The use of high resolution satellite data (IKONOS) in the establishment and maintenance of an urban
Geographical Information System.
Masters of Engineering (Surveying and Spatial Information Systems)
Eric W Richards
January 2009
Originality Statement
‘I hereby declare that this submission is my own work and to the best of my knowledge it
contains no materials previously published or written by another person, or substantial
proportions of material which have been accepted for the award of any other degree or
diploma at UNSW or any other educational institution, except where due
acknowledgement is made in the thesis. Any contribution made to the research by others,
with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the
thesis. I also declare that the intellectual content of this thesis is the product of my own
work, except to the extent that assistance from others in the project’s design and
conception or in style, presentation and linguistic expression is acknowledged.’
Signed…………………………………………………..
Date……………………………………………………..
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Acknowledgements
For an activity lasting this long it is needless to say there are a number of people who
have played a part in its final completion, some of whom may or may not know they
played a part.
I must first thank the staff at the University of NSW such as Brian Donnelly and Helve
who accommodated my arrivals, stays and departures to and from Canberra. A major
vote of appreciation goes to my supervisor Dr John Trinder who has shown significant
patience and has always provided timely and well needed feedback and advice by
correspondence, particularly after the unfortunate passing of Dr Ewan Masters early in
my starting of this study. Next comes all the industry participates who replied to my
questionnaire in particular Graham Boler from the Sutherland Shire Council and Chris
Comer at ACT Urban Services, also Noel Ward and Neil Fraser at SKM who provided
access to their practical experience. Finally the staff at Hobart City Council, Glenorchy
City Council, Mackay City Council and at the Tasmanian Governments’ “The LIST”
office who cheerfully supplied me with reference data and answered my questions.
The completion of this thesis would not have been possible without the scholarship
support of the Cooperative Research Centre for Spatial Information (CRC-SI). I would
like to thank in particular Graham Kernich and Michael Ridout for their support. In the
same light Dr Clive Fraser of the University of Melbourne who suggested I apply for the
CRC-SI scholarship and when he heard of my problems sourcing stereo high resolution
commercial satellite imagery supplied the control point data over Hobart and facilitated
access to the IKONOS sample imagery kindly supplied by Mr Gene Dial at Space
Imaging. The final person in this administrative loop to thank is Ms Elizabeth Milne from
the Department of Defence who on finding no previous precedence on allowing a
Defence staff member to accept such a scholarship gave me permission.
On a personal front I would also like to thank and acknowledge a range of friends, family
and work colleagues such as Dan Carmody, Richard Stanaway, Rae Absolom, Neil
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Sparks, Mel Clark, Owen Moss, John Gregs, Graham McCloy and my brother Adrian
who either allowed me to take time off or gave me quiet encouragement as well as the
occasional “Man, are you still doing that?”.
Finally the last paragraph goes to my wife Louise for her love, patience and unbelievable
tolerance; no words can ever describe or thank you for this devotion. I promise I will
finish the house renovations now that you are no longer a “Masters widow”. But first we
are going on a picnic. To Thomas and Lydia, you can now use the Dell.
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Abstract
The past years has seen the advent of the availability of high resolution commercial
satellite imagery. This study shows that whilst high resolution commercial satellite
imagery is capable of producing reasonable spatial data both in quality and cost for use in
an urban GIS the challenges of supplying this data commercially is not limited to simply
the provision of the imagery.
Since a significant amount of work has been done by others to examine and quantify the
technical suitability and limitations of high resolution commercial satellite imagery, this
study examines the practical limitations and opportunities presented with the arrival of
this new spatial data source. In order to do this a number of areas are examined; the
historical development of the satellite systems themselves, the business evolution of the
owning commercial ventures, Geographical Information Systems (GIS) data and service
requirements for a diverse range of spatial data applications and finally the evaluation
and comparison of the imagery as a spatial data source.
The study shows that high resolution commercial satellite imagery is capable of
providing spatial data and imagery for a variety of uses at different levels of accuracy as
well as opening up a new era in the supply and application of metric imagery. From a
technical approach high resolution commercial satellite imagery provides remote access,
one metre or better resolution, 11 bit imagery and a multispectral capability not
previously available from space. Equally as challenging is the process or achievement in
making the technical capability a reality in a commercial world requiring a financial
return at all levels; from the image vendors to the spatial science professional providing a
service to a paying customer. The imagery must be financially viable for all concerned.
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Contents
Chapter 1 - Introduction…………………………………………………………..
1.1 - Background…………….…………………………………………………….. 1.2 - The Usability of High Resolution Commercial Satellite Imagery……........ 1.3 - What Data and Source are required?............................................................. 1.4 - Selection of Collection Method..…………………………………….............. 1.5 - Objectives of the Research…………..………………………………………. 1.6 - Overview of the Thesis………….……………………………………………
Chapter 2 - High Resolution Commercial Satellite Imagery – Development and Characteristics………………………………………………………………...
2.1 - Available Satellite Sourced Data (1.0m resolution or better)…....………... 2.1.1 - Satellite Imagery Collection Technique…………………….............. 2.1.2 - Distortions……….…………………………………………………... 2.1.2.1 - Observer Distortions………….……………........................ 2.1.2.2 - Observed Distortions……………………………………… 2.1.3 - Accuracy……….……………………………………………………. 2.2 - Emergence of Commercial High Resolution Satellite Imagery….…........... 2.2.1 - Business Ventures………………………….………………………... 2.2.1.1 - ClearView and NextView Contracts………........................ 2.2.1.2 - Digital Globe……………………………………................ 2.2.1.3 - Space Imaging…………………………………………….. 2.2.1.4 - OrbImage……….…………………………………………. 2.2.1.5 - Geoeye Inc………….……………………………………... 2.2.1.6 - ImageSat…………………………………………………...
2.2.1.7 - Centre National d'Etudes Spatiales (CNES)…….................
2.3 - Satellite Systems…………….………………………………………………... 2.3.1 - IKONOS…………………………………………………………….. 2.3.2 - EROS A……………………………………………………………... 2.3.3 - EROS B…………………………...….…....………………………... 2.3.4 - Quickbird…………….……………………………............................ 2.3.5 - WorldView 1………………………………………………………... 2.3.6 - OrbView – 3………………………………………............................
2.4 - Terrestrial Based Methods…………………………………………………... 2.4.1 - Ground Survey………………………………………………………. 2.4.2 - Aerial Photogrammetry……………………………………………... 2.4.3 - Airborne Interferometric Synthetic Aperture Radar (InSAR)………. 2.4.4 - Light Detection and Ranging (LIDAR)………….………………….. 2.5 - Summary……………………………………………………………………...
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Chapter 3 - Imagery Applications in a Geographical Information System (GIS)………………………………………………………………………………...
3.1 - Introduction – Composition of a Geographical Information System (GIS)……………………………………………………………………………….. . 3.1.1 - GIS Data Acquisition………………..………………………………. 3.1.2 - GIS Preprocessing…………………………………………………... 3.1.3 - GIS Data Management……………………….................................... 3.1.4 - GIS Manipulation and Analysis……………………………………... 3.1.5 - Product Generation………………………………………………….. 3.2 - Imagery in Data Maintenance………………………………….................... 3.3 - Satellite Imagery versus Aerial Photography (Imagery)…………………..
3.3.1 - Cost of High Resolution Commercial Satellite Imagery..................... 3.3.2 - Application of Imagery………………………….…………………... 3.3.3 - Processing Tools Required…………………………………………..
3.4 - Imagery Applications and Considerations…….……………........................ 3.4.1 - Local Council Requirements………………………………………...
3.4.2 - Emergency Services…………….………………................................ 3.4.3 - Public Information (Street Directories, Mapping, General Spatial Data, Analytical Applications)……………………..
3.4.4 - Land Use Identification………………..……………………………. 3.5 - Summary……………………………………………………………………...
Chapter 4 - The Suitability of High Resolution Commercial Satellite Imagery as a Spatial Data Source…………………………………………….......................
4.1 - Introduction….………………………………………………………………. 4.1.1 - Imagery and Study Area………………..……………………………
4.2 - Ground Control Points Comparison…………..…………………………….
4.2.1 - Methodology………..……………………………………………….. 4.2.2 - Comparison of GPS RTK Coordinate Values to IKONOS Stereo Model Values……………….………………………………..
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4.3 - Feature Interpretation and Extraction…………..…………………………. 4.3.1 - Development of a Civil NIIRS Rating for Imagery Comparison........ 4.3.2 - Factors Affecting the Interpretation of Features in High Resolution
Commercial Satellite Imagery…..………………………………..…. 4.3.3 - Evaluation of Feature Interpolation and Extraction
from IKONOS Imagery………….…………………………………. 4.3.3.1 - Urban Trial area at Binya St, Glenorchy, Hobart…….…… 4.3.3.2 - Central Business District (CBD) area of Hobart……….….
4.3.3.3 - Summary Observations from Feature Interpolation and Extraction of Study Areas……………….……………
4.4 - Digital Elevation Model (DEM) Creation……………..………...…………. 4.4.1 - Derivation of Digital Elevation Models………….…………………. 4.4.2 - Comparison of Digital Elevation Models…………..……….............. 4.5 - Summary……….……………………………………………………..............
Chapter 5 - Conclusions and Recommendations………………………………...
5.1 - Conclusions………………….……………………………………………….. 5.1.1 - Objectives and Strategies…………..………………………………... 5.1.2 - Historical Development……………..………………………………. 5.1.3 - Business Evolution………………….………………………………. 5.1.4 - Geographical Information System (GIS) Requirements and Applications…………………………………………………..... 5.1.5 - Evaluation and Comparison of High Resolution Commercial Satellite Imagery as a Spatial Data Source….………………………
5.2 - The Future and Recommendations………………..………………………...
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Appendix A - Masters Imagery Users Survey – 2005
Appendix B - Hobart Ground Control Points Comparison
Appendix C - Compiled Drawings Extracted from IKONOS
Stereo Imagery showing Features, Contours
and Photo Locations
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List of Tables Table 2.1 - Resolution Classification of Satellite Imagery………….………………
Table 2.2 - Current and Planned High Resolution Satellite Images………………...
Table 2.3 - Summary of Distortions in High Resolution Satellite Imagery…………
Table 2.4 - Map Scale Estimates for the existing and proposed satellite data……....
Table 2.5 - Quickbird Imagery Price Changes for 2002…………………………….
Table 2.6 - DigitalGlobe ClearView and Next View Contract Awards……………..
Table 2.7 - Satellite Operational Parameters………………………………………...
Table 2.8 - Spacings between spot heights or measurements……………………….
Table 2.9 - Comparison of Spatial Data Sources………………………………........
Table 3.1 - Cost Comparison of Satellite Imagery…………………………………..
Table 3.2 - AUSIMAGE 2007 pricing…………………............................................
Table 3.3 - Features which can be captured from Quickbird imagery at national
(UK) Scales…..…………………………………………………………
Table 3.4 - Summary of the classification results for an IKONOS image using
maximum likelihood classification………...………..………………….
Table 4.1 - IKONOS Imagery Geometric Collection Parameters…………………..
Table 4.2 - IKONOS Imagery Set Details…………………………………………..
Table 4.3 - Mean, Minimum and Maximum Component Differences
and Vector Distance…………………………………………………….
Table 4.4 - Root Mean Square Error of Component and Vector Distance…….........
Table 4.5 - Definition of Civil NIIRS ratings…….…………………………………
Table 4.6 - Civil NIIRS ratings for imagery over Hobart………….………..............
Table 4.7 - Feature Classes identified in IKONOS Imagery………………..............
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List of Figures Figure 2.1 - One single image covers a large area………….……………………….
Figure 2.2 - IKONOS image acquisition technique……………….………………...
Figure 2.3 - Distortions due to Orbit variations and Earth Shape and Relief……….
Figure 2.4 - Effect of Roll, Pitch and Yaw………………………………………….
Figure 2.5 - Difference between the Physical Earth, Tangent Plane, Geoid
and Ellipsoid…………………………………………………………..
Figure 2.6 - IKONOS Satellite…………………………………………....................
Figure 2.7 - IKONOS Imagery - Dalrymple Bay, Queensland,
Australia. (May 23, 2005)……………………………………………...
Figure 2.8 - EROS A Satellite…………………………………….…........................
Figure 2.9 - EROS Imagery - Adelaide Cricket ground, 5th March 2002……...…...
Figure 2.10 - EROS B Satellite…………….………………………………………..
Figure 2.11 - EROS Imagery - Circular Quay, Sydney, 17 May 2006………...........
Figure 2.12 - Quickbird Satellite……………………………………........................
Figure 2.13 - Quickbird Imagery - Singapore, 21 March 2004………………...…...
Figure 2.14 - WorldView – 1 Satellite…………….………………………………...
Figure 2.15 - WorldView – 1 Imagery - Sydney, 31 December 2007……................
Figure 2.16 - OrbView–3 Satellite…………….…………………………………….
Figure 2.17 - Orbview Imagery……………….……………………………………..
Figure 2.18 - Typical Ground Survey Party…………………....................................
Figure 2.19 - Typical Detail Survey…………………………………………………
Figure 2.20 - Aerial Stereo Photography collect…………………............................
Figure 2.21 - Concept of IFSAR Mapping………………………………………….
Figure 2.22 - Intermap’s LearJet 36 STAR-3i System……………………………...
Figure 2.23 - LIDAR system………………………………………………………...
Figure 2.24 - LIDAR feature collection methodology……………………………...
Figure 3.1 - Relationship of Data in a GIS………………………………………….
Figure 3.2 - Data flow of a Geographic Information System (GIS)………………...
Figure 3.3 - Example of non quantitative reporting…………………........................
Figure 3.4 - Inability to identify assets in high resolution satellite imagery…….…..
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Figure 3.5 - Level of detail from aerial photography…….………………….............
Figure 3.6 - Mackay City Council Urban Aerial Photography……………………...
Figure 3.7 - Quickbird Stereo Pair over Sydney……………….…............................
Figure 3.8 - Example of Sentinel Product……………………...……………………
Figure 3.9 - Complexity of Scenes due to Ground Cover (IKONOS imagery)….….
Figure 4.1 - Extent of IKONOS Study Imagery over Hobart……………….………
Figure 4.2 - Urban area at Binya St, Glenorchy, Hobart………….............................
Figure 4.3 - CBD Central business district (CBD) of Hobart……………………….
Figure 4.5a - Vector Distance against Ellipsoidal Height…………….……………..
Figure 4.5b - Vector Distance against Easting…………….………………………...
Figure 4.5c - Vector Distance against Northing…………………………………….
Figure 4.5d - �Z (m) against Ellipsoidal Height……….……………………………
Figure 4.5e - �Z (m) against Easting……………….……………………………….
Figure 4.5f - �Z (m) against Northing…………….………………………………...
Figure 4.6 - Vector distances between IKONOS stereo model control points
and the RTK GPS reference data……………..………………..............
Figure 4.7 - Change in roof line, not apparent on IKONOS imagery
or feature extraction……………………………………………………
Figure 4.8 - Building Data from Glenorchy City Council………….……………….
Figure 4.9 - Features Extracted from IKONOS Imagery…………………………...
Figure 4.10 - Stormwater drains and Survey Mark………………….………………
Figure 4.11 - Example of track and steepness………………………………………
Figure 4.12 - Track Detail from Glenorchy City Council…………...........................
Figure 4.13 - Track Detail extracted from IKONOS stereo imagery…….………….
Figure 4.14 - Example of power line visible only by clearing not lines
and poles……………………………………………………………...
Figure 4.15 - Example of water main, corresponds with
Glenorchy City Council plans…………..............................................
Figure 4.16 - Glenorchy City Council plan detail of water main……….…………...
Figure 4.17a - IKONOS 8 bit Imagery……………..................................................
Figure 4.17b - IKONOS 11 bit Imagery…………………………………………….
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Figure 4.18 - Example of tops of reservoirs…….…………………………………...
Figure 4.19 - Features extracted from 11bit IKONOS Stereo Imagery………….….
Figure 4.20 - Example of roof tops and their roof line……………….......................
Figure 4.21 - Example of number of power poles and lines that cannot be seen on
IKONOS imagery……….……………………………………………
Figure 4.22 - New construction (mobile telephone relay station)…….……..............
Figure 4.23 - Example of new construction (children’s’ playground)…….………...
Figure 4.24 - Example of detail…………………………………………..................
Figure 4.25 - Hobart City Council data……………………………………...............
Figure 4.26 - Features extracted from 11bit IKONOS Stereo Imagery……….…….
Figure 4.27 - Hobart Midcity Hotel – example of roofline and level of
detail extracted possible………………………………………………
Figure 4.28 - Hobart City Council data……………………………………...............
Figure 4.29 - Features extracted from 11bit IKONOS Stereo Imagery………..........
Figure 4.30 - Example of roof line………….……………………………………….
Figure 4.31 - Hobart City Council data……………………………………...............
Figure 4.32 - Features extracted from 11bit IKONOS Stereo Imagery…….……….
Figure 4.33 - Example of building detail. This multistory car park does
not appear as one from a vertical perspective………………..............
Figure 4.34 - Hobart City Council data…………………………...............................
Figure 4.35 - Features extracted from 11bit IKONOS Stereo Imagery…….……….
Figure 4.36 - Example of roof line and detail from top of multistory car park..........
Figure 4.37 - Hobart City Council data…………………………...............................
Figure 4.38 - Features extracted from 11bit IKONOS Stereo Imagery……………..
Figure 4.39 - Example of roof top detail and the possibility of to
misinterpretation……….……………………………………………..
Figure 4.40 - Example of detail and interpretability…….………..............................
Figure 4.41 - Example of roof line and detail………….……………………………
Figure 4.42 - Hobart City Council data………….…………………………..............
Figure 4.43 - Features extracted from 11bit IKONOS Stereo Imagery………..........
Figure 4.44a - Sewerage Manhole……………………………………......................
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Figure 4.44b - Water Service – Size approx 0.15m……………...………………….
Figure 4.44c - IKONOS 8 bit Imagery……………………………………..............
Figure 4.44d - IKONOS 8 bit Imagery……………………………………………..
Figure 4.45 - 1:7000 Sullivans Cove Orthophoto……….…………………..............
Figure 4.46a - CBD Study Area: 5m IKONOS derived contours and 5m Hobart
12.5m DTM contours………………………………………………..
Figure 4.46b - CBD Study Area: 5m IKONOS derived contours and 5m 100m
GDA DTM contours…………………………………………………
Figure 4.47a - Urban Study Area: 5m IKONOS derived contours and 5m Hobart
12.5 DTM contours…………………………………..........................
Figure 4.47b - Urban Study Area: 5m IKONOS derived contours and 5m 100m
GDA DTM contours……………………..…………………………..
Figure 4.48a - CBD Longitudinal Section – Liverpool St……………......................
Figure 4.48b - CBD Longitudinal Section – Macquarie St………………………….
Figure 4.49a - Urban Longitudinal Section – South………………………………...
Figure 4.49b - Urban Longitudinal Section – North………………………………...
Figure 4.50 - Geoeye-1 Imagery – Cambridge, Massachusetts, 18 October 2008….
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CHAPTER 1
INTRODUCTION
1.1 Background
In 1987 the Soviet Union made an unexpected decision to allow images taken with the
Cosmos KFA-1000, MK-4 and MFK-6 satellites, all with resolutions of approximately
five meters, (Steinberg 1998) to be sold on the world-wide market. This was then
followed in 1992 by another unexpected decision by the Russian government to allow the
sale of imagery with a resolution of two to three metres taken with the KVR-10000 and
KFA-3000 satellites. These events could be considered to be the trigger for developments
in the area of high resolution commercial satellite imagery (Petrie 1999).
Since then, the last eight years have seen the advent of commercially available high
resolution satellite imagery from satellites such as IKONOS, which was launched by
Space Imaging from the United States (U.S.) in September 1999 to the more recent
Worldview 1 and Geoeye -1 satellites that were launched on the 18 September 2007 and
6 September 2008. In this period of eight years the new generation of imaging satellites
have taken the resolution of images from space from the initial quantum leap of barely
one metre to systems that promise to deliver resolutions of half a metre or less.
During this time a range of countries as well as the United States of America such as
Israel, Russia and Korea have deployed commercial satellites capable of obtaining high
resolution images of the order of one to two metres. This has ensured that the market has
had competition and variety.
Despite the promises given by the operators of these satellites the revolution in the use of
space based imagery anticipated from the availability of high resolution satellite imagery
has not occurred. In fact Government organisations (particularly the U.S. government)
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remain the chief customers of U.S. companies marketing high resolution commercial
satellite imagery. The existence for operators of more traditional aircraft based imaging
systems such as metric aerial photography and the latest airborne scanners such as the
Leica ADS40 continue to provide data with an accuracy, resolution and value that still
cannot be achieved from images sourced from satellites.
The competition that still exists from traditional systems has not prevented the growth of
the industry. Recent world events such as the September 11, 2001 terrorist attack on New
York and Washington in the U.S and the subsequent “War on Terror” leading to the
ongoing conflicts in both Iraq and Afghanistan have ensured a need for spaced based
imagery. This is because it provides a “no risk” look into the Worlds trouble spots for
customers such as the military, aid organisations and the media.
In addition to the world’s troubles, a good global economy over recent years has meant a
need for spatial data to assist in new infrastructure projects, particularly in third world
nations. After long periods in the late 1980s and 1990s when economic downturn meant
little money was available for the mapping of the environment, many countries and
organisations relied on older spatial data to fulfil needs. The improved economic times
coincided with the launch of the first commercial satellite capable of high resolution
imagery (IKONOS in 1999) and readily provided a market and need that could meet for
the first time with a system that collected spatial data and produced a product without
ever having to visit an area. This was a distinct advantage for infrastructure projects and
military planners as not only did it reduce risks to staff, but also removed or reduced the
need to deploy expensive survey parties and aircraft to remote locations.
1.2 The Usability of High Resolution Commercial Satellite Imagery
In 1998 Steinberg reported that the French publication, Air and Cosmos Aviation
International stated:
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“By the early years of the next millennium, the number of countries owning earth
observation satellites will have doubled… Earth observation is going to enter a new
phase this year, with more and more satellites going up, entry into service of the first
high-resolution and hyper-spectral instruments as well as deployment of the first private
commercial systems and new systems co-financed by government and private industry.
This should lead to broad changes of the landscape in this sector of activity over the next
10 years. The world market, which has seen no growth in the last decade, should finally
see that long hoped for expansion.”
The advent of high resolution commercial satellite imagery was lauded by the vendors
and supporters of such ventures as a revolution in the acquisition of metric imagery and
spatial data. It soon became apparent that this was not the case and with all new
technologies it had to find itself a place within the spatial data market.
The first years saw a rapid expansion and creation of authorised resellers of the imagery
but this was not to last as the realities of the market place started to occur. Recently
opened offices closed, as in the case of the Australian Space Imaging office. In answer to
these problems modification of licencing agreements and a series of special price offers
occurred. These were intended to attract new customers and woo back initial customers
lost through the initial restrictive licencing arrangements, delayed imagery and the
combination of high costs and lower resolution when compared to aerial photography.
From all this the high resolution commercial satellite imagery market has buoyed and
found its place. It is no longer common to hear how good the resolution or what an
alternative satellite imagery is, when compared to aerial photography. It is now a case of
how it complements and supplements aerial photography in cases such as providing a low
cost supplement in intervening years between higher resolution aerial photography
surveys in a mapping programme. High resolution commercial satellite imagery is also
now being used as a common option in rapid reporting and assessment over isolated areas
or disaster zones where it is either too difficult, dangerous or politically unacceptable to
deploy aircraft or ground crews.
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From a beginning that promised everything to a number of years attempting to find a
place in the spatial data market, high resolution commercial satellite imagery has filled a
niche. This niche is being in between good resolution aerial photography and the
requirement to easily obtain reasonable imagery over difficult areas or situations, as well
as basic supplementation of aerial photography in a mapping programme.
1.3 What Data and Source are required?
Spatial data is information that defines the geographic location as well as description of
features and boundaries on Earth. They can be either natural features, for example hills
and rivers, or constructed features such as buildings, pipelines or roads. Their position
can be acquired and stored as coordinates and together with their topology (vectors) as
well as imagery (raster). The spatial data required and the purpose for what it is intended
still dictates the methodology to be used in obtaining the information.
If, for example, imagery is required to monitor and provide base information for town
planning or utilities work, aerial photography or imaging are still the preferred solution.
If it is not possible to access the required airspace due to political, financial or isolation
issues, or persistent cloud cover makes it impossible or economically unviable to use
aircraft, high resolution satellite imaging could be the solution. High resolution satellite
images provide good planning scale data (1:5 000 to 1:10 000), without the requirement
to physically access the site or to maintain a field or aerial survey party on location until
atmospheric conditions are favourable.
Spatial data has become indispensable to the modern world. Not only is it used for
scientific purposes but also to:
(a) Map the Earth above and below sea level;
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(b) Prepare navigation charts for air, sea and land ;
(c) Establish cadastral boundaries of both private and public land;
(d) Develop and maintain databases of land use and natural resource information;
(e) Determine measures on the size, shape, gravity and magnetic fields of the
Earth (geodesy)
(Wolf: 2002: 9)
In today’s world that has taken for granted modern technology such as Global Navigation
Satellite Systems (GNSS), Geographic Information Systems (GIS) and mobile phones,
spatial data is critical for the success of these technologies. Without reliable and accurate
spatial data against which to represent any location based service, for example street
mapping showing latest road changes, the sophistication of the technology is worthless.
1.4 Selection of Collection Method
Before commencing the collection of purpose specific spatial data, the following need to
be considered (Wolf & Brinker: 1989: 310):
(a) Purpose of the survey or data;
(b) Map or data use (accuracy required);
(c) Map or output scale;
(d) Output format (such as digital data, orthoimage or hardcopy map);
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(e) Contour interval or Digital Terrain Model (DTM) post spacing;
(f) Cost;
(g) Equipment and time available to collect data;
(h) Experience and training of staff involved;
These factors will clearly influence the method chosen to collect the data, the most
important being cost and accuracy required. An example of how these factors together
play a part, is a mapping task located in an undeveloped region of the world. In such a
case, availability of funds could allow for a solution based on sophisticated technology
making the project simple to achieve for skilled technicians in a short timeframe. A
sophisticated solution could also be suitable in the same location if the concern was a
shortage of trained and experienced staff as the training liability could be lower due to the
automation and less requirement for labour. Though what must also be considered are the
benefits of a simple technical solution being labour intensive but providing an
opportunity for skilling a larger workforce due to the lower labour and equipment costs.
1.5 Objectives of the Research
This research does not dwell on improving the technical level of the data or imagery
sourced from high resolution commercial satellites, but demonstrate its practical
application and implementation. This has be done to provide both a background
knowledge to existing spatial science professionals on possible use (both in private and
public sector) and also highlighting its use by other professions or industry areas such as
engineers, local councils or emergency services which could benefit from the services
provided by spatial science professionals in this area.
The main objectives of this study are to:
6
(a) Provide an understanding of the imagery collection techniques and
methodology of high resolution commercial satellites.
(b) Give an understanding of the way the commercial and government interests
have influenced the development and sustainability of high resolution
commercial imaging satellites, particularly in the context of U.S. companies
and the U.S. government.
(c) Allay practical concerns in the application of using the imagery in regards to
cost, equipment (hardware and software) and training when compared to
more traditional or terrestrial means such as aerial photography or field
survey techniques.
(d) Give an assessment of the imagery’s ability to represent features and
phenomena on the ground to a practical level in a variety of applications.
(e) Provide a guide to possible uses and disadvantages of such imagery away
from the traditional qualifiers used by the spatial industry such as scale and
planimetric accuracy and more to its functionality in fulfilling needs.
1.6 Overview of the Thesis
This thesis has been organised into the following chapters. Chapter Two provides an
explanation of high resolution imagery from commercial satellites in regards to how the
imagery is collected and factors that need to be considered during the acquisition of the
imagery, followed by how these can impact on the accuracy and hence usability of the
imagery. Further since collecting satellite imagery is not the only issue but also there is
the commercial aspects, Chapter Two then provides an investigation of the business
ventures behind the current and future satellite systems, a comparison of the satellite
7
8
systems and imagery available, and then the infrastructure in regards to hardware,
software and training required in order to successfully exploit the imagery at the user or
customer level. Chapter Three defines an urban Geographic Information System (GIS)
within the context of various applications and some of the potential advantages and
limitations of using high resolution commercial satellite imagery within a GIS in
organisations with varying requirements (such as local councils and emergency services).
A practical investigation and evaluation of a sample set of high resolution commercial
satellite imagery is conducted in Chapter Four in order to quantify the practicalities and
versatility of using the imagery since ultimately the purpose of the spatial sciences is to
represent and define the shape of the earth and describe complex features upon it. This
chapter describes how well the imagery is capable of satisfying this.
Finally Chapter Five provides a summary, conclusion and recommendations for the
usability and applicability of high resolution commercial satellite imagery.
CHAPTER 2
HIGH RESOLUTION SATELLITE IMAGERY - DEVELOPMENT AND
CHARACTERISTICS
2.1 Available Satellite Sourced Data (1.0m resolution or better)
There are a variety of Earth observation satellites in orbit capable of imaging objects on the
earth with various degrees of resolution. Hart and McCleave (2002), provide a classification
of satellite imagery as shown in Table 2.1.
Classification Resolution Examples
Very Low � 300m NOAA (Oceanographic and Weather)
Low � 30m < 300m Landsat MSS
Medium � 3m < 30m Landsat TM, SPOT
High � 0.5m < 3m Quickbird, IKONOS
Table 2.1 - Resolution Classification of Satellite Imagery
Since 1999 a number of commercial high resolution imaging satellites have been launched or
are planned to be launched in the near future as displayed in Table 2.2, with further detail
being provided in Section 2.3.
2.1.1 Satellite Imagery Collection Techniques
Most of the high resolution Earth observation satellites referred to above, such as IKONOS
and Quickbird, use the “pushbroom” imaging technique which is based on a linear array
sensor that collects images by sweeping over the terrain in a similar manner to a broom. The
width of the array provides coverage across the satellite track, whilst the motion of the
satellite provides coverage along track. This method of collecting is in contrast to “staring” or
9
“spinning” sensors. A “staring” sensor points to an area and instantaneously collects images
of the area covered by the array. “Spinning” sensors rotate as they collect data of an area.
Satellite Launch Date Company/Country Resolution(m) (Pan)
LifeExpectancy
IKONOS 24/9/1999 Geoeye (US) 1.0 > 8.5 years
EROS A 5/12/2000 ImageSat (Israel) 1.8 10 years
Quickbird 18/10/2001 Digitalglobe (US) 0.6
Orbview - 3 26/6/2003 Geoeye (US) 1.0 At least 5 years
EROS B 25/4/06 ImageSat (Israel) 0.7 10 years
Resurs DK-1
(01-N5)15/06/06
NTs OMZ (Russia) 1.0 3 years
KOMPSAT-2 28/07/06Korean Aerospace Research Institute (KARI) (Korea)
1.0
IRS Cartosat 2 10/01/07 India 1.0
Worldview 1 18/09/07 Digitalglobe (US) 0.5 7.25 years
GeoEye-1 1st half 2008 Geoeye (US) 0.41 7 years.
WorldView -2 01/07/08 Digitalglobe (US) 0.5 7.25 years
EROS C 21/03/08 Israel 0.7 10 years
Pleiades-1 End of 2009 France 0.7 5 years
Pleiades-2 March 2011 France 0.7 5 years
Table 2.2 – Current and Planned High Resolution Satellite Images
The “pushbroom” sensor has the advantage that it can scan the terrain with a certain width as
the satellite orbits the earth (as opposed to a “staring” sensor which acts as a shutter camera
does) and hence collects images of larger areas than an imaging system based on a sensor
area array. This technique reduces the need for mosiacing in order to produce images of large
areas as shown in Figure 2.1 (DigitalGlobe Website: 2002). Typically the size of the image is
limited by the satellite’s onboard electronic storage capability.
10
Figure 2.1 – One single image covers a large area (Source: DigitalGlobe Website: 2008)
In addition, these satellites have highly manoeuvrable bodies or buses, which are sometimes
referred to as “agile buses”. As the imaging system does not move independently of the
satellite bus, the agility allows the satellite to tip and tilt in order to point the sensors at
specific areas. This pointing system is based on reaction wheels that can accelerate and
decelerate to change the satellite’s orientation (roll and pitch). In order to prevent any impact
of these movements on the imagery there is a “settle time” measured in milliseconds between
completion of the motion of the satellite bus and turning on the imaging system to prevent
image jitter or blurring (DigitalGlobe Website: 2002).
This high level of maneuverability achieved by the “agile bus” allows the satellites to collect
stereo images along their orbit path by pitching the satellite forward and backwards (as in
Figure 2.2), a technique that is known as “in-track” acquisition. This is opposed to “cross-
track” stereo, where the images in the stereo pair are collected on separate orbits. These orbits
are required to occur on different days which may result in unsuitable weather conditions for
the second acquisition date or inconsistencies between the two images of the stereo pair. As a
result the main advantage of using the “in-track” stereo collect is that it shares many benefits
of traditional airborne collection such as timeliness, constant illumination and constant
ground conditions (Gonzalez: 1998: 12).
11
Zhou and Li (2000) provide a good description of the example of stereo collection using the
agile bus of IKONOS (referred to in more detail in Section 2.3.1). The IKONOS satellite
travels at 7 km per second at an altitude of 680 km. For the forward looking image
acquisition, it pitches forward (fore-looking) 26° to begin collecting data (imagery) after
which it pitches to nadir viewing and collects data over the same ground area. Once this is
completed it pitches aft and collects the same area for the third time. Stereo pairs created
from forward, nadir and aft-looking ensure high quality collection of imagery as the images
are acquired under nearly the same ground and atmospheric conditions, with convergence
angles of 26° or 52° (Zhou & Li: 2000: 1104).
Figure 2.2 – IKONOS image acquisition technique (Dial & Grodecki:2003:11)
As with stereo aerial photography, the collection of stereo images from an imaging satellite
allows for the creation of digital terrain models and the extraction of features with three
dimensional coordinates (x, y and z). Such a capability provides versatility in the use of high
resolution satellite imagery, as it allows for the creation three dimensional spatial data
without the use of ground control, thus eliminating the need to visit a site to collect spatial
data such as digital elevation models (DEM) and cultural features.
2.1.2 Distortions
Toutin (2002) reports that geometric distortions that need to be corrected in high resolution
12
satellite imagery can be placed into two general categories:
(a) Observer Distortions: due to the platform or satellite itself in motion, or the image
sensor on board the satellite.
(b) Observed Distortions: due to the Earth’s atmosphere and the shape of the Earth.
Also, as most Geographical Information Systems (GIS) end user applications are represented
and conducted in referenced topographic maps coordinate systems, distortions due to the map
projection being used needs to be corrected (Toutin: 2002).
2.1.2.1 Observer Distortions
Distortions caused by the platform are mainly related to variations of the satellite’s elliptic
orbit around the Earth. The degree to which these variations will affect the image being
obtained is a function of the duration of image acquisition as well as the size of image being
sourced. Variations of the elliptic movement of the platform have the following impacts on
image geometry (Toutin: 2002):
(a) Altitude variations caused by the Earth’s curvature and topography affects the
focal length, changing the pixel spacing;
(b) Attitude variations of the roll (x), pitch (y) and yaw (z) axes cause changes in the
orientation and shape of high resolution images;
(c) Velocity variations affect the line spacing or can create line gaps or overlaps in the
resultant images.
There are distortion due to the imaging sensor (Toutin: 2002):
(a) Calibration parameters such as focal length and the instantaneous field of
view (IFOV).
13
(b) Panoramic distortions in combination with the oblique viewing system, Earth
curvature and the topographic relief change the ground pixel sampling interval along
the column or across track direction of image scan.
2.1.2.2 Observed Distortions
According to Toutin (2002) the Earth’s motion and topographic features can cause the
following distortions in satellite images:
(a) The Earth’s rotation generates lateral displacements across track depending on the
latitude.
(b) The Earth’s curvature creates variation in the image pixel spacing.
(c) Topographic relief generates a parallax in the direction of scan (scanning
azimuth).
Map projections cause the following deformations (Toutin: 2002):
(a) The approximation of the geoid by the chosen reference ellipsoid.
(b) The projection of the reference ellipsoid onto the plane of the chosen projection
(tangent plane).
Work undertaken, as detailed later in Chapter 4, indicates that whilst geometric distortions
can occur, there are no significant unpredictable influences in x, y or z directions that could
be attributed to Observer or Observed distortions. The only distortion that could be detected
was a directional bias in a west-north-west direction, but this was consistent across the entire
sample stereo pair.
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Observer Distortions
Impact on image geometry due to variation of the elliptic movement.
(a) Altitude variation in conjunction with focal length and the effect of the Earth’s curvature and topography changing the pixel spacing. (b) Attitude variations of the roll, pitch and yaw axes cause changes in the orientation and shape of high resolution images. (c) Velocity variations affect the line spacing or can create line gaps or overlaps in the resultant images. Distortions due to the imaging sensor.
(a) Calibration parameters such as focal length and the instantaneous field of view (IFOV).(b) Panoramic distortion in combination with the oblique viewing system, Earth curvature and the topographic relief, changes the ground pixel sampling along the column or direction (across track) of image scan.Observed Distortions
Distortions due to Earth’s motion and topographic features.
(a) The Earth’s rotation generates lateral displacements across track depending on the latitude.(b) The Earth’s curvature creates variation in the image pixel spacing. (c) The topographic relief generates a parallax in the scanning azimuth. Deformations caused by map projections.
(a) The approximation of the geoid by the chosen reference ellipsoid. (b) The projection of the reference ellipsoid onto the tangent plane.
Table 2.3 – Summary of Distortions in High Resolution Satellite Imagery
Figure 2.3 – Distortions due to Orbit variations and Earth Shape and Relief
Earth
Effect ofEarth Curvature
Effect ofTopographycreates a parallex in the scanning direction
Effect ofAltitude Change Satellite Orbit
15
Yaw (z)
Roll (x)
Pitch (y)
Figure 2.4 – Effect of Roll, Pitch and Yaw
Figure 2.5 – Difference between the Physical Earth, Tangent Plane, Geoid and Ellipsoid
ImageRoll
Pitch
Yaw
Pitch
Satellite Track
Note:(1) Rotation around the x axis is roll(2) Rotation around the y axis is pitch(3) Rotation around the z axis is yaw
Tangent Planeof the chosen map projection
Ellipsoid
PhysicalEarth
Geoid
16
2.1.3 Accuracy
To enable imagery to be used for practical purposes such as mapping, infrastructure planning
or a Geographical Information Systems (GIS) base, a qualitative accuracy value needs to be
able to be determined for it. Gonzalez (1998) by an empirical method, calculated that the
imagery resolution required for a specific map scale can be determined by:
GRD = (1/5) x 0.25 mm x Map Scale
= 5 x 10-5 x Map Scale Factor (in metre) (2.1)
where GRD = Ground Resolution Distance
In using this formula it is assumed that the smallest feature on a map has a dimension of at
least 0.25mm at map scale and that for an object to be identifiable on imagery with medium
contrast conditions, it must be imaged by at least one-fifth of the reported image ground
sampling distance (GSD) (Gonzalez:1998).
At this point it is worth explaining the difference between spatial resolution and Ground
Sampling Distance (GSD). Spatial resolution and GSD when referred to imagery have two
different meanings. Spatial resolution is defined as the size of the smallest object or distance
that can be resolved in the image (Lillesand et al: 1979). This differs from GSD which is the
area of ground covered by one pixel. This means that even though an image may have a GSD
of one metre, the smallest object (or spatial resolution) may be greater than one metre (Poon
et al: 2006)
An initial investigation using the above expression and based purely on vendor’s data, shows
that typical map scales that could be derived from the satellite systems are as shown in Table
2.4.
The estimates in Table 2.4 are based purely on the ground resolution of each of the satellites.
In producing data from such imagery standard scales would be used such as 1:10 000, 1:25
000, 1:50 000 and 1:100 000. This means that when outputting such data, the standard scales
are derived from the data directly.
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Satellite Resolution(m) (Pan) Map Scale
IKONOS 1.0 1: 20 000 EROS A 1.8 1: 36 000 Quickbird 0.6 1: 12 000 Orbview - 3 1.0 1: 20 000 EROS B 0.7 1: 14 000 Resurs DK-1
(01-N5)1.0 1: 20 000
KOMPSAT-2 1.0 1: 20 000 IRS Cartosat 2 1.0 1: 20 000 WorldView -1 0.5 1: 10 000 GeoEye-1 0.41 1: 8 200 WorldView -2 0.5 1: 10 000 EROS C 0.7 1: 14 000 Pleiades-1 0.7 1: 14 000
Pleiades-2 0.7 1: 14 000
Table 2.4 Map Scale estimates for the existing and proposed satellite data.
The use of scale to provide an indication of the suitability and accuracy of imagery can cause
uncertainty or misunderstanding in its application. From Table 2.4 above the largest scale that
could be derived from the current high resolution commercial satellite images is 1:10 000, but
typical scales being quoted from various sources range from 1:10 000 to 1:20 000.
The following example using high resolution commercial satellite imagery shows that the
final scale achieved is dependent on the outcome or application required. In Vietnam, Hanh
& Tuan (2005) reported that by ortho-rectifying Quickbird satellite imagery it can then be
used for topographic mapping updates to recognise new features in a map sheet. According to
their results, Hanh & Tuan established that:
(a) Quickbird imagery can be used as an alternative to aerial photographs for updating
topographic maps;
(b) This method can be applied for the scale 1:5000 and smaller, within permitted
18
errors.
(c) The updated features should be hydrology networks; residential areas and
vegetation cover.
This accuracy was achieved by orthorectifying the satellite image and then overlaying it on
the original topographic map to assess the accuracy. They found that changes could be clearly
seen in roads, watercourses and buildings and these were updated on the topographic map
using the orthorectified Quickbird imagery. Other information that needed to be updated was
collected in the field, based on changes which were found on the satellite image as primary
information.
From this they determined that in the case of work in Hanoi, Quickbird satellite imagery
could be used for topographic map revision with periods of 2 to 3 years between image
acquisition, instead of periods of 5 to 6 years when using aerial photographs.
Fraser et al (2002) also determined that by using IKONOS imagery with affine sensor
models, planimetric accuracy of 0.3 - 0.6 m and height accuracy of 0.5 - 0.9 m are readily
possible with only 3 to 6 ground control points. It is interesting to note that they state that
there are some limitations in the suitability of the imagery of 1m resolution because of the
variability of image quality from scene to scene. This will limit its application for such tasks
as building reconstruction and city modeling. The problems encountered by Fraser et al
include the user’s inability to control the date and time of image collection, the specification
of favourable sun angles and atmospheric conditions. Areas more specific to their
investigation included the identification of all buildings of a certain size and the
reconstruction of their form without excessive generalisation.
From this discussion it can be concluded that whilst it is possible to determine the positional
accuracy of the imagery, it is more difficult to determine the suitability of the imagery for a
specific application in terms of identifying features of interest. This issue will be furthered
discussed in Chapter 3.
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2.2 Emergence of Commercial High Resolution Satellite Imagery
The end of the 1990s saw commercial high resolution satellite imagery becoming available to
the wider community. Up until this time the use of imagery taken from such space platforms
was available only to government agencies and authorities. The successful launch of such
platforms as IKONOS, EROS A and Quickbird saw the start of a new era in space remote
sensing as systems now existed that provided images comparable with those available from
small scale aerial photography in terms of accuracy and potentially price.
According to Li (1998), high resolution imaging satellites have four advantages. They;
(1) Provide the highest resolution satellite data available to the civilian mapping
community. This could then be better than small scale aerial photography.
(2) Comprise an extremely long camera focal length of ten metres for capturing terrain
relief information from satellite orbit.
(3) Include fore, nadir and aft-looking linear CCD arrays supplying in-track stereo strips.
(4) Have a base-height (sensor baseline vs. orbit height) ratio of 0.6 and greater, which is
similar to that of aerial photographs.
Petrie (1999) indicates that the trigger for developments in the area of commercial high
resolution satellite imagery was the unexpected decision by the Soviet Union in 1987 to allow
images taken with the Cosmos KFA-1000, MK-4 and MFK-6, with resolutions of
approximately 5 metres (Steinberg 1998), to be sold on the world-wide market. This was
followed in 1992 by another unexpected decision by the Russian government allowing the
sale of imagery with a ground resolution of 2 to 3 m taken with the KVR-1000 and KFA-
3000 cameras (Petrie 1999).
Before the release of the Russian imagery President Carter had, under Presidential Directive
37 in 1978, limited the ground resolution of American space imagery to 10m. The initial
Russian decision described above resulted in an easing of this restriction by the Reagan
administration. In March 1994 President Clinton then issued Presidential Directive 23 in
20
which the development of commercial satellites capable of producing imagery down to the
1m ground resolution was permitted. A consequence of this directive was that licences were
issued to several American companies to commercialise hardware and software previously
supplied to United States military space defence programmes (Petrie 1999).
Subsequent to Presidential Directive 23, President Clinton then signed a further Executive
Order on 24 February 1995, directing the declassification of intelligence imagery acquired by
the first generation of United States photo-reconnaissance satellites. As a result of this
Executive Order more than 860,000 images of the Earth's surface, collected between 1960
and 1972, were declassified with the majority now being available for purchase through the
United States Geological Survey or USGS (United States Geological Survey: 2008)
Steinberg (1998) reports that at the time President Clinton issued his Presidential Directive
23, American industry leaders were claiming that the Soviet military threat had been replaced
by a commercial threat from French and Russian companies. These companies were
preparing to enter the high-resolution space image market with sales that had already reached
$700US million in 1994. Steinberg goes on to say that according to the French publication,
Air and Cosmos Aviation International:
“By the early years of the next millennium, the number of countries owning Earth
observation satellites will have doubled… Earth observation is going to enter a new
phase this year, with more and more satellites going up, entry into service of the first
high-resolution and hyper-spectral instruments, as well as deployment of the first
private commercial systems and new systems co-financed by government and private
industry. This should lead to broad changes of the landscape in this sector of activity
over the next 10 years. The world market, which has seen no growth in the last
decade, should finally see that long hoped for expansion.”
The change in the US restrictive policy also allowed US firms to compete in and capture a
large portion of this market, enabling the US to maintain some control over distribution of
high-resolution images, particularly in a crisis. In addition, these developments occurred at a
time when the US defence budget was reduced. Hence the development of commercial high-
resolution imaging systems was seen as a way of allowing the major firms that had mastered
this technology to maintain capabilities in this strategic area, without the massive budgets
21
provided previously by defence agencies (Steinberg 1998).
Even though the relaxation of the strict US regulations meant the beginning of a new age in
satellite imagery collection, it took until the end of the 1990s for the satellites to be realised,
with some unexpected results. Petrie (2001) points out that several of the US licensed
projects (for example GDE Systems, Boeing, Motorola) have progressed very slowly or been
terminated. There have been numerous delays, cost overruns and several expensive failures
either at launch or in actual operations.
It was not until the 24 September 1999 that the first high resolution imaging satellite,
IKONOS II was successfully launched. This was followed by the EROS A satellite which
was successfully launched on 5 December 2000. There was a 5 year delay between the
issuing of President Clinton’s Presidential Directive 23 and the first successful launch which
is a long time considering that the companies involved had previously developed high
resolution imaging satellites for the US Government.
2.2.1 Business Ventures
It is of interest at this point to examine a number of commercial companies that have
attempted or are attempting to develop the market for high resolution satellite imagery. The
ones to be looked at here are DigitalGlobe, Space Imaging, ImageSat International, Centre
National d'Etudes Spatiales (the French space agency, CNES), Orbimage and Geoeye.
2.2.1.1 ClearView and NextView Contracts
A significant factor that has assured the survival of the American companies that own and run
high resolution commercial satellite images has been the National Geospatial-Intelligence
Agency or NGA (previously known as the National Imagery and Mapping Agency or NIMA)
ClearView and NextView contracts. In 2003 the White House U.S Commercial Remote
Sensing Space Policy was released. This policy directed that all U.S. federal government
agencies to utilise commercial satellite imagery to the maximum extent possible.
(www.military-geospatial-technology.com website, accessed 5 February 2008).
22
Consequently the ClearView contract was signed in early 2003 and the NextView contract in
late 2003, the difference between the two contracts being:
(1) ClearView: Government contracts for imagery and imagery services from three
domestic satellites (two companies) at one meter spatial resolution.
(2) NextView: Government contracts for imagery and imagery services from current
and next generation (half meter resolution) domestic satellites.
The significance of these contracts to the suppliers of the imagery and to the NGA can be
seen from the following quote from the 2004 NGA Director, Lieutenant General James R.
Clapper (Geoeye website:2008):
“The agreement is a key development for both NGA and the commercial remote sensing
industry. We need the next generation of commercial satellites to help us do our job and we
look forward to working with both NextView vendors to bring this new capability to fruition”
The degree to which these contracts are sustaining the companies can be seen from Geoeye’s
(the current operators of the IKONOS and OrbView satellites) financial statement (Form 10-
K) from March 2007:
“Revenues for the years ended December 31, 2006, 2005 and 2004 were $151.2 million,
$40.7 million and $31 million, respectively. All of the $110.5 million increase in 2006
revenues over 2005 resulted from the operations acquired from Space Imaging. Revenues
generated by the operations acquired from Space Imaging which are now reported by the
Company’s SI Opco subsidiary, were $110.8 million for the period from January 10, 2006 to
December 31, 2006. Excluding the acquired operations, revenues were comparable with the
prior year. In 2006, our contracts under the ClearView program provide for NGA to pay us a
minimum of $36 million for IKONOS-related imagery products and $13 million for
Orbview-3 related imagery products.”
Further to this, Orbimage released a media statement in May 2005 that stated that revenues
for the company from US government contracts was approximately 49%, 34% and 80% of
23
the total company’s revenue for the years ending December 31, 2004, 2003 and 2002
respectively (Geoeye website:2008).
2.2.1.2 Digital Globe
DigitalGlobes’ history dates back to 1993 when the United States Department of Commerce
granted WorldView Imaging Corporation (Worldview), the first license to build and operate a
satellite system capable of collecting high resolution digital imagery (DigitalGlobe Website:
2002).
In January 1995, Earthwatch Incorporated (EarthWatch) was formed with the merger of the
commercial remote sensing ventures of Ball Aerospace and Worldview. Additional partners
included Hitachi, Datron, Telespazio and MDA (Petrie 2001).
This merger resulted in their first attempted satellite launch on December 24 1997. The first
satellite, Earlybird 1 was designed to collect 3m resolution panchromatic imagery and 15
metre multispectral imagery. It was successfully launched on a Start-1 rocket from Svobodny,
Russia but the satellite failed in orbit four days later due to the failure of the onboard power
system. In April 1998, EarthWatch declared the satellite a total loss, and used the insurance
funds to complete the continuing construction of the Quickbird satellites which were
designed to collect 1m panchromatic and 4m multispectral imagery. The company had
decided not to launch the second Earlybird satellite as the market window had closed for 3m
resolution data (DigitalGlobe Website: 2002).
In June 1998 EarthWatch’s CEO, Herb Satterlee secured substantial additional funding to
enable Earthwatch to complete the Quickbird satellites and the company’s DigitalGlobe
database. This was successfully achieved through a series of investments amounting to
US$186 million made by ITT Industries, the Morgan Stanley Investment House and Capital
Research in 1999. In addition, EarthWatch had a number of contracts from NASA for the
supply of airborne radar imagery of Central America and Alaska and from NIMA (NGA) for
the mapping of Panama. Even though a large part of these contracts were executed by
Canadian Intermap Technologies as a subcontractor, they still helped to generate revenue for
Earth Watch (Petrie 2001).
24
On the 20 November 2000, EarthWatch launched its second satellite, Quickbird 1 from the
Plesetek cosmodrome in Russia. Unfortunately it failed to reach orbit and re-entered Earth’s
atmosphere north east of Brazil and was destroyed (Petrie 2001). Then on August 31 2001
EarthWatch was granted permission by the U.S. Federal Communications Commission to
modify the altitude of Quickbird-2 from 600km to 450 - 470km, the reason being given that it
would improve the ground image resolution capability allowing EarthWatch to respond to
changing technological, market and regulatory conditions. In September 2001 EarthWatch
changed its name and became DigitalGlobe. This was followed by the successful launch of
the second Quickbird satellite on October 18 2001 and the beginning of the successful
collection of panchromatic and multispectral imagery (DigitalGlobe website: 2002).
It was from this successful launch that DigitalGlobe had to create its customer base and the
first twelve months saw a change in the prices of imagery as displayed in Table 2.5.
Over the following years the most significant customer base for Quickbird imagery was
Government and Universities. Of particular note is the NGA (formerly) NIMA ClearView
and NextView contracts where DigitalGlobe was awarded significant amounts as displayed in
Table 2.6.
It is quite clear that without the support of the NGA contracts DigitalGlobe would have
struggled to maintain liquidity. In fact it was the award of the 30 September 2003 contract in
excess of $500million USD that allowed DigitalGlobe to construct the first of the next
generation high resolution commercial satellites, WorldView I.
When the Quickbird satellite commenced operations in 2002, DigitalGlobe was claiming that
there were no plans to launch a comparable satellite until 2004. In the period between 2002
and 18 September 2007 when DigitalGlobe managed to successfully launch and deploy
WorldView I (with 0.5m resolution) the company’s claims varied on their launch date for
their next satellite as well as how the competition was faring, as is displayed (DigitalGlobe
website: 2002 to 2008):
25
Date Prices/Licensing Reason
8 May 2002
(a) Pan imagery from $30USD to $22.50USD per sq km
(b) Multispectral from $30USD to $25USD per sq km
(c) Data-bundle containing panchromatic and multispectral from $45USD to $30USD
(d) Pan-sharpened products from $37.50USD to $30USD
(e) Digitalglobe simplified their licensing scheme for multi-organisational purchases.
(f) Minimum purchase for standard imagery products delivered from archive was reduced from 64 sq km to 25 sq km. This meant that an archive order may be placed for well below $600USD.
In response to customers and
resellers demands for more
affordable products,
Digitalglobe reduced the pricing
for its Quickbird Imagery
products.
7 Oct 2002
(a) Pan/3-Band Colour: $13.50USD per sq km (40% discount)
(b) Multispectral: $15.00USD per sq km (40% discount)
(c) 4-Band Colour: $20.00USD per sq km (33% discount)
(d) Pan/MS Bundle: $20.00USD per sq km (33% discount)
For the month of October pricing was reduced for
archived Quickbird orders in honour of the one year
anniversary of the satellite launch.
Table 2.5 - Quickbird Imagery Price Changes for 2002 (DigitalGlobe website: 2002 to 2008)
26
Date Details
Jan 2003 DigitalGlobe awarded $72million USD contract to deliver high resolution satellite imagery to NIMA (now NGA) over a three year period, as part of a ClearView contract not to exceed $500million USD.
15 Sept 2003 DigitalGlobe awarded $9.8million USD ClearView contract with NIMA (now NGA) in competition with another ClearView contractor.
30 Sept 2003 DigitalGlobe awarded a contract in excess of $500million USD under NIMA (now NGA) NextView contract.
July 2005 DigitalGlobe awarded an additional $6.1million USD supplemental modification to the ClearView contract.
Jan 2006 DigitalGlobe awarded $24million USD satellite imagery capacity contract modification by NGA. A ClearView contract.
16 Mar 2006 DigitalGlobe awarded $12million USD satellite imagery capacity contract modification by the NGA. A ClearView contract.
Table 2.6 - DigitalGlobe ClearView and Next View Contract Awards (DigitalGlobe website: 2002 to 2008)
2002 - Currently there are no plans to launch a comparable commercial satellite until at least 2004.
2003 - The competition has no plans to launch a comparable satellite until at least 2006
2004 - March 23, 2004: The competition has no plans to launch a comparable satellite until at least 2007 - March 31, 2004: The superior technical capabilities of Digitalglobe’s Worldview system, scheduled for launch no later than 2006 - May 10, 2004: The competition has no plans to launch a comparable satellite until at least 2008
2005 - The company will launch its next generation Worldview 1 and Worldview 2 satellites no later than 2006 and 2008.
2006 - The company will launch its next generation Worldview 1 and Worldview 2 satellites in mid 2007 and anticipated launch in 2008 respectively.
2007 - 18 September – DigitalGlobe successfully launches WorldView I satellite
2008 - DigitalGlobe plans to complete construction of its next generation satellite WorldView II in late 2008.
27
The above shows the unpredictable nature of launching, sustaining and successfully
marketing the high resolution commercial satellite imagery business. Indeed if it had not been
for the support given by the NGA the success and survival of DigitalGlobe would have been
extremely unlikely.
2.2.1.3 Space Imaging
In December 1994, Space Imaging was founded in order to build the world’s first high
resolution commercial imaging satellite system. In November 1996 the company acquired the
Earth Observation Satellite Company (EOSAT). Space Imaging was a limited partnership
with Lockheed Martin Corp., Raytheon Company, Mitsubishi Corporation, Singapore’s Van
Der Horst Ltd., Korea’s Hyundai Motor Co., Europe’s Remote Sensing Affiliates, Swedish
Space Corporation, Thailand’s Loxley Public Company and other international investors
(Space Imaging website:2002).
In addition to owning and operating the IKONOS satellite, Space Imaging had the exclusive
rights to distribute outside India, the imagery from the Indian Remote Sensing Satellites
(resolution of 5m to 180m). Also the company had the rights to distribute digital imagery to
the US government’s National Imagery and Mapping Agency or NIMA (now NGA) from the
Canadian RADARSAT satellites (resolution of 8m to 100m). Space Imaging also distributed
European ERS imagery (resolution of 30m) and had access to an archive of imagery from the
Japanese Earth Resource Satellite JERS (resolution of 18m) (Space Imaging website:2002).
Lockheed Martin had, as a result of a preliminary study, conceived the Space Imaging
Satellite (SIS). When the appropriate licences were being issued in 1993 and 1994, Lockheed
Martin had initiated the further development of its Commercial Remote Sensing Satellites
(CRSS-1 and CRSS-2). In 1997 these were renamed IKONOS-1 and IKONOS-2 (IKONOS
being derived from the Greek word for “image”) (Petrie 2001). The IKONOS satellite system
was built by Lockheed Martin Commercial Space Systems, with the communications, image
processing and customer service elements built by the Raytheon Company. The camera was
built by Eastman Kodak. (Space Imaging website: 2002).
On the 27 April 1999 IKONOS-1 was launched by an Athena II (built by Lockheed Martin)
28
rocket from the Vandenberg Air Force Base in the United States. Unfortunately the satellite
never reached orbit. Investigations determined that the Athena II’s Orbit Adjust Module
(OAM) fourth stage with the payload fairing failed and the satellite did not separate properly.
This resulted in the rocket not achieving sufficient velocity to place the satellite into earth
orbit (Space Imaging website: 2002). Within five months Space Imaging had made ready the
IKONOS-2 replacement satellite and on the 24 September 1999 successfully launched the
satellite into orbit. By the 12 October 1999 the first high resolution image of Washington
D.C. had been released and Space Imaging had begun selling imagery by January 2000.
(Petrie 2001).
In December 1999, Space Imaging applied for a licence to build and launch a 0.5m resolution
panchromatic and 2m resolution multispectral imaging satellite. This licence application was
approved by the U.S. Government on 6 December 2000. The company expected to launch
this satellite sometime between 2005 and 2006 (Space Imaging website: 2002).
Similar to DigitalGlobe, Space Imaging had relied heavily on US government funding. In fact
in May 1998 Space Imaging had already signed a five-year contract with NIMA (now NGA)
for ordering commercial satellite imagery. This contract had a minimum guarantee of
$4.4million USD and the contract amount was expected to rise as NIMA further defined the
need for additional commercial imagery products. This was then followed in January 2003
with an award for a multi-year ClearView contract that a had a minimum value of
$120million USD for the first 3 years, with a 5 year ceiling of $500million USD (Geoeye
website:2008).
Space Imaging suffered a setback later in 2003 when it missed out on a NextView contract
that was awarded to Digitalglobe in September 2003 worth in excess of $500million USD. It
was not until July 2005 that Space Imaging was awarded a $5.88million USD mid year
supplemental contract by NGA. Then again on 6 January 2006 NGA awarded Space Imaging
a $24million USD one year contract extension and within 6 days it was announced that
Orbimage Holdings Inc. had finalised the acquisition of substantially all of Space Imaging’s
assets, for the purchase price of approximately $58.5million USD. The resultant company
was in future to be known under the brand name of Geoeye (Geoeye website:2008). Further
details which are provided in Section 2.2.1.5.
29
2.2.1.4 OrbImage
There are currently three OrbView satellites in orbit, which were launched and are controlled
by the Orbimage company. Orbimage could trace its origins back to an early proposal by
Eyeglass International (Petrie 2001). The Eyeglass project could be considered as a
commercial side product of remote sensing projects for the US intelligence community. The
company was established in 1994 based on a consortium of three US firms: Orbital Sciences,
Itek and GDE Systems. Each company was to undertake a specific role in the development of
the satellite system. Orbital Sciences was to have built the satellite bus and provide the launch
vehicle with their Taurus rocket. Itek was developing the sensors and on-board electronics
and GDE Systems was to be responsible for the image processing and data handling tasks
(Gupta 1994). When Itek and GDE Systems left the consortium, Orbital Sciences decided to
continue, largely on its own. Orbital Sciences received its licences in the middle of 1994 and
the Orbimage company was established later that same year. Effectively the company had
operated as an affiliate or subsidiary of the Orbital Sciences Corporation, which had built
launchers and satellites both for the US government agencies and non-government bodies and
also had large interests in electronic and optical imagers (Petrie 2001).
In April 1995 Orbimage began operations with the launch of OrbView-1. This satellite was
air launched from 40 000ft from a modified TriStar aircraft. OrbView-1 is a scientific
satellite with its main objective being meteorological research. It is capable of producing low
resolution images (spatial resolution 10km) of weather and is used for the mapping of
lightning strikes. OrbView-2 followed with its successful launch in August 1997 and
generates low to medium resolution imagery, mainly of oceanic areas. The imagery from
OrbView-2 has eight spectral bands, six in the visible and two in the near-infrared spectrum
with a spatial resolution of 1.1km. The imagery is used for oceanic and coastal research by
NASA and for operational purposes by the US Navy and commercial fishing fleets (Petrie
2001).
The first two OrbView satellites are not classified as high resolution satellites but two other
OrbView satellites, namely OrbView-3 and OrbView-4, were promoted as offering 1m
panchromatic and 4m multispectral digital imagery, with OrbView-4 planned to be the
world’s first commercial hyperspectral satellite (Orbimage website:2002). But as with other
companies involved in such ventures, Orbimage had its share of problems.
30
Orbimage made a business decision to launch OrbView-4 first before OrbView-3, but on the
21 September 2001 OrbView-4 failed to achieve a stable orbit when launched. OrbView-3
though was successfully launched on 26 June 2003 from a Pegasus rocket provided by the
Orbital Sciences Corporation from Vandenberg Air Force Base in California USA. The
OrbView-3 satellite is capable of producing 1m panchromatic and 4m multispectral imagery
(Orbimage website: 2003). Shortly after the failed launch of OrbView-4, Orbimage
announced that it was financially restructuring with a new Chairman of the Board, Lt.
General (Ret) James A. Abrahamson.
Similar to DigitalGlobe and Space Imaging, the Orbital Imaging Corporation (Orbimage) has
signed a number of contracts with NIMA (now NGA). The first one of these was in January
2001 when it signed the NIMA Production Prototype (NPP) contract. The NPP was an
Indefinite Delivery/Indefinite Quantity (IDIQ) type contract in which NIMA could order
products and services for one base year and two optional years. The maximum value of the
contract over the three year period was fixed at $100million USD. The objective of the NPP
contract was to develop, prototype and demonstrate new or improved geospatial and
intelligence products and data sets derived from commercial satellites and US government
imagery sources (Geoeye website:2008).
In March 2004 Orbimage was awarded a Clearview contract for a guaranteed minimum over
two years of $27.5million USD, with a minimum of $10.5million USD in the first year and
$12million USD in the second year. This contract did not prevent Orbimage from announcing
net losses for the three months ending March 31, 2003 and 2004 of $2.51million USD and
$8.39million USD. It is worth noting that from 2001 to 2004 Orbimage was in a constant
state of management and financial restructuring that included voluntary bankruptcy filing
(Geoeye website:2008).
In September 2004 the company was then awarded a major contract valued at approximately
$500million USD that would expire on the 30 September 2008. The awarding of this contract
provided Orbimage with both long term revenue commitments as well as the capital for the
development of what was their next satellite OrbView-5 (estimated project costs of
$502million USD) (Geoeye website: 2008).
31
As mentioned previously, on September 16 2005, Orbimage purchased Space Imaging for
approximately $58.5million USD and the resultant company was Geoeye Inc (Geoeye
website:2008).
2.2.1.5 Geoeye Inc.
As a result of the formation of Geoeye Inc. in January 2006 when Orbimage Holdings Inc.
acquired Space Imaging’s assets, Geoeye now operates a constellation of three remote-
sensing satellites, which are IKONOS, OrbView-2 and OrbView-3. In addition, Geoeye
successfully launched another satellite, Geoeye-1 on 6 September 2008. After a period of
calibration and checks the company released the first colour half meter ground resolution
image from Geoeye-1 on 8 Oct 2008. GeoEye-1 can simultaneously collect 0.41-meter
ground resolution panchromatic imagery as well as 1.65-meter multispectral imagery. It is
anticipated the company will begin selling imagery from Geoeye-1 towards the end of 2008
(Geoeye website: 2008).
Due to its heritage, Geoeye is a company that has enjoyed substantial revenue contracts from
NGA and up until March 2006 the value of Geoeyes’ ClearView contracts was $49million
USD (Geoeye website:2008). In fact as a result of contract awards from one of its founding
companies, NGA is supporting the project costs of the new Geoeye-1 satellite under a cost
sharing arrangement to a total of $237million USD out of the estimated project costs of
$502million USD. The NextView contract also provides for NGA to order approximately
$197million USD of imagery products beginning 1 February 2007 and continuing until six
quarters after the launch of Geoeye-1. It could be considered that as a result of the announced
delays in entering Geoeye-1 into service in February 2007, Geoeye and NGA agreed to
purchase $54million USD of imagery products from the company’s existing satellites from 1
February 2007 until 31 December 2007. The reliance for financial support on the contract
with NGA, seen in Geoeye’s statement in their Form 10-K, shows that they anticipate that
NGA will account for approximately half of the satellites’ imagery capacity during the period
previously stated (Form 10-K for Geoeye, Inc: 2007)
32
2.2.1.6 ImageSat
ImageSat is an international company established in 1997, originally named West Indian
Space. The two originating Israeli companies were Israel Aircraft Industries (IAI) a
manufacturer of aircraft, satellites and electronic systems, Electro-Optics Industries (El-Op) a
supplier of advanced military and commercial electro-optical systems. A third American
company, Core Software Technology (CST) was the supplier of software and services to
handle large databases of geospatial information (Petrie 1999).
Despite ImageSat International’s (including predecessor) existence only since 1997, Israel
has been involved in developing high resolution satellites since the late 1980s though little
information is available. In 1988, Israel launched the Ofeq-1 (Horizon-1) test satellite on a
three stage Shavit launcher. In order to avoid flying over Israel’s Arab neighbours an unusual
northeast flight path was used over the Mediterranean placing the satellite into a retrograde
orbit at an inclination of 143°. This orbit limited the satellite’s view to areas 37° north and
south of the equator. This 156kg satellite was explained to be a test vehicle designed to lead
the development of an orbital reconnaissance capability. Ofeq-1 re-entered the Earth’s
atmosphere in January 1989. Ofeq-2 was launched in April 1990 with similar weight and
technical characteristics to Ofeq-1 and an orbital life of three months (Steinberg 1998).
Then on 5 April 1995, Ofeq-3 which was Israel’s first fully operational reconnaissance
satellite, was successfully launched into orbit. This satellite was also placed in a retrograde
orbit with an inclination of 143.4° (Petrie 1999). Its higher perigee (369 km) and an orbital
maneuvering capability allowed for a longer life of one to three years. Its orbit took it over
sites in the Middle East, including Iraq. The head of the Israeli Space Agency (ISA)
described Ofeq-3 as “a very sophisticated platform on which many things can be placed.”
(Steinberg 1998). Whilst not much detail is available on the payload, it is reported to be
capable of obtaining both visible and ultraviolet imagery with a resolution of approximately
1m (Encyclopedia Astronautica Website:2002). Ofeq-3 eventually re-entered and burned up
in the Earth’s atmosphere in October 2000. Ofeq-4 was launched as a replacement satellite
but failed (Petrie 1999).
On 28 May 2002, Ofeq-5 was launched on an IAI/MLM Shavit satellite launcher from the
Palmachim missile test center on the Israeli Mediterranean coast. The satellite carries a
33
remote sensing payload that will enable it to acquire high resolution data for Israels’ national
needs. The satellite is believed to be capable of delivering both panchromatic and colour
images at approximately 0.8m (Defence Update International Website: 2002). Ofeq-6 was
subsequently launched on 6 September 2004 but unfortunately crashed into the
Mediterranean Sea during a launch which was attempting to place the satellite in a low Earth
orbit (LEO). The imagery from the Ofeq-6 satellite was intended to supplement coverage of
the Ofeq-5 satellite, which Israel was using to monitor troop movements, missile launches
and nuclear development efforts in neighbouring countries (Space News website: 2008).
Despite the significant setback caused by the loss of Ofeq-6 on launch, Israel managed to
successfully launch Ofeq-7 from the Palmahim Air Force Base on 11 June 2007. Details of
the satellite’s characteristics are difficult to obtain though its velocity is known to be
approximately 8m/second in an elliptical orbit at an altitude between 311 and 600km (Space
News website: 2008).
In December 1996, Israeli press published unconfirmed reports of an agreement between IAI,
Lockheed Martin and Mitsubishi in which IAI agreed to supply Ofeq images to the civilian
market through a satellite to be launched by the end of 1997 (Steinberg 1998). Consequent
events have shown this has not been the case and that imagery obtained from the Ofeq series
of satellites have appeared to have remained for the use of the Israeli government only. The
Israeli Ministry of Defence also has exclusive access to Middle Eastern coverage provided by
the commercial EROS remote sensing satellites, also built by IAI (Space News website:
2008).
The development of the Ofeq satellites is important, as their design is the one on which the
EROS satellites of ImageSAT International are based (Petrie 2001). ImageSat International
had intended to launch a constellation of up to six satellites by 2007 (ImageSat Website:
2002). In common with the American satellite projects, the launch of the first EROS satellite,
EROS-A had its share of both technical and financial problems. At first EROS-A was to be
launched in December 1999, then it was postponed until February 2000. In July 2000 the
financial problems were solved by a group of U.S. and French investors providing a US$90
million package. The acceptance of the financial assistance also resulted in the reorganisation
(including a change in top management) of the company and it was here the company’s name
was changed from West Indian Space to ImageSat International NV, incorporated in the
Netherlands Antilles. The EROS-A satellite, following the company’s reorganisation, was
34
then to be launched in October 2000, but it was not until 5 December 2000 that the satellite
finally launched on a Russian Start-1 launcher from Svobodny. The first images were
received successfully four days later, while the first images shown publicly were released on
the 18 January 2001. One of the limitations of the EROS satellite is that it has no on-board
recording and storage device so the satellite relies entirely on the images being transmitted in
real-time to ground receiving stations (Petrie 2001). ImageSat International though, has
signed strategic partnership agreements with a network of 12 Acquisition, Archiving and
Distribution (AAD) ground receiving, processing and distribution centres worldwide, but by
November 2001 there were only six countries with functioning AAD centres: Sweden,
Argentina, Japan, Italy, South Africa and Taiwan. In developing these partnerships ImageSat
International was dealing with receiving stations that already processed Landsat and SPOT
data. They supplied the stations with a free upgrade of hardware and software for them to
receive, process and archive imagery from the EROS satellites. Through this arrangement
ImageSat International obtains a share of the revenue (Wagner 2001).
EROS B which was successfully launched on 25 April 2006 from a Russian Start-1 rocket,
was designed to capture black and white (panchromatic) images at 0.7m resolution, compared
to its predecessor, EROS A which collects images at 1.9m resolution. In addition, EROS B
has larger onboard storage, improved pointing accuracy and a faster data communication link
than EROS A.
As with their U.S. based counterparts, ImageSat has had to consider financial constraints and
market influences in deciding company direction. In June 2004 they announced that they
would not launch the satellite formerly known as EROS B1 (now known as EROS C) but
instead an upgraded version of EROS A, the EROS B satellite. The ImageSat Chief
Executive Officer (CEO) at the time, Menashe Broder claimed the revised plan was a direct
response to a market demand in which national security customers were in need of multiple
collections of panchromatic, high resolution imagery as opposed to the civilian market that
the multispectral imagery was intended for (Space News website: 2008). In fact ImageSat
operates a purchasing arrangement whereby customers can exclusively buy collection areas
around the world and no one else will be able to obtain EROS imagery over these locations.
Whilst it is difficult to find details of ImageSat’s financial position, in 2003 Menashe Broder
claimed that government customers for intelligence quality imagery accounted for 99% of
35
ImageSat business.
The EROS C satellite is anticipated to be launched in 2009 into a sun synchronous orbit at an
altitude of about 500km. It will be equipped with sensors capable of producing both
panchromatic imagery at a resolution of 0.7m and multispectral imagery at a resolution of
2.8m with a swath of 11km at nadir. Its expected life is considered to be ten years (Apogee
website: 2008)
2.2.1.7 Centre National d'Etudes Spatiales (CNES)
Whilst not included in the category of high resolution satellites, the SPOT system continues
to be a source of satellite imagery. The SPOT Earth observation satellite system was first
approved in 1978 being designed by the French Space Agency (CNES) with cooperation
from Belgium and Sweden. The entire system consists of a series of orbiting satellites and
associated ground facilities for satellite control, acquisition programming, data reception and
imagery production. CNES has responsibility for the satellite in-orbit control and execution
and the satellite acquisition plan. Spot Image, a CNES subsidiary has responsibility for the
satellite’s daily activity plan definition, the reception of images transmitted to the Toulouse
station, the processing of the image telemetry to update image catalogues, the production and
development of products derived from satellite data and their commercialisation (CNES
Internal Newspaper: 2002).
The SPOT system has been operational since 1986 with the launch of SPOT 1 in February
1986, SPOT 2 in January 1990, SPOT 3 in September 1993 (failed November 1996), SPOT 4
in March 1998 and finally SPOT 5 in May 2002 (ACRES:2002). As the ground system was
only ever configured for the management of three satellites, at present only three satellites are
in use, being SPOT 2, 4 and 5. SPOT 1 has been temporarily deactivated in favour of SPOT 5
(CNES Internal Newspaper:2002), though in late 2003 SPOT 1 was de-orbited to place it on a
destructive re-entry into the Earth’s atmosphere in 15 years (Space News website: 2008).
SPOT 5 is considered as the last of the medium-resolution wide-area collection systems with
future imaging satellites systems being of the 1m resolution category (Geoinformatics:2002).
Similar to the other commercial ventures of high resolution commercial satellite imagery, the
36
37
SPOT series of satellites are linked both financially and technically with France’s military
reconnaissance satellite program. France’s military reconnaissance program is known as
Helios and is divided into two phases Helios I and Helios II, both comprising of two
satellites. In the Helios I phase, Helios IA and Helios IB were launched in July 1995 and
December 1999. These satellites both had a resolution of 1m and no infrared capability. Italy
and Spain reportedly have invested in Helios I and receive data at an amount proportional to
their funding (Hitchens: 2006).
Helios IIA was launched in December 2004 and produced its first images in January 2005.
Helios IIB, the second satellite in the series is due for launch in 2008. The Helios IIA which
weighs 4 200kg, was built by EADS Astrium as the prime contractor, with Alcatel Space
providing the imaging system and has a contractual service life of five years. Its resolution
according to the French Defense Ministry is “several 10s of centimetres”. The Helios II
platform is based on a nearly identical platform built by EADS Astrium to SPOT 5 (Space
News website: 2008).
In 2005 Spot Image reported revenues of approximately $85million USD which was a 19%
increase from 2004, though this was actually 12% after taking into account a partnership
cancellation with competitor DigitalGlobe. It was though, the company’s third straight year
of double digit revenue growth. Despite this, previously it was reported in 2003 that 60% of
images taken by SPOT satellites are used for defence purposes. With the advent of the
Pleiades satellite system in the near future, similar to their counterparts in the U.S. and Israel,
it can be anticipated that the French high resolution commercial satellite imaging industry
will be heavily supported by not only the French military but also the military of its European
partners (Space News website: 2008).
The Pleiades satellite system is the high resolution optical French component of the Franco-
Italian ORFEO (Optical and Radar Federated Earth Observation) program. These satellites
are claimed to be dual use satellites for both military and civilian users. Other partners in the
Pleiades project include Austria, Belgium, Spain and Sweden who will receive data in
proportion to their investments. The actual system will consist of two satellites capable of a
resolution of 0.7m. The first satellite is due to be launched in late 2008 and the second in
2009 or early 2010 (Hitchens: 2006).
IKO
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#4:
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Tabl
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7 Sa
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38
2.3 Satellite Systems
This section will describe the technical characteristics of commercial satellites that are
capable of acquiring high resolution imagery.
2.3.1 IKONOS
Figure 2.6 - IKONOS Satellite (Source: Geoeye Website, 2008)
The IKONOS satellite is currently in a 680km sun synchronous orbit at an inclination of
98.1°. In addition to collecting panchromatic and multispectral imagery, the IKONOS
satellite has the capability to collect stereo pairs. It has a revisit frequency of 2.9 days at 1m
resolution and 1.5 days at 1.5m resolution. Revisit times are more frequent for latitudes
greater than 40° and less frequent for latitudes closer to the equator. The nominal swath width
is 11km at nadir and 13km at 26° off-nadir (Space Imaging Website: 2002). Operational
parameters are listed in Table 2.7.
Figure 2.7 IKONOS Imagery - Dalrymple Bay, Queensland, Australia. (May 23, 2005) (Source: Geoeye Website, 2008)
39
Zhou and Li (2000) also describe the special characteristics of IKONOS, and compare them
with other systems. Their main points are:
(a) The system is based on a new optical system: a push broom camera with a 10m
focal length, folded to 2m through the use of a mirror system. It is designed to capture
both panchromatic images with a 1m resolution and multispectral images with a 4m
resolution.
(b) In addition to along-track stereo capability, the satellite imaging system is able to
roll in orbit to collect cross-track images at distances of 725 km on either side of the
ground track. Due to the satellite’s 680 km altitude, imagery maintains at least a 1m
ground sample distance (GSD) out to 350 km either side of nadir (Corbley: 1996).
(c) The system is also equipped with GPS antennas and three digital star trackers to
establish precise camera positions and attitudes. A rigid satellite platform has been
built to reduce the motion vibration of the platform and this contributes to the
integrity of the line-of-sight determination. The satellite orbits the Earth in a sun-
synchronous polar orbit, allowing it to traverse the planet every 98 minutes, crossing
the equator at the same local time (around 10:30 am) on each pass (Folchi, 1996).
2.3.2 EROS A
Figure 2.8 – EROS A Satellite (Source: ImageSat Website: 2008)
EROS A is a light weight low earth orbit (LEO) satellite with a single electro optical camera
system. The satellite is capable of capturing only high resolution panchromatic image data. It
40
orbits the Earth almost 15 times a day in a circular sun-synchronous near polar orbit at an
altitude of 480km, with the capability of delivering data in real time to 16 ground receiving
stations worldwide. As the satellite is highly maneuverable it can be quickly pointed and
stabilised to image customer specified sites on nadir or at oblique angles up to 45°, this
enables the satellite to view almost any site on Earth as often as two or three times a week.
The standard image resolution is 1.8m with an over sampled resolution of 1.0m at a swath of
12.7km at nadir for the standard resolution. (ImageSat Website: 2002). A list of the main
operational parameters can be seen in Table 2.7.
Figure 2.9 - EROS Imagery - Adelaide Cricket ground, 5th March 2002 (Source: Apogee Website: 2008)
2.3.3 EROS B
Figure 2.10 – EROS B Satellite (Source: ImageSat Website: 2008)
ImageSat claims to have launched the EROS B satellite to address market demand for higher
resolution and faster revisit of EROS satellites (ImageSat Website: 2008). Like its
predecessor it is a light, low earth orbiting satellite that is designed for fast maneuvering
41
between imaged targets (Apogee Website: 2008). It is slightly larger in appearance to the
EROS A satellite but has greater capabilities which include a larger improved camera which
provides a standard panchromatic resolution of 0.70m from an altitude of approximately
500km (ImageSat Website: 2008). The EROS B satellite has an increased onboard storage
capacity, which when combined with its agility permits collection of 190km long strip scenes
at any angle to the ground track (Apogee Website: 2008).
Figure 2.11- EROS Imagery - Circular Quay, Sydney, 17 May 2006 (Source: Apogee Website: 2008)
2.3.4 Quickbird
Figure 2.12 – Quickbird Satellite (Source: DigitalGlobe Website: 2008)
The Quickbird satellite which was manufactured by Ball Aerospace & Technologies Corp,
orbits the Earth at a 450km in a 98° sun-synchronous orbit (DigitalGlobe Website: 2002).
The satellite has a 61-72cm resolution for panchromatic imaging and 2.44-2.88m resolution
for multispectral imaging, depending upon the off-nadir viewing angle (0°-25°). It also has an
42
along-track and across track stereo capability (which has been used in the past, but is not
currently being offered for commercial customers), which provides a high revisit frequency
of 1 to 3.5 days, depending on the latitude (Toutin & Cheng 2002). Operational parameters
are listed in Table 2.7.
Figure 2.13- Quickbird Imagery - Singapore, 21 March 2004(Source: DigitalGlobe Website: 2008)
2.3.5 Worldview 1
Figure 2.14 – WorldView – 1 Satellite (Source: DigitalGlobe Website: 2008)
The Worldview satellite is advertised by DigitalGlobe as the most agile satellite ever flown
commercially. Even though it is only capable of obtaining panchromatic imagery, the
resolution is from 0.5m to 0.59m, which currently exceeds any of its competitors. It not only
has improved maneuverability when compared to Quickbird but also positional accuracy of
6.5m 90%CE compared to 23m 90%CE, resolution of 0.5m at nadir compared to 0.6m at
nadir and a larger swath width of 17.6km at nadir compared to 16.5km at nadir. It is now the
43
only DigitalGlobe satellite that is used to collect stereo imagery. Operational parameters are
listed in Table 2.7.
Figure 2.15 - WorldView – 1 Imagery - Sydney, 31 December 2007 (Source: DigitalGlobe Website: 2008)
2.3.6 OrbView - 3
Figure 2.16 – OrbView–3 Satellite (Source: Geoeye Website, 2008)
OrbView-3 collects 1m panchromatic and 4m multispectral imagery at a swath width of 8km.
It has a revisit rate of less than three days as a result of its ability to collect data up to 50° off
nadir and is capable of collecting 21 000 square kilometres per ten minute pass. (Geoeye
Website: 2008)
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Figure 2.17 – Orbview Imagery (Source: Global Land Cover Facility Website, 2008)
2.4 Terrestrial Based Methods
High resolution commercial satellite imagery provides a new source of imagery and spatial
data products. The following section provides a comparison of the current terrestrial
technology available that either complements or competes with high resolution commercial
satellite imagery. The methods detailed here are considered to be the more “traditional”
methods or “earth based systems” normally familiar and accessible to surveyors and related
spatial industries.
Existing methods of survey include:
(1) Ground Survey
(2) Aerial Photogrammetry
(3) Airborne InSAR
(4) Light Detection and Ranging (LIDAR)
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A discussion on cost is only given for ground surveys. A detailed discussion on cost
comparison between Aerial Photogrammetry and Satellite Imagery is contained in Chapter 3.
2.4.1 Ground Survey
The usual method of information collection by ground survey is by the use of an Electronic
Total Station which incorporates an electronic theodolite, electronic distance measuring
(EDM) equipment and a data recorder. Whilst there are Electronic Total Station systems that
can be operated by one person, it is more commonly a two person operation, where one
person operates the Electronic Total Station and the other controls the prism or “target” to
which physical measurement to the object is required (see Figure 2.16).
Figure 2.18 - Typical Ground Survey Party (Source: United States Army Publication, EM 1110-1-1005)
Ground survey is the most accurate and detailed method for obtaining spatial data. It has the
advantage that objects can be measured direct at close range and hence produce easily
consistently accurate results. Typically accuracy of +/- 1cm in the X and Y direction (or
Easting and Northing coordinates) and a vertical accuracy of +/- 5cm on soft surfaces (such
as grass) to +/- 1cm on hard surfaces (such as concrete) is obtainable (Uren & Price: 1992:
198)
When determining the required resolution of the Digital Elevation Model (or DEM)
consideration must be taken of how the spacing or grid size will affect the accuracy of the
final DEM. In terms of contour interval, Table 2.8 gives an indication of spacings between
spot heights or measurements in terms of a required contour interval (Uren & Price: 1992:
46
200).
Scale 1:50 1:100 1:200 1:500 1:1000
Contour vertical interval 0.05m 0.1m 0.25m 0.5m 1m
Spot level grid size 2m 5m 10m 20m 40m
Table 2.8 – Spacings between Spot Heights or Measurements
The establishment costs of a field survey party can vary but include a total station between
$10 000 to $20 000AUD, vehicle and ancillary equipment (such as spades, axes, chainsaws
etc) $20 000 to $30 000AUD. Typically a field survey party in Australia will cost
approximately $150 to $200 per hour depending on the circumstances. In typical field
operations a field survey party can observe between 250 to 1 000 points per day for a detail
survey. The numbers of observations are dependent on the complexity and ruggedness of the
site as well as the purpose for the survey.
During a ground survey the ability of “walking the ground” means that it is quite easy to
obtain accurate spatial data correctly representing the details of the ground. The disadvantage
of field surveys is that the cost can rapidly escalate for large areas that require a detailed
digital elevation model (DEM) and topographical and cultural details. As a result, ground
survey techniques and equipment are commonly used for such work as detail surveys,
engineering layout, “as constructed” or house block surveys covering small or discrete areas.
Figure 2.19 Typical Detail Survey
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When used in conjunction with high resolution commercial satellite imagery and its
derivative spatial data, ground survey can be a complementary source. As mentioned above
ground survey can be uneconomical both in time and money for large areas, but for a large
area project which contains some areas that cannot be observed on satellite images (due to
cloud or vegetation) or requiring higher positional accuracy or detail, ground survey can be
complementary.
2.4.2 Aerial Photogrammetry
Photogrammetry can be described as:
“the art, science, and technology of obtaining reliable information about physical objects
and the environment through processes of recording, measuring and interpreting
photographic images and patterns of recorded radiant electromagnetic energy and other
phenomena” (Wolf & Dewitt:2000:1)
Only images or photographs taken from airborne vehicles will be considered in this section.
As with satellite imagery, stereo imagery must be obtained to determine heights or relief from
aerial photography. In the case of aerial photography this means a 60% overlap between
successive photographs along each strip and 30% sidelap between strips if more than one
photograph width is required to cover the area. Suitable ground control points are also
required to ensure referencing to the ground coordinate system.
Aerial photography can be classified as either vertical or oblique. Vertical photography refers
to photos taken with the camera axis directed within 2° of vertical. By using detailed
photogrammetric instruments, computer software and hardware it is possible to rigorously
correct for tilt in the photos with no loss of accuracy (Wolf & Dewitt: 2000:5).
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Figure 2.20 – Aerial Stereo Photography collect
Oblique aerial photography refers to photography exposed with the camera axis tilted about
the flight direction. High oblique photography includes the horizon while low oblique
photography does not (Wolf & Dewitt: 2000:6).
The process of obtaining spatial data from aerial photogrammetry involves four separate
phases:
(a) Planning
(b) Establishment of ground control
(c) Acquisition of the photographs or images
(d) Processing or extracting the data.
Due to the amount of work and coordination required in using this method of spatial data
production, detailed planning is required. The initial point of determination which will govern
the nature of all subsequent steps, is the actual purpose for which the photography is being
49
flown. Normally aerial photography has either good metric qualities or high pictorial
qualities. Good metric qualities means that the photographs or images are to be used for
topographic mapping or purposes that require quantitative photogrammetric measurements.
Images of high pictorial qualities are used for qualitative analysis, for example photographic
interpretation or for the construction of orthophotos, photomaps and aerial mosaics (Wolf &
Dewitt:2000:409).
In recent years the advent of digital aerial cameras such as the Leica ADS40 and the Vexcel
UltraCam have caused a re-evaluation of aerial photography. These systems have many
enhanced features over their “wet film” predecessors. Their advantages include the almost
complete elimination of the need for ground control due to the integration of Global
Positioning Systems (GPS) technology and an Inertial Measuring Unit (IMU). Also they are
capable of obtaining multispectral as well as panchromatic images.
From the computing aspect the use of digital cameras or sensors allow for greater contrast in
images by using the digital images radiometric resolution. As computers store information in
binary format every number has a value of 0 or 1, to obtain more larger numbers, more binary
numbers need to be stringed together. Hence 8 bit data has 28 or 256 possible values which
when applied to imagery means that 8 bit data has 256 potential values for each pixel (when
viewing an image in gray tones 0 = black and 255 = white and there are 254 shades of grey in
between). Similarly 11 bit data (211 equates to 2048) increases the range to 2048 shades of
grey.
There are distinct similarities between digital aerial cameras and high resolution commercial
satellite imagery, such as the way the images are electronically captured, stored and then
transferred. This allows for more rapid turnaround for processing or recapture in the event of
images being found to be unsuitable. Whilst digital aerial cameras are primarily designed for
large scale mapping with pixel sizes as small as 5cm, they can be suitable for medium or
smaller scale imagery collects, particularly for aircraft which are capable of a ceiling of
approximately 8000m. In this instance the maximum ground sampling distance (GSD) would
be less than 1m which would then be comparable with high resolution commercial satellite
imagery. This with the ability to collect 12 bit imagery (IKONOS imagery being 11bit)
makes the use of digital aerial cameras a serious alternative to high resolution commercial
satellite imagery (Trinder: 2008).
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A more detailed comparison between aerial photography and high resolution commercial
satellite imagery is contained in Chapter 3. Aerial photography has the advantages that it
provides higher resolution, greater positional accuracy and also, depending on the cloud
cover, can be flown on demand at any time to achieve a desire outcome. Despite this, the
financial costs of aerial photography are generally greater, as well as the greater complexity
of collection when compared to high resolution commercial satellite imagery, such as the
establishment of ground control, flight planning, airspace clearance and staff skills required
to process.
2.4.3 Airborne Interferometric Synthetic Aperture Radar (InSAR)
Airborne Interferometric Synthetic Aperture Radar (InSAR) uses a variation of a
conventional Synthetic Aperture Radar (SAR). It combines a SAR system with another
spatially separated receiving antenna, to determine elevations. When a radar pulse is emitted
from the SAR antenna, the returning echoes are received by both SAR antennas, and the
phase difference between the two signals is measured. This phase difference is related to the
difference in geometric path length from the ground point. From the geometry of the
antennas, the phase difference can be converted into heights of ground points. Using the
interferometric phase, in addition to the standard along and cross track location of an image
point obtained with conventional SAR, the three dimensional coordinates of a point can be
determined within an accuracy of 0.3m to 3.0m (ASPRS: 2001:153).
InSAR has the advantage of having near weather-independent operation, cloud penetrating
capability and quick turnaround time. In areas which are notorious for cloud clover, such as
Papua New Guinea, InSAR provides an effective solution for mapping as it is capable of
penetrating weather that optical sensors cannot. Its disadvantage is that there are very few
InSAR systems in the world. Its application can be expensive for a small project or third
world application without outside financial and technological assistance.
In comparison to high resolution commercial satellite imagery, InSAR can be used to produce
a high resolution digital elevation model that can be used to orthorectify satellite imagery or
aerial photography. Whilst InSAR data has been used for feature extraction of spatial data
unless it is automated it can be a difficult and operator exhausting process. For this reason
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InSAR provides a good source of digital elevation model data (which does not necessarily
need to be recent), for use in the production of orthorectified images using high resolution
satellite imagery or aerial photography.
Figure 2.21 – Concept of IFSAR Mapping (Source: Xiaopeng Li, Intermap website, 2008)
Figure 2.22 - Intermap’s LearJet 36 STAR-3i System (Source: Xiaopeng, Intermap website, 2008)
2.4.4 Light Detection and Ranging (LIDAR)
Laser scanning or LIDAR (light detection and ranging) is a recent development in the area of
topographical data collection. The equipment consists essentially of a laser scanner, inertial
navigation system, GPS receiver, and controller and data recording computers fitted to an
airborne platform. It is capable of obtaining measurements of a very large number of points
every second, to 10 – 20cm accuracy with swathes several kilometers wide on the Earth’s
surface (Wolf & Dewitt: 2000:303).
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The principle of operation is that as the aircraft flies over the subject area a laser pulse is
transmitted to the terrain below and its return signal is detected by a sensor. The distance
from the scanner to the terrain can then be determined from the time delay between the
transmission and the return signal. This distance data is then combined with information from
the inertial navigation system, which determines the orientation of the aircraft, and the GPS
receiver which records the XYZ positions of the antenna. The results from the laser pulses
then effectively define vector displacements from specific points in the air to points on the
ground. As the laser is capable of generating pulses at the rate of thousands of pulses per
second the output is a dense pattern of measured X, Y, Z points on the terrain. At present the
accuracy obtained using this technology is of the order of 10 to 20cm (Wolf & Dewitt:
2000:304).
Figure 2.23 - LIDAR system. (Source: Burtch, 2002)
The use of LIDAR has a number of advantages which include:
(a) Measuring ground and above ground features. The density of points collected
mean that it is possible to determine manmade objects and infrastructure such as
building and powerlines.
(b) Defining the terrain under vegetation. Due to the large number of points collected
53
a sufficient number of laser pulses will penetrate the tree canopy and return, enabling
the determination of the terrain under the tree canopy.
(c) Rapid data acquisition, typically defining 500ha per hour or 100-150 linear km/hr.
(Jones:2008)
Figure 2.24 – LIDAR feature collection methodology (Source: AAM Hatch:2008)
Similar to InSAR the main use for LIDAR is the production of digital elevation models
which then can be used for a variety of applications. LIDAR though does have the distinct
advantages in that it can be used in the determination of heights of infrastructure such as
power lines or tree canopy heights due to the large number of laser pulses emitted. Which
means features can be extracted as with InSAR data.
Table 2.9 provides a comparison between the sources of spatial data and their data output
accuracy and where applicable image resolution. Whilst it can be seen that imagery and
digital elevation models from high resolution commercial satellite imagery are of a lower
accuracy and resolution of terrestrial based sources, what is not represented here is the
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advantages of spatial data sourced from spaced based means which in some situations can
counter the loss of accuracy and resolution. These advantages as well as the disadvantages
are examined in the following chapter.
HighResolution
Commercial Satellites
GroundSurvey
AerialPhotogrammetry
(Aerial Digital Cameras)
InSAR LIDAR
ImageResolution
Less than 1m
- 5 to 50cm - 15 to 40cm from
integrated cameras
DigitalElevationModelAccuracy
+/- 2m vertical
accuracy @ 1 sigma on
clear ground (with
control)
+/- 5cm (soft surfaces)
+/- 1cm (hard surfaces)
5 to 50cm @ 1 sigma on clear
ground
0.3m to 3.0m 10 to 50cm @ 1 sigma
on clear ground
Source AAMHatch2008
Uren & price:1992:198
AAMHatch 2008 ASPRS:2001:153
AAMHatch2008
Table 2.9 – Comparison of Spatial Data Sources
2.5 Summary
The availability of high resolution commercial satellite imagery has opened a new spatial
data collection source and methodology not previously available. Its ability to collect high
resolution imagery in both mono and stereo remotely from space means that not only can
imagery for a variety of uses be collected, but also other spatial data such as features and
digital elevation models can be extracted. Whilst the accuracy is not that of ground survey,
aerial photogrammetry or scanning (such as InSAR or LIDAR), it is sufficient to satisfy a
variety of requirements.
As a business venture the cost of establishing such a data source or industry has not been
cheap both in time and finance for any organisation or country involved. Few business
ventures have not had at least one major set back and none would have survived if it was not
for significant funding through work programs from their respective governments. Despite
these problems there is still progress being made to ensure that the next generation of high
55
56
resolution imaging commercial satellites will be constructed and successfully launched, such
as Digital Globe’s WorldView 1 and 2 satellites.
When compared to terrestrial based survey technologies such as aerial photogrammetry,
InSAR or LIDAR it has become clear that in some areas depending on the requirement for
small to medium scale mapping or revision, it is a direct competitor. It would be better to
consider high resolution commercial satellite imagery as a source or methodology in its own
right providing an alternative and or complement to other technologies.
CHAPTER 3
IMAGERY APPLICATIONS IN A GEOGRAPHICAL INFORMATION
SYSTEM (GIS)
3.1 Introduction – Composition of a Geographical Information System (GIS)
The significance of the advent of commercial high resolution satellite imagery is that
it has added another versatile source of data that can be used by the spatial
community. Consideration still needs to be given to accuracy assessments, suitability,
timeliness and cost compared with other sources of data to ensure that it is utilised to
its full capacity. This chapter will briefly cover a description of Geographical
Information Systems (GIS) and provide some points for consideration in regards to
the use of high resolution satellite imagery in a GIS.
Wolf (2000) describes a Geographical Information System or a GIS as any
information management system which can:
(a) Collect, store, and retrieve information based on its spatial location;
(b) Identify locations within a targeted environment which meet specific
criteria;
(c) Explore relationships among data sets within that environment;
(d) Analyse the related data spatially as an aid to making decisions about that
environment;
(e) Facilitate selecting and passing data to application-specific analytical
models capable of assessing the impact of alternatives on the chosen
environment; and
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(f) Display the selected environment both graphically and numerically either
before or after analysis.
As a note of caution Chernin and LeRoux (1999) state:
“As a result, GIS challenges us to understand the complexities of scale,
accuracy, and the photogrammetric process. To ignore or misunderstand these
complexities can cause unnecessary expenditures of money for the development of
highly accurate land bases.”
This view highlights the point that whilst spatial data has been available for thousands
of years, its only form until the advent of computer technologies was that of a map.
These maps came in a variety of themes, but were all in hardcopy form. Giger (2001)
highlights the point from Goodchild (1988) that:
“The ability of a Geographic Information System to analyse spatial data is
frequently seen as a key element in its definition and has often been used as a
characteristic to distinguish a GIS from systems whose primary objective is map
production.”
This can best be displayed by comparing a Computer Aided Drafting (CAD) package
such as AutoCAD or Microstation with a GIS. A CAD package is capable of creating
spatial data and cartographically presenting it in the form of a plan or map, which will
generally consist of a number of thematic layers with a common grid or coordinate
system, capable of being overlaid to provide a compiled product. A GIS will present
this data and its associated topological information, which describes its spatial
relationship with respect to neighbouring objects, and be capable of analysis. For
example, by using a CAD package an engineer or surveyor can design and draft a
housing estate subdivision including all the roads and allotment details. By importing
this data into a GIS and building the topology, it is possible then to perform analysis
of relationships to space facilities such as bus stops, community centres etc to ensure
they satisfy minimum or maximum requirements for residential dwellings.
A GIS exploits the full potential of analysis of spatial data for providing information
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of benefit to most areas of our community and businesses. These benefits are derived
from the way spatial data is stored in a GIS with textual attributes, position and
relationships in a digital format thus enabling manipulation with application software.
Aerial Photography 1995
IKONOS Satellite Imagery 2001
Cadastral Information
Road Network
Digital Terrain Model (DTM)
Building Footprints
AnalyticalOutput
Analysis and Manipulation Software
Relational Database
Figure 3.1 – Relationship of Data in a GIS
Before looking at the possible uses of satellite imagery in a GIS it is important to
understand the composition of a geographic information system. This concept can be
partly explained by the previous example comparing a CAD package application
versus a GIS, but the concept with a modern GIS goes further than this. With a GIS,
textual data is stored with the spatial data so relationships can be created and
manipulated to provide analysis of a geographical area or thematic topic. For
example, a digital street map compiled from cadastral data will enable a street address
to be located in an automobile’s Global Positioning System (GPS) navigation system,
in an automated map. If the same combination of data were augmented by tabulated
text data such as rates, unimproved land values, house hold income, utilities
59
consumption, persons per dwelling, it would then become possible to monitor and
model future trends in geographical areas from a suburb to a town council to a
regional area. Such modelling would allow the analysis of essential future services, or
considerations of planning as demographics of an area change or remain static over a
period of time.
A GIS, through linking data from various sources and giving it a spatial context,
provides a dynamic environment in which to exploit all the data. The following
description (Star and Estes: 1990) of the five basic components of a GIS is provided
to illustrate this point:
(a) Data acquisition;
(b) Preprocessing;
(c) Data management;
(d) Manipulation and analysis;
(e) Product generation.
3.1.1 GIS Data Acquisition
This is the process of identifying and collecting the varying data sets for the
application of the GIS and could involve a number of techniques and sources. In the
first instance it can be sourced from existing data such as computer scanned hardcopy
map products, ground surveys including feature vector information, aerial and satellite
imagery and terrain data. Spatially linked to this would be tabulated textual data such
as land rates, population demographics, crime incidents and home ownership,
depending on the output application or purpose of the GIS. This phase can be
considered the true foundation of the GIS since the correct data for the intended use
must be identified and located. The costs and time taken to acquire the data should not
be underestimated. The suitability and quality of any decisions and analysis derived
60
by using spatial data is limited and linked to the accuracy and precision of the datasets
from which they were derived (Star and Estes: 1990).
Data Acquisition
Preprocessing- Ingest- Data Translation- Transformation- Storage- Quality Control- Metadata
Data Management- Maintenance- Quality Control- Procedures
Manipulation andAnalysis- Training for users - Training forspecialist staff - Software tools
Product Generation- Textual report- Hardcopy (Paper)products
Field Surveys
CAD DrawingsVector Data
Sets
ScannedHardcopy Maps(Raster Datasets)
Digital Terrain Models
(Various Resolution)
Imagery- Various Resolution- Various Source - Aerial
Spatially LinkedTextual Data
- Satellite
- Derived vector andraster data (softcopy)
Figure 3.2 – Data flow of a Geographic Information System (GIS)
The issue of cost versus accuracy and quality is a critical problem not only for this
phase of the creation of a GIS but also the maintenance of the system. It should not be
assumed that expenditure of all available funding on highly accurate and detailed data
will ensure a system capable of high quality output. Data must be acquired that is
appropriate and suitable for the intended user base. For example, a local authority
may use a GIS to assist town planners and engineers to design and maintain services
in an urban environment. The acquisition of high resolution aerial photography may
be justified for this purpose as it will allow town planners to monitor development
and identify potential unauthorised construction. The digital elevation model (DEM)
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derived from the photography would be suitable for town planning purposes only,
such as identifying land parcel zoning and road corridors. Similarly, engineers would
be able to use the aerial photography and DEM to perform preliminary design work,
both in identifying features and approximating the topography. But for detailed design
a ground survey would need to be used to meet engineering construction
specifications of the order of centimetres, and ensure the most current information is
used, since the aerial photography could be dated (in excess of twelve months) when
design work is commenced or ground detail may be obscured by obstacles such as
vegetation
3.1.2 GIS Preprocessing
Preprocessing can be divided into two tasks, the first being collation, conversion and
extraction of existing data, such as maps, imagery and textual records and then
depositing this information into the computer database. This task can be extremely
time consuming especially when initially establishing a GIS. The importance of this
phase also cannot be underestimated since, unless the appropriate data is collected, the
GIS may not suit the applications it was intended for.
After importing the data the second task involves establishing a system for recording
and specifying the locations of the objects in the datasets. This means that all the data
must be related to a spatial reference, street address or Real Property description,
and/or underlying coordinate system. When this is complete it is possible to derive the
characteristics of any specified location in terms of the contents of any data layer in
the system (Star and Estes: 1990).
3.1.3 GIS Data Management
Whilst the quality of data is critical to the success of a GIS, once it is loaded
appropriate data management procedures are required allowing the creation of, access
to, and maintenance of the database. Effective data management ensures consistent
methods for data entry, update, deletion and retrieval. If this is done well, the system
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will run efficiently and responsively ensuring the functionality and suitability of the
system to the user (Star and Estes: 1990).
3.1.4 GIS Manipulation and Analysis
From the user’s perspective this component is often the focus of a GIS. It is in this
area of the system where the analytic operations are undertaken on the database
contents to derive information about the location. This can be displayed for example,
by determining of the gradients of the slopes of an area to establish potential routes
for a new road construction. Whilst the terrain information resides in the database in
the form of a DEM, software tools are required to derive the gradients from the
database (Star and Estes: 1990).
The manipulation and analysis of data in a GIS is not limited to engineering or town
planning requirements in an urban environment. Its application can be applied to other
areas to assist in either a decision making process or the determination of the delivery
of services. For example, a hardware retailer may use a GIS to assist in determining
the location of a new large retailing outlet by using the following data sets:
(a) Percentage of home ownership versus rental properties in a designated area
(data source: Bureau of Statistics);
(b) Traffic congestion patterns (data source: Government Transport
Authority);
(c) Road widths (data source: Local Government);
(d) Availability and location of suitable land parcels for development (data
source: Local and State Governments);
(e) Average financial income range and disposable income of house holds
(data source: Bureau of Statistics);
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(f) Age range of population (data source: Bureau of Statistics).
The suitability of a site or service requirements for the retail outlet could be
determined by applying the following principles:
(a) Home owners are more likely to do renovations than tenants;
(b) Low traffic congestion on weekends assists in traveling to and from retail
sites, particularly with bulky goods with short notice requirements;
(c) A large site allows for ample parking and loading areas for bulky goods;
(d) An area with a population suffering from financial stress (due for example
to high mortgage repayments) will have less income available for
renovations or improvements.
(e) An older or retired population may indicate the need or potential for a
larger service catering to tradespersons who maybe called to work in the
area.
Similarly the determination of health services in an residential area can be established
or improved by spatially linking population data sets in a GIS and then performing a
range of analysis to determine the requirement; be they aged care, new born clinics or
public transport restrictions which may highlight the requirement for a major medical
facility such as a hospital.
3.1.5 Product Generation
Product generation is the final component and could also be termed the output phase,
which could be in the form of a textual report based on an analysis derived by linking
statistical data such as housing prices of cadastral lots in a suburb, or a hardcopy map
plotting the location of bush fires in relation to houses.
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3.2 Imagery in Data Maintenance
The data used in a GIS must satisfy standards in quantity, quality and timeliness and
therefore it must be maintained or updated whenever changes occur. The updating
process can be a difficult problem both in terms of time, money and availability of
data. Updating or revision of maps has traditionally been achieved by techniques such
as obtaining information as new developments occur, when the number of changes
that have occurred has rendered the existing maps useless, or by invoking a
programme of map revision, such as in the case of Switzerland which updates its
national maps every six years. A significant issue with such methods is that changes
between revisions maybe lost or economic or political circumstances may prevent the
scheduled revision, resulting in the loss of historical data. High resolution satellite
imagery would be beneficial for updating purposes particularly in terms of time and
cost, when compared to aerial photography. High resolution satellite imagery provides
large area coverage cost effectively, which whilst at a lower resolution as will be
shown in this thesis, provides enough detail and accuracy when used in conjunction
with aerial photography and survey control.
3.3 Satellite Imagery versus Aerial Photography (Imagery)
In the collection of imagery data for compilation or revision of a GIS, metric imagery
or photography can play a significant role in spatial analysis and terrain representation
for a GIS. Of issue is whether to use aerial imagery or satellite sourced imagery or
both. Holland and Marshall (2003) provided a comparison of high resolution
Quickbird imagery and aerial imagery as follows:
Advantages of satellite derived imagery over aerial imagery include:
(a) A satellite is operational 365 days a year;
(b) Potentially no extra expense is incurred in attempting more than one image
capture as vendors of satellite imagery will continue to attempt to collect
imagery until parameters are met (for example until the agreed minimum
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percentage of cloud cover is achieved);
(c) The orbit of a satellite enables frequent re-visit times (every four days in
the case of the Quickbird satellite);
(d) Imagery can be post processed reasonably quickly;
(e) Air Traffic Control restrictions do not apply;
(f) Satellite derived imagery typically has a large ground coverage which in
the case of Quickbird is 16.5km by 16.5km. This reduces the need for
block adjustments and the creation of image mosaics;
(g) A satellite can easily access remote or restricted areas, weather permitting;
(h) No aircraft cameras are required to be maintained and financed and
depending on level of imagery collected there are minimum specialised
software and hardware requirements (such as Photogrammetric
Workstations).
Drawbacks of satellite imagery:
(a) Totally cloud free imagery can be difficult to collect and in tropical areas
often impossible;
(b) The typical off nadir viewing angle of up to 25 degrees may not be
acceptable in a dense urban area, or where the DEM is not ideal;
(c) The production processes required for high resolution satellite imagery can
be different to those of traditional photogrammetric data capture. In
particular, extra equipment, different production work practices and more
training may be required;
(d) the reliability of capture and delivery of imagery is an unknown quantity;
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(e) satellite image resolution still cannot meet the resolution of large scale
aerial imagery.
3.3.1 Cost of High Resolution Commercial Satellite Imagery
The cost of high resolution satellite imagery is often subject to change due to varying
offers from the vendors. The table below (Table 3.1) contains prices for imagery from
each of the subject satellites. Where possible a comparison has been obtained for
previous years’ pricing in order to provide an indication of the financial evolution of
the satellite imagery product.
The gaps between years and non continuance of pricing from year to year is an
indication of the changing policies within the vendor companies depending on
whether their pricing is readily available or only available “upon application”. The
varying pricing policy is particularly evident for different locations around the world.
For example, in 2002 the Space Imaging price for Geo 1m imagery varied from $18
USD/sq km in North America, $35 USD/sq km in Asia, $35 USD/sq km in Middle
East, to $44 USD/sq km in Japan.
However the cost of imagery has not varied greatly from 2002 to 2008. What has
changed is the way in which the image vendors have altered the licensing
arrangements in order to make the purchase of high resolution commercial satellite
imagery more attractive to organisations. An example is the introduction of multi-
organisation licensing, whereby a government department which purchases an image
is permitted to share the imagery with a certain number of government departments
within the same jurisdiction. This is different to when this type of imagery was first
available when each government department was required to purchase their own copy
of the imagery at full or a negotiated price. The continued competitiveness of the
pricing, particularly of the archive imagery is further indication of satellite imagery’s
lack of attraction to certain markets, such as a replacement for aerial photography.
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Satellite Imagery Scene Area 2002Cost
2004Cost
2007Cost
2008Cost
Archive 25 sq km $750 AUD ($30 AUD /sq km)
Standard(Archive)
25 sq km $18 USD /sq km
Standard and Priority
64 sq km $18 USD /sq km
Quickbird
Stereo 544 sq km $9792 USD a pair
Worldview Prebooked due to U.S. Defence Commitments
Geo 1m Archive 49 sq km New 100 sq km
$35 USD /sq km
0.8m Colour (4 band)
50 sq km $7.70 USD /sq km
New Capture 0.8m Colour (4 Band)
100 sq km 50 sq km (Multi-Site)
$19.80USD/sq km
New Capture 0.8m Stereo Pan
100 sq km $36 USD /sq km
IKONOS
Multi-Site product
50 sq km (Three sites collected in 12 months)
$1500AUD per site/date
Basic Enhanced Pan
384 sq km $21 USD /sq km
Multispectral 384 sq km $21 USD /sq km
Orbview (Did not start commercial sales until 2004) Stereo (Pan) 384 sq km $52.50
USD/sq km
Standard Scene 12.5km x 12.5km $1500 USD (Nov 2001)
Eros
25 sq km (offer only)
$5 AUD/sq km
2.5m Colour Merge (Archive)
Full Scene 60km x 60km
$10 125USD
2.5m Pan (Archive)
Full Scene 60km x 60km
$6 750USD
2.5m Colour Merge
Full Scene 60km x 60km
$11 475USD
2.5m Pan Full Scene 60km x 60km
$7 425USD
SPOT 5
2.5m Pan Full Scene 60km x 60km
$8 600AUD
Table 3.1 Cost Comparison of Satellite Imagery (Source: Vendor websites 2002 to 2008)
3.3.2 Application of Imagery
Despite earlier unsuccessful attempts by the vendors of high resolution commercial
satellite imagery to prove how applicable or comparable their product was to aerial
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sourced imagery, the greatest determining factor remains the application or outcome
required (Fraser N: 2008).
If there is a requirement to provide an image in order to non quantitatively report or to
assess the extent of an incident such as a natural disaster, satellite imagery provides a
versatile source through its ability to collect a large area of any point of the Earth
weather permitting. This is opposed to obtaining more quantitative results which is
possible with aerial metric photography or imagery, but this source is limited by its
area coverage and physical access to sites for acquiring the imagery and support data,
such as ground control points if required (Fraser N: 2008). An example of non
quantitative reporting from high resolution commercial satellite imagery can be seen
in Figure 3.3 below.
Khao Lak, Thailand, 13 Jan 2003 Figure 3.3a
Khao Lak, Thailand, 29 Dec 2004 Figure 3.3a
Source: Images acquired and processed by CRISP, National University of Singapore, IKONOS Imagery © CRISP 2004
The IKONOS images shown in Figure 3.3 were taken twelve months prior and four
days after the 2004 Boxing Day Tsumani Disaster in South East Asia. From these
images it is immediately possible to obtain an initial assessment of the damage
inflicted for preliminary planning purposes in an area prior to dispatching rescue
parties.
69
A more detailed quantitative example is acquisition of imagery over an education
facility, of the size of a typical university campus. It would take an estimated $3 000
AUD to have the aircraft and imaging equipment on site and a further $5 000 AUD to
fly over the site to obtain the imagery. This would provide an up-to-date image, but if
there have been no significant changes over a site, an option could be to obtain
archived mono Quickbird imagery, which can be obtained for $750 AUD covering a
minimum area of 25 square kilometers. Even if the requirement called for more recent
satellite imagery, it could be tasked for approximately $3 500 AUD, which is still
below the amount required for aerial photography. If the requirement were to provide
a DEM and three dimensional extraction and location of features over the area, this
would not be possible with mono satellite imagery and uneconomical with stereo
satellite imagery at approximately $18 000 AUD a stereo pair (Fraser N: 2008). From
this example it can seen that whilst high resolution commercial satellite imagery can
be competitive in price in certain circumstances, the competitiveness can lead to a
lack in versatility of the imagery.
The competitiveness of the market is demonstrated by how some suppliers of aerial
imagery have marketed their products, as in the case of the AUSIMAGE product. The
pricing in Table 3.2 for smaller imagery tiles at greater resolution (urban at 75 to
150mm and rural at 200 to 300mm) and spatial accuracy (urban at +/- 150mm to
200m and rural at +/- 500m) provides a significant viable alternative to high
resolution satellite imagery, particularly archived imagery.
3.3.3 Processing Tools Required
A variety of software capable of handling high resolution commercial satellite
imagery exists. The cost, both in financial terms and for training in the use of the
software is dependent on the desired outcome of the manipulation of the imagery. The
greatest cost is at the more detailed photogrammetric application and processing level
of the imagery, with the least being at the aesthetic enhancement level. The range of
software can be categorised into three levels:
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Table 3.2 – AUSIMAGE 2007 pricing(Source: SKM website 2008)
(a) Aesthetic Enhancement, where the purpose is simply to manipulate the
imagery in order to make it aesthetically pleasing (create a picture only), but
not cartographically manipulate or correct the imagery; any software accepting
the format of the imagery could be used, such as Corel Draw.
(b) Spatial or Spectral Analysis: this includes the derivation of dimensions of
features represented in an image and the spectral manipulation of the image in
order to derive analysis and extract relevant information. For example it can
include the spectral classification of an image to determine the nature of
vegetation cover in a subject area, and requires a more detailed software
package capable of spectral classification and specific image manipulation.
Software such as ERDAS Imagine, ENVI or IDIRISI all have this functionality.
(c) Photogrammetric Processing: this software is capable of providing the full
range of photogrammetrically derived products such as orthoimages, DEMs
71
and feature extraction to required industry and government standards. A
package such as BAE Socet Set is capable of performing these functions
The functionality of the software has a direct relationship to its cost. Software capable
of the functionality described in (a) and (b) above can cost between $1000 AUD to
$10 000 AUD but software capable of (c) such as Socet Set can cost in excess of $55
000 AUD together with similar hardware costs, depending on the functionality and
processing speed required.
Once the data (imagery) and tools (software and hardware) have been acquired the
question of training and skills sets need to be addressed. Most vendors of software
will provide, at a cost, training programs of various lengths to develop initial
proficiency in a software package. Depending on the desired outcome of the training,
its length may vary from a few days for a basic image manipulation package, through
to a number of weeks for a more detailed package such as Socet Set. A further
consideration is the one of skills sets of the operators. They can be partly developed
from theory lessons but they vary according to types of imagery. Aerial photography
is more difficult to use than satellite imagery due to the extensive use of ground
control, orientation and triangulation procedures required (Fraser N: 2008). This
means that in situations where skills sets maybe limited, such as those of local staff in
aid work conducted in a developing country, the simplicity of use of high resolution
commercial satellite imagery makes it more suitable than aerial photography in such a
situation. In such circumstances the final product may not adhere to strict cartographic
standards but it maybe “fit for purpose”.
3.4 Imagery Applications and Considerations
Imagery from any source be it aerial or satellite has a direct application within a GIS
as described previously, but each application or service specific GIS has unique
requirements of the imagery to meet the aims and outputs of the GIS. The following
section presents the requirements of imagery from a range of application specific GIS,
and then provides a comparison between the use of aerial photography and high
resolution commercial satellite imagery in each application.
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The areas to be discussed in this section further are:
(a) Local Council Requirements;
(b) Emergency Services;
(c) Public Information (Street Directories, Mapping, General Spatial Data,
Analytical Applications;
(d) Land Use Identification.
3.4.1 Local Council Requirements
Potentially local governments in Australia are one of the significant users of satellite
imagery. Most local governments have a GIS in which to store and manipulate their
spatial data, where the level of detail is dependent on each local government’s
priorities, revenue, requirements and access to skilled staff. Typical local government
spatial and spatially linked data includes land rates, land zoning, drainage, cadastre, as
constructed/as built, water reticulation, sewerage with an orthoimage background
derived from aerial photography or satellite imagery.
Imagery has been found to be of distinct benefit to local governments to assist their
staff in a variety of tasks, such as:
(a) Town planning;
(b) Engineering works;
(c) Health services;
(d) Emergency services.
In the areas of town planning and engineering, image data is used for initial
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overviews, including the proximity of residential dwellings to proposed developments
eg. entertainment venues such as hotels, or for displaying existing services and
infrastructure for upgrading engineering works, such as road widenings. For health
and emergency services, imagery together with other information such as statistical
and residential data, provide information to assist the decision maker in determining
needs of potential services. For example, an environmental officer may record refuse
overflow in certain areas and then use the imagery as a back drop to the statistical and
land use data to establish “choke points”. This can then be used to allocate more
refuse collection services and possibly different equipment to ensure efficient and
economic collection for the benefit of community health.
A major task for local governments is asset management for which satellite imagery
can provide assistance. However in the case of satellite imagery, despite the
reassurance of the vendors, the resolution of 0.6m to 1.0m is not adequate to record
features such as access holes and power lines as displayed in Figure 3.4. Experience
has shown that most local governments require image resolutions of better than 0.15m
or in mapping scale terms, 1:5 000.
Whilst aerial photography has the potential of providing greater detail this is
dependent on flying height, with the greater level of detail, being obtainable from a
lower flying height. This can be displayed in the case of a 152mm focal length aerial
camera, theoretically a flying height of 152m would produce a photo scale of 1:1 000
covering 5.3ha (53 000sq m) whereas a flying height of 760m will produce a photo
scale of 1:5 000 covering 130ha (1 300 000sq m) (Wolf: 2000). The result being that
whilst it is possible to obtain greater detail from aerial photography, image acquisition
costs increase as more images are required to cover the same area with greater detail.
Whilst the preference of some users is to source the highest resolution imagery
possible (typically by aerial photography), the cost of such collection can be
prohibited on a annual basis. For this reason some local governments opt for high
resolution aerial photography data collection for one year followed by a lower
resolution collection every three to five years, and then higher resolution data
collection (more expensive) five to seven years after the original collect. (Author,
2005: Appendix A). This has the advantage of limiting costs but also ensuring
74
suitable time intervals between acquisitions to be used for analysis for change
detection, as opposed to asset management. For example, in Figure 3.6 Mackay City
Council, chose aerial photography over satellite imagery. This was due to the greater
resolution afforded by aerial photography and the ability to update the authority’s
DEM. In addition, the effects of cloud in a tropical environment played a part as it
was possible for an aircraft to fly below the clouds to obtain the required imagery,
whereas a satellite which requires a cloud free area (MiMAPS, Mackay City Council:
2007).
Accesshole IKONOS 11 bit Imagery
Power lines IKONOS 11 bit Imagery
Figure 3.4 – Inability to identify assets in high resolution satellite imagery
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1:7000 scale Aerial Photography
Figure 3.5 – Level of detail from aerial photography
B
C
A
Figure 3.6a – City of Mackay May 2004 Urban Aerial Photography
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A-1
B-1
C-1
Figure 3.6b – City of Mackay May 2006 Urban Aerial Photography
The advantage of regular imagery collects, whether aerial or satellite, for monitoring
change within the urban environment can also be seen in Figure 3.6. Both images
were taken in May of each year and late in the week or on the weekend. Three areas
of change have been indicated in the two year period which assists in urban planning
and monitoring. The three areas of change can be interpreted as follows:
(a) A and A-1: increase in the patronage of the facility by the development of
car parking and extension of buildings.
(b) B and B-1: construction of a structure in a back yard of a dwelling or the
structure is revealed due to vegetation clearing.
(c) C and C-1: construction of a swimming pool in a backyard.
Both set of images were taken with similar parameters, such as a pixel size of 0.11m
and a positional accuracy of 0.3m and 0.4m.
Local governments undertake engineering works in the form of sewerage
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construction, drainage and water reticulation. Sourcing DEMs that satisfy the design
and construction of this infrastructure is of significant importance to local
governments. It is not unusual for local governments to forgo the expense of creating
their own DEMs and instead, use topographic data from mapping sources at various
scales such as 1:25 000 topographic maps for initial planning only and then use
existing higher accuracy DEMs derived from traditional detail field survey techniques
for engineering design work. This approach has the additional advantage of reducing
costs for data acquisition as the field survey performed to meet the engineering design
requirements can have a secondary use as a DEM data source in a GIS for other
applications (Boler, 2002 to 2003).
Some state governments have dealt with the issue of cost effectiveness of satellite
imagery by taking the initiative to recommend consolidated acquisition. An Example
of consolidated acquisition were revealed by the Study of Spatial Imagery Use &
Management in Queensland Government in August 2004 (Queensland Government,
Geoimage Pty Ltd, Spatial 3i Pty Ltd: 2004). The benefits of joint projects can be
displayed by the following stereo pair over the Sydney metropolitan area.
Figure 3.7 – Quickbird Stereo Pair over Sydney
Such a stereo pair covers a large area and with licensing arrangements catering for
multi users, this type of joint project becomes a realistic option to a consortium of
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organisations such as local governments. A stereo pair collect can be used to derive
the following information:
(a) Road layout;
(b) DEM;
(c) Orthophoto generation;
(d) Feature extraction.
3.4.2 Emergency Services
Emergency Services such as fire, ambulance, natural disaster response and defence
are significant consumers of high resolution commercial satellite imagery. The use of
any remote sensing data for emergency services (in regards to imagery collects,
dissemination and exploitation) can pose significant problems, resulting in restricting
the application of high resolution commercial satellite imagery. Previous experience
has shown that in order to bridge the gap between the emergency management
community and the remote sensing community, there is a need for:
(a) The specification of the type of remotely sensed information required by
emergency managers;
(b) Data brokers to provide emergency managers with information from
remotely sensed data;
(c) A national process to provide coordination and direction.
Of particular importance is the need to establish procedures for the acquisition of
imagery prior to the emergency to enable timely exploitation.
At an international level as a result of United Nations General Assembly resolution
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61/110 of 14 December 2006 the United Nations under its Office for Outer Space
Affairs (UNOOSA) established the programme, United Nations Platform for Space-
based Information for Disaster Management and Emergency Response (UN-
SPIDER). The purpose is to provide access to all countries and relevant international
and regional organisations to all types of space-based information and services
relevant to disaster management, including support and training to developing
countries. Further it also allows entities within the United Nations and international
bodies that work on space-related issues and disaster management to benefit from
increased coherence and synergy in using space science and technology to assist in
humanitarian developments (UNOOSA website, 2008)
There have been a number of examples in professional literature and general media to
highlight the usage of high resolution commercial satellite imagery for emergency
services. The most significant use prior to and during any emergency is the
visualisation aspect which allows situation awareness and planning to occur. Prior
inspection can reveal to Police offence locations, while during and after an event for
Fire Services the imagery can be used for example in Computer Aided Dispatch
(CAD), fire investigation, planning and incident management roles.
A particular problem for emergency services is the skill sets of the available staff,
particularly in the case of volunteer organisations such as Rural Fire Brigades and
State Emergency Services. Even more established emergency services such as the
Police often have only one qualified spatial professional, which affects the level of
exploitation available and hence restricts the use of high resolution commercial
satellite imagery as backdrop imagery. These volunteer organisations also face the
issue of acquiring suitable and current imagery and are often reliant on state
governments to provide funding or access.
Though emergency services clearly have difficulties accessing high resolution
commercial satellite imaging, an example of where a lower resolution imaging
satellite has significantly catered for emergency services is the Sentinel web service
provided by the Australian Federal Government. Imagery from the MODIS satellite
(ground resolution ranging from 250m to 1km) is downloaded and processed to
determine the location of probable bush fires within Australia. Output from the
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service provided by the Sentinel website is shown in Figure 3.8 below.
Figure 3.8 – Example of Sentinel Product
It is more likely that the satellite imagery will be used by emergency services in
conjunction with other sources of data such as aerial imagery. This was displayed in
the September 11 2001 terrorist attacks on the World Trade Centre in New York as
reported by Williamson and Baker (2002). Overhead imagery was used for the
purposes of:
(a) Orientating emergency workers since the environment was constantly
changing and devoid of landmarks;
(b) Locating smoldering fires that could flare up, posing a safety hazard to
emergency workers;
(c) Providing emergency planners an accurate method for estimating the
changing volume of the rubble piles as they were screened and removed;
(d) Assisting planners to create transportation routes for moving around the
site.
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Whilst aerial photography was used to provide the more detailed assessment, use of
high resolution satellite imagery was significant in the first days after the terrorist
attacks as civil and commercial aircraft were restricted from flying over the site.
3.4.3 Public Information (Street Directories, Mapping, General Spatial Data,
Analytical Applications)
Satellite imagery can be useful when acquiring data for digital mapping. An
othophotograph or orthorectified satellite image is commonly used in a GIS as a base
map over which vectors are laid. If the vector information is accurately positioned, all
the street segments, parcel boundaries and other vectors will coincide with their
locations in the image.
As described earlier in Chapter 2 it is not only the geometric quality that is important
in an image but also the information content. Jacobsen (2002) provided a comparison
between Swiss topographic maps of 1:25 000, Swiss orthoimages resampled to 1m
resolution from orthophotos of 0.3m pixel size, and IKONOS panchromatic images.
The results of this comparison indicated that on first view the information content is
similar, but further analysis revealed some misidentifications and unrecognised
buildings in a map based on the IKONOS image.
It can be seen from Table 3.3 that many of the feature types required for 1:10 000 to
1:50 000 scale mapping can be satisfactorily identified and captured with high
resolution commercial satellite imagery. Some features required for larger scale
mapping, such as roads and woodland boundaries at 1:2 500 can also be captured.
Exceptions to this are narrow linear features, such as electricity transmission lines,
walls, fences and hedges, which can be considered impossible to determine with
imagery of this resolution. Further to this, a combination of panchromatic and
multispectral imagery can help to differentiate between hedges and walls but in
general the imagery is unsuitable for the capture of these features. Holland and
Marshall (2003) concluded that results of the feature capture and geometric accuracy
indicated that Quickbird imagery showed the potential as a data source for 1:10 000
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83
scale mapping but could be used to derive topographic data up to scales as large as 1:6
000. The significant drawback is this type of imagery’s inability to resolve small
linear features, which if required for a product or data set would have to be derived
from another data source.
Mapping agencies are also required to identify changes to the landscape and add these
changes to the topographic database as soon as possible after they occur. These can be
done in several ways such as:
(a) Observations by field surveyors;
(b) Provision of planning information by local planning authorities or
commercial change detection agencies; and
(c) The supply of new development plans by architects and house building
consortia.
Use of satellite imagery could complement these sources of data by allowing
surveyors to find areas of change which could not be detected using other methods.
Holland and Marshall (2003) use the example of central London, where satellite
imagery potentially could provide regular images of an area, enabling field surveyors
to constantly monitor and capture topographic and cultural detail. By comparison in
rural areas, change intelligence requirements are different due to remoteness. In this
environment buildings may be constructed without planning permission, fields
subdivided or merged and vegetation removed or planted. In these areas imagery can
be a valuable tool for change intelligence, especially if this data is also used as a
source for the subsequent capture of the topographic change.
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Scale
Features Notes
1:1
250
1:2
500
1:10
000
1:25
000
1:50
000
Housing and associatedfeatures
All housing easy to identify. Capture of housing satisfactory if of uniform shape. Complex shapes with juts and recesses not possible to accurately depict.
N N M Y Y
Other buildings All uniform large buildings easy to detect, particularly industrial units. Complex, multilevel and roof structures difficult to clearly identify.
N N Y Y Y
Communication networks - roads
Kerbs and traffic calming features not clearly defined. Most white lines possible to identify. General road alignments very clear.
N Y Y Y Y
Dualcarriageways
Barriers not clear. General alignment very clear. Slopes (and any other height data) impossible to identify in a monoscopic image.
Y Y Y Y Y
Airports Edge of metalling clear. Major buildings okay. Small walkways and fine detail around buildings difficult to define.
N N Y Y Y
Railways Railway furniture not visible (signal posts, points etc). Single lines not visible. General alignment okay (centre of track). Major station detail clearly visible.
N N Y Y Y
Electricitytransmission Lines
Impossible to define actual lines. Transmission pylons possible to see in some instances. Single poles very difficult to see. N N N N N
Major sea defences to reduce flooding
All features clear to see. Groynes and promenades very clear. Y Y Y Y Y
Non-coastal sea defences
Weirs and dams stand out clearly. Finer detail not clear to see. Y Y Y Y Y
Major property boundaries
Large fences easy to see. Small fences very difficult to identify. N N N N Y
Major landscape changes
Very clear. Associated fences not so clearly defined. Y Y Y Y Y
Quarries and other surfaceworkings
Clear to see, but quarry permanent detail (e.g. conveyor belts), difficult to fully identify identify. M M Y Y Y
Field boundaries Clear to see, but difficult to classify. M M M M Y
Water features Clear to see. Small streams sometimes difficult. M M Y Y Y
All vegetation Vegetation is clearly defined if using pan-sharpened imagery. Y Y Y Y Y
Tracks and path Tracks clear to see. Unmade paths difficult to make out. Made paths in urban areas can be difficult to define.
M M M M M
Telephone boxes Difficult to define N N N N N
Extensions to commercial buildings
Major shape possible to define. Small juts and recesses not so easy.
M M Y Y Y
Minor property boundaries
Possible to see, but difficult to classify. M M M M Y
Tide lines Edge of water-line easy to see, but doubtful if imagery can be captured to coincide with high tide times.
N N N N N
Garages built after initial development
Clearly defined Y Y Y Y Y
Key:Y = Yes – feature can be captured N = No – feature cannot be successfully captured M = Maybe – in some circumstances Note: For 1:1 250 and 1:2 500 scales, even if features can be clearly identified, the geometric accuracy is not sufficient to meet the mapping specification
Table 3.3 – Features which can be captured from Quickbird imagery at national (UK) scales (Holland and Marshall, 2003)
3.4.4 Land Use Identification
The multispectral applications of satellite imagery must also be assessed when
looking at the practical use of satellite imagery. Whilst much has been written on the
potential of using the multispectral component of the high resolution commercial
satellite imagery, little has indicated the practicalities. The availability of colour
information in this instance would assist feature definition in that it would be easier to
locate objects featuring a contrasting colour such as roofs, trees, green areas and water
surfaces. An option in such an instance would be to use pan-sharpened images, which
are created by merging the colour information in a multispectral image with the
higher spatial resolution of a panchromatic image (Gianinetto et al, 2005).
A particular point to note is that the complexity of a scene directly affects the
selection and success of an automatic procedure for detecting building features (Chen
et al, 2001). This means that whilst the information content may exist in a high
resolution commercial satellite multispectral image, the complexity of the scene may
hinder its use for analysis or data extraction. This can be due to the variety of spectral
responses available in close proximity in an area, such as from roof tops, roads and
garden vegetation in the Central Business District of a city. This is opposite to the
situation with an urban scene where the transition from different ground cover is less
abrupt, as can be seen in Figure 3.9 below.
Trinder (2008) provides a warning in a review of research into the viability of spectral
classification using high resolution commercial satellite imagery. Of particular note is
the determination that the spectral resolution of high resolution commercial satellite
imagery is inadequate for extracting ground cover information based on typical pixel-
based classification techniques. In Table 3.4 Hirose et al (2004) provides a summary
of the potential of extracting thematic information from IKONOS images.
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Urban
Central Business District
Figure 3.9 – Complexity of Scenes due to Ground Cover (IKONOS imagery)
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Class Results of Maximum Likelihood Classification of an IKONOS Image
Water plants + Field check necessary Grass & Deciduous trees
+ Have similar appearance
Coniferous trees & bamboo
* Difficult to distinguish them
Bare soil + Relatively easy to identify Orchards & vegetable fields
* Visual interpretation must be used in conjunction with maximum likelihood classification
Rice Fields - Difficult to distinguish from agricultural fields if imagery is not when fields are submerged
Residential areas and manmade structures
* Distinction from residential area and manmade structures is problematic
Open Water + Relatively easy to distinguish Key: + : Acceptable, *: Moderate, -: Unacceptable
Table 3.4 – Summary of the classification results for an IKONOS image using maximum likelihood classification (Hirose et al:2004, Trinder: 2008)
3.5 Summary
A GIS provides an efficient and effective means in which to link spatial and textual
data to increase the efficiencies and usability of the spatial datasets. The utility of
storing and manipulating data in such a system means that more informed decisions
can be made to help organisations or individuals by the variety of ways by which the
data can be presented and sourced.
Imagery is a significant dataset available for the analysis or service provided by a
GIS. Both aerial and satellite imagery have roles, which in some cases compete, but
more often they complement each other as a result of their individual advantages and
disadvantages. As discussed, when only a representation of an area is required, high
resolution satellite imagery provides a good and economical alternative to aerial
photography both in terms of financial constraints and skills sets. But if more
quantitative information is required such as DEMs, three dimensional extraction and
location of features at larger scales, aerial photography provides a better solution. In
this case high resolution commercial satellite imagery cannot compete in terms of
resolution and accuracy but in some cases this depends on the area coverage and cost.
87
88
Further each of the applications of GIS discussed in this chapter has a different
requirement in respect of the types of datasets. It is this area where the utility of
imagery, in particular from satellites capable of high resolution imagery is examined
in this thesis. It is not enough to simply supply imagery once. It must also be capable
of being supplied repeatedly at various time intervals, be they days, months or years
depending on the application, with the same or better quality, as well as being suitable
for the purposes of the GIS.
CHAPTER 4
THE SUITABILITY OF HIGH RESOLUTION COMMERCIAL SATELLITE
IMAGERY AS A SPATIAL DATA SOURCE
4.1 Introduction
As seen from previous chapters high resolution commercial satellite imagery is a unique
source of spatial data. It is not only imagery providing a graphic representation of an area
but also can be used for feature extraction for mapping, digital elevation model creation
and change monitoring over a period of time. A major aim of this study is an evaluation
of the potentials and limitations of high resolution commercial satellite imagery. This
chapter is an investigation of the accuracy and the suitability of the imagery to fulfil the
requirements of representing an urban environment in a spatial data context. The topics
investigated are part of a typical spatial data creation process in a mapping organisation
and are listed below with the aims of the investigation for each component:
(a) Ground control points determination: Fundamental to the creation of any spatial data
is the establishment and use of control points which define the horizontal and vertical
ground positions of monuments or discernable features on an image. A comparison is
conducted in this chapter to assess the quality of coordinates of control points derived
photogrammetrically from a high resolution commercial satellite image stereo pair when
compared to values determined from a ground survey conducted by Real Time Kinematic
(RTK) Global Positioning System (GPS).
(b) Feature interpretation and extraction: The aim of this component is to determine the
potential of high resolution commercial satellite imagery to portray to a user both cultural
and topographical features in an area of interest. This work not only assesses the ability
of observers to distinguish certain features of interest but also the level of detail that can
be extracted.
89
(c) Digital Elevation Model (DEM) creation: The final area is the creation of Digital
Elevation Models or DEMs. High resolution commercial satellite stereo imagery enables
the creation of a DEM by the process of image matching using photogrammetric software
such as Socet Set as described in Chapter 3. This investigation gives a comparison
between existing data sets and those derived from the IKONOS stereo model over the
two study areas.
Whilst there is a multispectral component of high resolution commercial satellite imagery
as described in Chapter 3, given the complexity of the use of multispectral imagery as
detailed by Hirose et al (2004) and summarised by Trinder (2008) an evaluation of this
component of the imagery is outside the scope of this study.
4.1.1 Imagery and Study Areas
For the conduct of this work IKONOS images consisting of a stereo triplet, dated 22
February 2003, of the Greater Hobart area was obtained from Space Imaging (now
Geoeye Inc.) through the University of Melbourne. Reference data for comparison was
obtained from the Tasmanian government and two Hobart local councils.
The geometric collection parameters of the IKONOS imagery are shown below.
Source Image ID (Date 22 February 2003)
Start Time (GMT)
Sensor Azimuth
Sensor Elevation
Scan Azimuth
2003022200270380000011614288 00:27:03.8 329.4° 69.1° 180°2003022200272480000011614290 00:27:24.8 293.7° 75.1° 0°2003022200275430000011614289 00:27:54.3 235.7° 69.2° 180°
Table 4.1 – IKONOS Imagery Geometric Collection Parameters (Source: IKONOS Test Imagery Cover Letter: 2004)
90
The IKONOS imagery details are: Bits Dynamic
Range Adjustment (DRA)
Bands Format Product Identification (POIDs)
Remarks
8 Yes 1m RGB GeoTIFF 150442 150443 151127
Pan-sharpened, natural colour
11 No 1m Pan and 4-bands of 4m Multispectral (MSI)
GeoTIFF 149875 149879 149883
Absolute MSI radiometry with DRA off
11 No 1m Pan and 4-bands of 4m Multispectral (MSI)
NITF 2.0 149877 149881 149885
ClearView product format
11 No 1m Pan Stereo GeoTIFF 153452 UTM map-projected stereo. Relative orientation performed
Table 4.2 – IKONOS Imagery Set Details (Source: IKONOS Test Imagery Cover Letter: 2004)
Two different study areas were selected to provide a range of ground cover types, both
built and natural, and terrain, as follows:
(a) Urban area at Binya St, Glenorchy, Hobart (Figure 4.2);
(b) Central Business District (CBD) of Hobart (bounded by Bathurst St, Argle St,
Davey St and Harrington St) (Figure 4.3).
All photogrammetric work such as control point measurement, feature extraction and
digital elevation model creation were performed on the University of New South Wales
Socet Set Photogrammetric Workstation. The selection of the software was based on
commonly used or readily available software in regards to cost and training.
The stereo model was created without using ground control points or performing an
absolute orientation. The purpose of this methodology was to assess the inherit positional
and geometric accuracy of the imagery using the direct orientation from the Rational
Polynomial Coefficients or RPC of the images provided by Space Imaging. RPC, also
known as Rapid Positioning Capability or Rational Polynomial Model, are an
approximation to the rigorous sensor model and used for mapping the imaging system’s
pixel coordinates to ground points. The RPC model uses a pair of polynomial ratios to
91
approximate the geometric relationship between the camera and the surface of the Earth
(Hinson: 2007). In the case of IKONOS it is necessary to use this method since the
imaging systems sensor model is not supplied.
To further assess how well IKONOS imagery represents an urban environment, both
natural and built, in a spatial data context by comparison with reference data, a ground
truthing validation exercise was undertaken in Hobart on the 14 and 15 May 2007. The
field exercise was done in two phases:
(a) 14 May 2007 - survey of the urban study area at Binya St Glenorchy;
(b) 15 May 2007 - survey of the central business district study area of Hobart
(bounded by Bathurst St, Argle St, Davey St and
Harrington St).
The scope of the ground truthing covered:
(a) comparison of built environment features extracted;
(b) extent of utilities that can be determined or monitored using the imagery, such as
water, sewerage and power lines;
(c) the effect of resolution of the imagery on the representation of ground features.
92
Mt Wellington
CBD Area
Urban Area
N
Figure 4.1 – Extent of IKONOS Study Imagery over Hobart (Source: IKONOS POID: 153452)
93
Binya St
N
Figure 4.2a - Urban area at Binya St, Glenorchy, Hobart (Source: IKONOS POID: 150442)
Figure 4.2b – Anaglyph of Urban area at Binya St, Glenorchy, Hobart
(Source: Derived from IKONOS Stereo Pair POID: 153452)
94
N
Davey St
Argle St Bathurst St
Harrington St
Figure 4.3a - CBD Central business district (CBD) of Hobart (Source: IKONOS POID: 150442)
Figure 4.3b - Anaglyph of CBD Central business district (CBD) of Hobart
(Source: IKONOS POID: 150442)
95
4.2 Ground Control Points Comparison
4.2.1 Methodology
As discussed in 4.1 the purpose of this section is to assess between and as a result the
quality of coordinates extracted from IKONOS stereo imagery. As mentioned previously
in Chapter 2 the IKONOS satellite is equipped with GPS antennas and three digital star
trackers to establish precise camera positions and attitudes.
To support the IKONOS imagery data set, a control point data file consisting of 114
points created by the University of Melbourne was supplied. The control points ground
coordinates were determined by RTK GPS survey with a ground accuracy of
approximately 10 to 20cm in the X (Easting), Y (Northing) & Z (Height) direction. The
ground control points were spread over an area of approximately 12km by 13km with a
height variation of -2.49m to 1260.6m with respect to the WGS84 ellipsoid. The RTK
GPS control points were used as the reference data in the comparison of the X, Y & Z
coordinates of the points extracted from the IKONOS stereo model created in Socet Set.
4.2.2 Comparison of GPS RTK Coordinate Values to IKONOS Stereo Model Values
Once coordinate values were extracted from the IKONOS stereo model, four differences
between the data sets for each control point coordinate value were determined:
(a) X values;
(b) Y values;
(c) Z values;
96
(d) The vector distance derived for the X and Y differences for each point X, Y
coordinates from each data set.
The detailed results are contained in Appendix B with a summary of the results displayed
Table 4.3 below
�X (m) � Y (m) � Z (m)
X, Y Vector
Distance
�(�X² + �Y²)
(m)
Mean 1.9 -0.9 2.3 2.3
Minimum -0.4 -2.1 -1.9 0.8
Maximum 3.1 0.6 4.9 3.3
Table 4.3 – Mean, Minimum and Maximum Component Differences and Vector Distance
To provide a statistical evaluation, the Root Mean Square Error or RMSE of each
coordinate difference and the vector distance between the data sets were determined, as
summarised in Table 4.4.
Statistical
Level of
Confidence
�X (m) �Y (m) �Z (m)
X, Y Vector
Distance
�(�X² + �Y²)
(m)
68% 0.6 0.6 0.9 0.690% 0.9 1.0 1.6 0.995% 1.2 1.2 1.9 1.199% 1.6 1.6 2.5 1.4
Table 4.4 – Root Mean Square Error of Component and Vector Distance
According to Kay et al (2003) the RMSE approximately correspond to the residuals of
the observations within ± 1 standard deviation or 1-sigma of the measurements ie
approximately 68% of the residuals observed in an image would be expected equal to or
97
less than the RMSE, with remainder being larger. Further the 90%, 95%, or 99% levels
can be determined by multiplying the RMSE value by 1.65, 1.96 or 2.58 respectively.
The values are described as the statistical levels of confidence in Table 4.4.
The RMSE of the difference between reference and observed control points coordinate
values indicate that point coordinates derived from IKONOS stereo imagery would be
suitable for planimetric mapping between 1:1 000 to 1:5 000 scale. This scale range
requires a positional accuracy between ±0.3m to ±1.5m. It is doubtful that heights derived
from IKONOS stereo imagery would be suitable for this scale range as it is considered
that no point should be more in error that half the contour interval, the contour interval
for 1:1000 is 1m and 1:5000 is 5m.
Toutin, Chenier and Carbonneau (2002) make the point that IKONOS point accuracy
deteriorates in mountainous areas if the images are acquired with off-nadir viewing, and
so the product for these conditions will only meet requirements for mapping scales at
1:100 000. To determine if there was a relationship between point accuracy and location,
ie in terms of the Easting (X), Northing (Y) or Height (Z) values across the stereo model,
the X, Y vector distance (�(�X² + �Y²)) and the Height difference (�Z) between the data
sets were plotted against increasing Easting and Northing and ellipsoidal Heights (Figure
4.5). The correlation coefficient in order to determine interdependence was then
calculated for each data set represented in the graphs in Figure 4.5 by using the following
formula (Koffman et al: 1990):
Correlation Coefficient of (x, y) = � � � �� � � ���
����
��22 yyxx
yyxx (4.1)
Where x and y are the data sets with and being the mean values. A correlation
coefficient of close to or equal to -1.0 or 1.0 would indicate interdependence.
yx
98
It can be concluded from these figures that in this case there is no relationship between
differences between the control point coordinate values of the data sets and the Easting,
Northing and Height value of the coordinates.
Distance between Measured to Control against increasing Height
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
-2.49
23
-0.02
31
0.162
4
3.198
3
3.213
7
4.940
6
6.226
7
9.074
1
13.52
33
19.27
81
23.20
47
28.93
48
37.79
47
41.62
49
50.00
69
56.53
04
82.48
03
84.83
01
94.46
49
102.8
670
123.4
042
155.0
340
190.2
642
251.8
862
273.4
112
356.3
429
423.2
605
1256
.7574
1259
.0221
Height
Dis
tanc
e be
twee
n C
ontr
ol to
Mea
sure
d
Figure 4.5a – Vector Distance against Ellipsoidal Height
Correlation Coefficient: -0.07
99
Distance between Measured to Control against increasing Easting
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
5165
31.58
17
5189
44.50
98
5193
21.57
88
5198
85.35
91
5204
08.73
92
5212
30.94
50
5216
97.98
50
5221
50.84
33
5227
12.12
34
5232
03.75
41
5238
00.15
00
5239
43.25
95
5240
88.45
28
5244
35.52
64
5246
71.24
21
5249
27.08
03
5250
78.76
52
5253
54.73
92
5255
98.70
20
5256
82.14
62
5257
50.51
45
5258
25.34
78
5259
74.24
41
5261
19.51
20
5262
89.61
07
5263
37.61
32
5272
33.67
84
5283
11.91
61
5285
46.87
30
Easting
Dis
tanc
e be
twee
n C
ontr
ol to
Mea
sure
d
Figure 4.5b – Vector Distance against Easting
Correlation Coefficient: -0.119 Distance between Measured to Control against increasing Northing
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
5247
756.5
860
5248
332.7
390
5248
675.1
040
5249
055.3
150
5250
245.5
240
5250
498.3
860
5250
745.0
000
5250
839.3
120
5252
874.0
140
5253
671.0
910
5253
833.0
170
5253
954.2
500
5254
578.8
350
5254
867.2
200
5255
335.4
070
5255
380.4
310
5255
425.6
770
5255
444.2
290
5255
510.8
930
5255
977.1
920
5256
484.9
900
5256
839.1
840
5257
092.0
440
5257
380.9
040
5257
545.2
780
5258
150.3
800
5259
239.8
820
5260
177.4
520
5260
570.4
090
Northing
Dis
tanc
e be
twee
n C
ontr
ol to
Mea
sure
d
Figure 4.5c – Vector Distance against Northing
Correlation Coefficient: 0.025
100
Delta Z (Measured to Control) against increasing Height
-3
-2
-1
0
1
2
3
4
5
6
Z
-0.05
02
-0.00
35
1.362
9
3.211
7
4.938
3
4.984
8
8.950
4
13.12
07
17.38
37
21.35
12
27.50
97
36.59
35
41.50
25
47.11
07
52.99
04
78.38
15
84.78
57
93.85
94
96.81
64
114.2
219
137.7
189
183.7
533
232.1
978
256.6
078
351.6
752
423.0
093
689.8
207
1258
.2201
Height
Del
ta Z
bet
wee
n C
ontr
ol to
Mea
sure
d
Figure 4.5d – �Z (m) against Ellipsoidal Height
Correlation Coefficient: -0.184 Delta Z (Measured to Control) against increasing Easting
-3
-2
-1
0
1
2
3
4
5
6
X
5173
16.41
91
5193
20.58
68
5196
53.56
15
5203
70.45
84
5212
04.70
98
5215
61.92
81
5220
43.81
88
5226
28.17
84
5230
37.62
83
5236
68.88
82
5239
12.65
26
5240
76.99
39
5244
15.20
52
5245
76.46
23
5247
21.54
68
5249
85.13
46
5253
40.97
23
5255
72.09
6
5256
46.04
15
5257
36.09
04
5257
86.03
95
5259
01.43
16
5259
94.99
22
5262
40.76
49
5263
35.44
58
5266
44.58
85
5281
88.55
73
5284
61.59
54
Easting
Del
ta Z
bet
wee
n C
ontr
ol to
Mea
sure
d
Figure 4.5e – �Z (m) against Easting
Correlation Coefficient: 0.158
101
Delta Z (Measured to Control) against increasing Northing
-3
-2
-1
0
1
2
3
4
5
6
Y
5248
317.3
22
5248
555.7
44
5249
000.3
22
5249
563.0
5
5250
400.8
99
5250
696.9
77
5250
826.5
13
5252
698.2
75
5253
464.4
5
5253
786.0
66
5253
951.1
45
5254
509.7
65
5254
838.3
61
5255
299.2
95
5255
379.2
97
5255
394.0
81
5255
440.8
31
5255
453.5
44
5255
801.7
81
5256
336.9
99
5256
622.6
77
5257
023.2
13
5257
347.6
79
5257
453.3
63
5258
096.5
69
5259
239.4
72
5260
012.6
56
5260
565.5
13
Northing
Del
ta Z
bet
wee
n C
ontr
ol to
Mea
sure
d
Figure 4.5f – �Z (m) against Northing
Correlation Coefficient: -0.120
It is significant that when each of the vector distances between the IKONOS stereo model
and the RTK GPS reference data coordinate values are plotted as vectors the majority are
in the west-north-west direction (see Figure 4.6) indicating a directional bias within the
model.
Willneff and Poon (2006) indicated a similar bias with IKONOS imagery over Hobart
and attributed it to a RPC bias. Willneff and Poon (2006) also indicated that the RPC bias
could be corrected if ground control points of the image area are available. In fact
Baltsavias et al (2001) makes the point that by using 4 to 7 accurate and well distributed
ground control points and a simple translation or bias removal, absolute accuracies in X,
Y and Z of 0.4m to 0.5m and 0.6m to 0.8m can be achieved. It is clear that the ability to
resolve this directional bias without ground control points would be of significant
advantage to the application of IKONOS imagery. This would allow positional accuracies
approaching that of an earth based mapping system as detailed in Section 2.4 whilst still
maintaining the advantage of avoiding visiting the site by ground or accessing airspace.
102
This ability though is currently not available for stereo models based on orientations by
RPCs.
Figure 4.6 - Vector distances between IKONOS stereo model control points and
the RTK GPS reference data (Note: In order to visualize the direction vector each error distance has been
multiplied by a factor of 100.) The directional bias may also be due to a coordinate datum error. As both the images and
the RTK GPS control points are derived using the WGS84 datum, there maybe
discrepancies between the application of this datum within the satellites’ buses due to the
differing orbits or orientations. As they are both using the same datum and the IKONOS
satellite derives its position from the GPS satellites and star trackers, the directional bias
could be considered a systematic Observer Distortion as described in Chapter 2.
In summary the RPC of the IKONOS stereo imagery is capable of providing a horizontal
positional accuracy between 0.3m to 1.5m, though in heighting accuracy it cannot match
103
its horizontal component. A directional bias only was confirmed in this work; currently it
is only possible to resolve this bias by the introduction of ground control points to the
stereo model. The introduction of such points would unfortunately remove one advantage
of high resolution commercial satellite imagery that being remote access.
4.3 Feature Interpretation and Extraction
As detailed in Chapter 3 for high resolution commercial satellite imagery to be useful it
must have an application in the collection of spatial information in the form of ground
features. This section examines the factors affecting the suitability for high resolution
commercial satellite imagery to fulfill this role, when compared to other data sources,
such as aerial photography and spatial data sourced from ground survey methods.
4.3.1 Development of a Civil NIIRS Rating for Imagery Comparison
In order to provide an initial comparison across all images, it was considered appropriate
to assess the ability to extract features using the Civil NIIRS (National Imagery
Interpretability Rating Scale). A full description of Civil NIIRS can be found in Beloken
et al (1997). NIIRS was originally developed within the United States of America (USA)
defence community in the 1970s to include categories such as natural, agricultural and
urban/industrial and not simply military equipment as described in the original NIIRS. It
therefore can be utilised as a measure of image interpretability for:
(a) communicating the potential usefulness of imagery.
(b) specifying requirements for imagery.
(c) managing the tasking and collection of imagery.
(d) assisting the design and assessment of future imaging systems.
104
(e) qualitatively determining the performance of sensor systems and imagery
exploitation devices.
In order to assign a NIIRS rating to an image, analysts need to determine what tasks they
could achieve and/or what features they can see in the imagery, by taking into account
local scene content and image acquisition conditions. In effect the imagery analyst judges
the information potential of the image as opposed to making judgments about what was
or was not actually imaged.
The procedure for establishing an image’s Civil NIIRS rating is as follows:
(a) Decide which NIIRS rating level best describes the interpretability of the image
being viewed, by judging what interpretation tasks can or could be done, and what
items of interest can or could be seen on imagery of that interpretability. This
analysis will also not only reveal what items cannot be seen but also what tasks
cannot be done on imagery of that quality.
(b) Determine an appropriate rating level ranging from 0 to 9 according to the criteria
described in Table 4.5.
In determining the Civil NIIRS rating for imagery over the study areas presented in Table
4.6, first a rating level was determined from the values in Table 4.5 then a decimal rating
graded further on how well the image defined the Rating Level was applied. For example
in the case of Image ID 150442RGB for the CBD it was determined that it satisfied the
information requirements for Rating Level 4 to the satisfaction of a score of 8 out of 10:
hence a final Civil NIIRS score of 4.8 resulted.
105
Rating Level 0
Interpretability of the imagery is precluded by obscuration, degradation, or very poor resolution.
Rating Level 5 (� 0.75m to 1.2m GSD)
Identify Christmas tree plantations. Identify individual rail cars by type (eg gondola, flat, box) and locomotive by type (eg steam, diesel). Detect open bay doors of vehicle storage buildings. Identify tents (larger than two persons) at established recreational camping areas. Distinguish between stands of coniferous and deciduous trees during leaf off condition. Detect large animals (eg elephants, rhinoceros, giraffes) in grasslands.
Rating Level 1 (� 9.0m GSD)
Distinguish between major land use classes (eg urban, agricultural, forest, water, barren). Detect a medium sized port facility. Distinguish between runways and taxiways at a large airfield. Identify large area drainage patterns by type (eg dendritic, trellis, radial)
Rating Level 6 (� 0.40m to 0.75m GSD)
Detect narcotics intercropping based on texture. Distinguish between row (eg corn, soybean) crops and small grain (eg wheat, oats) crops. Identify automobiles as sedans or station wagons. Identify individual telephone/electric poles in residential neighbourhoods. Detect foot trails through barren areas.
Rating Level 2 (� 4.5m to 9.0m GSD)
Identify large (ie greater than 160 acre) center-pivot irrigated fields during the growing season. Detect large buildings (eg hospitals, factories). Identify road patterns, such as clover leaves, on major highway systems. Detect ice-breaker tracks. Detect the wake from a large (eg greater than 300’) ship.
Rating Level 7 (� 0.20m to 0.40m GSD)
Identify individual mature cotton plants in a known cotton field. Identify individual railroad ties. Detect individual steps on a stairway. Detect stumps and rocks in forest clearings and meadows.
Rating Level 3 (� 2.5m to 4.5m GSD)
Detect large area (ie larger than 160 acres) contour plowing. Detect individual houses in residential neighbourhoods. Detect trains or strings of standard rolling stock on railroad tracks (not individual cars). Identify inland waterways navigable by barges. Distinguish between natural forest stands and orchards.
Rating Level 8 (� 0.10m to 0.20 GSD)
Count individual baby pigs. Identify a USGS benchmark set in a paved surface. Identify grill detailing and/or the license plate on a passenger/truck type vehicle. Identify individual pine seedlings. Identify individual water lilies on a pond. Identify windshield wipers on a vehicle.
Rating Level 4 (� 1.2m to 2.5m GSD)
Identify farm buildings as barns, silos or residences. Count unoccupied railroad tracks along right of ways or in a railroad yard. Detect basketball court, tennis court, volleyball court in urban areas. Identify individual tracks, rail pairs, control towers, switching points in rail yards. Detect jeep trails through grassland.
Rating Level 9 (� less than 0.10 GSD)
Identify individual grain heads on small grain (eg wheat, oats, barley). Identify individual barbs on a barbed wire fence. Detect individual spikes in railroad ties. Identify bunches of pine needles. Identify an ear on large game animals (eg deer, elk, moose).
Table 4.5 – Definition of Civil NIIRS ratings (Source: Beloken & Emmons et al:1997)
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Civil NIIRS
Rating Comment Image ID/
Name Description Software
CBD Urban
150442RGB (GeoTIFF)
8 Bit, 1m Red-Green-Blue DRA – ON, Pan-sharpened, natural color
RemoteView Reader
4.8 4.8 - Good true colour
150443RGB (GeoTIFF)
8 Bit, 1m Red-Green-Blue DRA – ON, Pan-sharpened, natural color
RemoteView Reader
4.9 4.9 - CBD - “crisp” image - Urban - Brown tone reveals more
151127RGB (GeoTIFF)
8 Bit, 1m Red-Green-Blue DRA – ON, Pan-sharpened, natural color
RemoteView Reader
4.8 4.8 - Washed out
149875 (GeoTIFF)
11 Bit, 1m Pan, DRA - OFF
Remote View Reader
4.8 4.8
149879 (GeoTIFF)
11 Bit, 1m Pan DRA - OFF
Remote View Reader
4.8 4.8
149883 (GeoTIFF)
11 Bit, 1m Pan DRA - OFF
Remote View Reader
4.8 4.8
149877 (NITF)
11 Bit, 1m Pan DRA - OFF
Remote View Reader
5.1 5.1
149881 (NITF)
11 Bit, 1m Pan DRA - OFF
Remote View Reader
5.1 5.1
149885 (NITF)
11 Bit, 1m Pan DRA - OFF
Remote View Reader
5.1 5.1
153452 (GeoTIFF))
11 Bit, 1m Pan Stereo, UTM map-projected stereo, Relative orientation performed DRA - OFF
Remote View Reader
5.0 5.0
153452_001 (GeoTIFF))
11 Bit, 1m Pan Stereo, UTM map-projected stereo, Relative orientation performed DRA - OFF
Remote View Reader
5.0 5.0
1:7000 Sullivans Cove
(GeoTIFF)
“The List” DPIE, 1:7000 Sullivans Cove, Colour Orthophoto
Remote View Reader
6.0 Does not cover
Urban area
1:24 000 Orthophoto (GeoTIFF)
“The List” DPIE
Remote View Reader
5.00 4.5 - CBD – Clear - Urban – Washed out
CBD_15m_Ortho (GeoTIFF)
Created from 11 Bit, 1m Pan Stereo GeoTIFF, 15m post spacing DEM
Remote View Reader
4.8 Does not cover
Urban area
Urban_ortho (GeoTIFF)
Created from 11 Bit, 1m Pan Stereo GeoTIFF, 30m post spacing DEM
Remote View Reader
Does not cover CBD area
4.8
Table 4.6 – Civil NIIRS ratings for imagery over Hobart
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4.3.2 Factors Affecting the Interpretation of Features in High Resolution Commercial Satellite Imagery. Experience and evidence has shown that there are a variety of factors affecting the quality
and hence visual interpretability. This section provides a brief description of the factors
which affect image quality:
(a) Radiometric Resolution;
(b) Whether Dynamic Range Adjustment applied (DRA on or off);
(c) Colour versus panchromatic imagery;
(d) National Imagery Transfer Format (NITF) versus Geographic Tag Image
Format (GeoTIFF);
(e) Spatial Resolution and Ground Sampling Distance;
(f) Angle of collect of imagery (or obliquity).
(a) Radiometric Resolution
The principles of radiometric resolution or “bit depth” are described in Chapter 2. The
use of 11 bit imagery in situations such as low-level lighting conditions can be
advantageous as it improves the ability to enhance images or increase the “tonal” range of
the image. The difference can clearly be seen by comparing the 8 bit IKONOS imagery to
the 11 bit IKONOS imagery. As determined in this study despite the use of colour in the
8 bit data the panchromatic 11 bit data has the same NIIRS rating.
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(b) Effect of Dynamic Range Adjust applied (DRA on or off)
IKONOS imagery can be supplied with and without Dynamic Range Adjust (DRA)
applied, though only GeoTIFF imagery allows this option, as NITF imagery is only
available with DRA off. When DRA is turned on a tonal transfer curve converts the
absolute radiometry, which separately measures the intensity at each wavelength,
achieving a more natural appearance for visual interpretation. The effect is when the Civil
NIIRS was applied during this study that the 8 bit imagery with DRA-on achieved the
same or very nearly the same Civil NIIRS rating as the 11 bit data with DRA-off.
(c) Colour versus Panchromatic
Panchromatic imagery whilst black and white, covers the whole of the visible spectrum.
The advantage of colour is that the 8 bit colour imagery can achieve a similar Civil
NIIRS rating to the 11 bit Panchromatic image. A significant advantage of panchromatic
imagery is that the file size is substantially smaller than the 3 bands of the colour
imagery. Despite this, colour imagery has the distinct advantage of its visual appeal,
which can be more important than a higher dynamic range black and white image for the
casual observer.
(d) National Imagery Transfer Format (NITF) versus Geographic Tag Image Format
(GeoTIFF)
Two formats for IKONOS imagery were provided, GeoTIFF (Geographic Tagged Image
Format File) and NITF (National Imagery Transfer Format). The documentation
accompanying the trial imagery indicates that the GeoTIFF is supplied to civilian users,
while NITF is reserved for government users, presumably from the United States of
America. It can be noted in the Table 4.6 that the NITF file format has a higher NIIRS
rating than the GeoTIFF images, indicating that the NITF image format presents better
imagery quality.
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(e) Spatial Resolution and Ground Sampling Distance (GSD)
Spatial resolution is a function of the GSD, the smaller the GSD the greater the spatial
resolution, as well as the contrast of objects. This is apparent in the IKONOS trial
imagery where it is possible to detect a vehicle and depending on the size of the vehicle
determine the type, such as a bus versus a family car, but it is not possible to discriminate
between a sedan and station wagon. The spatial resolution of the 1:7000 Sullivans Cove
Aerial Colour Orthophoto provided by the Tasmanian Department of Primary Industries
and Water (DPIW) allows the discrimination between a sedan and station wagon.
(f) Angle of collect of imagery (or obliquity)
The angle of obliquity has the effect of increasing the GSD of the imagery and as a result
its geometric accuracy and resolution. The effect of obliquity is such that at nadir the
GSD for a IKONOS panchromatic image is 0.82m whereas at 26° off nadir it is 1.0m
(www.geoeye.com, products, 11/12/06). The off nadir angle is defined as the angle
between Nadir, the point directly below the sensor, and the point on the ground the sensor
is pointed at.
Poon et al (2006) also developed a rating scale for orthoimages based on all aspects of
digital imagery such as high and low contrast as well as area and linear features. For this
study the Civil NIIRS system was considered more suitable as it rates an image based
simply on what information can be determined and as such the usefulness of the image, it
is also a standard recognised rating system. This differs to Poon et al (2006) where the
characteristics of the image, such as how well features are defined or contrasted, are
rated.
This section demonstrates that there is more to considering the use of high resolution
commercial satellite imagery than simply the ground resolution for determining the
maximum scale and employment of final products. It has been shown that through the
application of the rating scale that there is no significant difference between the worst
assigned rating and the best even though there is a significant difference in the scale of
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the imagery. For instance as described earlier, IKONOS by empirical means is suited to
1:20 000 scale mapping based on its best rate on the Civil NIIRS of 5.0, whereas the 1:7
000 Sullivans Cove Orthophoto could only be considered one rating scale better at a Civil
NIIRS of 6.0. Further, the IKONOS 11 bit panchromatic imagery was rated the same as
the 8 bit colour imagery, this indicates that despite the appeal of colour, the 11 bit
panchromatic or “black and white” imagery can be considered more useful. Finally near
vertical aerial photography must be acquired with minimum tilt of the aircraft to ensure
integrity of the metric imagery, whereas for IKONOS imagery the obliquity can vary
from 21 degrees to 15 degrees with no loss in Civil NIIRS, further indicating the
versatility of the imagery and platform used in the image acquisition.
4.3.3 Evaluation of Feature Interpolation and Extraction from IKONOS Imagery
This section provides a comparison of what ground features (both built and natural) can
be detected or identified using IKONOS imagery. Compiled drawings extracted from
IKONOS stereo imagery showing features, contours and photo locations are contained in
Appendix C.
Due to the varying terrain and ground cover of the natural and built environment, the two
Study Areas are treated individually for comparison with other data sources and ground
truthing.
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4.3.3.1 Urban Trial area at Binya St, Glenorchy, Hobart
Figure 4.7 – Change in roof line, not apparent on IKONOS imagery or feature extraction
(Photo P1)
Figure 4.7a – IKONOS 8 bit Imagery Figure 4.7b – IKONOS 11 bit Imagery
Change in roof line
Figure 4.7c – IKONOS derived Orthophoto Figure 4.7d – 1:24 000 Orthophoto
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Figure 4.7 shows the roof line of a typical suburban house, the resolution displayed by all
comparison images makes it difficult to determine the actual outline. Data from
Glenorchy City Council indicated the level of detail they depict on the circled building is
a regular rectangular as displayed in Figure 4.8.
Figure 4.8 – Building Data from Glenorchy City Council
This is compared to the level of detail extracted from IKONOS stereo imagery using
Socet Set software in Figure 4.9. This segment of data better approximates the actual
shape of the building. Though the extra area at the lower end of the building could be due
to an extension of the original structure given the different reflectance of the roof (see
Figure 4.7a) and the indication from the Glenorchy City Council data dictionary that the
data was sourced from design plans.
Extra Building Area
Figure 4.9 – Features Extracted from IKONOS Imagery
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Figure 4.10 shows two stormwater drains and a survey mark both of which clearly cannot
be distinguished on any of the imagery due to their small size as shown below by the
orange circles.
Figure 4.10 – Stormwater drains and Survey Mark (No.10432) (Photo P2)
Figure 4.10a – IKONOS 8 bit Imagery Figure 4.10b – IKONOS 11 bit Imagery
Stormwater Drain
Stormwater Drain
Survey Mark 10432
Figure 4.10c – IKONOS derived Orthophoto Figure 4.10d – 1:24 000 Orthophoto
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Figure 4.11 displays an example of an unformed track and a steep slope. The Glenorchy
City Council data indicates the tracks are derived from engineering drawings, with a
similar level of detail as displayed in Figure 4.12. The IKONOS derived features are
shown in Figure 4.13 which shows the level of detail available was only a single line, as
opposed to both sides of the track being available from the City Council data.
Figure 4.11 – Example of track and steepness (Photo P3)
Figure 4.11a – IKONOS 8 bit Imagery Figure 4.11b – IKONOS 11 bit Imagery
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Figure 4.11c – IKONOS derived Orthophoto Figure 4.11d – 1:24 000 Orthophoto
Figure 4.12 – Track Detail from Glenorchy City Council
Figure 4.13 – Track Detail extracted from IKONOS stereo imagery
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Figure 4.14 shows a significant power line which is only detectable through ground
clearing or scarring with the poles not even detected by the shadow they cast on any of
the imagery
Figure 4.14 – Example of power line visible only by clearing not lines and poles
(Photo P4)
Figure 4.14a – IKONOS 8 bit Imagery Figure 4.14b – IKONOS 11 bit Imagery
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Figure 4.14c – IKONOS derived Orthophoto Figure 4.14d – 1:24 000 Orthophoto
Figure 4.15 – Example of water main, corresponds with Glenorchy City Council plans (Photo P5)
The water main marker shown in Figure 4.15 above relates to the Glenorchy City Council
plans shown below by the dashed blue line and orange arrow in Figure 4.16.
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119
Figure 4.16 – Glenorchy City Council plan detail of water main
The orange circle in Figures 4.17a and 4.17b indicate the approximate area of the water
main which cannot be detected by visible ground scarring (depression or clearing) or by
change in tone in the ground surface or vegetation.
Figure 4.17a – IKONOS 8 bit Imagery Figure 4.17b – IKONOS 11 bit Imagery
Fi
gure
4.1
8 –
Exam
ple
of to
ps o
f res
ervo
irs, o
lder
one
(abo
ve) i
s ci
rcul
ar (P
hoto
P6)
Fi
gure
4.1
8a –
Exa
mpl
e of
tops
of r
eser
voirs
, new
er o
ne (a
bove
) is
pol
ygon
shap
ed (P
hoto
P7)
Fi
gure
4.1
8b–
IKO
NO
S 8
bit
Imag
ery
Figu
re 4
.18c
– IK
ON
OS
11 b
it Im
ager
y Fi
gure
4.1
8d –
IKO
NO
S de
rived
Orth
opho
to
Figu
re 4
.18e
– 1:
24 0
00
Orth
opho
to
Old
O
ld
Old
O
ld
New
N
ew
New
N
ew
New
120
The reservoirs shown in Figures 4.18 and 4.18a give an example of the difficulty of
detecting the true shape of similarly shaped features, as shown for the old and new
reservoirs. During feature extraction using IKONOS 11 bit stereo imagery, it was not
possible to detect the difference between the new roof which is a polygon shape and the
old roof which is a circle, as displayed in Figure 4.19.
Figure 4.19 – Features extracted from 11bit IKONOS Stereo Imagery Both reservoirs are represented as circles and not as they should be.
A B
Figure 4.20 – Example of roof tops and their roof line (Photo P12)
121
Figure 4.20a – IKONOS 8 bit Imagery Figure 4.20b – IKONOS 11 bit Imagery
A
AA
B B
B B
A
Figure 4.20c – IKONOS derived Orthophoto Figure 4.20d – 1:24 000 Orthophoto
As in Figure 4.7, Figure 4.20 shows the details of the roof line in the Glenorchy City
Council feature data, compared with features extracted from 11 bit IKONOS imagery
below.
122
Figure 4.20e – Glenorchy City Council Data
A
B
A
B
Figure 4.20f – Features extracted from 11bit IKONOS Stereo Imagery
Whilst the purpose of this study is to assess the suitability of IKONOS imagery, in this
instance with both examples the information derived was incorrect in at least one part of
the structure, highlighting the importance of ground truthing and the danger of relying on
one source of data.
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Figure 4.21 – Example of number of power poles and lines that cannot be seen on IKONOS imagery (Photo P13)
Figure 4.21a – IKONOS 8 bit Imagery Figure 4.21b – IKONOS 11 bit Imagery
Figure 4.21c – IKONOS derived Orthophoto Figure 4.21d – 1:24 000 Orthophoto
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Figure 4.21 gives not only an idea of the level of complexity of data that can be derived
from overhead imagery but also the level of undetectable data such as urban power lines,
similar to Figure 4.14. Despite the amount of overhead wires as seen in Figure 4.21 it is
not possible to detect them in Figures 4.21a to d.
Figure 4.22 – New construction (mobile telephone relay station) Photo P8
Figure 4.23 – Example of new construction (children’s’ playground) (Photo P17)
125
126
Both Figures 4.22 and 4.23 give an example of the change detection capability of high
resolution commercial satellite imagery. The IKONOS imagery was taken in 2003 and
since then two constructions have occurred, which it can be easily seen were not in
existence in 2003. Regular collection plans at set time intervals have the potential for
monitoring developments at a local level. Also the coverage of one satellite image (16km
x 16km) allows trend analysis of an entire region to be potentially achieved with one
image collect.
4.3.3.2 Central Business District (CBD) area of Hobart
Figure 4.24 – Example of detail (Photo P24)
Detail of Building Line
Figure 4.24a – IKONOS 8 bit Imagery Figure 4.24b – IKONOS 11 bit Imagery
Figure 4.24c – IKONOS derived Orthophoto Figure 4.24d – 1:24 000 Orthophoto
Figure 4.25 - Hobart City Council data
127
Figure 4.26 – Features extracted from 11bit IKONOS Stereo Imagery Comparing the IKONOS imagery in Figure 4.24 with other imagery, such as higher
resolution aerial orthophotos, shows the level of building detail is comparable in this case
in all images regardless of source. This is in contrast to features extracted in both the
Hobart City Council data (Figure 4.25) and those derived from IKONOS stereo imagery
(Figure 4.26). Neither example of vector data gives a complete representation of ground
features. In the case of features derived from IKONOS imagery this maybe due to
interpreter/observer error due to unfamiliarity of the ground features. In the case of the
Hobart City Council data, which is based on a combination of digitised and ground
survey data, it may simply be due to incomplete data collection.
128
Figure 4.27 – Hobart Midcity Hotel – example of roofline and level of detail extracted possible (Photo P26)
Figure 4.27b – IKONOS 11 bit Imagery
Similar to Figure 4.24, the difference in the interpretation of features can be seen in
Figures 4.27, 4.28 and 4.29. In this case the IKONOS extracted features more closely
resemble the situation on the ground. This shows the usefulness of high resolution
commercial satellite imagery in defining the urban landscape.
129
Figure 4.28 - Hobart City Council data
Figure 4.29– Features extracted from 11bit IKONOS Stereo Imagery
Figures 4.30, 4.31 and 4.32 provide an example of consistency across all forms of
imagery (Figure 4.30) as well as the feature representation in a vector or line form
(Figures 4.31 and 4.32). It can be clearly seen that the arrowed building roof line is
accurately represented in all the images (Figure 4.30) and the building footprint is
consistently represented in the vector diagrams (Figures 4.31 and 4.32).
130
Figure 4.30 – Example of roof line (Photo P29)
Figure 4.30a – IKONOS 8 bit Imagery Figure 4.30b – IKONOS 11 bit Imagery
Figure 4.30c – IKONOS derived Orthophoto Figure 4.30d – 1:24 000 Orthophoto
131
132
Figure 4.31 - Hobart City Council data
Figure 4.32 – Features extracted from 11bit IKONOS Stereo Imagery
Figures 4.33, 4.34 and 4.35 are a good demonstration that the level of detail that is
obtainable from satellite imagery compares extremely favourably with the aerial
orthophoto. The deficiencies of the Council data are more probably due to the lack of
traditional survey detail in the Council data. This is a clear example where satellite or
aerial imagery can be of benefit.
Fi
gure
4.3
3 –
Exam
ple
of b
uild
ing
deta
il. T
his
mul
tisto
ry c
ar
park
doe
s no
t app
ear a
s on
e fr
om a
ver
tical
per
spec
tive.
(Pho
to
P30)
Figu
re 4
.33a
– T
op o
f mul
tisto
ry c
arpa
rk (P
hoto
P32
) Lo
okin
g so
uth
Fi
gure
4.3
3b –
IKO
NO
S 8
bi
t Im
ager
y
Fi
gure
4.3
3c –
IKO
NO
S 11
bit
Imag
ery
Fi
gure
4.3
3d –
IKO
NO
S de
rived
Orth
opho
to
Fi
gure
4.3
3e –
1:2
4 00
0 O
rthop
hoto
133
Figure 4.34 - Hobart City Council data
Figure 4.35 – Features extracted from 11bit IKONOS Stereo Imagery
Figures 4.36, 4.37 and 4.38 provide a good view of the complexity of city roof scapes at
close range. The extracted features from IKONOS imagery (Figure 4.38) compare
favorably with more traditional data and imagery sources in this example.
134
Figure 4.36 – Example of roof line and detail from top of multistory car park (Photo P33)
Looking North
Figure 4.36a – IKONOS 8 bit Imagery Figure 4.36b – IKONOS 11 bit Imagery
Figure 4.36c – IKONOS derived Orthophoto Figure 4.36d – 1:24 000 Orthophoto
135
Figure 4.37 - Hobart City Council data (orange arrow indicates direction of photograph P33 in Figure 4.36)
Figure 4.38 - Features extracted from 11bit IKONOS Stereo Imagery (orange arrow indicates direction of photograph P33 in Figure 4.36)
Figure 4.39 is an example of a mis-identified feature. Whilst the detail of the building
footprint is clearly apparent, the shape of the building has been interpreted incorrectly.
During feature extraction using the IKONOS stereo imagery the single building has been
interpreted as two buildings. This example demonstrates that whilst high resolution
satellite imagery can remove the requirement to visit a location for feature extraction, it
cannot remove the requirement to visit a location for ground truthing purposes.
136
137
Figure 4.39 – Example of roof top detail and the possibility of to misinterpretation. (Photo P36)
Figure 4.39a – IKONOS 11 bit Imagery
Figure 4.39b - Features extracted from 11bit IKONOS Stereo Imagery
Figu
re 4
.40
– Ex
ampl
e of
det
ail a
nd in
terp
reta
bilit
y
(Pho
to P
37)
Figu
re 4
.40e
– 1
:700
0 Su
lliva
ns C
ove
Orth
opho
to
Fi
gure
4.4
0a –
IKO
NO
S 8
bit
Imag
ery
Fi
gure
4.4
0b –
IKO
NO
S 11
bit
Imag
ery
Fi
gure
4.4
0c –
IKO
NO
S de
rived
Orth
opho
to
Figu
re 4
.40d
– 1
:24
000
Orth
opho
to
138
Figu
re 4
.41
– Ex
ampl
e of
roof
line
and
det
ail (
Phot
o P3
8)
Figu
re 4
.41e
– 1
:700
0 Su
lliva
ns C
ove
Orth
opho
to
Fi
gure
4.4
1a –
IKO
NO
S 8
bit
Imag
ery
Fi
gure
4.4
1b –
IKO
NO
S 11
bit
Imag
ery
Figu
re 4
.41c
– IK
ON
OS
deriv
ed O
rthop
hoto
Fi
gure
4.4
1d –
1:2
4 00
0 O
rthop
hoto
139
Figure 4.42 – Hobart City Council data
Fig 4.41
Fig 4.40
Fig 4.41
Fig 4.40
Figure 4.43 - Features extracted from 11bit IKONOS Stereo Imagery
Both Figures 4.40 and 4.41 show the comparison of IKONOS imagery with large scale
aerial orthophoto (1:7000). This larger scale imagery is clearly far superior to either the
IKONOS imagery or the 1:24 000 orthophoto, and displays best that currently it is
difficult to replace high resolution aerial photography with satellite imagery for certain
purposes or that satellite imagery has its limitations.
140
4.3.3.3 Summary of Observations from Feature Interpolation and Extraction of Study Areas From the examples shown in Sections 4.3.3.1 and 4.3.3.2 the following conclusions on
the use of high resolution commercial satellite imagery can be made:
(a) Due to the size of utility features, such as water or sewerage, as displayed below it is
not possible to detect or analyse these typical urban features. Even though sewer
manholes are approximately one metre in diameter, for the GSD of the IKONOS
imagery, they are undetectable. The level of detail that can be extracted varies and is a
function of the contrast of the object with respect to the background. Some one metre size
objects can be detected if they have high contrast compared to their background.
Generally objects of low contrast must be approximately 3 to 4 metres, or size of a small
car, to be identifiable.
Figure 4.44a – Sewerage Manhole (P25) Figure 4.44b- Water Service
– Size approx 0.15m (P14)
Figure 4.44c – IKONOS 8 bit Imagery Figure 4.44d – IKONOS 8 bit Imagery
141
(b) Whilst it is difficult to detect utility features, it is possible to detect some through
“ground scarring” in the form of vegetation clearing as displayed in Figure 4.14, but it is
not possible to determine the nature of the feature, only the evidence of human activity.
(c) The level of detail that can be discerned within an image varies for a given size of
feature, depending on its orientation and structure, for example:
- tops of the reservoirs. The two newer ones actually have polygons tops. Figure
4.18 shows the difference between the older reservoir with a circular top and a
newer one with a polygon shaped top.
- roof shape (roof line) of dwellings can be difficult to establish as resolution, sun
angle and tonal range appears to affect the level of detail that can be determined
(Figure 4.7).
(d) Mis-interpretation can easily occur on high resolution satellite imagery as depicted in
Figure 4.33 which shows a multi-storey carpark. With no cars parked on the roof
(IKONOS imagery was captured on Saturday 22 February 2003) and with the colour of
concrete similar to the ground, it is not possible to determine the building purpose unless
ground knowledge is obtained. This compares with another multistory carpark from
another image on another day, as shown below which is easily identified because cars are
parked on the roof. This clearly indicates that also timing of capture of the imagery is an
important factor in its usability.
Figure 4.45 – 1:7000 Sullivans Cove Orthophoto
142
(e) There is a rivulet running under the Hobart central business district which has recently
been built over. There is no evidence of this in the IKONOS imagery. Whilst in this case
it is not possible to detect an “old” and apparently lost natural feature there are many
cases where aerial and satellite imagery have been utilised to help locate the extent of an
archeological site that is “invisible” at ground level.
As described earlier in this section the purpose of using the Civil NIIRS rating is to
determine the information potential of an image not quantify the number of features that
could be correctly identified. This is not the normal mapping approach and significant
studies, such as Holland and Marshall (2003 and 2004) have been performed to relate the
suitability of high resolution commercial satellite imagery to map scale requirements. The
utility and versatility of the imagery through such properties as 11bit radiometric
resolution and single image large area coverage, add new properties for assessing
suitability of imagery for mapping purposes. This can be seen in that the IKONOS 11bit
imagery rates better than any of the sample imagery except the 1:7 000 scale aerial
orthophotos.
Further, in the sample imagery used the one metre resolution or GSD should put the Civil
NIIRS ratings for all the IKONOS imagery at five, but this is not the case as it ranges
from 4.8 to 5.1. This indicates that whilst it is possible to establish the scale from the
resolution, the information potential of the image may vary as a result of other factors
such as radiometric resolution or even the acquisition obliquity of the imagery. An
example of how other factors, which were not able to be explained during this study, can
affect interpretability is the difference between the quality of the aerial 1:24 000
Orthophoto from the CBD area with a Civil NIIRS rating of 5.0 to the Urban area, Civil
NIIRS rating 4.5. Whilst both images are produced at the same scale and so should be of
the same quality they are not.
As indicated in Chapter 2, using empirical formula a suitable map scale using IKONOS
imagery would be 1:20 000. But many studies have indicated, as previously described in
this thesis, that it could be used for scales up to 1:5 000 if the requirement to collect
143
linear features such as roads or watercourses were eliminated. The work in this section
confirms that it is difficult without additional information, such as ground scarring, to
detect some linear features such as power lines on IKONOS imagery. It must also be
noted that this shortfall is not restricted to high resolution commercial satellite imagery as
aerial photography of the two scales used in this study also presented difficulties for
adequately interpreting linear features such as power lines and tracks.
To provide a comparison with other work an evaluation was conducted using the
IKONOS 11 bit imagery against the classes of features evaluated for extraction at a scale
of 1:10 000 in Table 3.3 from Holland et al (2003), the results are contained in Table 4.7.
From Table 3.3 Feature 1:10 000 Identification on IKONOS 11 bit Imagery Image Civil
NIIRS
Housing and associated features Maybe Yes - though discrete changes in roof line as
in Fig 4.7 not possible to detect 5.0
Other buildings Yes Yes – all large or commercial buildings. 5.0 Communications networks - roads Yes Yes – though road markings on secondary
roads not clear 5.0
Dual carriageways Yes Yes – Barriers and road furniture maybe detectable but not clear
5.0
Airports Yes No example on image 5.0 Railways Yes Yes – Cannot determine detail 5.0 Electricity transmission lines No No – detectable only through ground scarring
and truthing see Fig 4.14 5.0
Major sea defences to reduce flooding Yes Yes – Port facilities clearly identifiable 5.0
Non-coastal sea defences Yes Yes – Dams an be identified, but detail unclear
5.0
Major property boundaries No Yes – Only through ground cover change 5.0 Major landscape changes Yes Yes - Distinct 5.0 Quarries and other surface workings Yes Yes - Distinct 5.0
Field boundaries Maybe Yes - Only through ground cover change 5.0 Water features Yes Yes – Small creeks difficult 5.0 All vegetation Yes Maybe – Depends on nature of vegetation. 5.0 Tracks and path Maybe Maybe – Depends on size and width of track,
See Figs 4.11 and 4.17 5.0
Telephone boxes No No 5.0 Extensions to commercial building Yes Yes 5.0
Minor property boundaries Maybe Yes – Only through ground cover change 5.0 Tide lines No No 5.0 Garages built after initial development Yes Yes 5.0
Table 4.7 - Feature Classes identified in IKONOS Imagery
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Using Table 4.7 it can be seen that there is agreement in the ability to detect the majority
of urban ground features or 70% of the feature classes tabulated using IKONOS 11 bit
panchromatic imagery with a Civil NIIRS of at least 5.0.
In order to ensure the quality of mapping data, mapping organisations attempt to quantify
the numbers of features that can be determined. Such as in the case of the UK Ordnance
Survey, which as part of its performance monitoring, requires that a minimum of 99.6%
of significant real world features are represented in their database within six months of
their completion (Holland and Marshall: 2004). In this study it was found to be difficult
to determine a definitive number of features that could be identified or extracted in an
IKONOS image when compared to other spatial datasets. This was because, regardless of
the dataset, there were always omissions or misinterpretations either due to the age or
incompletion of the dataset as displayed in Figures 4.7 to 4.9 and Figures 4.24 to 4.26.
This highlights the importance of ground truthing as part of the process of creating spatial
data. Whilst a significant advantage of high resolution commercial satellite imagery is the
ability to image areas where access is restricted, unquantifiable errors that occur due to
feature misinterpretation must be factored into any quality measured and consequently
risk assessment in the employment of the resultant spatial data, and therefore decisions
made as a result of analysing this derived data.
In summary when using IKONOS imagery for feature identification or extraction the
following need to be considered:
(a) Only one metre objects with high contrast can be detected. In reality objects need
to be at least 3 to 4 metres in size to be reliably detected on IKONOS imagery;
(b) It is possible to detect finely detailed linear features through ground scarring or
associated indicators such as change in vegetation or relief, but it is usually not
possible to identify the feature itself;
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(c) The level of detail that can be discerned will vary for a given size of feature
depending on its orientation, structure and contrast;
(d) Misinterpretation can easily occur and with all feature extraction or identification
ground truthing or other confirming checks should be conducted to provide
validation;
(e) Errors or omission exist in all spatial data sets and this needs to be considered in
any decision or product creation involving the data.
4.4 Digital Elevation Model (DEM) Creation
4.4.1 Derivation of Digital Elevation Models
As described in Chapter 2 one of the advantages of high resolution commercial satellite
imagery is the stereo imaging capacity, in particular its ability to obtain “in track” stereo,
enabling a large base-height ratio to be achieved, which is a favourable geometric
condition for elevation determination. Whilst stereo imagery allows the creation of
DEMs, the actual process used in softcopy photogrammetry is image matching where
corresponding points on the images of a stereo pair are matched and their object space
coordinates calculated by an intersection computation for DEM creation (Wolf: 2000).
For this comparison a DEM was created from the IKONOS stereo pair over each Study
Areas using the Socet Set Photogrammetric Workstation at the University of New South
Wales. The DEM over the CBD area had a grid post spacing of 15m and the Urban DEM
had 30m grid post spacing. Both DEMs were edited to “First Surface” as opposed to
“Bald Earth”. “First Surface” digital elevation models represent the ground but include
objects such as buildings and trees whereas “Bald Earth” digital elevation models are
reduced to ground level eliminating buildings and vegetation.
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As described in Section 4.1.1 the stereo models were created without using ground
control points, being only based on RPCs for exterior orientation. Consequently the
heights for the IKONOS derived DEM are based on WGS84 spheroidal heights as this is
the acquisition datum of the IKONOS imagery used. Once the IKONOS derived DEMs
were created they were then compared to two reference DEMs with post spacings of
100m and 12.5m supplied by the Tasmanian Department of Primary Industries and Water
(DPIW). The 100m post spacing data is based on 1:25 000 series mapping and the 12.5m
post space data DEM is derived from contours from 1:25 000 series mapping or better.
Both use the Australian Height Datum (AHD). Whilst it is stated in Chapter 2 that
IKONOS imagery would be suitable for 1:20 000 mapping, which is larger than the 1:25
000 mapping derived data that was used as the references in this study, these were the
best datasets commercially available without a specific collect occurring (such as
LIDAR). They represent DEMs that would be readily available for use in an urban GIS.
As noted the two reference DEMs are based on the AHD which approximates the Geoid
and the IKONOS derived DEM uses WGS84 spheroidal heights, at this point this
difference in height datum can only be resolved by applying ground control points to the
stereo model. Poon et al (2006) reports that readily available softcopy photogrammetric
workstations and software can extract elevation information from high resolution
commercial satellite imagery based on ground control points to accuracies of 4 to 9m
overall. Since many researchers report that a minimum number of ground control points
such as 4 to 8 are all that is required to achieve maximum possible accuracy for this type
of imagery, this would indicate that regardless of the use of ground control points in such
a stereo model, there is finite accuracy level that can be achieved. This will prevent the
imagery’s use for applications requiring more detail, such as civil engineering design and
restrict it to preliminary or proposal level design work at best.
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4.4.2 Comparison of Digital Elevation Models
From each of the data sets contours of 5m intervals were derived as shown in Figures
4.46 and 4.47 which display the 5m contours derived from each reference DEM overlaid
on the IKONOS derived contours. Although the potential accuracy of the IKONOS
imagery and the reference data sets would suit a larger contour interval such as 10m, a
5m contour interval was only used for comparison purposes between the data sets. Also
for comparison between the data sets, two longitudinal sections for each DEM were
extracted for each Study Area, as shown in Figures 4.48 and 4.49.
The area size and height differences along the longitudinal sections meant that there were
only a small number of points able to be used in the analysis, such as in the urban area
four and five points and the CBD ten and eleven points, along the longitudinal sections.
This limits the results as there is not enough data to perform a detail statistical analysis.
The use of this reference data set though is realistic as mentioned previously as it is
typical of the height data that is available in such an urban environment and to local
governments as opposed to LIDAR or InSAR data.
In order to identify the variations of heights along the longitudinal sections of the
reference data sets and the IKONOS derived DEM, an RMSE value was derived from the
differences in the heights of the data sets at 50m chainage intervals along the longitudinal
sections, the results being:
(a) CBD Study Area – Longitudinal Section 1 - Liverpool St
- RMSE IKONOS /100m GDA DTM = 0.3m - RMSE IKONOS / 12.5m Hobart DTM = 1.7m
(b) CBD Study Area – Longitudinal Section 2 - Macquarie St
- RMSE IKONOS / 100m GDA DTM = 1.0m - RMSE IKONOS / 12.5m Hobart DTM = 1.2m
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(c) Urban Study Area – Longitudinal Section 1 - South
- RMSE IKONOS / 100m GDA DTM = 7.4m - RMSE IKONOS / 12.5m Hobart DTM = 3.0m
(d) Urban Study Area Longitudinal Section 2 - North
- RMSE IKONOS / 100m GDA DTM = 2.7m - RMSE IKONOS / 12.5m Hobart DTM = 1.3m
The overall accuracy result of the test is better than that expected from the reference data
based on 1:25 000 mapping, which has a horizontal accuracy of ±17.5m and a vertical
accuracy of ±5m, this is due to the small sample size.
It can be seen from the above data and that there is consistency in all study areas data sets
in the form or shape of the terrain. The lack of gradient displayed in Figure 4.48 for the
CBD area as well as the difficulty in creating a DEM in a significantly built up area
makes it difficult to confirm the elevation in the CBD area. The urban area provides a
better comparison, primarily because it is less built up. Toutin et al (2002) reports a
similar difference in magnitude by comparing elevations from IKONOS stereo models
using ground control points. In his work Toutin et al (2002) reports a 6.5m linear error at
the 68% level of confidence (LE68) and a 10m linear error at the 90% level of confidence
(LE90) for a DEM, which includes ground cover and an accuracy over bare soils of 1.5m
LE68 and 3.5m LE90. The results from this study indicate that IKONOS derived DEMs
are comparable to the reference data sets in quality and accuracy. Apart from the large
RMSE error in the Urban Study Area Longitudinal Section 1 – South for the IKONOS /
100m GDA DTM of 7.4m all the results would indicate that without ground control
IKONOS stereo imagery would be suitable for the production of 10m interval contours
making the imagery suitable for 1:10 000 scale mapping. The difference in the Urban
Study Area Longitudinal Section 1 – South can be attributed to the large difference in
post spacing in the data sets and the undulating nature of the terrain, particularly between
chainages 250m and 300m along this longitudinal section shown in Figure 4.49a. Better
results overall could only be achieved by utilising ground control points when
establishing the IKONOS stereo model as discussed in Section 4.2 of this chapter.
Figure 4.46a – CBD Study Area: 5m IKONOS derived contours and 5m Hobart 12.5m DTM contours
Figure 4.46b – CBD Study Area: 5m IKONOS derived contours and 5m 100m GDA DTM contours
Figure 4.47a – Urban Study Area: 5m IKONOS derived contours and 5m Hobart 12.5 DTM contours
Figure 4.47b – Urban Study Area: 5m IKONOS derived contours and 5m 100m GDA DTM contours
Figure 4.48a - CB
D Longitudinal Section – Liverpool St
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Figure 4.48b - CB
D Longitudinal Section – M
acquarie S151
t
Figure 4.49a - Urban Longitudinal Section – South
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Figure 4.49b - Urban Longitudinal Section – N
orth 153
4.5 Summary
The studies conducted in this Chapter have shown that the positional accuracy and the
ability to extract features from high resolution commercial satellite imagery would
indicate that it is suitable for mapping for at least at the 1:10 000 scale level without the
use of ground control points. This represents the most versatile characteristic of high
resolution commercial satellite imagery.
The RPC provided by Space Imaging with the IKONOS stereo imagery is capable of
providing a horizontal positional accuracy between 0.3m to 1.5m, and even though its
heighting accuracies capability is lower, elevations derived from the imagery will meet
the requirements in both vertical and horizontal positional accuracy for 1:10 000
mapping.
In regards to feature identification or extraction, the IKONOS imagery can be seen as no
worse than aerial photography derived with the same ground resolution. The imagery is
capable of identifying ground features, both built and natural, regardless of scale subject
to its shortfalls in identification of linear features, which are also usually not visible on
higher resolution aerial photography. The IKONOS imagery also brings the advantage of
11 bit depth radiometric resolution which can increase the identification capability of the
imagery by allowing the detection of high contrast one metre size objects that would not
normally be visible at such a resolution.
The examples provided in this chapter have shown that the level of detail that can be
interpreted and positioned is at the individual building level without accessing the area,
with a minimum of technical expertise. This advantage of high resolution commercial
satellite imagery is an important one. What the imagery lacks in detail or positional
accuracy, it counters with its versatility provided by remote processing with minimal
ground survey required.
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The latest high resolution imaging commercial satellite, Geoeye-1 has similar
characteristics as the IKONOS satellite in that it is capable of acquiring 11bit imagery at
a maximum off nadir imaging of 60 degrees. Geoeye-1 differs in that it has a spatial
resolution for panchromatic imagery of 0.41m and multispectral imagery of 1.65m with a
claimed stereo positional accuracy of three metres within actual locations on the Earth
surface. This is opposed to IKONOS which has spatial resolution for panchromatic
imagery of 0.82m and multispectral imagery 3.2m and a precision stereo horizontal
positional accuracy of four meters and vertical accuracy of five metres (Geoeye Website:
2008). Given that this study has shown that the IKONOS stereo positional accuracy is
greater than the vendors claims it could be anticipated similar results for Geoeye-1,
potentially enabling satellite imagery to achieve the positional accuracy of aerial
photography. Further as can be seen from Figure 4.50 the increase spatial resolution
increases the ability to detect linear features, such as rowing shells of an approximate
width of 0.6m, an issue highlighted in this study and others (Geoeye Website: 2008).
Figure 4.50 – Geoeye-1 Imagery – Cambridge, Massachusetts, 18 October 2008 (Source: Geoeye Website: 2008)
In summary, high resolution commercial satellite imagery provides a source of
information from which to derive a range of spatial data such as vectors, points, features
(built and natural), land use identification through spectral analysis, and DEMs creation.
Whilst this shows the versatility of this imagery source, the practicalities and success of
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application of the data can vary. Whilst the positional accuracy and feature identification
can permit use for map scales between 1:1 000 to 1:6 000, if elevations are required as
well this will restrict the suitability of the imagery to a minimum of 1:10 000 scale.
CHAPTER 5
CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusions
5.1.1 Objectives and Strategies
The successful implementation of the commercial accessibility of high resolution satellite
imagery is a significant technological and business step in the spatial science industry
around the world. Access to and the availability of the service and imagery from these
satellites has provided a new data source in the terms of imagery, DEM creation and
feature extraction, as well as potential spatial science services such as the long term
monitoring of areas that are physically difficult to access due to physical, political,
disaster or time constraints.
The objectives of this thesis were:
(a) To provide an understanding of the imagery collection techniques and
methodology of high resolution commercial imaging satellites.
(b) To give an understanding of the way the commercial and government
interests have influenced the development and sustainability of high
resolution commercial imaging satellites;
(c) To allay practical concerns in the application of using the imagery in regards
to cost, equipment (hardware and software) and training;
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(d) To give an assessment of the imagery’s ability to represent features and
phenomena on the ground to a practical level in a variety of applications.
(e) to provide a guide to possible uses and disadvantages of such imagery away
from the traditional qualifiers;
Overall the work in this thesis has determined that the RPC of the IKONOS stereo
imagery can provide horizontal positional accuracy of between 0.3m to 1.5m, and though
the heighting accuracy is not at the same level it is suitable for the production of 10m
interval contours. This makes the imagery suitable for 1:10 000 scale mapping if
heighting is required to be sourced from the stereo model without the use of ground
control or for planimetric mapping between 1:1 000 to 1:5 000 scale if heighting is not
required.
Feature identification or extraction using IKONOS imagery can satisfy a variety of
requirements, but there are some points that need to be considered in the application.
Whilst the imagery is supplied at one metre GSD, only objects with high contrast can be
detected of this size, resulting in practical terms that an object needs to be at least 3 or 4
metres minimum in size to be detected on IKONOS imagery. Also even if the object or
feature is greater than 3 to 4 metres it can be difficult to detect finely detailed linear
features such as tracks or power lines on identification of the feature itself without some
associated indicators such as change in vegetation or relief. Similarly the level of detail
that can be discerned will vary for a given size of feature depending on its orientation and
structure.
Each of the objectives has been achieved. This study has shown that whilst high
resolution commercial satellite imagery is capable of producing reasonable spatial data
detail both in quality and cost for use in a urban GIS, its greatest advantage is its ability
to remotely access a site to obtain imagery for the creation of spatial data. In order to take
advantage of this benefit it must be accepted that there will be lower positional accuracy
as well as the potential of errors due to misidentification of features if ground truthing is
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not conducted. Consequently the only way that high resolution commercial satellite
imagery can compete with sources of higher resolution spatial data such as aerial
photography, is by the application of ground control and ground truthing. This will
reduce the advantage of the imagery. For this reason high resolution commercial satellite
imagery should be assessed as a spatial data source on its own merits and not as a
substitute or replacement for another technique.
To reach the conclusions, this study has reviewed and examined all aspects of the
development and application potential of high resolution commercial satellite imagery,
such as:
(a) Historical Development;
(b) Business Evolution;
(c) GIS data and service requirements for a diverse range of spatial data
applications;
(d) Evaluation and comparison as a spatial data source.
5.1.2 Historical Development
The historical development of high resolution commercial satellite imagery is one of
successes and failures. The initiator of this development can be found in decisions by the
former Soviet Union then subsequently the United States of America, to declassify high
resolution satellite technologies that were developed for military advantage during the
Cold War to be used for commercial application. These events led to a “challenge” from
other countries such as France and Israel which ensured that the U.S. was not going to
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easily dominate the market for the supply of high resolution commercial satellite
imagery, that in 1994 was already estimated to have reached $700US million.
Even though a number of National Governments around the world such as the U.S.,
France and Israel have heavily sponsored their countries’ commercial ventures into the
high resolution commercial satellite imagery market, the successful outcomes took a
number of years to achieve, as well as incurring financial losses. There is not one
company regardless of country of origin that did not suffer setbacks of either a technical
nature, such as failure to achieve orbit or malfunction when in orbit, or a financial loss as
a result of not being able to achieve a suitable market. If it were not for the support of
each of these companies’ governments by either supplying lucrative contracts or injecting
financial support, none of these companies or consortiums commercialising high
resolution satellite imagery would have been able to achieve results.
5.1.3 Business Evolution
The business of high resolution commercial satellite imagery started with extreme
confidence in its applicability and value to its targeted audiences. However the fortunes
of the companies resulted in several having to restructure to ensure their commercial
survival and return on investor’s capital.
A significant development in this evolution has been the licensing arrangements for
distribution of the high resolution commercial imagery. Initially the purchase of the
imagery was strictly on a one user per purchase or licence. Whilst the cost of the imagery
has not varied since the early years of its availability, the form of licensing has, since it is
now possible to purchase multi-organisation licenses. This increases the competitiveness
and attractiveness of using such imagery by government departments or authorities, since
sharing cost makes the imagery financially viable to source.
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A factor that has ensured the survival of many if not all of these commercial ventures is
access of government contracts. In particular defence contracts such as the U.S.
ClearView and NextView have ensured millions of dollars worth of business being made
available to such companies as DigitalGlobe and Geoeye Inc. This methodology of
funding is not limited to the U.S.; both Israel and France have used similar financial
techniques to continue to develop their countries’ imaging satellite capabilities.
Finally since there is no benefit to put in place such a infrastructure or service there are
also practical concerns in the implementation to ensure the imagery’s use and marketing
success. These practical concerns include cost, in time and money of such items as
hardware and software acquisition, training and data or imagery acquisition. It has been
shown that depending on the circumstances or application, the cost of the use of high
resolution commercial satellite imagery is no greater or less than other survey or spatial
data creation techniques.
5.1.4 Geographical Information System (GIS) Requirements and Applications
This study highlights the philosophy and aims behind the establishment, use and
consequently the maintenance of spatial data in a GIS. As a result the differing
requirements between the purposes for a particular GIS being established are revealed, be
it for Emergency Services or Local Government. The demands and requirements of the
users of a GIS determine whether high resolution commercial satellite imagery and
derived spatial data meets the subject GIS requirements.
It can be seen from this study that if a representation or overview of an area is required,
high resolution commercial satellite imagery provides an economical alternative at the
cost of resolution and positional accuracy. This loss of resolution and positional accuracy
for the sake of economies whilst initially could be seen as “penny pinching” is also
revolutionary in the utilisation of imagery. The ease of availability and competitive
pricing of high resolution commercial satellite imagery, in particular archived imagery,
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allows for monitoring and recording of the earth surface at a resolution not previously
available to the general public. This imagery can be of use in most public authorities and
local governments when it is supplemented in suitable intervals with higher resolution
and more expensive imagery or terrain data from aerial platforms.
5.1.5 Evaluation and Comparison of High Resolution Commercial Satellite Imagery
as a Spatial Data Source
As discussed in this thesis high resolution commercial satellite imagery provides more
than simply imagery. It can also be a source of feature data (both the natural and built
environment), terrain information (DEM) and land use identification using the
multispectral component of the imagery.
The suitability of the spatial data derived from the imagery is, as previous mentioned,
dependent on the purpose for which the data is intended to be used. From this study it is
clear that high resolution commercial satellite imagery cannot compete with the
geometric or resolution accuracy of a ground survey or aerial photography. But its
accuracy and resolution is such that it is suitable for planning or reconnaissance tasks as
well as an interval supplement to more accurate spatial data source collections.
In addition despite its lower accuracy and resolution, high resolution commercial satellite
imagery’s ability to provide reasonably accurate position and resolution without ground
control ensures its vendors a niche market.
5.2 The Future and Recommendations
December 2007 and September 2008 saw the launch of the Worldview-1 and Geoeye-1
satellites respectively, both of which boast capability of providing imagery of a resolution
of 0.5m or better. Other commercial imaging satellites from other countries due for
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launch in the near future include the ImageSat EROS C satellite from Israel with a
planned panchromatic resolution of 0.7m and the French CNES Pleiades satellite system
also with a resolution of 0.7m. The successful launch of these two satellites and the
pending launch of the others herald in the next chapter of the use of high resolution
commercial satellite imagery. Geoeye-1 in particular as mentioned in Chapter 4 promises
greater positional accuracy and resolution; this with the marketing lessons from the sales
of the first high resolution commercial satellite images will make the gap between
imagery sources smaller. Whilst high resolution commercial satellite imagery is finding a
place in the remote sensing and spatial industry market, it has not achieved its original
promise of revolutionising spatial data creation and imagery exploitation, as its vendors
attempted to make potential markets believe.
The next generation of high resolution commercial satellite imagery promises greater
accuracy, accessibility and more competitive costing. Only time and world events (such
as natural disasters and conflicts) will determine the success and nature of the business
that surrounds high resolution commercial satellite imagery.
Areas in which further investigation or development should be considered are:
(a) A change of attitude by spatial science professionals in not relating data to
scale, but to usability or “fit for purpose”, to make full use of modern imaging
techniques that allow for the sourcing and exploitation of 11 bit or greater
imagery and resolution;
(b) Investigating techniques to take full advantage of the 11 bit panchromatic
imagery capability;
(c) Methodology to improve the absolute accuracy of the imagery without the use
of ground control so as to remove the current requirement for ground or aerial
access to enhance the imagery’s inherent positional capability.
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APPENDIX A
MASTERS IMAGERY USERS SURVEY – 2005
Masters Imagery Users Survey - 2005
As part of the thesis a survey was conducted, this involved approaching current providers
and users of spatial data. The aim was to determine the nature of their existing uses of
spatial data (in particular imagery) and whether they have considered using high
resolution satellite imagery, and if not, why not? The survey was conducted in the middle
of 2005.
Each survey participant was asked to complete a questionnaire. Survey participants
varied from private companies, government, mining and emergency services.
The composition of the participants and the returns are contained in Table A-1 below
Percentage Government/Authority returned 50Percentage Private Companies returned 43.75Percentage Emergency returned 27.59Percentage Mining returned 0
Total Percentage Returned 35.44
Total Sent: 79 Government/Authority: 26 Private Companies: 16 Emergency: 29 Mining:8
Table A-1 – Survey Participants
The results are detailed in the Tables below
A-1
Gro
und
Sur
vey
Aer
ial P
hoto
grap
hyS
atel
lite
Imag
ery
Pur
chas
ed D
ata
Set
s
Gov
ernm
ent
108
410
Priv
ate
25
44
Em
erge
ncy
22
18
Min
ing
Tota
l14
159
22
Not
e: (1
) Aer
ial i
nclu
des
airb
orne
sen
sors
suc
h as
LID
AR
(2) P
urch
ased
dat
a se
ts in
clud
es to
pogr
aphi
c an
d ve
ctor
dat
a or
sup
plie
d
d
irect
from
Gov
ernm
ent D
epar
tmen
ts
(3
) Som
e or
gani
satio
ns u
se a
var
iety
of s
ourc
es
Tabl
e A
-2 –
Sou
rce
of D
ata
Sta
ndar
dS
urve
yE
quip
men
t S
urve
yS
oftw
are
GIS
Sof
twar
eP
hoto
-gr
amm
etric
M
obile
Uni
tsE
xter
nal
Pro
vide
r
Rem
ote
Sen
sing
S
oftw
are
Gov
ernm
ent
44
122
12
Priv
ate
33
54
11
Em
erge
ncy
6
11
Min
ing
Tota
l7
723
63
13
Not
e: S
ome
orga
nisa
tions
use
a v
arie
ty o
f sof
twar
e an
d eq
uipm
ent
Tabl
e A
-3 –
Fac
ilitie
s fo
r Pro
cess
ing
A-2
Terr
estri
al
Aer
ial
Sat
ellit
e
Gov
ernm
ent
111
5
Priv
ate
36
4
Em
erge
ncy
2
65
Min
ing
Tota
l6
2314
Not
e: S
ome
orga
nisa
tions
use
a v
arie
ty o
f im
ager
y
Tabl
e A
-4 –
Typ
es o
f Im
ager
y
Land
sat
IKO
NO
S Q
uick
bird
SP
OT
Ast
erH
yper
ion
Gov
ernm
ent
34
3
Priv
ate
54
45
11
Em
erge
ncy
3
14
Min
ing
Tota
l11
49
121
1N
ote:
Som
e or
gani
satio
ns u
se a
var
iety
of i
mag
ery
Tabl
e A
-5 –
If u
sed,
Sat
ellit
e D
ata
from
wha
t sys
tem
A-3
Too
Exp
ensi
ve
No
Sui
tabl
e So
ftwar
eN
o su
itabl
e ex
perie
nced
or
trai
ned
staf
f
Not
sui
tabl
e ac
cura
cy o
r re
solu
tion
No
suita
ble
reas
on o
r tas
k O
ther
sui
tabl
e da
ta
Gov
ernm
ent
21
51
Priv
ate
21
3
Em
erge
ncy
2
12
Min
ing
Tota
l6
11
81
3
Tabl
e A
-6 –
Rea
son
for N
ot U
sing
Sat
ellit
e D
ata
Tow
nP
lann
ing
Em
erge
ncy
Man
agem
ent
Land
Dev
elop
men
t E
nviro
nmen
tal
Pla
nnin
gIn
sura
nce
Ass
essm
ent
Min
eral
Exp
lora
tion
Map
ping
Lo
cal A
sset
A
sses
smen
t W
eb
Gov
ernm
ent
73
47
12
1
Priv
ate
22
43
11
11
Em
erge
ncy
6
1
Min
ing
Tota
l9
118
112
11
31
Tabl
e A
-7 –
Usi
ng Im
ager
y D
ata
for w
hat?
(Inc
lude
s Te
rrest
rial,
Aer
ial a
nd S
atel
lite)
A-4
Tow
nP
lann
ing
Em
erge
ncy
Man
agem
ent
Land
Dev
elop
men
t E
nviro
nmen
tal
Pla
nnin
gIn
sura
nce
Ass
essm
ent
Min
eral
Exp
lora
tion
Map
ping
Lo
cal A
sset
A
sses
smen
t W
eb
Gov
ernm
ent
34
24
1
Priv
ate
22
33
11
11
Em
erge
ncy
5
1
Min
ing
Tota
l5
115
81
11
11
Tabl
e A
-8 –
Usi
ng S
atel
lite
Imag
ery
Dat
a fo
r wha
t?
Pan
chro
mat
ic
Mul
tispe
ctra
lO
rtho-
Cor
rect
ed (i
nclu
des
Pan
Sha
pene
d)
Ste
reo
Gov
ernm
ent
55
2
Priv
ate
44
32
Em
erge
ncy
4
5
Min
ing
Tota
l13
145
2
Tabl
e A
-9 –
Lev
el o
f Pro
cess
ing
(Sat
ellit
e Im
ager
y)
A-5
Orth
oim
age
Dra
inag
e C
adas
tral
As
Bui
ltW
ater
Tran
spor
tA
ddre
ss
Poi
ntFe
atur
es
Vec
tor
Dat
a
(Var
ious
)R
oads
Bui
ldin
gFo
otpr
ints
Sew
erAs
set
Even
tLo
catio
ns
(eg
offe
nces
) Zo
ning
Gov
ernm
ent
811
138
82
11
12
22
2
2
Priv
ate
54
44
41
11
11
11
1
Em
erge
ncy
5
45
23
11
12
11
Min
ing
Tota
l18
1922
1415
43
34
43
33
12
Tabl
e A
-10
– D
ata
in G
IS
A-6
APPENDIX B
HOBART GROUND CONTROL POINTS COMPARISON
PointNumber X Y Z Measured X Measured Y Measured Z
Delta X (Measured to
Control)
Delta Y (Measured to Control)
Delta Z (Measured to Control)
Resultant Bearing (dd.mmss)
(Measured to Control)
Resultant Distance (Measured to Control) Bearing (0<x<90) Bearing
(90<x<180)Bearing
(180<x<270)Bearing
(270<x<360)
B1_01 519653.5615 5259759.2210 94.4649 519655.2140 5259758.5930 97.0150 1.6525 -0.6280 2.5501 290.4819 1.7678 1B1_02 519885.3591 5260271.7760 59.0294 519887.2550 5260271.0930 62.1290 1.8959 -0.6830 3.0996 289.4803 2.0152 1B1_03 518944.5098 5259596.6710 129.6879 518946.1070 5259595.7990 131.1380 1.5972 -0.8720 1.4501 298.3757 1.8197 1B1_05_A 516531.5817 5256907.7950 356.3429 516534.1290 5256907.7290 356.5430 2.5473 -0.0660 0.2001 271.2903 2.5482 1B1_05_C 516542.0633 5256846.9530 359.2805 516544.0940 5256845.7270 361.6310 2.0307 -1.2260 2.3505 301.0715 2.3721 1B1_07 520082.3401 5257453.3630 132.5842 520082.3100 5257452.0890 135.7340 -0.0301 -1.2740 3.1498 1.2113 1.2744 1B1_08 517316.4191 5258409.8570 367.1250 517318.4250 5258409.6620 366.9750 2.0059 -0.1950 -0.1500 275.3309 2.0154 1B1_10 517048.9390 5260565.5130 450.2904 517051.3230 5260564.7210 450.4900 2.3840 -0.7920 0.1996 288.2309 2.5121 1B2_01 521934.7996 5256622.6770 45.5947 521936.5310 5256621.2620 48.6450 1.7314 -1.4150 3.0503 309.1513 2.2361 1B2_02 522043.8188 5256839.1840 39.6236 522045.7400 5256837.4710 41.9240 1.9212 -1.7130 2.3004 311.4328 2.5740 1B2_03 521697.9850 5257023.2130 39.2630 521699.6620 5257021.5540 41.8130 1.6770 -1.6590 2.5500 314.4108 2.3589 1B2_04 521825.5608 5257347.6790 27.5097 521827.4820 5257345.9650 30.4600 1.9212 -1.7140 2.9503 311.4346 2.5746 1B2_05 521244.2592 5260185.9910 0.7069 521245.6920 5260184.3870 2.6070 1.4328 -1.6040 1.9001 318.1336 2.1508 1B2_06 521204.7098 5260177.4520 0.6069 521206.1430 5260175.8480 3.5070 1.4332 -1.6040 2.9001 318.1307 2.1510 1B2_07 523037.6283 5258693.1430 0.1624 523039.5490 5258691.4300 2.9120 1.9207 -1.7130 2.7496 311.4343 2.5736 1B2_08 523668.8882 5258150.3800 7.3790 523671.5410 5258148.5020 9.5790 2.6528 -1.8780 2.2000 305.1806 3.2503 1B2_09 523203.7541 5256484.9900 41.6249 523206.0290 5256483.7100 44.6750 2.2749 -1.2800 3.0501 299.2207 2.6103 1B2_10 522628.1784 5257380.9040 17.3837 522630.3430 5257379.1360 19.1840 2.1646 -1.7680 1.8003 309.1428 2.7949 1B2_11 522324.0339 5257408.2910 21.3512 522325.9550 5257406.5780 24.1010 1.9211 -1.7130 2.7498 311.4333 2.5739 1B2_13 524076.9939 5258096.5690 -0.0502 524079.4570 5258094.9900 2.5000 2.4631 -1.5790 2.5502 302.3956 2.9258 1B2_14 521030.8770 5256557.8930 82.4803 521032.2000 5256555.8020 85.3300 1.3230 -2.0910 2.8497 327.4050 2.4744 1B3_01 527445.4790 5260012.6560 8.9504 527447.5640 5260011.6750 12.8500 2.0850 -0.9810 3.8996 295.1214 2.3043 1B3_02 526240.7649 5259239.4720 9.0741 526242.3620 5259238.6000 9.2740 1.5971 -0.8720 0.1999 298.3803 1.8196 1B3_03_1 524934.1553 5257747.3290 6.2341 524936.9180 5257745.9390 7.3840 2.7627 -1.3900 1.1499 296.4230 3.0927 1B3_03_2 524927.0803 5257782.5930 6.2267 524929.7080 5257781.7460 9.7770 2.6277 -0.8470 3.5503 287.5157 2.7608 1B3_04 526289.6107 5257431.6620 13.5590 526291.8060 5257431.1680 16.9590 2.1953 -0.4940 3.4000 282.4010 2.2502 1B3_05 526224.2370 5257545.2780 21.0215 526226.5660 5257544.2410 23.7210 2.3290 -1.0370 2.6995 293.5942 2.5494 1B3_06 528311.9161 5259239.8820 104.9297 528312.8370 5259239.4190 107.4300 0.9209 -0.4630 2.5003 296.4131 1.0307 1B3_09 525376.5568 5256599.6490 43.5848 525378.2400 5256600.2950 45.8350 1.6832 0.6460 2.2502 249.0023 1.8029 1B3_10 524301.3349 5260570.4090 51.6418 524303.7430 5260568.5860 54.3920 2.4081 -1.8230 2.7502 307.0742 3.0203 1B3_11 523539.9911 5260765.5710 -2.1197 523542.3750 5260764.7790 -3.4700 2.3839 -0.7920 -1.3503 288.2256 2.5120 1B3_12 528188.5573 5257126.4120 -2.3928 528190.6730 5257126.7050 -0.3430 2.1157 0.2930 2.0498 262.0655 2.1359 1B3_15 528546.8730 5257092.0440 10.2284 528547.8490 5257091.8250 13.1780 0.9760 -0.2190 2.9496 282.3849 1.0003 1B3_16 528567.1831 5257096.5720 10.5894 528568.5120 5257096.7850 12.4890 1.3289 0.2130 1.8996 260.5338 1.3459 1B4_01 520370.4584 5255001.2570 167.4864 520371.8370 5254999.4100 168.9360 1.3786 -1.8470 1.4496 323.1606 2.3048 1B4_02 520425.2441 5255532.4430 112.9719 520426.8110 5255530.2970 115.3720 1.5669 -2.1460 2.4001 323.5208 2.6572 1B4_03 520408.7392 5255510.8930 114.2219 520409.6840 5255509.3990 118.1220 0.9448 -1.4940 3.9001 327.4102 1.7677 1B5_01 524435.5264 5256336.9990 19.8141 524437.8010 5256335.7190 22.3640 2.2746 -1.2800 2.5499 299.2232 2.6100 1B5_02 524524.0475 5254609.1740 41.5025 524525.9680 5254607.4610 44.5530 1.9205 -1.7130 3.0505 311.4411 2.5735 1B5_03 523996.6945 5254353.3170 58.7682 523998.1270 5254351.7130 60.3180 1.4325 -1.6040 1.5498 318.1404 2.1506 1B5_04 524088.4528 5253833.0170 95.0857 524090.4830 5253831.7920 96.7860 2.0302 -1.2250 1.7003 301.0638 2.3711 1B5_06 523943.2595 5256150.4760 25.0198 523945.7230 5256148.8970 27.4200 2.4635 -1.5790 2.4002 302.3912 2.9261 1B5_07 524040.4751 5255977.1920 29.3035 524043.2370 5255975.8010 31.2530 2.7619 -1.3910 1.9495 296.4330 3.0924 1B5_08 524381.5024 5253170.5180 183.7533 524383.9110 5253168.6950 187.6530 2.4086 -1.8230 3.8997 307.0721 3.0207 1B5_10 522577.1765 5254438.9760 157.7802 522578.6090 5254437.3720 160.5300 1.4325 -1.6040 2.7498 318.1357 2.1506 1B5_11 522751.1700 5254509.7650 137.7189 522753.3900 5254508.2410 139.4690 2.2200 -1.5240 1.7501 304.2834 2.6928 1B5_12 522150.8433 5255659.3960 96.8164 522150.4350 5255658.7190 98.9160 -0.4083 -0.6770 2.0996 31.0539 0.7906 1B5_13 523839.7101 5256333.7690 23.2047 523841.9850 5256332.4880 26.0050 2.2749 -1.2810 2.8003 299.2242 2.6108 1B5_15 523239.5673 5255453.5440 155.0340 523242.3300 5255452.1540 158.0840 2.7627 -1.3900 3.0500 296.4230 3.0927 1B6_01 524721.5468 5254838.3610 24.9900 524723.6870 5254837.6230 27.7400 2.1402 -0.7380 2.7500 289.0132 2.2639 1B6_02 524576.4623 5254867.2200 28.9348 524578.9810 5254865.8840 29.7350 2.5187 -1.3360 0.8002 297.5609 2.8511 1B6_03 524671.2421 5254578.8350 36.5935 524673.5710 5254577.7980 38.9440 2.3289 -1.0370 2.3505 294.0008 2.5493 1B6_04 525304.6625 5254737.5750 52.9904 525307.0470 5254736.7830 55.9900 2.3845 -0.7920 2.9996 288.2241 2.5126 1B6_05 524552.7865 5253726.1480 93.8594 524555.3050 5253724.8120 94.8090 2.5185 -1.3360 0.9496 297.5622 2.8509 1B6_06 524942.0197 5253464.4500 123.4042 524944.1600 5253463.7120 125.9540 2.1403 -0.7380 2.5498 289.0046 2.2640 1B6_07 525460.7668 5252874.0140 78.3815 525463.0960 5252872.9780 80.8320 2.3292 -1.0360 2.4505 293.5914 2.5492 1B6_08 526119.5120 5253671.0910 29.5483 526122.3840 5253670.1890 31.5480 2.8720 -0.9020 1.9997 287.2628 3.0103 1B6_09 525354.7392 5255359.6530 13.5233 525356.8790 5255358.9150 16.0230 2.1398 -0.7380 2.4997 289.0118 2.2635 1B6_10 528461.5954 5255801.7810 1.3629 528462.8150 5255801.5060 4.0130 1.2196 -0.2750 2.6501 282.4120 1.2502 1B6_14 524687.0220 5252131.3130 228.3833 524689.1070 5252130.3320 232.0830 2.0850 -0.9810 3.6997 295.1222 2.3043 1B6_16_0A 525993.4555 5255425.6770 -0.0184 525995.3520 5255424.9940 2.2820 1.8965 -0.6830 2.3004 289.4821 2.0157 1B6_16_0B 525994.9922 5255427.3060 -0.0231 525997.1870 5255426.8120 2.0770 2.1948 -0.4940 2.1001 282.4105 2.2497 1B6_16_0C 525975.8259 5255445.9100 -0.0126 525977.3990 5255446.0690 2.4870 1.5731 0.1590 2.4996 264.1342 1.5811 1B6_16_0D 525974.2441 5255444.2290 -0.0035 525975.2750 5255444.2530 2.3470 1.0309 0.0240 2.3505 268.3959 1.0312 1B6_17_0A 525771.1967 5255299.2950 4.9848 525773.9350 5255298.9350 7.0850 2.7383 -0.3600 2.1002 277.2923 2.7619 1B6_17_0D 525825.3478 5255290.3550 4.9406 525826.8070 5255290.2400 6.6740 1.4592 -0.1150 1.7334 274.3022 1.4637 1B6_17_0E 525832.7879 5255335.4070 4.9516 525834.2450 5255334.6920 6.5740 1.4571 -0.7150 1.6224 296.0814 1.6231 1B6_17_0F 525840.1708 5255380.4310 4.9526 525841.6830 5255379.1430 7.0740 1.5122 -1.2880 2.1214 310.2520 1.9864 1B6_17_0I 525786.0395 5255389.3510 4.9230 525788.8840 5255388.2030 6.6740 2.8445 -1.1480 1.7510 291.5842 3.0674 1B6_17_0J 525778.6079 5255344.3260 4.9383 525781.4460 5255343.7510 7.0240 2.8381 -0.5750 2.0857 281.2711 2.8958 1B6_18_0A 525727.1947 5255379.2970 3.2137 525728.7130 5255379.2120 6.0640 1.5183 -0.0850 2.8503 273.1215 1.5207 1B6_18_0D 525736.0904 5255433.4370 3.2066 525737.8270 5255432.2550 5.3140 1.7366 -1.1820 2.1074 304.1427 2.1007 1B6_18_0E 525691.0545 5255440.8310 3.1983 525693.1320 5255439.7470 5.0640 2.0775 -1.0840 1.8657 297.3317 2.3433 1B6_18_0F 525646.0415 5255448.2150 3.2057 525648.6260 5255446.9410 5.3640 2.5845 -1.2740 2.1583 296.1426 2.8814 1B6_18_0I 525637.1335 5255394.0810 3.2117 525640.1880 5255393.4900 5.2140 3.0545 -0.5910 2.0023 280.5702 3.1111 1B6_18_0J 525682.1462 5255386.6800 3.2223 525684.7490 5255386.5400 5.4140 2.6028 -0.1400 2.1917 273.0444 2.6066 1B6_19 526605.5934 5253786.0660 83.3369 526606.6240 5253786.0900 85.8870 1.0306 0.0240 2.5501 268.3957 1.0309 1B6_20 525750.5145 5252976.0440 52.5855 525752.3320 5252976.1480 57.4360 1.8175 0.1040 4.8505 266.4415 1.8205 1B6_21 524415.2052 5253773.5640 102.8670 524417.1810 5253772.0940 105.3170 1.9758 -1.4700 2.4500 306.3824 2.4627 1B6_22_0A 526335.4458 5253934.5450 85.0177 526337.8060 5253934.7830 86.8680 2.3602 0.2380 1.8503 264.1431 2.3722 1B6_22_0B 526337.6132 5253937.6420 85.0253 526339.0310 5253936.8130 86.6180 1.4178 -0.8290 1.5927 300.1855 1.6424 1B6_22_0C 526313.9306 5253954.2500 84.7857 526315.8350 5253953.3070 86.3680 1.9044 -0.9430 1.5823 296.2035 2.1251 1B6_22_0D 526311.6155 5253951.1450 84.8301 526313.6590 5253950.4650 86.4680 2.0435 -0.6800 1.6379 288.2420 2.1537 1B6_23 527486.2915 5252698.2750 19.2781 527488.0540 5252698.1350 21.6280 1.7625 -0.1400 2.3499 274.3155 1.7681 1B7_01 520303.0523 5248555.7440 689.8207 520303.6990 5248554.0610 693.8210 0.6467 -1.6830 4.0003 338.5850 1.8030 1B7_02 519309.5615 5250656.0970 1256.7574 519311.5130 5250655.6580 1259.2570 1.9515 -0.4390 2.4996 282.4041 2.0003 1B7_03 519316.5863 5250748.1190 1260.6792 519318.0500 5250747.7900 1262.7290 1.4637 -0.3290 2.0498 282.4005 1.5002 1B7_07 519321.5788 5250826.5130 1259.0221 519323.9630 5250825.7200 1257.1220 2.3842 -0.7930 -1.9001 288.2351 2.5126 1B7_09 519402.4983 5250745.0000 1258.2201 519404.0960 5250744.1280 1259.8200 1.5977 -0.8720 1.5999 298.3730 1.8202 1B7_10 519320.5868 5250669.0580 1257.3182 519322.4830 5250668.3750 1258.9680 1.8962 -0.6830 1.6498 289.4831 2.0155 1B7_12 519413.7240 5250786.0160 1256.8439 519415.9980 5250784.7360 1258.9440 2.2740 -1.2800 2.1001 299.2228 2.6095 1B8_01 522733.0340 5250245.5240 190.2642 522735.2540 5250243.9990 192.2640 2.2200 -1.5250 1.9998 304.2853 2.6933 1B8_02 523828.0043 5249000.3220 251.9132 523830.4130 5248998.4990 254.2130 2.4087 -1.8230 2.2998 307.0706 3.0208 1B8_03 523800.1500 5248990.4190 251.8862 523802.3700 5248988.8950 255.0860 2.2200 -1.5240 3.1998 304.2821 2.6928 1B8_04 521236.8777 5247756.5860 423.0093 521237.7680 5247754.8480 425.9590 0.8903 -1.7380 2.9497 332.5233 1.9528 1B8_04_A 521230.9450 5247763.4150 423.2605 521231.6460 5247761.9760 425.6100 0.7010 -1.4390 2.3495 334.0138 1.6007 1B8_05 521561.9281 5248345.7110 433.8235 521563.9590 5248344.4860 437.0730 2.0309 -1.2250 3.2495 301.0614 2.3717 1B8_06 523912.6526 5248541.3180 284.7487 523915.0060 5248539.2510 288.5490 2.3534 -2.0670 3.8003 311.1720 3.1322 1B8_08 522712.1234 5249281.3580 287.0207 522713.2030 5249279.3220 290.5710 1.0796 -2.0360 3.5503 332.0402 2.3045 1B9_01 526619.1199 5250362.5850 16.1257 526621.2600 5250361.8470 19.3260 2.1401 -0.7380 3.2003 289.0118 2.2638 1B9_02 526644.5885 5250260.4020 13.1207 526647.4600 5250259.4990 14.4210 2.8715 -0.9030 1.3003 287.2652 3.0101 1B9_03 525901.4316 5250400.8990 50.0069 525903.5720 5250400.1610 52.9570 2.1404 -0.7380 2.9501 289.0100 2.2641 1B9_04 525572.0960 5250498.3860 82.7498 525574.4800 5250497.5940 84.5000 2.3840 -0.7920 1.7502 288.2254 2.5121 1B9_05 525340.9723 5249563.0500 224.8773 525343.1130 5249562.3570 228.3770 2.1407 -0.6930 3.4997 287.5534 2.2501 1B9_06 524985.1346 5249369.9590 273.4112 524987.6290 5249369.6540 275.6110 2.4944 -0.3050 2.1998 276.5800 2.5130 1B9_07 528381.2208 5248675.1040 56.5304 528382.6290 5248674.5300 59.3800 1.4082 -0.5740 2.8496 292.0932 1.5207 1B9_08 528435.6921 5248332.7390 96.3145 528436.3450 5248333.3610 97.5650 0.6529 0.6220 1.2505 226.2318 0.9018 1B9_11 527233.6784 5250839.3120 -2.4923 527234.7330 5250838.3060 -0.4920 1.0546 -1.0060 2.0003 313.3856 1.4575 1B9_12 525299.2432 5251065.1480 47.1107 525301.1940 5251064.7090 49.3610 1.9508 -0.4390 2.2503 282.4056 1.9996 1B9_13 526190.8319 5250696.9770 37.7947 526193.2160 5250696.1850 40.7450 2.3841 -0.7920 2.9503 288.2259 2.5122 1B9_14 525602.4682 5248887.8400 254.3795 525604.6330 5248886.0720 254.6300 2.1648 -1.7680 0.2505 309.1419 2.7950 1B9_15 525598.7020 5248150.7630 256.6078 525600.7870 5248149.7810 259.1080 2.0850 -0.9820 2.5002 295.1311 2.3047 1B9_16 524709.9635 5249055.3150 351.6752 524712.1040 5249054.5770 354.4750 2.1405 -0.7380 2.7998 289.0132 2.2642 1B9_17 525078.7652 5248317.3220 232.1978 525081.0940 5248316.2850 233.9980 2.3288 -1.0370 1.8002 294.0011 2.5493 1
1.9330 -0.9764 2.2724 2.2670 2 0 9 103 TotalsMean = 1.9330 Mean =-0.9764 Mean = 2.2724 Mean = 2.2670Min = -0.4083 Min = -2.1460 Min = -1.9001 Min = 0.7906Max = 3.0545 Max = 0.6460 Max = 4.8505 Max = 3.2503
ConfidenceInterval
RMSE RMSE RMSE RMSE
Delta X (m) Delta Y (m) Delta Z (m)X, Y Resultant Distance (m)
68% 0.606 0.621 0.982 0.5590% 0.9996 1.024 1.62 0.90795% 1.187 1.217 1.924 1.07899% 1.563 1.602 2.533 1.419
Table B-1 - Hobart Ground Control Points ComparisonB-1
APPENDIX C
COMPILED DRAWINGS EXTRACTED FROM IKONOS STEREO IMAGERY
SHOWING FEATURES, CONTOURS AND PHOTO LOCATIONS
Figure C-1 - Compiled Drawings Extracted from IKONOS Stereo Imagery showing Features, Contours and Photo Locations over Urban Study Area C-1
Figure C-2 - Compiled Drawings Extracted from IKONOS Stereo Imagery showing Features, Contours and Photo Locations over CBD Study Area C-2
