF AG
Service Provider: GAF Editor: A. Broszeit
Client: WB Date: 24.04.2018
Product: Urban Green Areas
Overview of QC-Sheets and Processing Steps Sheet used Sheet filled
in
Requirements
0.1 Requirements Yes Yes
Specifications of Input Data
1.1 List of EO Data Yes Yes
1.2 List of In-situ Data Yes Yes
1.3 List of Ancillary Data Yes Yes
Data Quality Checks
2.1 EO Data Quality Yes Yes
2.2 In-situ Data Quality Yes Yes
2.3 Ancillary Data Quality Yes Yes
Pre-Processing of EO Data
3.1 Geometric Correction Yes Yes
3.1.1 Data Fusion Yes Yes
3.2 Data Processing Yes Yes
Thematic Processing
4.1 Classification Yes Yes
Accuracy Assessment
5.1 Thematic Accuracy Yes Yes
5.2 Error Matrices Yes Yes
Delivery Checks / Delivery
6.1 Completeness Yes Yes
6.2 Compliancy Yes Yes
Glossary (index numbers in the QA/QC tables refer to the glossary at the end of this document) Further QC-relevant Documents:
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Comments / Characteristics:
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0.1 Requirements
Product 1 (28) Urban Green Areas 2005 and 2015
Abstract
Urban Green Areas information product contains spatial explicit information on green areas within the urban extent. Green areas include linear green features such as river alignments, hedges and trees, as well as public parks, private gardens, forested areas, etc. Urban Green Areas & Change dataset is based on Very High Resolution (VHR) satellite imagery by means of automated classification processing techniques.
Service / Product Specifications
Area Coverage
Country: Tanzania A) Wall-to-wall: n/a
City: Arusha Selected Sites: Area of Interest defined with end-users
Area km² Core Urban: 211 B) Sampling based: n/a
Time Period - Update Frequency
A) Baseline Year(s): B) Update Frequency
2005 and 2015/2016 2 points in time
Comments: None
Geographic Reference System
WGS84 UTM Zone 37S
Mapping Classes and Definitions
Single date
0 Non-urban green area
1 Urban green area
Change product
0 Non-urban green area
1 Permanent urban green area
2 Loss of urban green area
3 New urban green area
Cloud and Cloud Shadow Detection and Removal
n/a
Spatial Resolution
1 m
Minimum Mapping Unit (MMU)
1 m²
Data Type & Format
Raster data in GEOTIFF
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Bit Depth
8 bit for *.tif raster files
Class Coding
Class Code Class Name RGB Code
Single date
0 Non-urban green area 240/240/240
1 Urban green area 56/168/0
Change product
0 Non-urban green area 255/245/235
1 Permanent urban green area 56/168/0
2 Loss of urban green area 255/100/100
3 New urban green area 152/230/0
Metadata
ISO standard 19115 "Metadata" and ISO 19139 "XML Scheme Implementation"
Service / Product Quality
Thematic Accuracy
> 80%
Positional Accuracy
3 m (CE90)
Delivery Procedure
Service Provision
Online via FTP
Delivery Date
End of April 2018
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1.1 List of EO Data
Sensoren (8) GE-1, WV-2, QB-2
Incoming Date
Acquisition Date
Proc. Level
Path / Row
AOI - City / Region / Country
Spatial Res. No. of Bands
Cloud Cover (Data Provider)
Projection / Spheroid (16)
Data Format (3)
Bit Depth (5)
Header / Metadata (2)
File Name [e.g yymmdd; tbd...]
WV-2, GE-1
1.
16SEP17081149-M2AS_R1C1-056358854010_01_P001
31.03.2017
17.09.2016
LV2A R1C1 Arusha, TZA
MUL: 2m; PAN: 0.5m
4 0% UTM 37S .tiff 16 Bit
.xml
16SEP17081149-M2AS_R1C2-056358854010_01_P001
31.03.2017
17.09.2016
LV2A R1C2 Arusha, TZA
MUL: 2m; PAN: 0.5m
4 0% UTM 37S .tiff 16 Bit
.xml
16SEP17081149-M2AS_R1C3-056358854010_01_P001
31.03.2017
17.09.2016
LV2A R1C3 Arusha, TZA
MUL: 2m; PAN: 0.5m
4 0% UTM 37S .tiff 16 Bit
.xml
16SEP17081149-M2AS_R2C1-056358854010_01_P001
31.03.2017
17.09.2016
LV2A R2C1 Arusha, TZA
MUL: 2m; PAN: 0.5m
4 0% UTM 37S .tiff 16 Bit
.xml
16SEP17081149-M2AS_R2C2-056358854010_01_P001
31.03.2017
17.09.2016
LV2A R2C2 Arusha, TZA
MUL: 2m; PAN: 0.5m
4 0% UTM 37S .tiff 16 Bit
.xml
16SEP17081149-M2AS_R2C3-056358854010_01_P001
31.03.2017
17.09.2016
LV2A R2C3 Arusha, TZA
MUL: 2m; PAN: 0.5m
4 0% UTM 37S .tiff 16 Bit
.xml
2. 15DEC30080039-M2AS-056358854010_01_P002
31.03.2017
30.12.2015
LV2A Arusha, TZA
MUL: 2m; PAN: 0.5m
4 0% UTM 37S .tiff 16 Bit
.xml
3. 15JAN04081530-M2AS-056358854010_01_P003
31.03.2017
04.01.2015
LV2A Arusha, TZA
MUL: 2m; PAN: 0.5m
4 4.7e-02%
UTM 37S .tiff 16 Bit
.xml
Quickbird-2
4.
05DEC14082246-M2AS_R1C1-056358854020_01_P001
31.03.2017
05.12.2005
LV2A R1C1 Arusha, TZA
MUL: 2m; PAN: 0.6m
4 0% UTM 37S GeoTIFF
16 Bit
.imd,
.rpb,
.til, xml
05DEC14082246-M2AS_R1C2-056358854020_01_P001
31.03.2017
05.12.2005
LV2A R1C2 Arusha, TZA
MUL: 2m; PAN: 0.6m
4 0% UTM 37S GeoTIFF
16 Bit
.imd,
.rpb,
.til, xml
05DEC14082246-M2AS_R2C1-056358854020_01_P001
31.03.2017
05.12.2005
LV2A R2C1 Arusha, TZA
MUL: 2m; PAN: 0.6m
4 0% UTM 37S GeoTIFF
16 Bit
.imd,
.rpb,
.til, xml
05DEC14082246-M2AS_R2C2-056358854020_01_P001
31.03.2017
05.12.2005
LV2A R2C2 Arusha, TZA
MUL: 2m; PAN: 0.6m
4 0% UTM 37S GeoTIFF
16 Bit
.imd,
.rpb,
.til, xml
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5.
05SEP15081836-M2AS_R1C1-056358854020_01_P002
31.03.2017
15.09.2005
LV2A R1C1 Arusha, TZA
MUL: 2m; PAN: 0.6m
4 0% UTM 37S GeoTIFF
16 Bit
.imd,
.rpb,
.til, xml
05SEP15081836-M2AS_R2C1-056358854020_01_P002
31.03.2017
15.09.2005
LV2A R2C1 Arusha, TZA
MUL: 2m; PAN: 0.6m
4 0% UTM 37S GeoTIFF
16 Bit
.imd,
.rpb,
.til, xml
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1.2 List of In-situ Data
Dataset 1 (14) Incoming Date Acquisition Date AOI - City / Region / Country
Data Type (4)
Projection / Spheroid (16)
No. of Sample Plots
Sampling Design (22)
Positional Accuracy (11)
Purpose (9)
No In-situ Data Used
Lineage:
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1.3 List of Ancillary Data
Dataset 1 (14)
LULC Date of Receipt from Client
Metadata (2)
Reference Mapping Date / Date of Creation
Geographic Area/- City / Region / Country
Area Coverag
e
Data Type (4)
Data Format (3)
Projection / Spheroid (16)
Positional Accuracy (11)
No. of Classes
Thematic
Accuracy
Availability of Class Definitions
EO4SD-Urban Land Use/Land Cover Baseline Product
2017-07-30 Yes
(INSPIRE) 2006 and 2016
Arsuha (urban area) 100% Vector *.shp EPSG:32736, WGS 84 / UTM zone 36S
< 3 m 24 88% Yes
Lineage(29): /
Source (30):: GAF AG
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2.1 EO Data Quality
Sensoren(8)
WV, GE GE-1, WV-2, QB-2
Ba
cku
p
Re
ad
ab
ility
(1
)
Ch
eck
He
ad
er
/ M
eta
da
ta
(2)
Band Specifications
Pro
ject
ion
/ S
ph
ero
id (1
6)
Sce
ne
Lo
cati
on
Co
mp
lete
ne
ss o
f
Ad
dit
ion
al D
ata
Da
ta F
orm
at
(3)
Bit
De
pth
(5)
Clo
ud
Co
ver
(in
tern
al
che
ck)
Ra
dio
me
try
To
po
gra
ph
y
Dro
pp
ed
Lin
es
/ A
rte
fact
s (7
)
Acc
ep
tan
ce S
tatu
s
Comment
s File Name [e.g yymmdd; tbd...]
No
. o
f B
an
ds
Ba
nd
Re
gis
tra
tio
n
Sp
ect
ral/
Sp
ati
al
Re
solu
tio
n
GeoEye-1/Worldview-2
1. 16SEP17081149-M2AS-056358854010_01_P001
Y - Q Y - V Q Y - V Q Y - V Q Y - V Q Y - Q Y - V Y - V Y - V Q Y - Q Y - Q
Y - V Q
Y - V Q
Y - V
N - V Q
Yes none
2. 15DEC30080039-M2AS-056358854010_01_P002
Y - Q Y - V Q Y - V Q Y - V Q Y - V Q Y - Q Y - V Y - V Y - V Q Y - Q Y - Q
Y - V Q
Y - V Q
Y - V
N - V Q
Yes none
3. 15JAN04081530-M2AS-056358854010_01_P003
Y - Q Y - V Q Y - V Q Y - V Q Y - V Q Y - Q Y - V Y - V Y - V Q Y - Q Y - Q
Y - V Q
Y - V Q
Y - V
N - V Q
Yes none
Quickbird-2
4. 05DEC14082246-M2AS-056358854020_01_P001
Y - Q Y - V Q Y - V Q Y - V Q Y - V Q Y - Q Y - V Y - V Y - V Q Y - Q Y - Q
Y - V Q
Y - V Q
Y - V
N - V Q
Yes none
5. 05SEP15081836-M2AS-056358854020_01_P002
Y - Q Y - V Q Y - V Q Y - V Q Y - V Q Y - Q Y - V Y - V Y - V Q Y - Q Y - Q
Y - V Q
Y - V Q
Y - V
N - V Q
Yes none
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2018 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 10
2.2 In-situ Data Quality
Dataset 1 (14) No Data used
Ba
cku
p
Re
ad
ab
ility
(1)
Ch
eck
He
ad
er
/ M
eta
da
ta (2
)
Ext
en
t
Pro
ject
ion
/ S
ph
ero
id
(16
)
Sp
ati
al R
eso
luti
on
Da
ta F
orm
at
(3)
Bit
De
pth
(5)
Lo
cati
on
Co
mp
lete
ne
ss
Ge
om
. M
isa
lign
me
nt
Pla
usi
bili
ty
Dro
pp
ed
Lin
es
/
Art
efa
cts
(7)
Acc
ep
tan
ce S
tatu
s
Comments File Name [e.g yymmdd; tbd...]
No In-situ Data Used
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2018 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 11
2.3 Ancillary Data Quality
Dataset 1 (14)
LULC
Re
ad
ab
ility
(1
)
Ch
eck
He
ad
er
/Me
tad
ata
(2)
Da
ta C
ove
rage
Ma
tch
es
Se
rvic
e A
rea
(%
)
Pro
ject
ion
/ S
ph
ero
id
, E
PS
G(1
6)
Sp
ati
al R
eso
luti
on
a
nd
un
it (
e.g
. m
, k
m)
Da
ta F
orm
at
(3)
Bit
De
pth
(5)
Co
mp
lete
ne
ss
(Ve
cto
r: A
ttri
bu
te
Ta
ble
; R
ast
er:
Th
em
ati
c V
alu
es)
Ge
om
. M
isa
lign
me
nt
Dro
pp
ed
Lin
es
/ A
rte
fact
s (
EO
da
ta
on
ly)(
7)
Uti
lity
for
Cu
rre
nt
Pro
ject
File Name [e.g yymmdd; tbd...]
EO4SD-Urban LULC Product ☒ Yes
☐ No
☒ Complete (INSPIRE/ISO19119)
☐ Incomplete
☐ not available
☒ Yes
☐ No
☐ Unknown If no: xxx%
☒ Correct
☐ Incorrect
☐ Unknown EPSG: 32749
n.a. *.shp n.a. ☒ Complete
☐Incomplete
☒ No
☐ Yes
☐ Unknown If yes: XX m
☒ No
☐ Yes
☐ n.a. If yes: xxx %
☐ None
☐ Partial
☒ Full
Comments: /
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
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3.1 Geometric Correction
Sensoren (8) GE-1, WV-2, QB-2
Pro
cess
ing D
ate
AO
I
Cit
y /
Re
gio
n /
Co
un
try
Pro
ject
ion
/ S
ph
ero
id (1
6)
No
. &
RM
S (
m)
of
GC
Ps
(17
)
No
. &
RM
S (
m)
of
TP
s (1
8)
No
. &
RM
S (
m)
of
CP
s (1
9)
Dig
ita
l E
leva
tio
n M
od
el
(DE
M)
Mo
de
l /
Alg
ori
thm
(13
)
Re
sam
plin
g
Me
tho
d (2
0)
Va
lida
tio
n R
ep
ort
s
Acc
ep
tan
ce S
tatu
s
Output File Name [e.g yymmdd; tbd...] N
o. File Name [e.g yymmdd; tbd...]
GeoEye-1/Worldview-2
1. 15DEC30080039-M2AS-056358854010_01_P002_toa_PSH
12.04.2017
Arusha TZA
UTM37S / WGS84
MGCPs 11;
RMSE x1,17; y
1,07
4; RMSE 0,61; y 0,67
N/A SRTM
30 0 order polynom
CC Yes Yes
o15DEC30080039-M2AS-056358854010_01_P002_toa_PSH
2. 16SEP17081149-M2AS-056358854010_01_P001_toa_PSH
12.04.2017
Arusha TZA
UTM37S / WGS84
MGCPs 11;
RMSE x1,17; y
1,07
4; RMSE 0,61; y 0,67
N/A SRTM 30
0 order polynom
CC Yes Yes
o16SEP17081149-M2AS-056358854010_01_P001_toa_PSH
3. 15JAN04081530-M2AS-056358854010_01_P003_toa_PSH
12.04.2017
Arusha TZA
UTM37S / WGS84
MGCPs 11;
RMSE x1,17; y
1,07
4; RMSE 0,61; y 0,67
N/A SRTM 30
0 order polynom
CC Yes Yes
o15JAN04081530-M2AS-056358854010_01_P003_toa_PSH
Quickbird-2
4. 05DEC14082246-M2AS-056358854020_01_P001_toa_PSH
12.04.2017
Arusha TZA
UTM37S / WGS84
MGCPs 15;
RMSE x1,01; y0,79
N/A N/A SRTM 30
Affin, 1st order poynom
CC Yes Yes
o05DEC14082246-M2AS-056358854020_01_P001_toa_PSH
5. 05SEP15081836-M2AS-056358854020_01_P002_toa_PSH
12.04.2017
Arusha TZA
UTM37S / WGS84
MGCPs 15;
RMSE x1,01; y0,79
N/A N/A SRTM 30
Affin, 1st order poynom
CC Yes Yes
o05SEP15081836-M2AS-056358854020_01_P002_toa_PSH
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Development – Urban Project QA/QC Sheets developed by GAF AG
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3.1.1 Data Fusion
Dataset 1 (14) Input A Input B Output
Acc
ep
tan
ce S
tatu
s
Comments
Filename 16SEP17081149-M2AS-
056358854010_01_P001 16SEP17081149-P2AS-
056358854010_01_P001 16SEP17081149-M2AS-
056358854010_01_P001_toa_PSH
none
Sensor GeoEye-1 GeoEye-1 GeoEye-1
Method PCI / Pansharp2 PCI / Pansharp2 PCI / Pansharp2
Spatial Resolution / (MS/Pan)
2m 0,5m 0,5m 0,5m 0,5m MS
Band Combination BGR NIR PAN BGR NIR
Data Format (3) & Bit Depth (5)
*.tif 16 Bit u *.tif 16 Bit u *.pix 16 Bit u Yes
Dataset 2 (14) Input A Input B Output
Acc
ep
tan
ce S
tatu
s
Comments
Filename 15DEC30080039-M2AS-
056358854010_01_P002 15DEC30080039-P2AS-
056358854010_01_P002 15DEC30080039-M2AS-
056358854010_01_P002_toa_PSH
none
Sensor GeoEye-1 GeoEye-1 GeoEye-1
Method PCI / Pansharp2 PCI / Pansharp2 PCI / Pansharp2
Spatial Resolution / (MS/Pan)
2m 2m 0,5m Pan 0,5m MS
Band Combination BGR NIR PAN BGR NIR
Data Format (3) & Bit Depth (5)
*.tif 16 Bit u *.tif 16 Bit u *.pix 16 Bit u Yes
Dataset 3 (14) Input A Input B Output
Acc
ep
tan
ce S
tatu
s Comments
Filename 15JAN04081530-M2AS-
056358854010_01_P003 15JAN04081529-P2AS-
056358854010_01_P003 15JAN04081530-M2AS-
056358854010_01_P003_toa_PSH none Sensor Worldview-2 Worldview-2 Worldview-2
Method PCI / Pansharp2 PCI / Pansharp2 PCI / Pansharp2
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Development – Urban Project QA/QC Sheets developed by GAF AG
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Spatial Resolution / (MS/Pan)
2m MS 0,5m Pan 0,5m MS
Band Combination BGR NIR PAN BGR NIR
Data Format (3) & Bit Depth (5)
*.tif 16 Bit u *.tif 16 Bit u *.pix 16 Bit u Yes
Dataset 4 (14) Input A Input B Output
Acc
ep
tan
ce S
tatu
s
Comments
Filename 05DEC14082246-M2AS-
056358854020_01_P001 05DEC14082245-P2AS-
056358854020_01_P001 05DEC14082246-M2AS-
056358854020_01_P001_toa_PSH
none
Sensor Quickbird-2 Quickbird-2 Quickbird-2
Method PCI / Pansharp2 PCI / Pansharp2 PCI / Pansharp2
Spatial Resolution / (MS/Pan)
2m MS 0,6m Pan 0,6m MS
Band Combination BGR NIR PAN BGR NIR
Data Format (3) & Bit Depth (5)
*.tif 16 Bit u *.tif 16 Bit u *.pix 16 Bit u Yes
Dataset 5 (14) Input A Input B Output
Acc
ep
tan
ce S
tatu
s
Comments
Filename 05SEP15081836-M2AS-
056358854020_01_P002 05SEP15081836-P2AS-
056358854020_01_P002 05SEP15081836-M2AS-
056358854020_01_P002_toa_PSH
none
Sensor Quickbird-2 Quickbird-2 Quickbird-2
Method PCI / Pansharp2 PCI / Pansharp2 PCI / Pansharp2
Spatial Resolution / (MS/Pan)
2m MS 0,6m Pan 0,6m MS
Band Combination BGR NIR PAN BGR NIR
Data Format (3) & Bit Depth (5)
*.tif 16 Bit u *.tif 16 Bit u *.pix 16 Bit u Yes
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
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3.2 Data Processing
Sensoren (8)
GE-1, WV-2, QB-2 Atmospheric Correction
Radiometric Processing (15)
Topographic Normalisation
OTHER Calculation:
Acc
ep
tan
ce S
tatu
s
Ba
cku
p
Comment
No.
File Name [e.g yymmdd; tbd...] Processing
Date
pro
cess
ed
Software / Method
pro
cess
ed
Software / Method
pro
cess
ed
Software / Method
pro
cess
ed
Software / Method
GeoEye1/Worldview-2
1. 16SEP17081149-M2AS-056358854010_01_P001
12.04.2017 No N/A No N/A No N/A Yes GAFmap / TOA
Yes No none
2. 15DEC30080039-M2AS-056358854010_01_P002
12.04.2017 No N/A No N/A No N/A Yes GAFmap / TOA
Yes No none
3. 15JAN04081530-M2AS-056358854010_01_P003
12.04.2017 No N/A No N/A No N/A Yes GAFmap / TOA
Yes No none
Quickbird-2
4. 05DEC14082246-M2AS-056358854020_01_P001
12.04.2017 No N/A No N/A No N/A Yes GAFmap / TOA
Yes No none
5. 05SEP15081836-M2AS-056358854020_01_P002
12.04.2017 No N/A No N/A No N/A Yes GAFmap / TOA
Yes No none
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
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4.1 Classification
Sensors (8) GE-1, WV-2, QB-2
Processing Date
Cloud Masking Thematic
Classification Manual
Enhancement Mosaicking
Acc
ep
tan
ce S
tatu
s
Ba
cku
p
Comment No.
File Name [e.g yymmdd; tbd...]
pro
cess
ed
Software / Method
pro
cess
ed
Software / Method
pro
cess
ed
Software /
Method
pro
cess
ed
Software / Method
1.
16SEP17081149-M2AS-056358854010_01_P001 15DEC30080039-M2AS-056358854010_01_P002 15JAN04081530-M2AS-056358854010_01_P003
06.10.2017 no No clouds Yes ERDAS IMAGINE
Yes
GAFMap and ArcMap/Visual interpretation
Yes
ArcGIS 10 / Mosaic Dataset with manual neatline selection
Yes Yes
2.
05DEC14082246-M2AS-056358854020_01_P001 05SEP15081836-M2AS-056358854020_01_P002
06.10.2017 no No clouds Yes ERDAS IMAGINE
Yes
GAFMap and ArcMap/Visual interpretation
Yes
ArcGIS 10 / Mosaic Dataset with manual neatline selection
Yes Yes
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Development – Urban Project QA/QC Sheets developed by GAF AG
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5.1 Thematic Accuracy
No. Product Name
Processing Date
Area Coverage
No
. o
f C
lass
es
Re
fere
nce
Da
ta
Sa
mp
le S
ize
Sa
mp
ling U
nit
(21
)
Sa
mp
ling
De
sign
(22
)
Sa
mp
le E
xclu
sio
n
Cri
teri
a (2
3)
Th
em
ati
c A
ccu
racy
(2
4)
Acc
ep
tan
ce S
tatu
s
Comment
1.
Urban Green Areas 2005
08.10.2017 Full AOI (urban core area)
4 VHR EO Data
215 per strata
POINT Stratified random sampling None, all used 92.56% Yes None
2.
Urban Green Areas 2015
08.10.2017 Full AOI (urban core area)
4 VHR EO Data
215 per strata
POINT Stratified random sampling None, all used 94.88% Yes none
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Development – Urban Project QA/QC Sheets developed by GAF AG
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5.2 Error Matrices
Urban Green Area 2015 Reference Data
Totals 0 - Non-Urban Green Area 1 - Urban Green Area
0 - Non-Urban Green Area 118 1 119
1 - Urban Green Area 10 86 96
Totals 128 87 215
Accuracy Statistics z= 1.96 Overall Accuracy: 94.88%
95% Confidence Interval: 91.71% 98.06%
Class Name Producer’s Accuracy User’s Accuracy
0 - Non-Urban Green Area 92.19% 99.16%
1 - Urban Green Area 98.85% 89.58%
Urban Green Area 2005 Reference Data
Totals 0 - Non-Urban Green Area 1 - Urban Green Area
0 - Non-Urban Green Area 97 9 106
1 - Urban Green Area 7 102 109
Totals 104 111 215
Accuracy Statistics z= 1.96 Overall Accuracy: 92.56%
95% Confidence Interval: 88.82% 96.30%
Class Name Producer’s Accuracy User’s Accuracy
0 - Non-Urban Green Area 93.27% 91.51%
1 - Urban Green Area 91.89% 93.58%
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Development – Urban Project QA/QC Sheets developed by GAF AG
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6.1 Completeness
INPUT DATA
No.
Item AOI Coverage [km²]
Area Coverage [km²]
Completeness of Coverage (25)
No. of Scenes Scenes used in Production
Completeness of Verification (27)
Metadata Comments
1. VHR EO Data Arusha
211 km² 100% 5 5 100% Yes none
2. Ancillary Data N/A N/A N/A N/A N/A Yes none
PRODUCTS
No.
Product (28) AOI Coverage [km²]
Product Coverage [km²]
Completeness of Coverage (25)
Completeness of Classification (26)
Unclassifiable Area [%]
Completeness of Verification (27)
Metadata Comments
1. Urban Green Areas 2005 and 2015 and change
Arusha 211 km² 100% 100% 0% 100% Yes none
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Development – Urban Project QA/QC Sheets developed by GAF AG
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6.2 Compliancy
Product 1 (28) Urban Green Areas
Abstract
Urban Green Areas information product contains spatial explicit information on green areas within the urban extent. Green areas include linear green features such as river alignments, hedges and trees, as well as public parks, private gardens, forested areas, etc. Urban Green Areas & Change dataset is based on Very High Resolution (VHR) satellite imagery by means of automated classification processing techniques.
Service / Product Specifications
Area Coverage
Requirements Achieved Specifications Compliancy Comments
Country 1: Tanzania Country 1: Tanzania YES None
A) Wall-to-wall: Arusha city A) Wall-to-wall: Arusha city
Core Urban Area (211 km²) Core Urban Area (211 km²)
Area of Interest. Arusha 211 km², defined by the national user.
B) Sampling based: B) Sampling based:
N/A N/A
Time Period - Update Frequency
Requirements Achieved Specifications Compliancy Comments
Baseline Year(s): Baseline Year(s): Yes
See EO data acquisition dates. 2005 and 2015 (+/- 1 years) 2005 and 2015/2016
B) Update Frequency B) Update Frequency Yes --
No update No update
Geographic Reference System
Requirements Achieved Specifications Compliancy Comments
WGS84 / UTM 37S WGS84 / UTM 37S YES None
Mapping Classes and Definitions
Requirements Achieved Specifications Compliancy
Single date
0 Non-urban green area 0 Non-urban green area YES
1 Urban green area 1 Urban green area YES
Change product
0 Non-urban green area 0 Non-urban green area YES
1 Permanent urban green area 1 Permanent urban green area YES
2 Loss of urban green area 2 Loss of urban green area YES
3 New urban green area 3 New urban green area YES
255 No data available due to absence of historic VHR imagery
255 No data available due to absence of historic VHR imagery
YES
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
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Cloud and Shadow Detection and Removal
Requirements Achieved Specifications Compliancy Comments
N/A N/A
Spatial Resolution
Requirements Achieved Specifications Compliancy Comments
1 m 1 m YES None
Minimum Mapping Unit (MMU)
Requirements Achieved Specifications Compliancy Comments
1 m² 1 m² YES None
Data Type
Requirements Achieved Specifications Compliancy Comments
Raster data Raster data YES None
Bit Depth
Requirements Achieved Specifications Compliancy Comments
N/A N/A
Data Format
Requirements Achieved Specifications Compliancy Comments
GEOTIFF GEOTIFF YES None
Class Coding
Requirements Achieved Specifications Compliancy
N/A N/A
Metadata
Requirements Achieved Specifications Compliancy Comments
ISO standard 19115 "Metadata" and ISO 19139 "XML Scheme Implementation"
ISO standard 19115 "Metadata" and ISO 19139 "XML Scheme Implementation"
YES None
Service / Product Quality
Thematic Accuracy
Requirements Achieved Specifications Compliancy Comments
> 80% > 80% YES None
Positional Accuracy
Requirements Achieved Specifications Compliancy Comments
3 m (CE90) 3 m (CE90) YES None
Delivery Procedure
Service Provision
Requirements Achieved Specifications Compliancy Comments
Online via FTP Online via FTP YES None
Delivery Date
Requirements Achieved Specifications Compliancy Comments
End of October 2017 Third Delivery April 2018 YES None
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
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Glossary
Quality Checks
Prefix Suffix Explanation
Y (Yes) - V Visually checked
N (No) - Q Quantitatively/qualitatively checked
N/A - V Q Visually and quantitatively/qualitatively checked
- N/A Not applicable
Readability (1) Check readability of all required input data. Can the data be stored again?
Header / Metadata (2) Check Image Header Information and/or Metadata for completeness / distinctive features.
Data Format (3) For digital data, please give file format (e.g. *.tiff, *.shp).
Data Type (4) Please specify the type of the data (e.g. raster, vector or analogue).
Bit Depth (5) Please give pixel depth and sign of raster data (e.g. 8 bit unsigned integer).
Dynamic Range (6) Check dynamic range of all image bands. Visual check of dynamic range should be accompanied by histograms and statistics.
Dropped Lines & Artefacts (7) Check Image for dropped lines and other artefacts. If such occurs, please give description of extent and influence in the Comments section.
Sensor 1, 2, … (8) Please delete / add additional sensor sections as necessary.
Purpose (9)
The purpose and use of the given auxiliary or reference data should be stated:
ORHTOrectification, GEOmetric correction, REFerence, VERification source, POSitional ACCuracy assessment, THEmatic ACCuracy assessment.
Sampling Methodology (10)
Methodology of in situ or reference/auxiliary data sampling scheme should be outlined. In case of sample plots, also state how the plot positions have been determined (e.g. from GPS measurements, topographic maps, terrestrial triangulation, EO data, etc.) and how the sampling grid was established.
Positional Accuracy (11)
Positional accuracy of collected in situ data should be given. For reference/auxiliary data it MUST be given. If unknown, the data’s use must be explained. For DEM or other data with 3D information please specify both vertical and horizontal Positional Accuracy. For analogue data (e.g. maps) try to give approximate accuracy related to mapping scale.
Completeness (12) Data should be checked for spatial/temporal/content gaps.
Model / Algorithm (13)
Give the name of the software and its version. Specify the software module/algorithm used for: a) geometric correction, e.g., Polynomial and its degree, Rational Functions, Thin Plate Spline, etc. b) classification, e.g., ISODATA, Maximum Likelihood, Neural Networks, etc.
Dataset 1, 2, … (14) Please delete / add additional dataset sections as necessary.
Radiometric Processing (15) State whether (and which) radiometric processing was applied (e.g., contrast enhancement, histogram matching, bundle block adjustment, radiometric normalisation, filtering, etc.).
Projection; Spheroid / Ellipsoid (16)
Always specify completely, i.e. at minimum the Projection (+Zone, if applicable), Spheroid / Ellipsoid, Map Datum. Give additional information if necessary to unambiguously define the reference system.
Ground Control Points (GCPs) (17)
Give the number, distribution and RMS of used Ground Control Points (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of GCPs should be attached as a snapshot, or described in the Comments section.
Tie Points (TPs) (18)
In case of mosaicking, give the number of used Tie Points and their total RMS (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of TPs should be attached as a snapshot, or described in the Comments section.
Check Points (CPs) (19)
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The real measure of positional accuracy and the only measure which should be examined as to its Acceptable Range. Give the independent Check Points’ total RMS (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of CP's should be attached as a snapshot, or described in the Comments section.
Resampling Method (20) Specify, if / which resampling algorithm has been used (e.g. NN=Nearest Neighbour, BIL=Bilinear Interpolation, CC=Cubic Convolution).
Sampling unit (21) Specify sampling unit of Accuracy Assessment as POINT, FRAME, POLYGON and how it is treated (e.g. pixel center, polygon centre, etc.).
Sampling Design (22) SYST=Systematic, RAND=Random, STRAT=Stratified, SBCLASS=Stratified by class, SBAREA=Stratified by area
Sample exclusion criteria (23)
Describe which criteria you apply for sampling point selection resp. exclusion of certain points. For example, if the point is too close to a class boundary (less than 1 pixel), it is excluded. If the selected sample point is not representative of the class , it is excluded. For these reasons, it is recommended to oversample by 10% to compensate for sample point exclusion.
Thematic Accuracy (24)
Provide a detailed description of the Accuracy Assessment results in the form of error matrices showing commission and omission errors, user’s , producer’s and overall accuracies and other measures of Thematic Accuracy, as the confidence level (usually fixed at 95%) and the respective confidence interval, at least for the overall accuracy. If classification is done in a phased approach, e.g. if Forest Area and subsequently Forest Type are mapped, independent reports have to be produced.
Completeness of Coverage (25) State whether the coverage is limited to a subset, or portion of the final product.
Completeness of Classification (26)
State whether classification was constrained to a subset, or portion of the final product.
Completeness of Verification (27)
State whether verification applied to lineage, positional, or thematic accuracy is constrained to a subset, or portion of the final product.
Product (28) Please delete / add additional product sections as necessary.
Lineage (29) Definition (INSPIRE 2015): Lineage is “a statement on process history and/or overall quality of the spatial data set. Where appropriate it may include a statement whether the data set has been validated or quality assured, whether it is the official version (if multiple versions exist), and whether it has legal validity. The value domain of this element is free text.”
Source(30) Definition (INSPIRE, 2015): “This is the description of the organisation responsible for the establishment, management, maintenance or distribution of the resource. This description shall include: name of the organisation and contact email address.”
Earth Observation for Sustainable
Development Urban Project QA/QC Sheets developed by GAF AG
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Page 16
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Earth Observation for Sustainable Development - Urban
Quality Assurance and Quality Control Sheets These QA/QC Templates were prepared by GAF AG compliant with ISO 9001:2008 Quality Management System
standards and can only be used by Partners in the current EO4SD-Urban Project.
Project Title: EO4SD-Urban
Project Leader: GAF AG
Service Provider: GAF Editor: A. Broszeit
Client: WB Date: 02.11.2017
Product: Planned/Unplanned Settlement Areas
Overview of QC-Sheets and Processing Steps Sheet used
Sheet filled in
Requirements
0.1 Requirements Yes Yes
Specifications of Input Data
1.1 List of EO Data Yes Yes
1.2 List of In-situ Data Yes Yes
1.3 List of Ancillary Data Yes Yes
Data Quality Checks
2.1 EO Data Quality Yes Yes
2.2 In-situ Data Quality Yes Yes
2.3 Ancillary Data Quality Yes Yes
Pre-Processing of EO Data
3.1 Geometric Correction Yes Yes
3.1.1 Data Fusion Yes Yes
3.2 Data Processing Yes Yes
Thematic Processing
4.1 Classification Yes Yes
Accuracy Assessment
5.1 Thematic Accuracy Yes Yes
5.2 Error Matrices Yes Yes
Delivery Checks / Delivery
6.1 Completeness Yes Yes
6.2 Compliancy Yes Yes
Glossary (index numbers in the QA/QC tables refer to the glossary at the end of this document) Further QC-relevant Documents:
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© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 2
Comments / Characteristics:
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© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 3
0.1 Requirements
Product 1 (28) Planned and Unplanned Settlement Areas 2005 and 2015
Abstract
The product Planned and Unplanned Settlements contains spatial explicit information on the two settlement types within the core urban area. The distinction into the two settlement types is restricted to the residential area (LULC class 11), and to commercial areas (LULC class 1211) which also have a residential component. The Planned and Unplanned Settlements, and Change dataset is developed, based on Very High Resolution (VHR) satellite imagery by means of visual interpretation.
Service / Product Specifications
Area Coverage
Country: Tanzania A) Wall-to-wall: n/a
City: Arusha Selected Sites: Area of Interest defined with end-users
Area km² Core Urban: 211 B) Sampling based: n/a
Time Period - Update Frequency
A) Baseline Year(s): B) Update Frequency
2005 and 2015/2016 2 points in time
Comments: None
Geographic Reference System
WGS84 UTM Zone 37S
Mapping Classes and Definitions
Single date
Planned settlements
Unplanned settlements
Change product
No change in planned settlement area
No change in unplanned settlement area
Expansion of planned settlement area
Expansion of unplanned settlement area
Decrease of planned settlement area
Decrease of unplanned settlement area
Unplanned to planned settlement area
Cloud and Cloud Shadow Detection and Removal
n/a
Spatial Resolution
n.a. (Product provided as Shapefile)
Minimum Mapping Unit (MMU)
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Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 4
0.25 ha
Data Type & Format
Shapefile *.shp
Bit Depth
n.a.
Class Coding
Class Name RGB Code
Single date
Planned settlements 132/0/168
Unplanned settlements 168/0/0
Change product
No change in planned settlement area 132/0/168
No change in unplanned settlement area 168/0/0
Expansion of planned settlement area 197/0/255
Expansion of unplanned settlement area 255/0/0
Decrease of planned settlement area 168/132/167
Decrease of unplanned settlement area 255/100/100
Unplanned to planned settlement area 255/0/197
Metadata
ISO standard 19115 "Metadata" and ISO 19139 "XML Scheme Implementation"
Service / Product Quality
Thematic Accuracy
> 80%
Positional Accuracy
RMSE < 15 m
Delivery Procedure
Service Provision
Online via FTP
Delivery Date
End of October 2017
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Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 5
1.1 List of EO Data
Sensoren (8) GE-1, WV-2, QB-2
Incoming Date
Acquisition Date
Proc. Level
Path / Row
AOI - City / Region / Country
Spatial Res. No. of Bands
Cloud Cover (Data Provider)
Projection / Spheroid (16)
Data Format (3)
Bit Depth (5)
Header / Metadata (2)
File Name [e.g yymmdd; tbd...]
WV-2, GE-1
1.
16SEP17081149-M2AS_R1C1-056358854010_01_P001
31.03.2017
17.09.2016
LV2A R1C1 Arusha, TZA
MUL: 2m; PAN: 0.5m
4 0% UTM 37S .tiff 16 Bit
.xml
16SEP17081149-M2AS_R1C2-056358854010_01_P001
31.03.2017
17.09.2016
LV2A R1C2 Arusha, TZA
MUL: 2m; PAN: 0.5m
4 0% UTM 37S .tiff 16 Bit
.xml
16SEP17081149-M2AS_R1C3-056358854010_01_P001
31.03.2017
17.09.2016
LV2A R1C3 Arusha, TZA
MUL: 2m; PAN: 0.5m
4 0% UTM 37S .tiff 16 Bit
.xml
16SEP17081149-M2AS_R2C1-056358854010_01_P001
31.03.2017
17.09.2016
LV2A R2C1 Arusha, TZA
MUL: 2m; PAN: 0.5m
4 0% UTM 37S .tiff 16 Bit
.xml
16SEP17081149-M2AS_R2C2-056358854010_01_P001
31.03.2017
17.09.2016
LV2A R2C2 Arusha, TZA
MUL: 2m; PAN: 0.5m
4 0% UTM 37S .tiff 16 Bit
.xml
16SEP17081149-M2AS_R2C3-056358854010_01_P001
31.03.2017
17.09.2016
LV2A R2C3 Arusha, TZA
MUL: 2m; PAN: 0.5m
4 0% UTM 37S .tiff 16 Bit
.xml
2. 15DEC30080039-M2AS-056358854010_01_P002
31.03.2017
30.12.2015
LV2A Arusha, TZA
MUL: 2m; PAN: 0.5m
4 0% UTM 37S .tiff 16 Bit
.xml
3. 15JAN04081530-M2AS-056358854010_01_P003
31.03.2017
04.01.2015
LV2A Arusha, TZA
MUL: 2m; PAN: 0.5m
4 4.7e-02%
UTM 37S .tiff 16 Bit
.xml
Quickbird-2
4.
05DEC14082246-M2AS_R1C1-056358854020_01_P001
31.03.2017
05.12.2005
LV2A R1C1 Arusha, TZA
MUL: 2m; PAN: 0.6m
4 0% UTM 37S GeoTIFF
16 Bit
.imd,
.rpb,
.til, xml
05DEC14082246-M2AS_R1C2-056358854020_01_P001
31.03.2017
05.12.2005
LV2A R1C2 Arusha, TZA
MUL: 2m; PAN: 0.6m
4 0% UTM 37S GeoTIFF
16 Bit
.imd,
.rpb,
.til, xml
05DEC14082246-M2AS_R2C1-056358854020_01_P001
31.03.2017
05.12.2005
LV2A R2C1 Arusha, TZA
MUL: 2m; PAN: 0.6m
4 0% UTM 37S GeoTIFF
16 Bit
.imd,
.rpb,
.til, xml
05DEC14082246-M2AS_R2C2-056358854020_01_P001
31.03.2017
05.12.2005
LV2A R2C2 Arusha, TZA
MUL: 2m; PAN: 0.6m
4 0% UTM 37S GeoTIFF
16 Bit
.imd,
.rpb,
.til, xml
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Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 6
5.
05SEP15081836-M2AS_R1C1-056358854020_01_P002
31.03.2017
15.09.2005
LV2A R1C1 Arusha, TZA
MUL: 2m; PAN: 0.6m
4 0% UTM 37S GeoTIFF
16 Bit
.imd,
.rpb,
.til, xml
05SEP15081836-M2AS_R2C1-056358854020_01_P002
31.03.2017
15.09.2005
LV2A R2C1 Arusha, TZA
MUL: 2m; PAN: 0.6m
4 0% UTM 37S GeoTIFF
16 Bit
.imd,
.rpb,
.til, xml
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 7
1.2 List of In-situ Data
Dataset 1 (14) Incoming Date Acquisition Date AOI - City / Region / Country
Data Type (4)
Projection / Spheroid (16)
No. of Sample Plots
Sampling Design (22)
Positional Accuracy (11)
Purpose (9)
No In-situ Data Used
Lineage:
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Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 8
1.3 List of Ancillary Data
Dataset 1 (14)
LULC Date of Receipt from Client
Metadata (2)
Reference Mapping Date / Date of Creation
Geographic Area/- City / Region / Country
Area Coverag
e
Data Type (4)
Data Format (3)
Projection / Spheroid (16)
Positional Accuracy (11)
No. of Classes
Thematic
Accuracy
Availability of Class Definitions
EO4SD-Urban Land Use/Land Cover Baseline Product
2017-07-30 Yes
(INSPIRE) 2005 and 2015
Arsuha (urban area) 100% Vector *.shp EPSG:32736, WGS 84 / UTM zone 36S
< 3 m 24 88% Yes
Lineage(29): /
Source (30):: GAF AG
Dataset 2 (14)
Report Date of Receipt from Client
Metadata (2)
Reference Mapping Date / Date of Creation
Geographic Area/- City / Region / Country
Area Coverag
e
Data Type (4)
Data Format (3)
Projection / Spheroid (16)
Positional Accuracy (11)
No. of Classes
Thematic
Accuracy
Availability of Class Definitions
Arusha Master Plan 2015-2035 Volume 1: Main Report
2016-12-21 n.a. 2016-07-22 Arusha/ Tanzania Arusha Document
pdf n.a. n.a. n.a. n.a. n.a.
Lineage(29): The Master Plan of Arusha was used as a reference while mapping planned and unplanned settlements in 2015.
Source (30):: Received from MaryGrace Weber.
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 9
2.1 EO Data Quality
Sensoren(8)
WV, GE GE-1, WV-2, QB-2
Ba
cku
p
Re
ad
ab
ility
(1
)
Ch
eck
He
ad
er
/ M
eta
da
ta
(2)
Band Specifications
Pro
ject
ion
/ S
ph
ero
id (1
6)
Sce
ne
Lo
cati
on
Co
mp
lete
ne
ss o
f
Ad
dit
ion
al D
ata
Da
ta F
orm
at
(3)
Bit
De
pth
(5)
Clo
ud
Co
ver
(in
tern
al
che
ck)
Ra
dio
me
try
To
po
gra
ph
y
Dro
pp
ed
Lin
es
/ A
rte
fact
s (7
)
Acc
ep
tan
ce S
tatu
s
Comment
s File Name [e.g yymmdd; tbd...]
No
. o
f B
an
ds
Ba
nd
Re
gis
tra
tio
n
Sp
ect
ral/
Sp
ati
al
Re
solu
tio
n
GeoEye-1/Worldview-2
1. 16SEP17081149-M2AS-056358854010_01_P001
Y - Q Y - V Q Y - V Q Y - V Q Y - V Q Y - Q Y - V Y - V Y - V Q Y - Q Y - Q
Y - V Q
Y - V Q
Y - V
N - V Q
Yes none
2. 15DEC30080039-M2AS-056358854010_01_P002
Y - Q Y - V Q Y - V Q Y - V Q Y - V Q Y - Q Y - V Y - V Y - V Q Y - Q Y - Q
Y - V Q
Y - V Q
Y - V
N - V Q
Yes none
3. 15JAN04081530-M2AS-056358854010_01_P003
Y - Q Y - V Q Y - V Q Y - V Q Y - V Q Y - Q Y - V Y - V Y - V Q Y - Q Y - Q
Y - V Q
Y - V Q
Y - V
N - V Q
Yes none
Quickbird-2
4. 05DEC14082246-M2AS-056358854020_01_P001
Y - Q Y - V Q Y - V Q Y - V Q Y - V Q Y - Q Y - V Y - V Y - V Q Y - Q Y - Q
Y - V Q
Y - V Q
Y - V
N - V Q
Yes none
5. 05SEP15081836-M2AS-056358854020_01_P002
Y - Q Y - V Q Y - V Q Y - V Q Y - V Q Y - Q Y - V Y - V Y - V Q Y - Q Y - Q
Y - V Q
Y - V Q
Y - V
N - V Q
Yes none
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Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 10
2.2 In-situ Data Quality
Dataset 1 (14) No Data used
Ba
cku
p
Re
ad
ab
ility
(1)
Ch
eck
He
ad
er
/ M
eta
da
ta (2
)
Ext
en
t
Pro
ject
ion
/ S
ph
ero
id
(16
)
Sp
ati
al R
eso
luti
on
Da
ta F
orm
at
(3)
Bit
De
pth
(5)
Lo
cati
on
Co
mp
lete
ne
ss
Ge
om
. M
isa
lign
me
nt
Pla
usi
bili
ty
Dro
pp
ed
Lin
es
/
Art
efa
cts
(7)
Acc
ep
tan
ce S
tatu
s
Comments File Name [e.g yymmdd; tbd...]
No In-situ Data Used
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 11
2.3 Ancillary Data Quality
Dataset 1 (14)
LULC
Re
ad
ab
ility
(1
)
Ch
eck
He
ad
er
/Me
tad
ata
(2)
Da
ta C
ove
rage
Ma
tch
es
Se
rvic
e A
rea
(%
)
Pro
ject
ion
/ S
ph
ero
id
, E
PS
G(1
6)
Sp
ati
al R
eso
luti
on
a
nd
un
it (
e.g
. m
, k
m)
Da
ta F
orm
at
(3)
Bit
De
pth
(5)
Co
mp
lete
ne
ss
(Ve
cto
r: A
ttri
bu
te
Ta
ble
; R
ast
er:
Th
em
ati
c V
alu
es)
Ge
om
. M
isa
lign
me
nt
Dro
pp
ed
Lin
es
/ A
rte
fact
s (
EO
da
ta
on
ly)(
7)
Uti
lity
for
Cu
rre
nt
Pro
ject
File Name [e.g yymmdd; tbd...]
EO4SD-Urban LULC Product ☒ Yes
☐ No
☒ Complete (INSPIRE/ISO19119)
☐ Incomplete
☐ not available
☒ Yes
☐ No
☐ Unknown If no: xxx%
☒ Correct
☐ Incorrect
☐ Unknown EPSG: 32749
n.a. *.shp n.a. ☒ Complete
☐Incomplete
☒ No
☐ Yes
☐ Unknown If yes: XX m
☒ No
☐ Yes
☐ n.a. If yes: xxx %
☐ None
☐ Partial
☒ Full
Comments: /
Dataset 2(14) Report
Re
ad
ab
ility
(1)
Ch
eck
He
ad
er
/Me
tad
ata
(2)
Da
ta C
ove
rage
M
atc
he
s S
erv
ice
A
rea
(%
)
Pro
ject
ion
/ S
ph
ero
id
, E
PS
G(1
6)
Sp
ati
al R
eso
luti
on
a
nd
un
it (
e.g
. m
, k
m)
Da
ta F
orm
at
(3)
Bit
De
pth
(5)
Co
mp
lete
ne
ss
(Ve
cto
r: A
ttri
bu
te
Ta
ble
; R
ast
er:
Th
em
ati
c V
alu
es)
Ge
om
. M
isa
lign
me
nt
Dro
pp
ed
Lin
es
/
Art
efa
cts
(E
O d
ata
o
nly
)(7
)
Uti
lity
for
Cu
rre
nt
Pro
ject
File Name [e.g yymmdd; tbd...]
Arusha Master Plan 2015-2035 Volume 1: Main Report
☒ Yes
☐ No n.a. n.a. n.a. n.a. *.shp n.a. n.a. n.a. n.a.
☐ None
☐ Partial
☒ Full
Comments: /
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3.1 Geometric Correction
Sensoren (8) GE-1, WV-2, QB-2
Pro
cess
ing D
ate
AO
I
Cit
y /
Re
gio
n /
Co
un
try
Pro
ject
ion
/ S
ph
ero
id (1
6)
No
. &
RM
S (
m)
of
GC
Ps
(17
)
No
. &
RM
S (
m)
of
TP
s (1
8)
No
. &
RM
S (
m)
of
CP
s (1
9)
Dig
ita
l E
leva
tio
n M
od
el
(DE
M)
Mo
de
l /
Alg
ori
thm
(13
)
Re
sam
plin
g
Me
tho
d (2
0)
Va
lida
tio
n R
ep
ort
s
Acc
ep
tan
ce S
tatu
s
Output File Name [e.g yymmdd; tbd...] N
o. File Name [e.g yymmdd; tbd...]
GeoEye-1/Worldview-2
1. 15DEC30080039-M2AS-056358854010_01_P002_toa_PSH
12.04.2017
Arusha TZA
UTM37S / WGS84
MGCPs 11;
RMSE x1,17; y
1,07
4; RMSE 0,61; y 0,67
N/A SRTM
30 0 order polynom
CC Yes Yes
o15DEC30080039-M2AS-056358854010_01_P002_toa_PSH
2. 16SEP17081149-M2AS-056358854010_01_P001_toa_PSH
12.04.2017
Arusha TZA
UTM37S / WGS84
MGCPs 11;
RMSE x1,17; y
1,07
4; RMSE 0,61; y 0,67
N/A SRTM 30
0 order polynom
CC Yes Yes
o16SEP17081149-M2AS-056358854010_01_P001_toa_PSH
3. 15JAN04081530-M2AS-056358854010_01_P003_toa_PSH
12.04.2017
Arusha TZA
UTM37S / WGS84
MGCPs 11;
RMSE x1,17; y
1,07
4; RMSE 0,61; y 0,67
N/A SRTM 30
0 order polynom
CC Yes Yes
o15JAN04081530-M2AS-056358854010_01_P003_toa_PSH
Quickbird-2
4. 05DEC14082246-M2AS-056358854020_01_P001_toa_PSH
12.04.2017
Arusha TZA
UTM37S / WGS84
MGCPs 15;
RMSE x1,01; y0,79
N/A N/A SRTM 30
Affin, 1st order poynom
CC Yes Yes
o05DEC14082246-M2AS-056358854020_01_P001_toa_PSH
5. 05SEP15081836-M2AS-056358854020_01_P002_toa_PSH
12.04.2017
Arusha TZA
UTM37S / WGS84
MGCPs 15;
RMSE x1,01; y0,79
N/A N/A SRTM 30
Affin, 1st order poynom
CC Yes Yes
o05SEP15081836-M2AS-056358854020_01_P002_toa_PSH
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3.1.1 Data Fusion
Dataset 1 (14) Input A Input B Output
Acc
ep
tan
ce S
tatu
s
Comments
Filename 16SEP17081149-M2AS-
056358854010_01_P001 16SEP17081149-P2AS-
056358854010_01_P001 16SEP17081149-M2AS-
056358854010_01_P001_toa_PSH
none
Sensor GeoEye-1 GeoEye-1 GeoEye-1
Method PCI / Pansharp2 PCI / Pansharp2 PCI / Pansharp2
Spatial Resolution / (MS/Pan)
2m 0,5m 0,5m 0,5m 0,5m MS
Band Combination BGR NIR PAN BGR NIR
Data Format (3) & Bit Depth (5)
*.tif 16 Bit u *.tif 16 Bit u *.pix 16 Bit u Yes
Dataset 2 (14) Input A Input B Output
Acc
ep
tan
ce S
tatu
s
Comments
Filename 15DEC30080039-M2AS-
056358854010_01_P002 15DEC30080039-P2AS-
056358854010_01_P002 15DEC30080039-M2AS-
056358854010_01_P002_toa_PSH
none
Sensor GeoEye-1 GeoEye-1 GeoEye-1
Method PCI / Pansharp2 PCI / Pansharp2 PCI / Pansharp2
Spatial Resolution / (MS/Pan)
2m 2m 0,5m Pan 0,5m MS
Band Combination BGR NIR PAN BGR NIR
Data Format (3) & Bit Depth (5)
*.tif 16 Bit u *.tif 16 Bit u *.pix 16 Bit u Yes
Dataset 3 (14) Input A Input B Output
Acc
ep
tan
ce S
tatu
s Comments
Filename 15JAN04081530-M2AS-
056358854010_01_P003 15JAN04081529-P2AS-
056358854010_01_P003 15JAN04081530-M2AS-
056358854010_01_P003_toa_PSH none Sensor Worldview-2 Worldview-2 Worldview-2
Method PCI / Pansharp2 PCI / Pansharp2 PCI / Pansharp2
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Spatial Resolution / (MS/Pan)
2m MS 0,5m Pan 0,5m MS
Band Combination BGR NIR PAN BGR NIR
Data Format (3) & Bit Depth (5)
*.tif 16 Bit u *.tif 16 Bit u *.pix 16 Bit u Yes
Dataset 4 (14) Input A Input B Output
Acc
ep
tan
ce S
tatu
s
Comments
Filename 05DEC14082246-M2AS-
056358854020_01_P001 05DEC14082245-P2AS-
056358854020_01_P001 05DEC14082246-M2AS-
056358854020_01_P001_toa_PSH
none
Sensor Quickbird-2 Quickbird-2 Quickbird-2
Method PCI / Pansharp2 PCI / Pansharp2 PCI / Pansharp2
Spatial Resolution / (MS/Pan)
2m MS 0,6m Pan 0,6m MS
Band Combination BGR NIR PAN BGR NIR
Data Format (3) & Bit Depth (5)
*.tif 16 Bit u *.tif 16 Bit u *.pix 16 Bit u Yes
Dataset 5 (14) Input A Input B Output
Acc
ep
tan
ce S
tatu
s
Comments
Filename 05SEP15081836-M2AS-
056358854020_01_P002 05SEP15081836-P2AS-
056358854020_01_P002 05SEP15081836-M2AS-
056358854020_01_P002_toa_PSH
none
Sensor Quickbird-2 Quickbird-2 Quickbird-2
Method PCI / Pansharp2 PCI / Pansharp2 PCI / Pansharp2
Spatial Resolution / (MS/Pan)
2m MS 0,6m Pan 0,6m MS
Band Combination BGR NIR PAN BGR NIR
Data Format (3) & Bit Depth (5)
*.tif 16 Bit u *.tif 16 Bit u *.pix 16 Bit u Yes
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Development – Urban Project QA/QC Sheets developed by GAF AG
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3.2 Data Processing
Sensoren (8)
GE-1, WV-2, QB-2 Atmospheric Correction
Radiometric Processing (15)
Topographic Normalisation
OTHER Calculation:
Acc
ep
tan
ce S
tatu
s
Ba
cku
p
Comment
No.
File Name [e.g yymmdd; tbd...] Processing
Date
pro
cess
ed
Software / Method
pro
cess
ed
Software / Method
pro
cess
ed
Software / Method
pro
cess
ed
Software / Method
GeoEye1/Worldview-2
1. 16SEP17081149-M2AS-056358854010_01_P001
12.04.2017 No N/A No N/A No N/A Yes GAFmap / TOA
Yes No none
2. 15DEC30080039-M2AS-056358854010_01_P002
12.04.2017 No N/A No N/A No N/A Yes GAFmap / TOA
Yes No none
3. 15JAN04081530-M2AS-056358854010_01_P003
12.04.2017 No N/A No N/A No N/A Yes GAFmap / TOA
Yes No none
Quickbird-2
4. 05DEC14082246-M2AS-056358854020_01_P001
12.04.2017 No N/A No N/A No N/A Yes GAFmap / TOA
Yes No none
5. 05SEP15081836-M2AS-056358854020_01_P002
12.04.2017 No N/A No N/A No N/A Yes GAFmap / TOA
Yes No none
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4.1 Classification
Sensors (8) GE-1, WV-2, QB-2
Processing Date
Cloud Masking Thematic
Classification Manual
Enhancement Mosaicking
Acc
ep
tan
ce S
tatu
s
Ba
cku
p
Comment No.
File Name [e.g yymmdd; tbd...]
pro
cess
ed
Software / Method
pro
cess
ed
Software / Method
pro
cess
ed
Software /
Method
pro
cess
ed
Software / Method
1.
16SEP17081149-M2AS-056358854010_01_P001 15DEC30080039-M2AS-056358854010_01_P002 15JAN04081530-M2AS-056358854010_01_P003
01.06.2017 no No clouds Yes
GAFMap and ArcMap/visual mapping
Yes
GAFMap and ArcMap/Visual interpretation
Yes
ArcGIS 10 / Mosaic Dataset with manual neatline selection
Yes Yes
2.
05DEC14082246-M2AS-056358854020_01_P001 05SEP15081836-M2AS-056358854020_01_P002
07.08.2017 no No clouds Yes
GAFMap and ArcMap/visual mapping
Yes
GAFMap and ArcMap/Visual interpretation
Yes
ArcGIS 10 / Mosaic Dataset with manual neatline selection
Yes Yes
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Development – Urban Project QA/QC Sheets developed by GAF AG
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5.1 Thematic Accuracy
No. Product Name
Processing Date
Area Coverage
No
. o
f C
lass
es
Re
fere
nce
Da
ta
Sa
mp
le S
ize
Sa
mp
ling U
nit
(21
)
Sa
mp
ling
De
sign
(22
)
Sa
mp
le E
xclu
sio
n
Cri
teri
a (2
3)
Th
em
ati
c A
ccu
racy
(2
4)
Acc
ep
tan
ce S
tatu
s
Comment
1. Settlements 2015
13.10.2017 Full AOI (urban core area)
2 VHR EO Data
107 per strata
POINT Stratified random sampling None, all used 94.39% Yes
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5.2 Error Matrices
Settlements 2015 Reference Data
Totals Planned Settlement Area Unplanned Settlement Area
Planned Settlement Area 97 10 107
Unplanned Settlement Area 2 105 107
Totals 99 115 214
Accuracy Statistics z= 1.96 Overall Accuracy: 94.39%
95% Confidence Interval: 91.08% 97.71%
Class Name Producer’s Accuracy User’s Accuracy
Planned Settlement Area 97.98% 90.65%
Unplanned Settlement Area 91.30% 98.13%
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6.1 Completeness
INPUT DATA
No.
Item AOI Coverage [km²]
Area Coverage [km²]
Completeness of Coverage (25)
No. of Scenes Scenes used in Production
Completeness of Verification (27)
Metadata Comments
1. VHR EO Data Arusha
211 km² 100% 5 5 100% Yes none
2. Ancillary Data N/A N/A N/A N/A N/A Yes none
PRODUCTS
No.
Product (28) AOI Coverage [km²]
Product Coverage [km²]
Completeness of Coverage (25)
Completeness of Classification (26)
Unclassifiable Area [%]
Completeness of Verification (27)
Metadata Comments
1.
Planned and Unplanned Settlement Areas 2005 and 2015 and change
Arusha 211 km² 100% 100% 0% 100% Yes none
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Development – Urban Project QA/QC Sheets developed by GAF AG
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6.2 Compliancy
Product 1 (28) Planned and Unplanned Settlement Areas 2005 and 2015
Abstract
The product Planned and Unplanned Settlements contains spatial explicit information on the two settlement types within the core urban area. The distinction into the two settlement types is restricted to the residential area (LULC class 11), and to commercial areas (LULC class 1211) which also have a residential component. The Planned and Unplanned Settlements, and Change dataset is developed, based on Very High Resolution (VHR) satellite imagery by means of visual interpretation.
Service / Product Specifications
Area Coverage
Requirements Achieved Specifications Compliancy Comments
Country 1: Tanzania Country 1: Tanzania YES None
A) Wall-to-wall: Arusha city A) Wall-to-wall: Arusha city
Core Urban Area (211 km²) Core Urban Area (211 km²)
Area of Interest. Arusha 211 km², defined by the national user.
B) Sampling based: B) Sampling based:
N/A N/A
Time Period - Update Frequency
Requirements Achieved Specifications Compliancy Comments
Baseline Year(s): Baseline Year(s): Yes
See EO data acquisition dates. 2005 and 2015 (+/- 1 years) 2005 and 2015/2016
B) Update Frequency B) Update Frequency Yes --
No update No update
Geographic Reference System
Requirements Achieved Specifications Compliancy Comments
WGS84 / UTM 37S WGS84 / UTM 37S YES None
Mapping Classes and Definitions
Requirements Achieved Specifications Compliancy
Single date
Planned settlements Planned settlements YES
Unplanned settlements Unplanned settlements YES
Change product
No change in planned settlement area No change in planned settlement area YES
No change in unplanned settlement area No change in unplanned settlement area YES
Expansion of planned settlement area Expansion of planned settlement area YES
Expansion of unplanned settlement area Expansion of unplanned settlement area YES
Decrease of planned settlement area Decrease of planned settlement area YES
Decrease of unplanned settlement area Decrease of unplanned settlement area YES
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Unplanned to planned settlement area Unplanned to planned settlement area YES
Cloud and Shadow Detection and Removal
Requirements Achieved Specifications Compliancy Comments
N/A N/A N/A
Spatial Resolution
Requirements Achieved Specifications Compliancy Comments
N/A N/A N/A None
Minimum Mapping Unit (MMU)
Requirements Achieved Specifications Compliancy Comments
0.25 ha 0.25 ha YES None
Data Type
Requirements Achieved Specifications Compliancy Comments
Vector data Vector data YES None
Bit Depth
Requirements Achieved Specifications Compliancy Comments
N/A N/A
Data Format
Requirements Achieved Specifications Compliancy Comments
ESRI SHP ESRI SHP YES None
Class Coding
Requirements Achieved Specifications Compliancy
N/A N/A
Metadata
Requirements Achieved Specifications Compliancy Comments
ISO standard 19115 "Metadata" and ISO 19139 "XML Scheme Implementation"
ISO standard 19115 "Metadata" and ISO 19139 "XML Scheme Implementation"
YES None
Service / Product Quality
Thematic Accuracy
Requirements Achieved Specifications Compliancy Comments
> 80% > 80% YES None
Positional Accuracy
Requirements Achieved Specifications Compliancy Comments
RMSE < 15 m 15m YES None
Delivery Procedure
Service Provision
Requirements Achieved Specifications Compliancy Comments
Online via FTP Online via FTP YES None
Delivery Date
Requirements Achieved Specifications Compliancy Comments
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End of October 2017 End of October 2017 YES None
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Glossary
Quality Checks
Prefix Suffix Explanation
Y (Yes) - V Visually checked
N (No) - Q Quantitatively/qualitatively checked
N/A - V Q Visually and quantitatively/qualitatively checked
- N/A Not applicable
Readability (1) Check readability of all required input data. Can the data be stored again?
Header / Metadata (2) Check Image Header Information and/or Metadata for completeness / distinctive features.
Data Format (3) For digital data, please give file format (e.g. *.tiff, *.shp).
Data Type (4) Please specify the type of the data (e.g. raster, vector or analogue).
Bit Depth (5) Please give pixel depth and sign of raster data (e.g. 8 bit unsigned integer).
Dynamic Range (6) Check dynamic range of all image bands. Visual check of dynamic range should be accompanied by histograms and statistics.
Dropped Lines & Artefacts (7) Check Image for dropped lines and other artefacts. If such occurs, please give description of extent and influence in the Comments section.
Sensor 1, 2, … (8) Please delete / add additional sensor sections as necessary.
Purpose (9)
The purpose and use of the given auxiliary or reference data should be stated:
ORHTOrectification, GEOmetric correction, REFerence, VERification source, POSitional ACCuracy assessment, THEmatic ACCuracy assessment.
Sampling Methodology (10)
Methodology of in situ or reference/auxiliary data sampling scheme should be outlined. In case of sample plots, also state how the plot positions have been determined (e.g. from GPS measurements, topographic maps, terrestrial triangulation, EO data, etc.) and how the sampling grid was established.
Positional Accuracy (11)
Positional accuracy of collected in situ data should be given. For reference/auxiliary data it MUST be given. If unknown, the data’s use must be explained. For DEM or other data with 3D information please specify both vertical and horizontal Positional Accuracy. For analogue data (e.g. maps) try to give approximate accuracy related to mapping scale.
Completeness (12) Data should be checked for spatial/temporal/content gaps.
Model / Algorithm (13)
Give the name of the software and its version. Specify the software module/algorithm used for: a) geometric correction, e.g., Polynomial and its degree, Rational Functions, Thin Plate Spline, etc. b) classification, e.g., ISODATA, Maximum Likelihood, Neural Networks, etc.
Dataset 1, 2, … (14) Please delete / add additional dataset sections as necessary.
Radiometric Processing (15) State whether (and which) radiometric processing was applied (e.g., contrast enhancement, histogram matching, bundle block adjustment, radiometric normalisation, filtering, etc.).
Projection; Spheroid / Ellipsoid (16)
Always specify completely, i.e. at minimum the Projection (+Zone, if applicable), Spheroid / Ellipsoid, Map Datum. Give additional information if necessary to unambiguously define the reference system.
Ground Control Points (GCPs) (17)
Give the number, distribution and RMS of used Ground Control Points (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of GCPs should be attached as a snapshot, or described in the Comments section.
Tie Points (TPs) (18)
In case of mosaicking, give the number of used Tie Points and their total RMS (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of TPs should be attached as a snapshot, or described in the Comments section.
Check Points (CPs) (19)
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The real measure of positional accuracy and the only measure which should be examined as to its Acceptable Range. Give the independent Check Points’ total RMS (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of CP's should be attached as a snapshot, or described in the Comments section.
Resampling Method (20) Specify, if / which resampling algorithm has been used (e.g. NN=Nearest Neighbour, BIL=Bilinear Interpolation, CC=Cubic Convolution).
Sampling unit (21) Specify sampling unit of Accuracy Assessment as POINT, FRAME, POLYGON and how it is treated (e.g. pixel center, polygon centre, etc.).
Sampling Design (22) SYST=Systematic, RAND=Random, STRAT=Stratified, SBCLASS=Stratified by class, SBAREA=Stratified by area
Sample exclusion criteria (23)
Describe which criteria you apply for sampling point selection resp. exclusion of certain points. For example, if the point is too close to a class boundary (less than 1 pixel), it is excluded. If the selected sample point is not representative of the class , it is excluded. For these reasons, it is recommended to oversample by 10% to compensate for sample point exclusion.
Thematic Accuracy (24)
Provide a detailed description of the Accuracy Assessment results in the form of error matrices showing commission and omission errors, user’s , producer’s and overall accuracies and other measures of Thematic Accuracy, as the confidence level (usually fixed at 95%) and the respective confidence interval, at least for the overall accuracy. If classification is done in a phased approach, e.g. if Forest Area and subsequently Forest Type are mapped, independent reports have to be produced.
Completeness of Coverage (25) State whether the coverage is limited to a subset, or portion of the final product.
Completeness of Classification (26)
State whether classification was constrained to a subset, or portion of the final product.
Completeness of Verification (27)
State whether verification applied to lineage, positional, or thematic accuracy is constrained to a subset, or portion of the final product.
Product (28) Please delete / add additional product sections as necessary.
Lineage (29) Definition (INSPIRE 2015): Lineage is “a statement on process history and/or overall quality of the spatial data set. Where appropriate it may include a statement whether the data set has been validated or quality assured, whether it is the official version (if multiple versions exist), and whether it has legal validity. The value domain of this element is free text.”
Source(30) Definition (INSPIRE, 2015): “This is the description of the organisation responsible for the establishment, management, maintenance or distribution of the resource. This description shall include: name of the organisation and contact email address.”
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 1
Earth Observation for Sustainable Development - Urban
Quality Assurance and Quality Control Sheets These QA/QC Templates were prepared by GAF AG compliant with ISO 9001:2008 Quality Management System
standards and can only be used by Partners in the current EO4SD-Urban Project.
Project Title: EO4SD-Urban
Project Leader: GAF AG
Service Provider: GAF Editor: D. Angelova
Client: WB Date: 02.11.2017
Product: Population Distribution and Density
Overview of QC-Sheets and Processing Steps Sheet used
Sheet filled in
Requirements
0.1 Requirements Yes Yes
Specifications of Input Data
1.1 List of EO Data Yes Yes
1.2 List of In-situ Data Yes Yes
1.3 List of Ancillary Data Yes Yes
Data Quality Checks
2.1 EO Data Quality Yes Yes
2.2 In-situ Data Quality Yes Yes
2.3 Ancillary Data Quality Yes Yes
Pre-Processing of EO Data
3.1 Geometric Correction Yes Yes
3.1.1 Data Fusion Yes Yes
3.2 Data Processing Yes Yes
Thematic Processing
4.1 Classification Yes Yes
Accuracy Assessment
5.1 Thematic Accuracy Yes Yes
5.2 Error Matrices Yes Yes
Delivery Checks / Delivery
6.1 Completeness Yes Yes
6.2 Compliancy Yes Yes
Glossary (index numbers in the QA/QC tables refer to the glossary at the end of this document) Further QC-relevant Documents:
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 2
Comments / Characteristics:
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 3
0.1 Requirements
Product 1 (28) Population Distribution and Density Change 2005 and 2015
Abstract
The Population Distribution and Density Change product contains spatial explicit information about population distribution changes at a certain degree, within the Core Urban Districts of Arusha. The following eight change classes are identified, based on a frequency distribution analysis of the changes that occur: Unchanged Population Distribution; Up to –100% decrease; Up to 200% increase; 201% - 400% increase; 401% - 600% increase; 601% - 800% increase; 801% - 1000% increase; More than 1000% increase. The Population Distribution and Density Change product is estimated for all objects which belong to the residential class (LULC class 11), using 5 different data sources: LULC Product, WorldPop data (2015) with a spatial resolution of 100m, official population census data from 2002 and 2012, Soil Sealing layer and Administrative Boundaries.
Service / Product Specifications
Area Coverage
Country: Tanzania A) Wall-to-wall: n/a
City: Arusha Selected Sites: Area of Interest defined with end-users
Area km² Core Urban: 211 km2 B) Sampling based: n/a
Time Period - Update Frequency
A) Baseline Year(s): B) Update Frequency
2005 and 2015/2016 2 points in time
Comments: None
Geographic Reference System
WGS84 UTM Zone 37S
Mapping Classes and Definitions
Single date
1 Population Density between 0 – 150 inhabitants/ km2
2 Population Density between 151 – 300 inhabitants/ km2
3 Population Density between 301 – 600 inhabitants/ km2
4 Population Density between 601 – 1500 inhabitants/ km2
5 Population Density between 1501 – 3000 inhabitants/ km2
6 Population Density between 3001 – 5000 inhabitants/ km2
7 Population Density between 5001 – 9000 inhabitants/ km2
8 Population Density between 9001 – 18000 inhabitants/ km2
9 Population Density between 18001 - 41000 inhabitants/ km2
Change product
1 Unchanged Population Distribution
2 Up to -100% decrease
3 Up to 200% increase
4 201% - 400% increase
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 4
5 401% - 600% increase
6 601% - 800% increase
7 801% - 1000% increase
8 More than 1000% increase
Cloud and Cloud Shadow Detection and Removal
n/a
Spatial Resolution
n.a. (Product provided as Shapefile)
Minimum Mapping Unit (MMU)
n.a.
Data Type & Format
Shapefile *.shp
Bit Depth
n.a.
Class Coding
Class Code Class Name RGB Code
Single date
1 Population Density between 0 – 150 inhabitants/ km2 242/241/162
2 Population Density between 151 – 300 inhabitants/ km2 250/245/105
3 Population Density between 301 – 600 inhabitants/ km2 255/255/38
4 Population Density between 601 – 1500 inhabitants/ km2 255/183/0
5 Population Density between 1501 – 3000 inhabitants/ km2 255/85/0
6 Population Density between 3001 – 5000 inhabitants/ km2 247/5/122
7 Population Density between 5001 – 9000 inhabitants/ km2 240/7/228
8 Population Density between 9001 – 18000 inhabitants/ km2 161/14/230
9 Population Density between 18001 - 41000 inhabitants/ km2 93/26/201
Change product
1 Unchanged Population Distribution 156/156/156
2 Up to -100% decrease 112/153/89
3 Up to 200% increase 242/237/194
4 201% - 400% increase 237/215/114
5 401% - 600% increase 242/176/99
6 601% - 800% increase 237/175/175
7 801% - 1000% increase 196/113/96
8 More than 1000% increase 120/35/35
Metadata
ISO standard 19115 "Metadata" and ISO 19139 "XML Scheme Implementation"
Service / Product Quality
Thematic Accuracy
> 80%
Positional Accuracy
RMSE < 15 m
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 5
Delivery Procedure
Service Provision
Online via FTP
Delivery Date
End of October 2017
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 6
1.1 List of EO Data
Sensoren (8) No Data used
Incoming Date
Acquisition Date
Proc. Level
Path / Row
AOI - City / Region / Country
Spatial Res.
No. of Bands
Cloud Cover (Data Provider)
Projection / Spheroid (16)
Data Format (3)
Bit Depth (5)
Header / Metadata (2)
File Name [e.g yymmdd; tbd...]
No EO Data Used
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 7
1.2 List of In-situ Data
Dataset 1 (14) Incoming Date Acquisition Date AOI - City / Region / Country
Data Type (4)
Projection / Spheroid (16)
No. of Sample Plots
Sampling Design (22)
Positional Accuracy (11)
Purpose (9)
No In-situ Data Used
Lineage:
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 8
1.3 List of Ancillary Data
Dataset 1 (14)
LULC Date of Receipt from Client
Metadata (2)
Reference Mapping Date / Date of Creation
Geographic Area/- City / Region / Country
Area Coverage
Data Type (4)
Data Format (3)
Projection / Spheroid (16)
Positional Accuracy (11)
No. of Classes
Thematic Accuracy
Availability of Class Definitions
EO4SD-Urban Land Use/Land Cover Baseline Product
2017-07-30 Yes
(INSPIRE) 2005 and 2015
Arusha (urban area) 100% Vector *.shp EPSG:32736, WGS 84 / UTM zone 37S
< 3 m 24 88% Yes
Lineage(29): The Urban Land Use/Land Cover Baseline Product and the derived Soil Sealing layer are used to interpolate the population distribution and density for each residential unit, within the Core Urban Districts of Arusha.
Source (30): GAF AG
Dataset 2 (14)
Report Date of Receipt from Client
Metadata (2)
Reference Mapping Date / Date of Creation
Geographic Area/- City / Region / Country
Area Coverage
Data Type (4)
Data Format (3)
Projection / Spheroid (16)
Positional Accuracy (11)
No. of Classes
Thematic Accuracy
Availability of Class Definitions
Arusha Master Plan 2015-2035 Volume 1: Main Report
2016-12-21 n.a. 2016-07-22 Arusha/ Tanzania Arusha Document
pdf n.a. n.a. n.a. n.a. n.a.
Lineage(29): The Arusha Master Plan 2015-2035 is used to define class thresholds for both current and historic status of the population density in Arusha.
Source (30): Received from MaryGrace Weber.
Dataset 3 (14)
Population Raster
Dataset
Date of Receipt from Client
Metadata (2)
Reference Mapping Date / Date of Creation
Geographic Area/- City / Region / Country
Area Coverage
Data Type (4)
Data Format (3)
Projection / Spheroid (16)
Positional Accuracy (11)
No. of Classes
Thematic Accuracy
Availability of Class Definitions
WorldPop Dataset 2017-09-01 Yes 2015 Arusha/Tanzania 100% Raster Geotiff Geographic, WGS84
n.a. n.a. n.a. n.a.
Lineage(29): The WorldPop Dataset is used to interpolate the current population data for each residential object in the Core Urban Districts of Arusha.
Source (30): Downloaded from WorldPop: http://www.worldpop.org.uk/
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 9
Dataset 4 (14)
Report Date of Receipt from Client
Metadata (2)
Reference Mapping Date / Date of Creation
Geographic Area/- City / Region / Country
Area Coverage
Data Type (4)
Data Format (3)
Projection / Spheroid (16)
Positional Accuracy (11)
No. of Classes
Thematic Accuracy
Availability of Class Definitions
2012 POPULATION AND HOUSING CENSUS
2017-09-01 n.a. 2012-08-26 Tanzania National coverage
Document
pdf n.a. n.a. n.a. n.a. n.a.
Lineage(29): The 2012 Population and Housing Census report is used to project the number of inhabitants per district, and to adjust the population residuals in the current Population Distribution and Density product.
Source (30): Downloaded from United Republic of Tanzania, Ministry of East African Cooperation: http://meac.go.tz/
Dataset 5 (14)
Report Date of Receipt from Client
Metadata (2)
Reference Mapping Date / Date of Creation
Geographic Area/- City / Region / Country
Area Coverage
Data Type (4)
Data Format (3)
Projection / Spheroid (16)
Positional Accuracy (11)
No. of Classes
Thematic Accuracy
Availability of Class Definitions
2002 POPULATION AND HOUSING CENSUS
2017-09-01 Yes
(The World Bank)
2002-08-22 Tanzania National coverage
Document
pdf n.a. n.a. n.a. n.a. n.a.
Lineage(29): The 2002 Population and Housing Census report is used to interpolate the population data in the historic Population Distribution and Density product.
Source (30): Downloaded from the IPUMS – International Website: https://international.ipums.org/international/
Dataset 6 (14)
Administrative
Boundaries
Date of Receipt from Client
Metadata (2)
Reference Mapping Date / Date of Creation
Geographic Area/- City / Region / Country
Area Coverage
Data Type (4)
Data Format (3)
Projection / Spheroid (16)
Positional Accuracy (11)
No. of Classes
Thematic Accuracy
Availability of Class Definitions
TZA_adm3 2017-09-01 Yes
2015 Tanzania
National coverage
Vector *.shp WGS 84 unknown n.a. unknown Yes
Lineage(29): This shapefile provides with information, regarding the administrative boundaries at a district level in Tanzania.
Source (30): Downloaded from Global Administrative Areas: http://www.gadm.org/country
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 10
2.1 EO Data Quality
Sensoren(8)
WV, GE No Data used
Ba
cku
p
Re
ad
ab
ility
(1
)
Ch
eck
He
ad
er
/ M
eta
da
ta
(2)
Band Specifications
Pro
ject
ion
/ S
ph
ero
id (1
6)
Sce
ne
Lo
cati
on
Co
mp
lete
ne
ss o
f A
dd
itio
na
l D
ata
Da
ta F
orm
at
(3)
Bit
De
pth
(5)
Clo
ud
Co
ver
(in
tern
al
che
ck)
Ra
dio
me
try
To
po
gra
ph
y
Dro
pp
ed
Lin
es
/ A
rte
fact
s (7
)
Acc
ep
tan
ce S
tatu
s
Comment
s File Name [e.g yymmdd; tbd...]
No
. o
f B
an
ds
Ba
nd
Re
gis
tra
tio
n
Sp
ect
ral/
Sp
ati
al
Re
solu
tio
n
No EO Data Used
n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 11
2.2 In-situ Data Quality
Dataset 1 (14) No Data used
Ba
cku
p
Re
ad
ab
ility
(1)
Ch
eck
He
ad
er
/ M
eta
da
ta (2
)
Ext
en
t
Pro
ject
ion
/ S
ph
ero
id
(16
)
Sp
ati
al R
eso
luti
on
Da
ta F
orm
at
(3)
Bit
De
pth
(5)
Lo
cati
on
Co
mp
lete
ne
ss
Ge
om
. M
isa
lign
me
nt
Pla
usi
bili
ty
Dro
pp
ed
Lin
es
/
Art
efa
cts
(7)
Acc
ep
tan
ce S
tatu
s
Comments File Name [e.g yymmdd; tbd...]
No In-situ Data Used n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a n.a /
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 12
2.3 Ancillary Data Quality
Dataset 1 (14) LULC
Re
ad
ab
ility
(1
)
Ch
eck
He
ad
er
/Me
tad
ata
(2)
Da
ta C
ove
rage
M
atc
he
s S
erv
ice
A
rea
(%
)
Pro
ject
ion
/ S
ph
ero
id
, E
PS
G(1
6)
Sp
ati
al R
eso
luti
on
a
nd
un
it (
e.g
. m
, k
m)
Da
ta F
orm
at
(3)
Bit
De
pth
(5)
Co
mp
lete
ne
ss
(Ve
cto
r: A
ttri
bu
te
Ta
ble
; R
ast
er:
Th
em
ati
c V
alu
es)
Ge
om
. M
isa
lign
me
nt
Dro
pp
ed
Lin
es
/ A
rte
fact
s (
EO
da
ta
on
ly)(
7)
Uti
lity
for
Cu
rre
nt
Pro
ject
File Name [e.g yymmdd; tbd...]
EO4SD-Urban LULC Product ☒ Yes
☐ No
☒ Complete (INSPIRE/ISO19119)
☐ Incomplete
☐ not available
☒ Yes
☐ No
☐ Unknown If no: xxx%
☒ Correct
☐ Incorrect
☐ Unknown EPSG: 32749
n.a. *.shp n.a. ☒ Complete
☐Incomplete
☒ No
☐ Yes
☐ Unknown If yes: XX m
☒ No
☐ Yes
☐ n.a. If yes: xxx %
☐ None
☐ Partial
☒ Full
Comments:
Dataset 2 (14) Report
Re
ad
ab
ility
(1)
Ch
eck
He
ad
er
/Me
tad
ata
(2)
Da
ta C
ove
rage
M
atc
he
s S
erv
ice
A
rea
(%
)
Pro
ject
ion
/ S
ph
ero
id
, E
PS
G(1
6)
Sp
ati
al R
eso
luti
on
an
d u
nit
(e
.g.
m, k
m)
Da
ta F
orm
at
(3)
Bit
De
pth
(5)
Co
mp
lete
ne
ss
(Ve
cto
r: A
ttri
bu
te
Ta
ble
; R
ast
er:
Th
em
ati
c V
alu
es)
Ge
om
. M
isa
lign
me
nt
Dro
pp
ed
Lin
es
/ A
rte
fact
s (
EO
da
ta
on
ly)(
7)
Uti
lity
for
Cu
rre
nt
Pro
ject
File Name [e.g yymmdd; tbd...]
Arusha Master Plan 2015-2035 Volume 1: Main Report
☒ Yes
☐ No
☐ Complete
☐ Incomplete
☒ not available
☒ Yes
☐ No
☐ Unknown If no: xxx%
n.a. n.a. *.pdf n.a. n.a. n.a. n.a.
☐ None
☒ Partial
☐ Full
Comments: The Master Plan is used to define proper thresholds for the population density classes in Arusha.
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 13
Dataset 3 (14) Population Raster Dataset
Re
ad
ab
ility
(1)
Ch
eck
He
ad
er
/Me
tad
ata
(2)
Da
ta C
ove
rage
M
atc
he
s S
erv
ice
Are
a
(%)
Pro
ject
ion
/ S
ph
ero
id
, E
PS
G(1
6)
Sp
ati
al R
eso
luti
on
an
d u
nit
(e
.g.
m, k
m)
Da
ta F
orm
at
(3)
Bit
De
pth
(5)
Co
mp
lete
ne
ss
(Ve
cto
r: A
ttri
bu
te
Ta
ble
; R
ast
er:
Th
em
ati
c V
alu
es)
Ge
om
. M
isa
lign
me
nt
Dro
pp
ed
Lin
es
/ A
rte
fact
s (
EO
da
ta
on
ly)(
7)
Uti
lity
for
Cu
rre
nt
Pro
ject
File Name [e.g yymmdd; tbd...]
WorldPop Dataset ☒ Yes
☐ No
☐ Complete
☒ Incomplete
☐ not available
☒ Yes
☐ No
☐ Unknown If no: xxx%
☒ Correct
☐ Incorrect
☐ Unknown EPSG: 32749
100m *.tif 32 Bit ☒ Complete
☐Incomplete
☒ No
☐ Yes
☐ Unknown If yes: XX m
☒ No
☐ Yes
☐ n.a. If yes: xxx %
☐ None
☒ Partial
☐ Full
Comments: The WorldPop Dataset is used only for the estimation of the current Population Distribution and Density product.
Dataset 4 (14) Report
Re
ad
ab
ility
(1
)
Ch
eck
He
ad
er
/Me
tad
ata
(2)
Da
ta C
ove
rage
M
atc
he
s S
erv
ice
Are
a (
%)
Pro
ject
ion
/ S
ph
ero
id
, E
PS
G(1
6)
Sp
ati
al R
eso
luti
on
an
d u
nit
(e
.g.
m, k
m)
Da
ta F
orm
at
(3)
Bit
De
pth
(5)
Co
mp
lete
ne
ss
(Ve
cto
r: A
ttri
bu
te
Ta
ble
; R
ast
er:
Th
em
ati
c V
alu
es)
Ge
om
. M
isa
lign
me
nt
Dro
pp
ed
Lin
es
/
Art
efa
cts
(E
O d
ata
o
nly
)(7
)
Uti
lity
for
Cu
rre
nt
Pro
ject
File Name [e.g yymmdd; tbd...]
2012 POPULATION AND HOUSING CENSUS
☒ Yes
☐ No
☐ Complete
☐ Incomplete
☒ not available
n.a. n.a. n.a. *.pdf n.a. n.a. n.a. n.a.
☐ None
☐ Partial
☒ Full
Comments: The 2012 Population Census report is used to derive the number of inhabitants per district in 2015 in Arusha.
Dataset 5 (14) Report
Re
ad
ab
ility
(1)
Ch
eck
He
ad
er
/Me
tad
ata
(2)
Da
ta C
ove
rage
M
atc
he
s S
erv
ice
Are
a (
%)
Pro
ject
ion
/ S
ph
ero
id
, E
PS
G(1
6)
Sp
ati
al R
eso
luti
on
an
d u
nit
(e
.g.
m, k
m)
Da
ta F
orm
at
(3)
Bit
De
pth
(5)
Co
mp
lete
ne
ss
(Ve
cto
r: A
ttri
bu
te
Ta
ble
; R
ast
er:
Th
em
ati
c V
alu
es)
Ge
om
. M
isa
lign
me
nt
Dro
pp
ed
Lin
es
/
Art
efa
cts
(E
O d
ata
o
nly
)(7
)
Uti
lity
for
Cu
rre
nt
Pro
ject
File Name [e.g yymmdd; tbd...]
2002 POPULATION AND HOUSING CENSUS
☒ Yes
☐ No
☒ Complete
☐ Incomplete
☐ not available
n.a. n.a. n.a. *.pdf n.a. n.a. n.a. n.a.
☐ None
☐ Partial
☒ Full
Comments: The 2002 Population Census report is used to derive the number of inhabitants per district in 2005 in Arusha.
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 14
Dataset 6 (14)
Administrative Boundaries
Re
ad
ab
ility
(1)
Ch
eck
He
ad
er
/Me
tad
ata
(2
)
Da
ta C
ove
rage
Ma
tch
es
Se
rvic
e A
rea
(%
)
Pro
ject
ion
/ S
ph
ero
id ,
E
PS
G(1
6)
Sp
ati
al R
eso
luti
on
an
d u
nit
(e.g
. m
, k
m)
Da
ta F
orm
at
(3)
Bit
De
pth
(5)
Co
mp
lete
ne
ss (
Ve
cto
r:
Att
rib
ute
Ta
ble
; R
ast
er:
Th
em
ati
c V
alu
es)
Ge
om
. M
isa
lign
me
nt
Dro
pp
ed
Lin
es
/ A
rte
fact
s
(EO
da
ta o
nly
)(7
)
Uti
lity
for
Cu
rre
nt
Pro
ject
File Name [e.g yymmdd; tbd...]
TZA_adm3 ☒ Yes
☐ No
☐ Complete
☒ Incomplete
☐ not available
☒ Yes
☐ No
☐ Unknown If no: xxx%
☒ Correct
☐ Incorrect
☐ Unknown EPSG: 32749
n.a. *.shp n.a. ☒ Complete
☐Incomplete
☒ No
☐ Yes
☐ Unknown If yes: XX m
☒ No
☐ Yes
☐ n.a. If yes: xxx %
☐ None
☐ Partial
☒ Full
Comments: /
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 15
3.1.1 Data Fusion
Dataset 1 (14) Input A Input B Output
Acc
ep
tan
ce S
tatu
s
Comments
Filename n.a n.a n.a
none
Sensor n.a n.a n.a
Method n.a n.a n.a
Spatial Resolution / (MS/Pan) n.a n.a n.a n.a n.a n.a
Band Combination n.a n.a n.a
Data Format (3) & Bit Depth (5) n.a n.a n.a n.a n.a n.a n.a
3.1 Geometric Correction
Sensoren (8) No Data used
Pro
cess
ing D
ate
AO
I
Cit
y /
Re
gio
n /
Co
un
try
Pro
ject
ion
/ S
ph
ero
id (1
6)
No
. &
RM
S (
m)
of
GC
Ps
(17
)
No
. &
RM
S (
m)
of
TP
s (1
8)
No
. &
RM
S (
m)
of
CP
s (1
9)
Dig
ita
l E
leva
tio
n M
od
el
(DE
M)
Mo
de
l /
Alg
ori
thm
(13
)
Re
sam
plin
g
Me
tho
d (2
0)
Va
lida
tio
n R
ep
ort
s
Acc
ep
tan
ce S
tatu
s
Output File Name [e.g yymmdd; tbd...] No. File Name [e.g yymmdd; tbd...]
No EO Data Used
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 16
3.2 Data Processing
Sensoren (8)
No Data used Atmospheric Correction
Radiometric Processing (15)
Topographic Normalisation
OTHER Calculation:
Acc
ep
tan
ce S
tatu
s
Ba
cku
p
Comment
No. File Name [e.g yymmdd; tbd...] Processing
Date
pro
cess
ed
Software / Method
pro
cess
ed
Software / Method
pro
cess
ed
Software / Method
pro
cess
ed
Software / Method
No EO Data Used
4.1 Classification
Sensors (8) No Data used
Processing Date
Cloud Masking Thematic
Classification Manual
Enhancement Mosaicking
Acc
ep
tan
ce S
tatu
s
Ba
cku
p
Comment
No. File Name [e.g yymmdd; tbd...]
pro
cess
ed
Software / Method
pro
cess
ed
Software / Method
pro
cess
ed
Software / Method
pro
cess
ed
Software / Method
No EO Data Used
5.1 Thematic Accuracy
No. Product Name Processing Date
Area Coverage
No
. o
f C
lass
es
Re
fere
nce
Da
ta
Sa
mp
le S
ize
Sa
mp
ling U
nit
(21
)
Sa
mp
ling
De
sign
(22
)
Sa
mp
le E
xclu
sio
n
Cri
teri
a (2
3)
Th
em
ati
c A
ccu
racy
(2
4)
Acc
ep
tan
ce S
tatu
s
Comment
1.
Population Distribution and Density Change 2005 and 2015
13.10.2017
Full AOI (Core Urban Districts)
8 n.a n.a n.a n.a n.a n.a Yes
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Development – Urban Project QA/QC Sheets developed by GAF AG
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5.2 Error Matrices
The estimated population per district corresponds to the 2012 population and housing census data.
In a disaggregation procedure, such as the one herein described, the outcome of the process is never less accurate than the original source data. By disaggregating
numerical data from one coarse geometry to a finer geometry, we always gain detail and approximate ground truth without the risk of deteriorating the source
information. The degree to which the disaggregation approximates reality, however, varies greatly, and it depends chiefly on: 1) the quality of the ancillary data
and 2) the appropriateness of the disaggregation algorithm and its parameters.
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 18
6.1 Completeness
INPUT DATA
No. Item AOI Coverage [km²]
Area Coverage [km²]
Completeness of Coverage (25)
No. of Scenes Scenes used in Production
Completeness of Verification (27)
Metadata Comments
1. Ancillary Data Arusha 211 km² 100% 1 1 100% Yes none
PRODUCTS
No. Product (28) AOI Coverage [km²]
Product Coverage [km²]
Completeness of Coverage (25)
Completeness of Classification (26)
Unclassifiable Area [%]
Completeness of Verification (27)
Metadata Comments
1.
Population Distribution and Density Change 2005 and 2015
Arusha 211 km² 100% 100% 0% 100% Yes none
Earth Observation for Sustainable
Development – Urban Project QA/QC Sheets developed by GAF AG
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6.2 Compliancy
Product 1 (28) Population Distribution and Density Change 2005 and 2015
Abstract
The Population Distribution and Density Change product contains spatial explicit information about population distribution changes at a certain degree, within the Core Urban Districts of Arusha. The following eight change classes are identified, based on a frequency distribution analysis of the changes that occur: Unchanged Population Distribution; Up to –100% decrease; Up to 200% increase; 201% - 400% increase; 401% - 600% increase; 601% - 800% increase; 801% - 1000% increase; More than 1000% increase. The Population Distribution and Density Change product is estimated for all objects which belong to the residential class (LULC class 11), using 5 different data sources: LULC Product, WorldPop data (2015) with a spatial resolution of 100m, official population census data from 2002 and 2012, Soil Sealing layer and Administrative Boundaries.
Service / Product Specifications
Area Coverage
Requirements Achieved Specifications Compliancy Comments
Country 1: Tanzania Country 1: Tanzania YES None
A) Wall-to-wall: Arusha city A) Wall-to-wall: Arusha city
Core Urban Area (211 km²) Core Urban Area (211 km²)
Area of Interest. Arusha 211 km², defined by the national user.
B) Sampling based: B) Sampling based:
N/A N/A
Time Period - Update Frequency
Requirements Achieved Specifications Compliancy Comments
Baseline Year(s): Baseline Year(s): Yes None
2005 and 2015 (+/- 1 years) 2005 and 2015
B) Update Frequency B) Update Frequency Yes None
No update No update
Geographic Reference System
Requirements Achieved Specifications Compliancy Comments
WGS84 / UTM 37S WGS84 / UTM 37S YES None
Mapping Classes and Definitions
Requirements Achieved Specifications Compliancy
Single date
Population Density between 0 – 150 inhabitants/ km2 0 – 150 inhabitants/ km2 YES
Population Density between 151 – 300 inhabitants/ km2 151 – 300 inhabitants/ km2 YES
Population Density between 301 – 600 inhabitants/ km2 301 – 600 inhabitants/ km2 YES
Population Density between 601 – 1500 inhabitants/ km2 601 – 1500 inhabitants/ km2 YES
Population Density between 1501 – 3000 inhabitants/ km2 1501 – 3000 inhabitants/ km2 YES
Population Density between 3001 – 5000 inhabitants/ km2 3001 – 5000 inhabitants/ km2 YES
Population Density between 5001 – 9000 inhabitants/ km2 5001 – 9000 inhabitants/ km2 YES
Population Density between 9001 – 18000 inhabitants/ km2 9001 – 18000 inhabitants/ km2 YES
Population Density between 18001 - 41000 inhabitants/ km2 18001 - 41000 inhabitants/ km2 YES
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Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 20
Change product
Unchanged Population Distribution Unchanged Population Distribution YES
Up to -100% decrease Up to -100% decrease YES
Up to 200% increase Up to 200% increase YES
201% - 400% increase 201% - 400% increase YES
401% - 600% increase 401% - 600% increase YES
601% - 800% increase 601% - 800% increase YES
801% - 1000% increase 801% - 1000% increase YES
More than 1000% increase More than 1000% increase YES
Cloud and Shadow Detection and Removal
Requirements Achieved Specifications Compliancy Comments
N/A N/A N/A
Spatial Resolution
Requirements Achieved Specifications Compliancy Comments
N/A N/A N/A None
Minimum Mapping Unit (MMU)
Requirements Achieved Specifications Compliancy Comments
N/A N/A N/A None
Data Type
Requirements Achieved Specifications Compliancy Comments
Vector data Vector data YES None
Bit Depth
Requirements Achieved Specifications Compliancy Comments
N/A N/A
Data Format
Requirements Achieved Specifications Compliancy Comments
ESRI SHP ESRI SHP YES None
Class Coding
Requirements Achieved Specifications Compliancy
N/A N/A
Metadata
Requirements Achieved Specifications Compliancy Comments
ISO standard 19115 "Metadata" and ISO 19139 "XML Scheme Implementation"
ISO standard 19115 "Metadata" and ISO 19139 "XML Scheme Implementation"
YES None
Service / Product Quality
Thematic Accuracy
Requirements Achieved Specifications Compliancy Comments
> 80% The estimated population per district corresponds to the 2012 population and housing census data.
YES None
Positional Accuracy
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Development – Urban Project QA/QC Sheets developed by GAF AG
© 2017 GAF AG All Rights Reserved. Unless otherwise indicated, the templates of these QA/QC pages are copyrighted by GAF AG. No part of these pages, either text or image may be used for any purpose other than use in the EO4SD-Urban Project. Therefore, reproduction, modification, storage in a retrieval system or retransmission, in any form or by any means, electronic, mechanical or otherwise, for reasons other than personal use, is strictly prohibited without prior written permission Page 21
Requirements Achieved Specifications Compliancy Comments
RMSE < 15 m 15m YES None
Delivery Procedure
Service Provision
Requirements Achieved Specifications Compliancy Comments
Online via FTP Online via FTP YES None
Delivery Date
Requirements Achieved Specifications Compliancy Comments
End of October 2017 End of October 2017 YES None
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Development – Urban Project QA/QC Sheets developed by GAF AG
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Glossary
Quality Checks
Prefix Suffix Explanation
Y (Yes) - V Visually checked
N (No) - Q Quantitatively/qualitatively checked
N/A - V Q Visually and quantitatively/qualitatively checked
- N/A Not applicable
Readability (1) Check readability of all required input data. Can the data be stored again?
Header / Metadata (2) Check Image Header Information and/or Metadata for completeness / distinctive features.
Data Format (3) For digital data, please give file format (e.g. *.tiff, *.shp).
Data Type (4) Please specify the type of the data (e.g. raster, vector or analogue).
Bit Depth (5) Please give pixel depth and sign of raster data (e.g. 8 bit unsigned integer).
Dynamic Range (6) Check dynamic range of all image bands. Visual check of dynamic range should be accompanied by histograms and statistics.
Dropped Lines & Artefacts (7) Check Image for dropped lines and other artefacts. If such occurs, please give description of extent and influence in the Comments section.
Sensor 1, 2, … (8) Please delete / add additional sensor sections as necessary.
Purpose (9)
The purpose and use of the given auxiliary or reference data should be stated:
ORHTOrectification, GEOmetric correction, REFerence, VERification source, POSitional ACCuracy assessment, THEmatic ACCuracy assessment.
Sampling Methodology (10)
Methodology of in situ or reference/auxiliary data sampling scheme should be outlined. In case of sample plots, also state how the plot positions have been determined (e.g. from GPS measurements, topographic maps, terrestrial triangulation, EO data, etc.) and how the sampling grid was established.
Positional Accuracy (11)
Positional accuracy of collected in situ data should be given. For reference/auxiliary data it MUST be given. If unknown, the data’s use must be explained. For DEM or other data with 3D information please specify both vertical and horizontal Positional Accuracy. For analogue data (e.g. maps) try to give approximate accuracy related to mapping scale.
Completeness (12) Data should be checked for spatial/temporal/content gaps.
Model / Algorithm (13)
Give the name of the software and its version. Specify the software module/algorithm used for: a) geometric correction, e.g., Polynomial and its degree, Rational Functions, Thin Plate Spline, etc. b) classification, e.g., ISODATA, Maximum Likelihood, Neural Networks, etc.
Dataset 1, 2, … (14) Please delete / add additional dataset sections as necessary.
Radiometric Processing (15) State whether (and which) radiometric processing was applied (e.g., contrast enhancement, histogram matching, bundle block adjustment, radiometric normalisation, filtering, etc.).
Projection; Spheroid / Ellipsoid (16)
Always specify completely, i.e. at minimum the Projection (+Zone, if applicable), Spheroid / Ellipsoid, Map Datum. Give additional information if necessary to unambiguously define the reference system.
Ground Control Points (GCPs) (17)
Give the number, distribution and RMS of used Ground Control Points (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of GCPs should be attached as a snapshot, or described in the Comments section.
Tie Points (TPs) (18)
In case of mosaicking, give the number of used Tie Points and their total RMS (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of TPs should be attached as a snapshot, or described in the Comments section.
Check Points (CPs) (19)
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The real measure of positional accuracy and the only measure which should be examined as to its Acceptable Range. Give the independent Check Points’ total RMS (as average per scene) as obtained from the Geometric Correction, in meters [m]. Optionally, also RMSx and RMSy may be given. Distribution of CP's should be attached as a snapshot, or described in the Comments section.
Resampling Method (20) Specify, if / which resampling algorithm has been used (e.g. NN=Nearest Neighbour, BIL=Bilinear Interpolation, CC=Cubic Convolution).
Sampling unit (21) Specify sampling unit of Accuracy Assessment as POINT, FRAME, POLYGON and how it is treated (e.g. pixel center, polygon centre, etc.).
Sampling Design (22) SYST=Systematic, RAND=Random, STRAT=Stratified, SBCLASS=Stratified by class, SBAREA=Stratified by area
Sample exclusion criteria (23)
Describe which criteria you apply for sampling point selection resp. exclusion of certain points. For example, if the point is too close to a class boundary (less than 1 pixel), it is excluded. If the selected sample point is not representative of the class , it is excluded. For these reasons, it is recommended to oversample by 10% to compensate for sample point exclusion.
Thematic Accuracy (24)
Provide a detailed description of the Accuracy Assessment results in the form of error matrices showing commission and omission errors, user’s , producer’s and overall accuracies and other measures of Thematic Accuracy, as the confidence level (usually fixed at 95%) and the respective confidence interval, at least for the overall accuracy. If classification is done in a phased approach, e.g. if Forest Area and subsequently Forest Type are mapped, independent reports have to be produced.
Completeness of Coverage (25) State whether the coverage is limited to a subset, or portion of the final product.
Completeness of Classification (26)
State whether classification was constrained to a subset, or portion of the final product.
Completeness of Verification (27)
State whether verification applied to lineage, positional, or thematic accuracy is constrained to a subset, or portion of the final product.
Product (28) Please delete / add additional product sections as necessary.
Lineage (29) Definition (INSPIRE 2015): Lineage is “a statement on process history and/or overall quality of the spatial data set. Where appropriate it may include a statement whether the data set has been validated or quality assured, whether it is the official version (if multiple versions exist), and whether it has legal validity. The value domain of this element is free text.”
Source(30) Definition (INSPIRE, 2015): “This is the description of the organisation responsible for the establishment, management, maintenance or distribution of the resource. This description shall include: name of the organisation and contact email address.”