CCI BIOMASS DATA ACCESS REQUIREMENTS DOCUMENT YEAR 1cci.esa.int/sites/default/files/Biomass D1.3...

CCI

BIOMASS

DATA ACCESS REQUIREMENTS DOCUMENT YEAR 1

VERSION 1.0

DOCUMENT REF: CCI_BIOMASS_DARD_V1

DELIVERABLE REF: D1.3-DARD

VERSION: 1.0

CREATION DATE: 2018-11-15

LAST MODIFIED 2018-11-15

Ref ESA CCI Biomass -Data Access Requirements Document

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© Aberystwyth University and GAMMA Remote Sensing, 2018 This document is the property of the CCI-Biomass partnership, no part of it shall be reproduced or transmitted without the

express prior written authorization of Aberystwyth University and Gamma Remote Sensing AG.

Document Authorship

NAME FUNCTION ORGANISATION SIGNATURE DATE

PREPARED R Lucas Aberystwyth Uni.

PREPARED P Bunting Aberystwyth Uni.

PREPARED A Siggins Aberystwyth Uni.

PREPARED H. Kay Aberystwyth Uni.

PREPARED M. Santoro Gamma RS

PREPARED

PREPARED

PREPARED

PREPARED

PREPARED

VERIFIED S. Quegan Science Leader Sheffield University

APPROVED

Document Distribution

ORGANISATION NAME QUANTITY

ESA Frank Seifert

Document History

VERSION DATE DESCRIPTION APPROVED 0.1 2018-10-01 First draft version

1.0 2018-11-15 Finalised version

Document Change Record (from Year 1 to Year 2)

VERSION DATE DESCRIPTION APPROVED


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TABLE OF CONTENTS

List of Figures ..................................................................................................................... 5

List of Tables ...................................................................................................................... 6

Symbols and acronyms ....................................................................................................... 7

1. Introduction ................................................................................................................ 9

1.1. Purpose and scope ........................................................................................................... 9

1.2. Applicable Documents .................................................................................................... 10

2. Background ................................................................................................................ 10

2.1. The CCI Biomass Project ................................................................................................. 10

2.2. Need for global biomass data ......................................................................................... 10

2.3. Summary of Data Specific User Requirements ................................................................ 11

3. Processing plan .......................................................................................................... 11

3.1. Phase 1 Processing Chain ................................................................................................ 11

3.2. Phase 1 Products ............................................................................................................ 12

3.3. Main satellite input data for Phase 1 .............................................................................. 12 3.3.1. Satellite sensor data .................................................................................................................... 12 3.3.2. Products derived from satellite sensor data ............................................................................... 13

4. Satellite data requirements for global AGB maps ....................................................... 15

4.1. Satellite sensor data ....................................................................................................... 15 4.1.1. X-band SAR data .......................................................................................................................... 18 4.1.2. C-band SAR data .......................................................................................................................... 18 4.1.3. L-band SAR .................................................................................................................................. 19 4.1.4. Spaceborne LIDAR ....................................................................................................................... 20 4.1.5. Spaceborne optical data .............................................................................................................. 20

4.2. Phase 1 Processing Chain ................................................................................................ 20

4.3. Satellite sensor data ....................................................................................................... 20

4.4. Satellite sensor data ....................................................................................................... 21

5. Requirements for supportive ancillary data ................................................................ 23

5.1. Biomes ........................................................................................................................... 23

5.2. Vegetation ..................................................................................................................... 25 5.2.1. Vegetation lifeform ..................................................................................................................... 25 5.2.2. Canopy cover ............................................................................................................................... 25 5.2.3. Canopy height ............................................................................................................................. 26 5.2.4. Leaf type: Broad-leaved, Needle-leaved or Aphyllous ............................................................... 26 5.2.5. Phenology: Evergreen, Semi-Evergreen and Deciduous forests. ................................................ 26 5.2.6. Vegetation stratification ............................................................................................................. 26 5.2.7. Dominant plant types and species .............................................................................................. 27 5.2.8. Vegetation optical depth ............................................................................................................. 27


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5.3. Topography .................................................................................................................... 30

5.4. At-ground Surface Conditions ......................................................................................... 30 5.4.1. Urban environments ................................................................................................................... 30 5.4.2. Bare surface materials ................................................................................................................ 31 5.4.3. Water inundation ........................................................................................................................ 31 5.4.4. Woody plant structures .............................................................................................................. 31

5.5. At surface and within volume environmental conditions ................................................ 35 5.5.1. Land Surface Temperatures ........................................................................................................ 35 5.5.2. Precipitation ................................................................................................................................ 35 5.5.3. Snow Cover (Extent and Fraction) ............................................................................................... 40 5.5.4. Soil Moisture ............................................................................................................................... 40 5.5.5. Fires (Extent and Severity). ......................................................................................................... 40 5.5.6. Sea Level ...................................................................................................................................... 40

6. Requirements for validation data ............................................................................... 42

6.1. Validation data ............................................................................................................... 42

7. Data procurement process ......................................................................................... 54

7.1. Procurement and agreements ........................................................................................ 54

8. Science References ..................................................................................................... 55


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LIST OF FIGURES

Figure 1: The processing chain behind the CCI Biomass Project .......................................................................... 12

Figure 2. An overview of the satellite sensor data to be used for generating global AGB maps. ........................ 13

Figure 3. Overview of products used to support and refine woody AGB retrievals. ............................................ 14

Figure 4. The FAO Land Cover Classification System (LCCS) Taxonomy. .............................................................. 15

Figure 5. Overview of at-ground surface conditions ............................................................................................ 30

Figure 6: Influence of environmental conditions on biomass retrieval algorithms ............................................... 35


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LIST OF TABLES

Table 1. Applicable documents ............................................................................................................................ 10

Table 2. Satellite sensor data sources with known sensitivity to AGB ................................................................. 16

Table 3. Satellite products used to generate the global AGB change maps ......................................................... 16

Table 4. Satellite sensor data needed to generate the AGB and AGB change maps. Note: information for datasets to which access has not been confirmed (JERS-1 single scenes) is not provided. .................................. 17

Table 5. Characteristics of data needed for pre-processing (orthorectification and topographic correction) of satellite sensor data. ............................................................................................................................................. 21

Table 6. Characteristics of data needed for landscape segmentation ................................................................. 21

Table 7. Characteristics of additional data required for pre-processing satellite sensor data ............................. 22

Table 8. Data required for biome definition ......................................................................................................... 24

Table 9. Data layers required for refining quantitative descriptions of vegetation structure and floristics. ....... 28

Table 10. Additional data layers required for refining quantitative descriptions of vegetation structure and floristics ................................................................................................................................................................. 29

Table 11. Layers required for quantitative descriptions of actual/probable at-ground surface states. ............... 33

Table 12. Additional layers required for quantitative descriptions of actual/probable at-ground surface states. ............................................................................................................................................................................... 34

Table 13. Additional layers required for quantitative descriptions of actual/probable at-ground surface states. ............................................................................................................................................................................... 37

Table 14. Layers required for quantitative descriptions of at-surface and within-volume conditions ................. 38

Table 15 Layers required for quantitative descriptions of at-surface and within-volume conditions. ................ 39

Table 16. Potential validation sites for CCI Biomass ............................................................................................. 43

Table 17. Validation sites being used in CCI Biomass where further measurements are being acquired. ........... 48

Table 18. Ground and airborne datasets and derived products used for validating the CCI Biomass Product. ... 53

Table 19. Additional datasets providing support to the generation of the CCI Biomass global AGB map ........... 53

Table 20. Agreements for data access by the CCI-Biomass project in Phase I. .................................................... 54

Table 21. Agreements for data access by the CCI-Biomass project in Phase 2. ................................................... 55


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SYMBOLS AND ACRONYMS

(A)ATSR (Advanced) Along Track Scanning Radiometer

ALOS Advanced Land Observing Satellite

ASAR Advanced Synthetic Aperture Radar

AOD Aerosol Optical Depth

ATBD Algorithm Theoretical Basis Document

CCI Climate Change Initiative

CCI-Biomass Climate Change Initiative – Biomass

CEOS Committee on Earth Observation Satellites

CEOS-WGCV Committee on Earth Observing Satellites Working Group on Calibration and Validation

CMC Climate Modelling Community

CMUG Climate Modelling User Group

CRS Coordinate Reference System

DARD Data Access Requirements Document

DEM Digital Elevation Model

DLR Deutsches Zentrum für Luft- und Raumfahrt

ECV Essential Climate Variables

EEA European Environmental Agency

ENVISAT ESA Environmental Satellite

EO Earth Observation

ERS European Remote Sensing Satellite

ESA European Space Agency

FAO Food and Agriculture Organization

GCOS Global Climate Observing System

GCS Geographic Coordinate System

GDAL Geospatial Data Abstraction Library

GFED Global Fire Emissions Database

GlobCover ESA DUE project

GLWD Global Lakes and Wetlands Database

GTOS Global Terrestrial Observing System

HH Horizontal-Horizontal

HV Horizontal-Vertical

ICESAT GLAS Ice, Cloud, and land Elevation Satellite Geoscience Laser Altimeter System

IIASA International Institute of Applied Systems Analysis

IMS Interactive Multisensor Snow and Ice Mapping System

JAXA Japan Aerospace Exploration Agency


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JRC Joint Research Centre

L1, L2, L3 Level 1, Level 2, Level 3 etc.

LC Land Cover

LCCS Land Cover Classification System

MERIS Medium Resolution Imaging Spectrometer

MMU Minimum Mapping Unit

MVC Maximum Value Composite

NASA National Aeronautics and Space Administration

NDVI Normalized Difference Vegetation Index

NSIDC National Snow and Ice Data Center

PALSAR Phased Array type L-band Synthetic Aperture Radar

PFT Plant Function Type

PSD Product Specification Document

PUG Product User Guide

PVASR Product Validation and Algorithm Selection Report

PVP Product Validation Plan

S1, S2 Sentinel-1, Sentinel-2

SAR Synthetic Aperture Radar

SLC Single Look Complex

SLSTR Sea and Land Surface Temperature Radiometer

SPOT Satellite Pour l'Observation de la Terre

SPOT-VGT SPOT-VEGETATION

SR Surface Reflectance

SRTM Shuttle Radar Topography Mission

SWBD SRTM Water Body Dataset

TM Thematic Mapper

UNFCCC United Nations Framework Convention on Climate Change

UR User Requirement

USGS United States Geological Survey

WGS84 World Geodetic System 84

WSM Wide Swath Mode

WWF World Wildlife Fund


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1. Introduction

1.1. Purpose and scope

The Data Access Requirement Document (DARD) identifies all data required as input to the European Space Agency’s (ESA) Climate Change Initiative (CCI) Biomass project. The document describes the datasets that are needed to generate global maps of the Essential Climate Variable (ECV) above ground biomass (AGB) in woody vegetation for the mid 1990s, 2007-2010, 2017/18 and 2018/19. Consideration is also given to forthcoming sensors for subsequent and future epochs.

The document identifies:

• Satellite sensor data from the European Space Agency (ESA) and Third-Party Missions (TPM)

• Additional datasets that support the retrieval of AGB at a global level.

• In situ and relevant airborne observation data sources to support development and validation of retrieval algorithms.

For each data source, the DARD includes:

• Information about all data, products and data sources.

• Differentiation of the data sources (satellite sensor data required for AGB retrieval, data to support satellite data pre-processing, spatial and temporal coverages, data volumes (where known) and formats).

• Reference to technical specification documents.

• Indications of data quality and reliability (where appropriate and available).

• An overview of data access and a description of the ordering delivery mechanisms. Focus is on the use of freely available data.

The DARD establishes how and where different datasets can lead to improvements in the retrieval of the AGB.


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1.2. Applicable Documents

Table 1. Applicable documents

ID TITLE ISSUE DATE

AD-1 Invitation to Tender AO/1-9041/17/I-NB

AD-2 Response to Invitation to ESA Tender AO/1-9041/17/I-NB

AD-3 D1.1 User Requirements Document

AD-4 D1.2 Product Specification Document

AD-5 CCI Fire – Overview: https://www.esa-fire-cci.org/node/1

AD-6 CCI Landcover – Overview: https://www.esa-landcover-cci.org/?q=overview

AD-7 CCI High-Resolution Landcover: http://cci.esa.int/HRLandcover

AD-8 CCI Land Surface Temperature: http://cci.esa.int/lst

AD-9 CCI Permafrost: http://cci.esa.int/Permafrost

AD-10 CCI Snow: http://cci.esa.int/node/274/

AD-11 CCI Soil Moisture - Overview: https://www.esa-soilmoisture-cci.org/node/93

2. Background

2.1. The CCI Biomass Project

The ESA CCI programme aims to realize the full potential of the long-term global Earth Observation (EO) archives as a significant and timely contribution to the ECV databases required by United Nations Framework Convention on Climate Change (UNFCCC). All products will be assessed against requirements from the Global Climate Observing System (GCOS) for ECV and the Climate Modelling Community (CMC), represented within the CCI program by the Climate Modelling User Group (CMUG).

The CCI Phase I provided a unique opportunity for the European EO science community to define and validate innovative approaches for continuously generating and updating a comprehensive and consistent set of ECV global satellite-based data products in the long term – i.e. decades hence. The focus was on a major sustained and coordinated scientific effort to review and improve underlying processing, retrieval and validation methods.

2.2. Need for global biomass data

A substantive amount of carbon is stored within the Earth’s vegetation and hence changes in vegetation biomass in terms of amount and spatial extent has major implication on global climate. In the past, many of these changes have largely been natural but substantive losses have occurred due primarily to


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a rapid and accelerating increase in human populations. Such changes have been either direct (e.g., clearance of forests so support expansion of agriculture and settlements) or indirect (through negative or positive feedback mechanisms which have led to variability in global climate). Hence, biomass has been defined by GCOS as one of 50 ECVs. It is a critical component of the Earth System as changes impact on surface energy budgets and land surface water balances, influencing the concentration of greenhouse gases in the atmosphere and impacting on ecosystem services.

2.3. Summary of Data Specific User Requirements

The CCI Biomass Project will generate four datasets that quantify the global distribution of live AGB of woody vegetation from EO data and across several decades and years (mid 1990s, 2007-2010, 2017/18 and 2018/19). The resulting products will be made available to climate change scientists to advance understanding of the global carbon cycle and climate system and future climate change scenarios. CCI Biomass recognizes the requirements of GCOS and the climate science community as well as stakeholders with interests in this area (e.g., carbon scientists and the REDD+ community). For the climate modelling community, the requirement is for AGB to be provided wall-to-wall over the entire globe for all major woody biomes at a 500 m to 1 km spatial resolution and a relative error of less than 20% where AGB exceeds 50 Mg ha-1 and a fixed error of 10 Mg ha-1 where the AGB is below that limit.

3. Processing plan

3.1. Phase 1 Processing Chain

In Phase 1, the CCI Biomass Project will be delivering global spatial datasets of the AGB in woody vegetation using available datasets, initially for 2017/18 (in Year 1) and then for 2018/19 using primarily ALOS-2 PALSAR-2 and Sentinel-1 SAR data but including (if available) data from other sensors (e.g., GEDI). The processing system will be developed to facilitate its application in future epochs and using new sensors (including the NASA GEDI, NASA/ISRO’s NISAR and ESA’s BIOMASS) and also those prior, including the mid 1990s and 2007-2010. These epochs have been selected largely because of the availability of L-band Synthetic Aperture Radar (SAR), which shows greatest sensitivity of AGB because of greater penetration of L-band microwaves into the forest volume and greater interaction with woody components. However, C-band SAR data are also to be used and all retrievals will be informed by referencing a range of environmental variables that influence microwave interactions. An overview of the processing chain (for the latter epochs) is given in Figure 1. A thorough description of the CCI-AGB processing chain is available in the CCI Biomass Phase 1 Algorithm Theoretical Basis Document (ATBD).


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Figure 1: The processing chain behind the CCI Biomass Project

3.2. Phase 1 Products

At the end of the 3-year long Phase 1, the key global datasets for the end-users will:

1) Global maps of AGB at 100 m spatial resolution for the following epochs:

a. 2017/18

b. 2018/19

c. 2007-2010

2) Global maps of AGB change between all epochs at 100 m spatial resolution.

3) Associated estimates of uncertainty.

3.3. Main satellite input data for Phase 1

In Phase 1 the CCI Biomass project aims to generate global maps of the AGB of woody vegetation for the latter epochs (2007-2010, 2017/18 and 2018/19) with appropriate validation (Technical Proposal, 2017). In addition, the estimation of biomass for the mid-1990s will be investigated in terms of the possibilities offered by the EO data available at that time (primarily Japanese Earth Resources Satellite (JERS-1) SAR. To achieve this, the project will focus on relevant satellite sensor data and derived products available for these epochs.

3.3.1. Satellite sensor data

The main satellite sensor data of relevance to the global retrieval of AGB in woody vegetation at the global level will be Synthetic Aperture Radar (SAR) data (X-, C-, S- and L-band), surface reflectance (%) from optical sensors and Light Detection and Ranging (LIDAR), as indicated in Figure 2. These data are provided by increasingly diverse range and number of sensors (operated by space agencies, including the ESA, the National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA). Not all of the satellite sensor data indicated in Figure 2 will be used because of limitations in coverage (e.g., The ERS-1 SAR), cost of purchase (e.g., Tandem-X backscatter data and high-resolution Digital Terrain Models (DTMs) and the experimental nature of some (e.g., MOLI, NOVASAR).


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Figure 2. An overview of the satellite sensor data to be used for generating global AGB maps.

3.3.2. Products derived from satellite sensor data

Satellite observations, whether from radar, optical or lidar sensors, are influenced by spatial and temporal variability in Earth states and conditions (Figure 3). Hence, a number of satellite products will be used to better describe these, with particular focus on:

a) The vertical and horizontal of plant components (i.e., foliage, branches, trunks and above ground roots; i.e., vegetation structure), floristics, seasonality and plant functional types (PFTs).

b) Topographic position (e.g., topographic altitude, slope and aspect). c) At-ground surface states (e.g., urban areas, bare surface materials, inundation (including tidal)

state). d) At surface and within volume environmental conditions (e.g., snow cover, vegetation moisture,

freeze/thaw state).

In the cases of c) and d), consideration will be given to the conditions prevailing at or close to the time of the acquisition of the satellite sensor data as well as over longer time-frames (e.g., water or snow hydroperiod).


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Figure 3. Overview of products used to support and refine woody AGB retrievals.

The derived data layers largely align with components of the Food and Agriculture Organisation’s (FAO) Land Cover Classification System (LCCS) taxonomy (Figure 4) and can (in some cases) provide more refined descriptions of land cover types and states and often at higher spatial resolution compared to the CCI Land Cover (LC) products. These descriptions can be enhanced using environmental variables not used in the LCCS taxonomy (e.g., dominant species type). Nevertheless, layers generated through the sister CCI project Land Cover (LC) can also be used.

A particular advantage of the approach adopted is that the ancillary data layers are based on robust units of measurement, including canopy height and layering (m), canopy cover (%), water and snow hydro-period (days) and land surface temperature (oC). As such, these units are the target variables for many existing and forthcoming sensors. For example, canopy height has been, is being or will be quantified by sensors such as the Shuttle Radar Topographic Mission (SRTM), ICESAT GLAS, Tandem-X, ICESAT-2 and GEDI. Hence, focusing on retrieval of unit measures provides capacity for more consistent retrieval and change comparisons over time and hence supports the longevity of the approach.


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Figure 4. The FAO Land Cover Classification System (LCCS) Taxonomy.

4. Satellite data requirements for global AGB maps This section defines the data requirements for the processing chain needed to retrieve AGB for the different epochs.

4.1. Satellite sensor data

The primary focus of the CCI Biomass project is to make use of the sensitivity of both C- and L-band SAR backscatter data to AGB but to also maximise opportunities for utilizing information on forest height (e.g., from interferometric SAR and spaceborne LIDAR) as this measure provides opportunities for refined retrieval of biomass or where retrieval from SAR data is compromised. The main satellite sensors to be used are listed in Table 2 and the change products will be based on comparison between epochs (Table 3). An overview of the availability and characteristics of these satellite sensor data is provided in Table 4.


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Table 2. Satellite sensor data sources with known sensitivity to AGB

GLOBAL AGB DATABASE REFERENCE

PERIOD DATA SOURCE

Current baseline map of woody AGB

2017-2018 ALOS-2 PALSAR-2 L-band HH and HV backscatter data (25 m) Sentinel-1 C-band SAR dual-pol backscatter data (150 m) Landsat-8 surface reflectance mosaic (30m) Tandem-X Interferometric products (90 m)

Woody AGB from past and future epochs

2018-2019 ALOS-2 PALSAR-2 L-band SAR HH and HV backscatter data (25 m) Sentinel-1 C-band SAR dual-pol backscatter data (150 m) Landsat-8 surface reflectance mosaic (30m) Tandem-X Interferometric products (90 m) ICESAT-2 GEDI

2007-2010 ALOS-1 PALSAR-1 L-band SAR HH and HV backscatter data (25 m) ENVISAT ASAR C-band SAR single-pol backscatter data (1,000m) Landsat-7 surface reflectance mosaic (30m) SRTM Interferometric products (90m)

1996 JERS-1 L-band SAR HH-pol backscatter data (30m) ERS-1/2 C-band SAR VV-pol backscatter and coherence data (20m) SRTM Interferometric products (90m)

Table 3. Satellite products used to generate the global AGB change maps

GLOBAL AGB DATABASE REFERENCE

PERIOD DATA SOURCE

Datasets generated in Table 2.

Change between 2017/18 and 2018/19

AGB maps for 2017/18 and 2018/19

Change between 2007-2010 and 2017/18

AGB maps for 2007-10 and 2017/18

Change between mid 1990s and 2007-2010

AGB maps for 1990s and 2007-2010


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© Aberystwyth University and GAMMA Remote Sensing, 2018 This document is the property of the CCI-Biomass partnership, no part of it shall be reproduced or transmitted without the express prior written authorization of Aberystwyth University and Gamma

Remote Sensing AG.

Table 4. Satellite sensor data needed to generate the AGB and AGB change maps. Note: information for datasets to which access has not been confirmed (JERS-1 single scenes) is not provided.

PRODUCT SOURCE ACCESS COVERAGE VOLUME (Tb) FORMAT COMMENTS SATELLITE SENSOR SPATIAL TEMPORAL INPUT OUTPUT

Envisat

ASAR WSM ESA Cat 1 Proposal 9204 Open Global 2002-2012 30 Available at Gamma

Envisat

ASAR IMM ESA Cat 1 Proposal 9204 Open Global 2002-2012 2 Available at Gamma

Envisat

ASAR IMM ESA Cat 1 Proposal 9204 Open Global 2002-2012 8 Available at Gamma

Sentinel-1A/B

IWS ESA Google Earth Engine1

Open Global 2014 & 2016 -

100 yr-1 Available at Gamma *Processed data

ALOS-1

PALSAR- 1 JAXA Open Global (25 m)

2006-2011 TBD Available at Gamma and Aberystwyth U.

ALOS-2

PALSAR- 2 JAXA Open Global (25m)

2015-2016 TBD Available at Gamma and Aberystwyth U.

ICESAT

GLAS NSIDC Open Global 2003-2008 0.06 Available at Gamma and Aberystwyth U.

ICESAT-2

NASA Open Global 2018 - TBD TBD

GEDI NASA Open 60°N-56°S 2018- < 1 Access online

SRTM NASA Open 60°N - 56°S (90 m)

2000 < 1 Access online

TANDEM-X

DLR Global 2009-present

TBD Access online (to DTM)

1Processed datasets


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4.1.1. X-band SAR data

The primary source of the X-band SAR data is the Tandem-X mission, which was launched by the Deutsches Zentrum für Luft- und Raumfahrt (DLR). TanDEM-X consists of two twin satellites (TerraSAR-X and TanDEM-X) flying in an interferometric constellation with zero temporal baseline. Data have been acquired since 2009 and were coordinated to provide multiple interferometric pairs with the aim of characterizing land elevation globally. In particular, the interferometric data from which elevation is estimated provides information on the height of vegetation. However, an estimate of vegetation height requires an accurate and high-resolution digital terrain model (DTM) and a modelling framework that relates vegetation parameters to the scattering phenomena in the vegetation layers (e.g., the Interferometric Water Cloud Model or the Random Volume over Ground model). TanDEM-X data are not free of charge; even for scientific use, a limited number of scenes can be obtained. Most appealing to the CCI Biomass project are global datasets generated from backscatter and interferometric data at reduced resolution (i.e., 3 arcseconds DEM, single-pass coherence as well as SAR backscatter), which are becoming available while the project is ongoing. The global DTM was released in 2018 albeit at 90 m spatial resolution.

4.1.2. C-band SAR data

C-band microwaves largely interact with the upper surface of vegetation canopies and single date imagery are limited for retrieval of AGB. However, dense time-series of ENVISAT data have been shown to facilitate retrieval of AGB with limited saturation and Sentinal-1 C-band SAR data are showing similar potential. Because of the long repeat-pass interval between acquisition (6- to 35-days), interferometric datasets are not sufficiently reliable to characterize biomass globally and are, therefore, not addressed in this document. The ENVISAT Advanced SAR (ASAR) Wide Swath Mode (WSM) is processed to 150 m spatial resolution and the swath width is 400 km. First operational in 2002, the mission ended in 2012. While operational, the ENVISAT provided data on a 35-day orbit, with daily repeat observations at the equator and weekly at the poles possible with beam steering from different orbits. The most suitable modes for operation were the Image mode (IM), wide swath mode, and global monitoring mode (GMM). The GMM was intended for monitoring change at a global scale but is the lowest resolution (1 km). The WSM is at finer resolution (150 m) but the swath width is limited to 400km. The IM can capture data at higher resolutions (ranging from 30m to 150m) with narrower swath widths of between 56-100 km. The global mode of operation was specifically designed such that maps could be produced and compared across dates as sources of variance caused by the sensor were able to be reduced or removed. Bringing all ASAR observations to 1 km spatial resolution, allowed for global coverage and almost daily observations, thus providing sufficient data to generate moderate resolution estimates of biomass globally (as demonstrated through the ESA DUE GlobBiomass project). Sentinel-1 SAR have been acquiring C-band since 2014 with one unit (1A) and since 2016 with two units (1A and 1B). Following a ramp-up phase, both satellites acquired data on a routine basis and pre-developed observational plans. Sentinel-1A and -1B operate over land in the Interferometric Wide Swath mode, achieving a spatial resolution of approximately 20 m and covering a swath of 250 km. This results in large data volumes and has resource implications in terms of time and costs. As a trade-off


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between data amount and preservation of details in the SAR imagery, it was decided to process Sentinel-1 data to 150 m spatial resolution. Herewith, the Sentinel-1 data would be consistent with the previous use of the ENVISAT WSM. Sentinel-1 data are available free of charge from various platforms (Copernicus hub, Amazon Web Service, Google Earth Engine) with the possibility of implementing pre-processing tools to obtain the data in desired projection and formats. Sentinel-1 also acquires in the Extended Wide Swath mode (EWS), mostly over land of polar regions. EWS has larger swath width than IWS but reduced spatial resolution. Its use is not intended unless the coverage of IWS does not guarantee the performance of multi-temporal biomass retrieval approaches in which case EWS might still be useful for gap filling in some cases. Interferometric C-band SAR data were acquired by the Shuttle Radar Topographic Mission (SRTM) in 2000 and have been used to provide information on the height of several forest types (e.g., mangroves) and across regions. These data have potential to provide estimates of AGB through relationships established with the canopy height. However, as in the case of TanDEM-X, canopy height estimation requires information on the elevation of the terrain and a modelling framework to relate vegetation height to the height estimated with interferometry. During the 1990s, the European Remote Sensing (ERS) satellites -1 and -2 acquired C-band data every 35 days over several regions repeatedly. Between 1995 and 1999, ERS-1/-2 flew in a tandem constellation with 1-day repeat-pass, allowing for high-quality coherence datasets. Coherence was found to be particularly suitable for retrieving the growing stock volume (GSV; which relates to biomass) in boreal forests and distinguishing differentiating biomass levels in other biomes. Together with JERS-1 SAR backscatter and coherence, the ERS-1/-2 coherence will be assessed to demonstrate global mapping of biomass for the 1990s.

4.1.3. L-band SAR

L-band SAR is sensitive primarily to the woody components of vegetation, with interactions being primarily but not exclusively with trunks at HH (through double bounce scattering) and larger branches at HV (volume scattering). To date, only the Japanese L-band SAR operated by the JAXA have provided global coverage for the mid 1990s (JERS-1 SAR), 2006-2011 (Advanced Land Observing Satellite (ALOS) Phase Arrayed L-band SAR (PALSAR)) and 2015 to the present day (2018; ALOS-2 PALSAR-2).

While JERS-1 could acquire only VV-polarized data at approximately 30 m spatial resolution, the ALOS PALSAR provided Fine Beam Single (FBS), Fine Beam Dual (FBD), Polarimetric (PLR) and ScanSAR data, with a spatial resolution of 20 m, 25 m, 30 m and 100 m, respectively. The ALOS-2 PALSAR-2 instrument operates FB and ScanSAR modes in dual-polarization at higher spatial resolution compared to ALOS-1 (20 m and 50 m) as well as the PLR mode. The main advantage of the ALOS-1 PALSAR-1 and ALOS-2 PALSAR-2 data is that they were acquired through a carefully designed data acquisition strategy.

Currently, JERS-1 SAR data are available free of charge whereas access to ALOS-1 PALSAR-1 and ALOS-2 PALSAR-2 data are restricted to a limited number of scenes per year. Global datasets of JERS-1 SAR, ALOS-1 PALSAR-1 and ALOS-2 PALSAR-2 have been released by JAXA in the form of mosaics of the SAR backscatter with a nominal spatial resolution of 25 m. The mosaics are provided on an annual basis (1996, 2007-2010, 2015-2017) and are generated from strip data that have been path processed, ortho-corrected and topographically adjusted and combined using the Sigma-SAR IMAGE and MOSAIC processing by JAXA.

In addition, mosaics of ALOS PALSAR and ALOS-2 PALSAR-2 ScanSAR backscatter data have been generated by JAXA on a cycle basis (46 days). These are available to the Science Team of JAXA’s Kyoto


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and Carbon (K&C) Initiative but are still not publicly released. These data are available at 50 m spatial resolution. However, the main limitation is that the ScanSAR data are only available for the tropics and, as with the FBD mosaics, these are not spatially consistent, and geolocation is not optimal.

4.1.4. Spaceborne LIDAR

NASA’s Ice, Cloud and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) provided spaceborne LIDAR data from 2003–2008 and data for 2018 will be provided by NASA’s ICESAT-2 at a global level and Global Ecosystem Dynamics Investigation (GEDI) LIDAR (primarily between 50o N and 50o S) from late 2018. ICESat 2 was successfully launched in October 2018. ICESAT GLAS was primarily designed to measure ice sheet mass balance, atmospheric aerosols, as well as land topography over the polar regions. However, GLAS also made select (samples not raster) measurements around the globe and provided height information (including vertical profiles of the distribution of plant material) for vegetated areas. Both ICESAT-2 and GEDI will be providing products, including canopy cover, ground height and canopy height as gridded (raster) layers.

4.1.5. Spaceborne optical data

Optical data that are useful to CCI Biomass are primarily from the Landsat series. In GlobBiomass, the global mosaic of Landsat-7 reflectances from the Global Forest Change endeavour (https://earthenginepartners.appspot.com/science-2013-global-forest) were used to support the re-scaling of C-band 1,000 m estimates of biomass to a pixel size of 30 m. The same use is envisaged in CCI Biomass, supporting the re-scaling of Sentinel-1 estimates of biomass to the spatial resolution of the ALOS mosaics. Landsat data are preferred to Sentinel-2 data because the data are in an already ready-to-use form.

In addition, optical data can be used to segment the global landscape to better establish the distribution of AGB (e.g., in riverine zones, remnant strips) but also in the retrieval of canopy cover (which can relate to AGB). These data are not described in these sections but instead are outlined later. To this scope, Sentinel-3 data may also be of interest provided that global composites become available over the course of the project. Data requirements for pre-processing

4.2. Phase 1 Processing Chain

This section defines the data requirements for the pre-processing chain for satellite sensor data.


The data acquisition and processing will be undertaken for each epoch, initially for 2017/18 and then for 2018/19 and 2007-10 with change measures then used. In addition, a proof of concept for the 1990s will be considered.

The characteristics and availability of the datasets required for pre-processing of spaceborne SAR, optical and/or LIDAR data are outlined in Table 5, these data include digital elevation models required for orthorectification and topographic correction of the satellite sensor data (


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Table 6). The data of image acquisition, including with mosaics, is essential in order to determine the environmental conditions occurring at the time of the overpass (Table 7).


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Table 5. Characteristics of data needed for pre-processing (orthorectification and topographic correction) of satellite sensor data.

PRODUCT SOURCE ACCESS COVERAGE VOLUME (Tb) FORMAT COMMENTS SPATIAL TEMPORAL INPUT OUTPUT

SRTM-3 DEM, v 4.1

srtm.csi.cgiar.org viewfinderpanaramas.org

Open +/- 60°N (90 m)

2000 0.0134 Available at Gamma, and FSU Jena

Russian topographic maps

viewfinderpanaramas.org Open > 60°N 1950-2000 0.0094 Available at Gamma, and FSU Jena

Canadian Digital Elevation Dataset

www.geobase.ca/geobase/en/data/cded/index.html

Open > 60°N 1950-2010 0.0018 Available at Gamma, and FSU Jena

Elevation dataset of Alaska

www.webgis.com/terr_us1 deg.html

Open > 60°N Undated 0.002 Available at Gamma, and FSU Jena

TanDEM-X DEM

Open1 90 m 2011-2015 TBD Available at Gamma, FSU Jena and Aberystwyth U.

1For science use; 12 and 30 m datasets available through application but restricted in volume.

Table 6. Characteristics of data needed for landscape segmentation

PRODUCT SOURCE ACCESS COVERAGE VOLUME FORMAT COMMENTS SPATIAL TEMPORAL INPUT OUTPUT

Sentinel-1

ESA data hub Open 10 m 5 days Geotiff

Available at Gamma

Sentinel-21 MSI Level 1

ESA data hub Open 10 -20 m 5 days 165 72 Geotiff

Landsat-8 OLI/TIRS L1T2

earthexplorer.usgs.gov Open 30 m 16 days 33 24 Geotiff

1 Sentinel-2 Multi-Spectral Instrument (MSI) L1: Data volume has been estimated on the assumption that S2A generates 750 TB L1

2 Landsat 8 L1 data volume has been extrapolated from one scene (1 Gb per scene)


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Remote Sensing AG.

Table 7. Characteristics of additional data required for pre-processing satellite sensor data

PRODUCT SOURCE ACCESS COVERAGE VOLUME FORMAT COMMENTS SPATIAL TEMPORAL INPUT OUTPUT

ENVISAT ESA Cat 1 Proposal 9204 Open Global 30 Available at Gamma Sentinel-1 IWS date of acquisition ESA

Google Earth Engine1 Open Global 100 yr-1 Available at Gamma

*Processed data ALOS PALSAR date of acquisition JAXA Open Global

(25 m) 2006-2011 Available at Gamma and

Aberystwyth U. ALOS-2 PALSAR-2 date of acquisition

JAXA Open Global (25m)

2015-2016 Available at Gamma and Aberystwyth U.

ENVISAT Attitude and Orbit

earth.esa.int 2002-2012 0.011 Pre-processing undertaken within AMORGOS


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5. Requirements for supportive ancillary data This section defines the data requirements for the processing chain needed to retrieve AGB for the different epochs.

Following from the ESA DUE project, the CCI Biomass is focusing on refining algorithms for different biomes and primarily the wet tropics, dry tropics and boreal and temporal regions. However, previous definitions of these biomes have relied upon the use of relatively coarse datasets (e.g., World Wide Fund for Nature (WWF) Ecoregions). Hence, within CCI Biomass, efforts will focus on refined biome datasets and/or improving the definitions of the biomes and appropriate application of the BIOMASAR algorithm used within the ESA DUE GlobBiomass project.

Improvements to the retrieval of AGB using the BIOMASAR algorithms can be made through reference to vegetation structure, topographic position, underlying topography (affecting SAR ground returns) and surface and volume conditions (affecting SAR vegetation returns). The following sections outline the range of data layers that can be used to better characterize sites but also to support concurrent classifications of the vegetated landscape.

5.1. Biomes

With the ESA DUE GlobBiomass project, the extent of biomes was determined using the WWF EcoRegions maps. Refinement of the biome extent is however needed to avoid artificial boundaries and hence the use of a biome map linked with a structural classification is considered to be beneficial. Table 8 provides details on the updated Köppen-Geiger climate map of the world which can be used to better inform on the distribution of biomes.


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Remote Sensing AG.

Table 8. Data required for biome definition PRODUCT SOURCE ACCESS COVERAGE VOLUME (Tb) FORMAT COMMENTS

SPATIAL TEMPORAL INPUT OUTPUT

Updated Köppen-Geiger climate

map of the world people.eng.unimelb.edu.a

u/mpeel/koppen.html

Open1 0.1 x 0.1o 2002-2012 1.061 ascii

raster

1Megabytes


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5.2. Vegetation

The high diversity of plant structures occurs as a consequence of environmental conditions but also biogeographic preferences of species. Primary descriptors of vegetation structure are metrics describing the height and cover of vegetation (which relates to woody lifeform), canopy cover, canopy height, leaf type, phenological classes and stratification (Table 9). These descriptors vary as a function of growth (regeneration) and degradation stage.

For the retrieval of AGB, and particularly from X-, C- and L-band SAR data, consideration needs to be given to these variables as these provide a measure of the vertical and spatial distribution, density, size and orientation of plant components (depending on whether present or otherwise; e.g., due to leaf fall) with which microwaves interact. Knowledge of these components can assist in quantifying the transmissivity and interactions of microwaves as they pass through the forest volume. It can be noted that in addition to the forest structure, the dielectric constants of the plant components (linked to water content and freeze/thaw status) impacts the radar backscatter signals. The use of time series SAR data is important to account for these resulting variations.

5.2.1. Vegetation lifeform

Vegetation lifeforms are defined on the basis of structural similarities and plants and, within the FAO LCCS classification, are divided into categories of woody (primarily forests; trees and shrubs), herbaceous (forbs and graminoids) and cryptograms (mosses and lichens). Whilst regional datasets of lifeforms are available, the only consistent and time-variant dataset is that produced by the CCI Land Cover project, with this discriminating primarily between trees, shrubs and grasslands.

Knowledge of the extent of forests is essential for CCI Biomass and can also be determined by combining spatial information on height and cover. Specifically, the FRA (2015) defines forest as land spanning more than 0.5 hectares (ha) with trees higher than 5 m and a canopy cover of more than 10 % or trees able to reach these thresholds in situ (excluding those that under agriculture or urban land use). -Structural classifications of woody vegetation can also be generated using a combination of height and cover layers. As an illustration, Australia cross tabulates these layers to generate structural classes according to the taxonomy of Specht (1970) that define the extent of low, medium, tall and very tall forests that are sparse, open or closed. Forest biomass also generally increases with vegetation height and cover as forests mature. Hence, structural classifications based on height and cover are a useful surrogate for AGB and can assist the targeting of retrieval algorithms. Further information on these layers is provided below.

5.2.2. Canopy cover

Tree cover represents the proportional, vertically projected area of vegetation (including leaves, stems, branches, etc.) of woody plants above a given height. The continuous classification of cover enables better depiction of land cover gradients compared to traditional discrete classification schemes. Importantly for detection and monitoring of forest changes (e.g., deforestation and degradation), tree cover provides a measurable attribute upon which to define forest cover and its changes.


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A number of global layers representing canopy cover have been generated (Hansen et al., 2013; Sexton et al., 2013). The Hansen et al. (2013) dataset is only for 2010 and very dense forests sometimes appear to have low cover (e.g., in the Congo Basin) also estimates are not available for coastal areas (e.g., mangroves). The Sexton et al. (2013) Landsat Vegetation Continuous Fields provides global coverage for 2000, 2005, 2010 and 2015 although there are noticeable omissions in areas of persistent cloud cover of topographic shadowing. Both datasets are affected by the Landsat SLC, which is sometimes observed in the product in some areas.

5.2.3. Canopy height

Extrapolation of ICESat-derived height metrics to form a continuous surface has been a common approach to the estimation of canopy height. Lefsky et al. (2010) associated footprints with forest area segment (based on spectral and textural heterogeneity properties) generated from MODIS data. For segments without GLAS data, canopy height prediction equations were developed for each of 6 geographic regions using Cubist (a rule-based modelling approach) and attributes (forest cover fraction, brightness PC1, greenness PC2, MODIS land cover and biome type). Simard et al. (2011) employed a Random Forest regression tree method to model RH100 values based on global climate and vegetation variables (precipitation, precipitation seasonality, annual mean temperature, temperature seasonality, elevation, percent forest cover and protection status) for areas not covered by GLAS waveforms. All maps were interpolated to a 1 km grid. Los et al. (2012) derived a gridded global vegetation height map directly from ICESat GLAS data, with filters (e.g., slope, elevation, area under first Gaussian) applied to remove anomalous data. For CCI Biomass, the map of Simard et al. (2011) will be used.

5.2.4. Leaf type: Broad-leaved, Needle-leaved or Aphyllous

Of relevance to the CCI Biomass project is the two 300 m resolution land cover maps generated by the CCI Land Cover Project for five-year periods from 1998-2002 and 2008-2012. The classes mapped relate to the Plant Functional Types (PFTs) used in many models, including climate. These include information on leaf type (broad-leaved or needle-leaved), although aphyllous leaf types, which include palms, whether natural or semi-natural or in plantations (e.g., palm oil, coconut), are not currently represented. In Phase II, the CCI Land Cover Project will be producing global annual land cover maps from the 1990s through to 2015 based on coarse resolution satellite sensor data including from the NOAA AVHRR, SPOT-VGT, MERIS and PROBA-V and a map for 2016 based on Sentinel-3 OLCI and SLSTR composites and associated metadata, with these providing more up-to-date temporal information on forest change.

5.2.5. Phenology: Evergreen, Semi-Evergreen and Deciduous forests.

Currently, the CCI Land Cover Project has generated global land surface (LS) seasonality products and associated metadata, with these relating to vegetation greenness (as a function of temporal analysis of Normalized Difference Vegetation Index (NDVI) data. The land cover classifications for the two periods 1998-2002 and 2008-2012 provide information on the extent of evergreen and deciduous forests although do not consider semi-evergreen forests (e.g., those occurring in Southeast Asia and Southern Africa).

5.2.6. Vegetation stratification

Knowledge of the vertical stratification of forests can be quantified through reference to data from spaceborne LIDAR. As illustration, Scarth et al. (2018) segmented and classified the Australian continent


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using a combination of ALOS PALSAR and Landsat-derived Foliage Projective Cover (FPC). Then, from intersecting ICESat waveform data, vertical foliage profiles and height metrics (e.g., 95 % percentile height, mean height and the height to maximum vegetation density) were extracted for each of the classes generated. ICESat metrics and profiles were then assigned to all remaining segments across Australia with the same class allocation. Integrated Landsat sensor, ALOS PALSAR ICESat GLAS data. Through this approach, an indication of the number of distinct layers within the vertical profile of vegetation was obtained. Such an approach is being developed through CCI Biomass.

5.2.7. Dominant plant types and species

Knowledge of the extent of dominant forest species can inform the AGB retrieval algorithm as this allows differentiation of both excurrent (pine-like) and decurrent (spreading crowns). The former forest type allocates a greater proportion of biomass to the trunks whilst the crowns of the latter can contain an equal amount of more of the biomass. Other forest species have different morphological characteristics, including prop roots.

5.2.8. Vegetation optical depth

The thermal emission arising from the Earth surface at microwave frequencies depends on the soil characteristics which controls the soil emissivity. In the presence of vegetation, part of the soil emission is absorbed and scattered. The extinction effect is parameterised by the Vegetation Optical Depth (VOD) that can be estimated using radiative transfer theory. VOD was shown to be linked to vegetation water content to the vegetation structure, which determines its dependence on the incidence angle and on the polarization of the radiation.

VOD samples the vegetation canopy, including woody vegetation, which uses root zone soil moisture. Passive microwave L-band radiometry (1.4 GHz, 21 cm) observations, which are less attenuated through the vegetation canopy, are capable of sampling the vegetation layer up to higher biomass values compared to higher-frequency observations and VOD has recently been related to AGB (Rodriguez-Fernandez et al., 2018) .


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Table 9. Data layers required for refining quantitative descriptions of vegetation structure and floristics.

PRODUCT SOURCE ACCESS COVERAGE VOLUME FORMAT COMMENTS/USE SPATIAL TEMPORAL INPUT OUTPUT

Vegetation lifeform (trees, shrubs versus herbaceous)

CCI land cover maps.elie.ucl.ac.be/CCI/viewer/download.php

Open

300 m 1998-2002 0.0017 GeoTIFF

AVHRR HRPT (1992-1999) SPOT-VGT (1999-2012) PROBA-V (2013 - 2015) 2008-2012 0.0028

Canopy cover (%) Hansen et al. (2013) landcover.usgs.gov/glc/TreeCoverDescriptionAndDownloads.php

Open 80oN-60oS 1 arcsec (~ 30 m)

2010 8 BIT From: Landsat sensor data; Integer values of 0 -100 %

Sexton et al. (2013) landcover.org/data/landsatTreecover/

Open Global 30 m

20001, 2010, 2015

GeoTIFF From Landsat sensor data. Includes water, cloud and shadow masks

Canopy height (m) Simard et al. (2011) /csdms.colorado.edu/wiki/Data:Global_Forest_Heights#Data_format

Open 1 km 2005 From: ICESAT GLAS data

Vegetation leaf type (needle-leaved, broad-leaved and mixed)

CCI land cover maps.elie.ucl.ac.be/CCI/viewer/download.php

Open

1 km

1998-2002 0.0017 GeoTIFF

AVHRR HRPT (1992-1999) SPOT-VGT (1999-2012) PROBA-V (2013 - 2015) (only provides evergreen and deciduous)

2008-2012 0.0028 Vegetation phenological classes (evergreen, deciduous, semi-evergreen)

1998-2002 0.0017 2008-2012 0.0028

Vegetation stratification ICESAT: NSIDC Open Global 2003-2008 0.06 Access online

ICESAT-2: NASA

Open Global 2018 - TBD TBD

GEDI - NASA Open 60°N-56°S 2018- < 1 Access online

1Annual for North and South America from 2010 to 2015.


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Table 10. Additional data layers required for refining quantitative descriptions of vegetation structure and floristics

PRODUCT SOURCE ACCESS COVERAGE VOLUME FORMAT COMMENTS/USE CLASS SPATIAL TEMPORAL INPUT OUTPUT

Vegetation species/types

Mangroves Global Mangrove Watch www.globalmangrovewatch.org

Open 25 m 2010 TBD Geotiff Mangrove extent mapped using ALOS PALSAR

mosaics and Landsat sensor composites

Vegetation optical depth www.geo.vu.nl/~jeur/lprm/

Open Between 38 & 56k

km

1987 onwards

TBD SSM/I (1987-2018+), TRMM-TMI (1998-2018+)

AMSR-E (2002-2011)


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5.3. Topography

Topographic information is needed to understand the SAR signal but also the reflectance characteristics of surfaces and the height metrics retrieved from spaceborne LIDAR. The essential measures are slope and aspect, but altitude also needs to be considered. The surface topography (roughness) is also essential in informing the retrieval of biomass (e.g., using the water cloud model). Essential datasets are listed in Table 5.

5.4. At-ground Surface Conditions

At-ground surface conditions can influence the interaction of microwaves (particularly at longer wavelengths) and hence the retrieval of AGB. These include urban structures, bare surface materials, water inundation and root structures (as indicated previously; e.g., buttress roots or prop roots) (Table 11,Figure 5)

Figure 5. Overview of at-ground surface conditions

5.4.1. Urban environments

In human-occupied landscapes, urban infrastructure is a component of the forested landscapes and SAR interactions with artificial surfaces (e.g., buildings, transport infrastructure), and particularly at L-band, leads to incorrect retrievals of AGB. Estimates of AGB are typically stronger in such areas because of greater interactions with solid structures. Knowledge of the extent of urban areas is therefore desirable and can be obtained through reference to existing global layers, with the raster layers of impervious surfaces (%) and associated built up and human settlement (extent) provided by the Socioeconomic Data and Applications Centre (SEDAC) being the most detailed (30 m spatial resolution) and also relatively up to date (2010). From these layers, areas of urban infrastructure can be used to establish where the retrieval of AGB is in error or might be compromised.


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5.4.2. Bare surface materials

Surface materials, whether consolidated (e.g., rocks) or unconsolidated (e.g., boulders, gravels or muds/sands) influence the surface scattering from SAR and can lead to errors in retrieval. Whilst global maps of bare surface materials are not available and would need to be generated from collations at the country or regional level, these can be inferred from existing layers, including the global digital terrain models and mangrove extent maps (for unconsolidated materials; muds and sands).

5.4.3. Water inundation

Water inundation under the canopy is a significant unknown as flooded forests typically exhibit higher L-band and sometimes C-band backscatter than those without flooding and hence influence the ground scattering terms. The JRC Global Surface Water Product (Pekel et al., 2016) is the only global water occurrence product and provides a single water occurrence surface for the period 1984 to 2015, alongside change in intensity between 1984-1999 and 2000-2015, surface water seasonality, and annual water occurrence as a percentage of years when water was present. These products are provided at a 25 m spatial resolution. However, being Landsat-based, there are artefacts due to cloud cover and Landsat 7 SLC failure, particularly in regions of poor acquisition density such as Africa. The greatest limitation of this dataset however is that it only detects surface water and therefore where tall woody vegetation is present, water can generally not be mapped. Therefore, the ALOS-1 PALSAR-1 and ALOS-2 PALSAR-2 SCANSAR product should be investigated for mapping the maximum inundated forest extent and occurrence throughout the tropics using, for example, the approaches or products generated by De Grandi et al. (2010) and Arnesen et al. (2013) for Central Africa and South Africa respectively. Information on lake water extent might also be generated by ESA’s Lake CCI, although most areas defined will be open water and without woody vegetation.

5.4.4. Woody plant structures

In some forest types, larger above ground root structures influence microwave interactions with vegetation canopies, with these including prop roots (e.g., mangroves, regenerating tropical forests) and buttress roots (tropical/subtropical forests). The impact of root systems is most evident for tall (typically > ~ 10 m) mangroves dominated by Rhizophora species, particularly at L-band HH but also HV (Asbridge et al., 2016) where the backscattering coefficient may be equivalent to non-vegetated areas because of reduced scattering from the large prop roots. Mangroves are therefore problematic in terms of biomass estimation due to attenuation of the large above ground root systems producing lower backscatter than forests of similar biomass. The Global Mangrove Watch (GMW) has derived mangrove extent maps for 1996, 2007, 2008, 2009, 2010, 2015 and 2016 using the JAXA JERS-1, ALOS-1 PALSAR-1 and ALOS-2 PALSAR-2 data and for 2010 alongside Landsat composites. As JAXA produce new and updated 25 m mosaics for 2017 and onwards these will also be processed to create updated mangrove extent maps which correspond with the biomass map dates for the CCI Biomass project. However, being predominantly SAR based and 25 m resolution the GMW products have confusion over complex and highly disturbed regions (e.g., in and around aquaculture). Secondly, the changes detected by the GMW were water to mangroves and mangroves to water. However, there are many areas where mangroves have been converted to terrestrial ecosystems and habitats and the inclusion of optical Landsat and Sentinel-2 can be used to improve these classifications. This work is being undertaken by the GMW team through projects such as Mangrove Capital Africa and the data will be rolled out globally in the near future. It is anticipated that CCI Biomass will be able to use those improved products as and when they become available.


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Tall mangroves with root systems can be identified by using the Global Mangrove Watch (GMW; Table 10) global mangrove extent layers to identify where mangroves occur, canopy height layers (derived from the SRTM and potentially Tandem-X) to differentiate tall mangroves and thresholding the L-band HH data to determine those with prop roots (those without exhibit an L-band backscatter similar to that of terrestrial forests). Adjustments to the water cloud model parameterization can also be made with reference to these layers and a better understanding of microwave interaction with root structures.

Other factors (Table 12) that can influence the microwave scattering, particularly at lower frequencies, include hollow stems (e.g., as typically of many larger savanna species) and particularly those that contain large amounts of water (e.g., regenerating Cecropia species in South and Central America). High densities of trees of small size (e.g., as in the case of brigalow (Acacia) regrowth in Australia; Lucas et al., 2014), colonizing mangroves or low shrublands are also not detected from L-band SAR and the integration of optically-derived measures (e.g., canopy cover) is advocated.


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© Aberystwyth University and GAMMA Remote Sensing, 2018 This document is the property of the CCI-Biomass partnership, no part of it shall be reproduced or transmitted without the express prior written authorization of Aberystwyth University and

Gamma Remote Sensing AG.

Table 11. Layers required for quantitative descriptions of actual/probable at-ground surface states.

PRODUCT SOURCE ACCESS COVERAGE VOLUME (Tb) FORMAT COMMENTS/USE CLASS SPATIAL TEMPORAL INPUT OUTPUT

Urban structures

Impervious surfaces (%)

sedac.ciesin.columbia.edu/data/set/ulandsat-gmis-v1

Open 30 m 2010 Some areas compromised by Landsat SLC-off and

cloud cover Built-up and human settlement extent (HBASE)

http://sedac.ciesin.columbia.edu/data/set/ulandsat-hbase-v1

30 m 2010

Bare surface materials

Under mangroves

Global Mangrove Watch www.globalmangrovewatch.org

Open 25 m 2010 TBD Geotiff Assumed to be unconsolidated beneath

mangroves Topographically related

DTMs listed; primarily derived from SRTM and Tandem X.

Open Typically, 90 m

2000, 2011-2015

Geotiff Inferred extent based on elevation, slope and

ruggedness Water inundation

Occurrence and

reoccurrence

EC Joint Research Centre (JRC) //global-surface-water.appspot.com/

Open 30 m 1984-2015 0.020 Provides historical context of inundated areas.

May exclude inundated vegetation that is dense. Seasonality 2014-2015

Permanent extent

ESA CCI Land Cover maps.elie.ucl.ac.be/CCI/viewer/download.php

Open 150 m 2005-2010 Geotiff and

NetCDF

A reference layer of water extent and potential water

mask.

ALOS-2 PALSAR-2

Open Global (25m)

2015-2016 Geotiff


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Gamma Remote Sensing AG.

Table 12. Additional layers required for quantitative descriptions of actual/probable at-ground surface states.

PRODUCT SOURCE ACCESS COVERAGE VOLUME (TB) FORMAT COMMENTS/USE CLASS SPATIAL TEMPORAL INPUT OUTPUT

Woody plant structure

Mangroves (large prop roots)


Open 25 m 2010 TBD Geotiff Within the mapped area of mangrove extent,

mangroves with large prop root systems can be

differentiated as those that are tall and with low

L-band HH backscatter


Open +/- 60°N (90 m)

2000 0.0134 Geotiff

ALOS-1 PALSAR

Open Global (25 m)

2006-2011 Geotiff

ALOS-2 PALSAR-2

Open Global (25m)

2015-2016 Geotiff


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5.5. At surface and within volume environmental conditions

Environmental conditions influence the radar return at C- and L-band and hence consideration of those prevailing at the time of image acquisition can inform on the retrieval of woody AGB. These include land surface temperature, precipitation (i.e., surface moisture conditions), snow cover, soil moisture, fire conditions and sea level (Table 13, Table 15, Table 15 and Figure 6).

Figure 6: Influence of environmental conditions on biomass retrieval algorithms

5.5.1. Land Surface Temperatures

Land surface temperature can indicate whether forested landscapes (including within the extensive boreal regions of Siberia and North America) are frozen (or otherwise) at the time of the satellite overpass. Freeze/thaw cycles influence the radar backscatter and hence the AGB retrieval algorithm. For CCI Biomass, CPC Global Daily Temperature data, interpolated from ground-based measurements, can indicate freeze and thaw conditions. The CCI Land Surface Temperature Project is producing a 25-year LST record (1995-2000) from ATSR to Sentinel-3 IR CDR), 22 years (1988 to 2020) Passive Microwave and 10 year (2010-2020) merged IR CDR. Data are available at a 0.5o grid spacing day and night for polar orbiters and 3-hourly for Geostationary. The final specification of the products will be based on the user requirements. Seasonal soil freeze/thaw data are also available as part of the CCI Permafrost Project and these would be relevant in tundra or taiga areas with woody vegetation.

5.5.2. Precipitation

Precipitation at the time of the satellite overpass can influence both optical and microwave signatures and compromise retrieval of AGB as it influences the amount of water on and within the vegetation and soil (Lucas et al., 2010). Global daily precipitation data are available from the CPC Global Unified Guage-


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Based Analysis of Daily Precipitation. Monthly precipitation data are also available, and these can be used to indicate the likelihood of dry or wet conditions, particularly in the dry tropics.


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Remote Sensing AG.

Table 13. Additional layers required for quantitative descriptions of actual/probable at-ground surface states.

PRODUCT SOURCE ACCESS COVERAGE VOLUME (Tb) FORMAT COMMENTS/USE CLASS SPATIAL TEMPORAL INPUT OUTPUT

Woody plant structure

Mangroves (large prop roots)


Open 25 m 2010 TBD Geotiff Within the mapped area of mangrove extent,

mangroves with large prop root systems can be

differentiated as those that are tall and with low

L-band HH backscatter


Open +/- 60°N (90 m)

2000 0.0134 Geotiff

ALOS-1 PALSAR

Open Global (25 m)

2006-2011 Geotiff

ALOS-2 PALSAR-2

Open Global (25m)

2015-2016 Geotiff


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Remote Sensing AG.

Table 14. Layers required for quantitative descriptions of at-surface and within-volume conditions


Land surface Temperature (oK) CPC Global Daily Temperature //www.esrl.noaa.gov/psd/data/gridded/data.cpc.globaltemp.html

Open 89.75N-89.75S, 0.25E-

359.75E. 0.5o

1979-2010 Monthly and daily

89 Mb for each

yearly file

TBD NetCDF From: Interpolated measurements

ESA CCI land surface temperature http://cci.esa.int/lst

Open TBD 1993-2018 TBD TBD TBD TBD

Precipitation (rainfall; mm) CPC Global Unified Gauge-Based Analysis of Daily Precipitation //www.esrl.noaa.gov/psd/data/gridded/data.cpc.globalprecip.html

Open 89.75N-89.75SN,

0.25E-359.75E

0.5o

1981-2010 Monthly and daily

89 Mb for each

yearly file

NetCDF From: Interpolated rain gauge data.


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Table 15 Layers required for quantitative descriptions of at-surface and within-volume conditions.


Snow cover Extent (area) NASA Daily snow cover L3 Global 500 m grid V6.1

//nsidc.org/data/MYD10A1/versions/6

Open 500 m 2002-present

Daily

3.5 Mb per file

HDF-EOS2 (V2.17)

From: AQUA MODIS using the Normalised Difference

Snow Index (NDSI)

NASA Daily snow cover L3 Global 500 m grid V6. 1

//nsidc.org/data/MOD10A1/versions/6

Open 500 m 2002-present

Daily

3.5 Mb per file

HDF-EOS2 (V2.17)

From: TERRA MODIS using the Normalised Difference

Snow Index (NDSI)

NASA 8-day snow cover L3 Global 500 m grid V6. 1

https://nsidc.org/data/MYD10A2/versions/6

Open 500 m 2002-present 8-day

3.5 Mb per file

HDF-EOS2 (V2.17)


Snow Index (NDSI)

NASA 8-day snow cover L3 Global 500 m grid V6. 1

//nsidc.org/data/MOD10A2/versions/6

Open 500 m 2002-present 8-day

3.5 Mb per file

HDF-EOS2 (V2.17)


Snow Index (NDSI)

NASA monthly snow cover L3 Global 0.05o grid V6. 1

//nsidc.org/data/MYD10CM/versions/6

Open 500 m 2002-present Monthly

3.5 Mb per file

HDF-EOS2 (V2.17)


Snow Index (NDSI)

NASA monthly snow cover L3 Global 0.05o grid V6. 1

//nsidc.org/data/MOD10CM/versions/6

Open 500 m 2002-present Monthly

3.5 Mb per file

HDF-EOS2 (V2.17)


Snow Index (NDSI)

ESA CCI Snow http://cci.esa.int/data1

Open 500 m 1980s to present

TBD TBD New ESA CCI

Fraction (%) ESA CCI Snow http://cci.esa.int/data1

Open 500 m 1980s to present

TBD TBD New ESA CCI

1Forthcoming


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5.5.3. Snow Cover (Extent and Fraction)

Snow cover influences both optical and microwave signals from forested areas. At a global level and on a daily, 8-day and monthly basis, snow cover is mapped routinely using the NASA MODIS and AQUA sensors and data are available from 2002 onwards. The daily and 8-day products can identify whether the SAR data used for AGB retrieval are snow covered at the time of observation whilst the monthly data can indicate whether the area of observation is likely to be covered in snow. The fractional cover can also indicate the relative amounts (e.g., depth) of snow if the vegetation type and structure is known a priori. The CCI Snow Cover Project is focusing on generating homogenous, well calibrated long-term time series of global snow area extent and mass from multi-sensor satellite data. Snow covered area will be generated daily at 1 km resolution and 100 m in complex terrain from 1990 onwards and will be available for the CCI Biomass target epochs. Uncertainty maps will accompany each daily product. The full time-series from AVHRR data will be available in Year 2 of the project for evaluation and feedback.

5.5.4. Soil Moisture

Soil moisture influences microwave scattering in particular and knowledge of daily and monthly amounts can indicate the utility of both C- and L-band SAR data for retrieval of AGB. The CCI Soil Moisture Project has generated global soil moisture products on a 0.25o grid and on a daily basis from 1989 to 2017, although estimates as far back as 1978 are available. The number of valid observations increase particularly after 2007. These estimates have been generated using 12 active passive and active microwave L2 products at ku, X-, C- and L-band observing at a resolution of 25-100 km with a revisit time ranging from 1 to 7 days. Three products are available, with one each generated from active and passive data and another using a combination of the two. The latter is considered to be the most reliable product.

5.5.5. Fires (Extent and Severity).

Fire influences both radar and optical signals due to the loss of foliage and often woody material occurs depending on fire severity. Knowledge of the occurrence and severity of fires can inform on the likely success of the AGB retrieval algorithm and can indicate whether in situ datasets used in algorithm development and validation are still appropriate. The CCI Fire Project is generating global maps of fire extent and severity (based the fraction of area burned and the number of patches) and these can be linked with satellite observations to establish the likelihood and extent of disturbance and the likely impacts on the AGB retrieval algorithms.

5.5.6. Sea Level

Sea level changes impact on coastal forests and can influence their extent, structural characteristics and species types. For example, in northern Australia, sea level rise has resulted in inland intrusion of sea water with mangroves replacing the freshwater (e.g., paperbark) forests. Asbridge et al. (2016) also highlighted the strong correspondence between sea level fluctuation and mangrove extent by species


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type. Awareness of longer-term sea level changes is important for global estimation of AGB as changes in the extent of forests (through both losses and gains) and amount of biomass accumulated may be dictated by these fluctuations.

Ref ESA CCI Biomass – Phase I. Data Access Requirements Document Year 1

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6. Requirements for validation data

6.1. Validation data

A number of datasets are required to support the assessment of the CCI Global Above Ground Biomass Map Product (GA-MP), with these including the airborne but also in situ datasets. Many studies have previously compared AGB products generated by other organisations and often at a country, regional or global level but these comparisons are compromised by their own uncertainties. Hence, in Phase I, CCI Biomass is working with international collaborators (e.g., NASA through the CEOS LPV) to establish a robust reference dataset of acceptable accuracy that can be used for comparison of all maps that exist or are being generated (including those from this project).

A number of airborne datasets are also available across the World, many of which associated with detailed ground measurements (plot-based inventories, Terrestrial Laser Scanner data). These datasets, particularly derived AGH products at high resolution, provide a basis for validating the global AGB retrieval algorithm as it is developed through the project. Table 16 summarizes the sites that could be made available to the CCI Biomass community, including information on permanent forest inventories, airborne lidar scanning (ALS) data, and/or terrestrial lidar scanning (TLS) data. The current version of the site list has been assessed with an emphasis on tropical regions, as this is a major challenge. More work needs to be done to include sites with permanent inventories, ALS and TLS information, in lower-biomass vegetation, and temperate/boreal environments. A total of 50 sites have currently been assessed, and these cover over 1500 ha of permanent inventory plots.

Many more studies can be mobilized to validate the CCI Biomass products (Table 18) through national forest inventories. These include many small-sized vegetation inventories that span environmental gradients in countries or regions. A separate dataset is being built for validation, improved from the validation database of the GlobBiomass project (Table 17).

Additional datasets of use include the land cover classifications generated through the ESA CCI High Resolution Land Cover (HRLC) project at 10–30 m spatial resolution (Table 19). These data will be generated using historical datasets every five years and, from these, change products will be derived. The land cover maps are not global but focus solely on the African Sahel, north-eastern Siberia and the Amazon Basin with these regions selected because of their vulnerability to climate change and the differential impacts that are likely to arise. These data provide a useful reference to compare with the environmental variables generated through CCI Biomass.


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Table 16. Potential validation sites for CCI Biomass

REGION COUNTRY SITE NAME CODE COORDINATOR PRIMARY CONTACT LATITUDE LONGITUDE

PLOT_

No

AREA_

UNIT ALS TLS

Africa Cameroon Dja DJA AfriTRON SL Lewis and B Sonke 3.1502 12.9997 18 1 no no

Africa Cameroon Korup KRP ForestGEO D Kenfack 5.0739 8.8547 1 50 no no

Africa CAR M'Baiki MBK TmFO B Herault and P Sist 4.5000 18.6000 10 4 no no

Africa

Congo-

Brazzaville

Nouabale-

Ndoki NBL AfriTRON

SL Lewis, C Clark and J

Poulsen 2.1858 16.3240 41 1 no no

Africa Cote d'Ivoire La Tene LTN CIRAD B Herault 6.6667 -5.5000 25 4 no no

Africa DRC Ituri ITR ForestGEO D Kenfack 1.4368 28.5826 4 10 no no

Africa DRC Malebo MLB WWF (?) JF Bastin -2.4977 16.5061 21 1 yes no

Africa DRC Salonga SLN AfriTRON SL Lewis -1.7044 20.5437 16 1 NA no

Africa DRC Yangambi YNG AfriTRON

E Kearsley and H

Verbeek 0.8338 24.5084 20 1 NA NA

Africa Gabon Lope LOP AfriTRON SL Lewis -0.2004 11.5886 3 & 11 0.5 & 1 yes yes

Africa Gabon Mabounie MBN AMAP N Barbier -0.7610 10.5565 12 1 yes NA

Africa Gabon Mondah MND NASA S Saatchi 0.5653 9.3515 19 1 yes yes


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Africa Gabon Rabi RAB ForestGEO D Kenfack -1.9250 9.8812 1 25 yes yes

Africa Ghana Ankasa ANK AfriTRON Y Malhi and SL Lewis 5.2864 -2.6923 3-5 1 yes yes

Africa Mozambique Gorongosa GRN Edinburgh E Mitchard -18.7667 34.5000 15 1 no NA

Africa Nigeria Ngel Nyaki NGL ForestGEO D Kenfack 7.0680 11.0566 1 20 no no

America Bolivia La Chonta CHN TmFO M Pena Claros and P Sist -15.7830 -62.9170 12 4 no no

America Brazil Caxiuana CXN RAINFOR and TEAM OL Phillips -1.7483 -51.4858 10 1 no no

America Brazil

Chico

Mendes CHM RAINFOR

S Cerruto Ribeiro and OL

Phillips -10.6505 -68.5003 8 1 no no

America Brazil

Ilha do

Cardoso ILH ForestGEO S Davies -25.0955 -47.9573 1 10 no no

America Brazil Manaus MNS

ForestGEO +

RAINFOR S Davies + AA de Oliveira -2.4417 -59.7858 1 + >10 25 + 1 no no

America Brazil

Nova

Xavantina NVX RAINFOR B Marimon and O Phillips -14.7099 -52.3499 8 1 no no

America Brazil Paragominas PRG TmFO P Sist -3.6670 -48.1670 3 24.5 NA NA

America Colombia Amacayacu AMC ForestGEO S Davies -3.8091 -70.2678 1 25 no no

America Colombia Choco CHC NASA S Saatchi 4.0565 -77.0665 45 & 15 0.25 &1 yes no


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America Colombia La Planada LPL ForestGEO S Davies 1.1558 -77.9935 1 25 no no

America Costa Rica La Selva LSL OTS J Kellner 10.4205 -84.0124 18 & 3 0.5 & 4 yes yes

America Costa Rica

Osa

Peninsula OSP Wien University F Hofhansl 8.7013 -83.5418 20 1 yes yes

America

French

Guiana BAFOG BAF Guyafor C Bedeau and G Derroire 5.4905 -53.9871 4 4 no no

America

French

Guiana

Montagne

Tortue MNT Guyafor C Bedeau and G Derroire 4.2214 -52.4119 3 4-10 yes NA

America

French

Guiana Nouragues NRG RAINFOR J Chave 4.0689 -52.6827 11 0.5-12 yes yes

America

French

Guiana Organabo ORG Guyafor C Bedeau and G Derroire 5.4716 -53.4781 6 4 no no

America

French

Guiana Paracou PRC TmFO G Derroire 5.2669 -52.9311 16 & 1

6.25 &

25 yes yes

America Panama

Barro

Colorado

Island BCI ForestGEO S Davies 9.1543 -79.8461 1 50 yes NA

America Peru Allpahuayo / AJH RAINFOR T Baker and OL Phillips -3.9493 -73.4295 15 0.5-1.5 yes no


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Jenaro

Herrera

America Peru Tambopata TMB RAINFOR OL Phillips -12.8344 -69.2816 9 1 yes yes

Asia-

Pacific Australia

Litchfield

Savanna LTC TERN J Armston -13.1491 130.7868 2 1 yes yes

Asia-

Pacific Australia

Robson

Creek RBS TERN J Armston -17.1201 145.6323 1 25 yes NA

Asia-

Pacific India

Karnataka_Y

ellapur KRN AMAP M Réjou-Méchain 14.9650 74.7122 20 1 yes NA

Asia-

Pacific Indonesia Malinau MLN TmFO P Sist 2.8670 116.6670 24 1 no no

Asia-

Pacific Indonesia STREK STR TmFO P Sist 2.0000 117.2500 18 4 no no

Asia-

Pacific

Malaysia-

Borneo

Danum

Valley DNM ForestGEO + Leeds

DFRP Burslem and S

Davies + OL Phillips 5.1019 117.6880 1 + 3 50 + 1 yes NA

Asia-

Pacific

Malaysia-

Borneo Lambir LMB ForestGEO S Davies 4.1865 114.0170 1 52 no no

Asia- Malaysia- Sepilok SPL Aberdeen, Leeds and D Coomes, DFRP 5.8581 117.9483 9 & 1 4 & 2 yes NA


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Pacific Borneo Cambridge Burslem, OL Phillips and

R Nilus

Asia-

Pacific

Malaysia-

Penisular Pasoh PSH ForestGEO + TmFO S Davies + P Sist 2.9820 102.3130 1 + 1 50 + 6 no no

Asia-

Pacific

Papua New

Guinea Wanang WNN ForestGEO

V Novotny and Stuart

Davies -5.2500 145.2670 1 50 no no

Asia-

Pacific Taiwan Fushan FSH ForestGEO S Davies 24.7614 121.5550 1 25 yes no

Asia-

Pacific Thailand Doi Inthanon DIN ForestGEO S Davies 18.5833 98.4333 1 15 no no

Asia-

Pacific Thailand

Huai Kha

Khaeng HKK ForestGEO S Davies 15.6324 99.2170 1 50 no no

Asia-

Pacific Thailand Mo Singto MSN ForestGEO + AMAP

S Davies + M Réjou-

Méchain 14.4333 101.3500

1 + ca.

10 30 + 1 yes NA


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Table 17. Validation sites being used in CCI Biomass where further measurements are being acquired.

ID CONTINENT COUNTRY/

REGION LOCATION

EXT-

ENT 1

VEGETATION

TYPE(S) 2 YEAR(S)

N.

PLOTS

PLOT SIZE

(ha)

PERMANENT

PLOT REFERENCE

CONTACT

PERSON

DATA

ACCESS 3

AFR1 Africa DRC Lukenie L

F

(concession) 2007-2010 1157 0.5 No

Hirsch et al.,

2013 N. Bayol

AO - MoU

required

AFR2 Africa Sierra Leone Gola Forest L F 2005-2007 609 0.125 No

Lindsell and

Klop, 2013 C. Tayleur AO

AFR4 Africa Ethiopia Kafa L F - W 2011-2013 119 0.126 No

De Vries et

al., 2012

V.

Avitabile AO - GBI

AFR5 Africa Ghana Ankasa L F 2012 34 0.05 No

Vaglio Laurin

et al., 2013

G. Vaglio

Laurin AO

AFR5 Africa Ghana

Bia Boin,

Dadieso L F 2012-2013 40 0.16 No

Pirotti et al.,

2014

G. Vaglio

Laurin AO

AFR6 Africa Tanzania

Eastern

Arc

Mountain L F 2007-2010 24 0.08 - 1 Yes

Willcock et

al., 2014

S.

Willcock AO

AFR7 Africa DRC Yangambi L F (Intact) 2011-2012 20 1 Yes

Kearsley et

al., 2013

H.

Verbeeck AO


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AFR8 Africa

Guinea-

Bissau

Guinea-

Bissau N

F - S - M (Live

& dead) 2007-2008 112 0.125 No

Carreiras et

al. (2012)

J.

Carreiras AO - GBI

AFR9 Africa Mozambique Lugela L W - S 2011 51 0.126 No

Carreiras et

al., 2013

J.

Carreiras AO - GBI

AFR1

0 Africa Cameroon

Mbam

Djerem

National

Park L F - S 2007 24 0.2 - 1 No

Mitchard et

al., 2011

E.

Mitchard AO

AFR1

1 Africa Uganda Uganda N F - W - S 2000-2005 897 0.25 Yes Drichi, 2003

V.

Avitabile AO - GBI

AFR1

2 Africa Uganda Budongo L F 2008 114 0.16 No

Avitabile et

al., 2012

V.

Avitabile AO - GBI

AFR1

3 Africa Uganda Budongo L F 2008 27 0.5 - 1 No

Mitchard et

al., 2009

E.

Mitchard AO

AFR1

4 Africa Mozambique Nhambita L F - W 2006-2009 96 0.1 - 2.2 No

Ryan et al.,

2012 C. Ryan AO

AFR1

5 Africa Madagascar

Madagasca

r N F 2007-2013 1003 max. 0.13

or 0.28 ha

No / Not

clear

Vieilledent et

al. 2016 G.

Vieilleden FA


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(4) t

SAM

2 S. America Brazil Brazil N F

2009 -

2013 124 0.16 - 1 No

Embrapa,

2014

Michael

Keller FA

SAM

3 S. America Guyana Guyana L F

2010 -

2011 111 0.126 No

Guyana

Forestry

Commission

Nasheta

Dewnath AO - GBI

SAM

4 S. America Peru

Madre de

Dios L F 2014 9 0.15 No in prep.

Martin

Herold AO - GBI

SAM

5 S. America Brazil Manaus L SF 2014 23 0.6 No in prep.

J.

Carreiras AO - GBI

CAM

1 C. America Mexico Mexico N F 2004-2008 4296 0.16 Yes

de Jong,

2013 B. de Jong AO

NAM

1 N. America Alaska Alaska N F 2002-2014 605 0.04 Yes

Liang et al.,

2015

Jingjing

Liang AO

NAM

2 N. America USA Oregon L F 2000-2007 85 NA No

Luyssaert et

al., 2007

S.

Luyssaert FA

ASI1 Asia Vietnam

Quang

Nam P F 2007-2009 2994 0.05 No Avitabile et

al., 2014;

V.

Avitabile AO - GBI


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Avitabile et

al., in review

ASI2 Asia Laos Xe Pian L F 2011-2012 122 0.1 - 0.126 No

WWF and

ÖBf (2013)

V.

Avitabile AO - GBI

ASI3 Asia Indonesia Sabah L

F

(concession)

2005 -

2008 104 0.5 - 1.5 No

Morel et al.,

2011 A. Morel AO

ASI4 Asia Indonesia Indonesia L F 2009-2010 82 0.015 No

Wijaya et al.,

2013 A. Wijaya AO

ASI5 Asia Asia

India,

China,

Indonesia L F (Intact) circa-2010 132 0.25 - 20 No

Slik et al.,

2013, 2014 F. Slik AO

ASI7 Asia Nepal Nepal N F 2013-2015 1236 0.075-0.05 No NA

Hammad

Gilani AO

ASI8 Asia Indonesia Indonesia L M 31 0.015 No

SWaMP/TWI

NCAM_in

prep. A. Wijaya AO

ASI9 Asia Vietnam

Quang

Nam P F 2011-2012 89

0.01 -

0.126 No

Avitabile et

al., 2014;

Avitabile et

V.

Avitabile AO - GBI


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al., in review

AUS1 Australia Australia Australia N F - Sh 1980-2016 11095 0.01 - 25 Yes

Paul et al.,

2016

Richard

Lucas FA

EU1 EU Sweden Sweden N F (FAO) 2007-2014 19010 0.015 No NA

Mats

Nilsson FA

EU2 EU Spain Spain N F 1997-2007 60070 0.196 Yes

Magrama

(n.d)

Geerten

Hengevel

d FA

EU3 EU

The

Netherlands

The

Netherland

s N F 2012-2013 3100

5-20m

radius 5 Yes (50%)

Schelhaas et

al. 2014,

Geerten

Hengevel

d FA

EU4 EU Croatia Croatia N F 2006-2008 6026

0.004 -

0.126 Yes

Cienciala et

al., 2008;

Tabacchi et

al., 2011

Jura

Cavlovic AO 1 L-Local, N-National, P-Province

2 F-Forest, M-Mangrove, S-Savanna, SF-Secondary forest, SH-Shurubland, W-Woodland

3 AO-Ask data owner, FA-Free Access, GBI-GlobBiomass Internal

4 Variable, dependant on tree size

5 Density dependant


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Table 18. Ground and airborne datasets and derived products used for validating the CCI Biomass Product.

PRODUCT SOURCE ACCESS COVERAGE VOLUME FORMAT COMMENTS/USE


Forest Observation System (FOS)1 forest-observation-system.net

Open 0.25 - 50 ha

2005 - 2017

excel tables

To support validation of pre-2017 global AGB maps

Australian Plant Biomass Library (APBL)

data.auscover.org.au/xwiki/bin/view/Product+pages/Biomass+Plot+Library

Open 0.5 – 1 ha 1936-present (as collected)

70 Mb Vector (.shp)

Originally developed to support validation of pre-

2015 maps for the Australian continent

1Includes data from the Forest Global Earth Observatory (CTFS-ForestGEO), ForestPlots.net (including RAINFOR, AfriTRON and T-FORCES) and the IIASA network (northern Eurasia)

Table 19. Additional datasets providing support to the generation of the CCI Biomass global AGB map

PRODUCT SOURCE ACCESS COVERAGE VOLUME FORMAT COMMENTS/USE


High resolution land cover products ESA CCI High Resolution Land Cover cci.esa.int/data

Open 10-30 m Every 5 years

To support validation of pre-2017 global AGB maps

Ref ESA CCI Biomass – Phase I. Data Access Requirements

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7. Data procurement process

7.1. Procurement and agreements

The following tables list the agreements and proposals for data acquisition for the CCI-Biomass project in Phase I (Table 20) and in Phase 2 (Table 21) together with their respective status.

Table 20. Agreements for data access by the CCI-Biomass project in Phase I.

NO AGREEMENT / PROPOSAL

DATASET (SENSOR, SPATIAL AND TEMPORAL COVERAGE)

SUBMITTED ACCEPTED RECEIVED

1 Agreement with CCI Fire

Fire disturbance (CCI output) not available yet

not available yet

not available yet


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Table 21. Agreements for data access by the CCI-Biomass project in Phase 2.

SECTION DATASET (SENSOR, SPATIAL AND TEMPORAL COVERAGE)

AGREEMENT / PROPOSAL SUBMITTED ACCEPTED RECEIVED

2 Sentinel 1 Data access through Sentinel-1 science hub

October 2014

October 2014

3 Sentinel 2 Data access principally announced/granted via ESA data hub

2014-01-24 by email via A. Chadwick

email by P. Potin, forwarded by A. Chadwick 25.03.2014

4 Sentinel 3 Data access principally announced/granted via ESA data hub

2014-01-24 by email via A. Chadwick

email by P. Potin, forwarded by A. Chadwick 25.03.2014

5 Landsat-8 Open access, but we may ask for offline delivery

8. Science References

Ablain, M., Cazenave, A., Larnicol, G., Balmaseda, M., Cipollini, P., Faugère, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen, P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko, S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J. (2015). Improved sea level record over the satellite altimetry era (1993–2010) from the Climate Change Initiative project. Ocean Science, 11, pp. 67-82.

Arnesen, A. S., Silva, T. S. F., Hess, L. L., Novo, E. M. L. Rudorff, C. Chapman, B. D., McDonald, K. C. (2013). Monitoring flood extent in the lower Amazon River floodplain using ALOS/PALSAR ScanSAR images. Remote Sensing of Environment, 130(15), pp. 51-61.

Asbridge, E., Lucas, R. M., Ticehurst, C. and Bunting, P. (2016). Mangrove response to environmental change in Australia’s Gulf of Carpentaria. Ecology and Evolution, 6(11), pp. 3523-3539

Avitabile, V., Baccini, A., Friedl, M. A., Schmullius, C. (2012) Capabilities and limitations of Landsat and land cover data for aboveground woody biomass estimation of Uganda. Remote Sensing of Environment, 117, pp. 366–380.


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Avitabile, V. (2014) Carbon stocks of vegetation in the Vu Gia Thu Bon river basin, central Vietnam. Technical Report. Land Use and Climate Change Interactions in Central Vietnam (LUCCi) project.

Carreiras, J. M., Vasconcelos M. J., & Lucas R. M. (2012). Understanding the relationship between aboveground biomass and ALOS PALSAR data in the forests of Guinea-Bissau (West Africa). Remote Sensing of Environment,121, pp. 426-442.

Carreiras, J., Melo, J. B., & Vasconcelos, M. J. (2013). Estimating the above-ground biomass in miombo savanna woodlands (Mozambique, East Africa) using L-band synthetic aperture radar data. Remote Sensing, 5(4), pp. 1524-1548.

Chuvieco, E., Pettinari, M.L., Lizundia-Loiola, J., Storm, T., Padilla Parellada, M. (2018). ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Pixel product, version 5.1. Centre for Environmental Data Analysis.

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