CCI BIOMASS PRODUCT VALIDATION PLANcci.esa.int/sites/default/files/biomass D2.5 Product Validation...

CCI

BIOMASS

PRODUCT VALIDATION PLAN

YEAR 1

VERSION 1.0

DOCUMENT REF: CCI_BIOMASS_PVP_V1

DELIVERABLE REF: D2.5-PVP

VERSION: 1.0

CREATION DATE: 2018-11-15

LAST MODIFIED 2019-03-03

Ref CCI Biomass Product Validation Plan v1

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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 Sytze de Bruin Wageningen University

PREPARED Martin Herold Wageningen University

PREPARED Arnan Araza Wageningen University

PREPARED

PREPARED

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 2019-02-09 First draft version

1.0 2019-02-22 Revised version

Document Change Record (from Year 1 to Year 2)

VERSION DATE DESCRIPTION APPROVED


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CONTENTS

Table of contents .............................................................................................................. 3

List of tables ..................................................................................................................... 4

List of figures .................................................................................................................... 5

Symbols and acronyms ...................................................................................................... 6

1. Introduction ............................................................................................................... 8

2. Concepts .................................................................................................................. 10

2.1. Definitions................................................................................................................... 10

2.2. Uncertainty metrics ..................................................................................................... 10

3. Uncertainty assessment ........................................................................................... 12

3.1. Sources of uncertainty in biomass maps ....................................................................... 12

3.2. Approach for assessing uncertainty of biomass maps ................................................... 12

3.3. Uncertainty propagation map - plot comparison........................................................... 12 3.3.1. Definition of the error model ....................................................................................................... 12 3.3.2. Identification of the error model ................................................................................................. 13 3.3.3. Model-based uncertainty prediction ........................................................................................... 15

4. Independent validation ............................................................................................ 16

4.1. Characteristics of the validation dataset ....................................................................... 16 4.1.1. Sources of reference data ............................................................................................................ 16 4.1.2. Sampling design ........................................................................................................................... 16

4.2. Response design .......................................................................................................... 18 4.2.1. Data harmonization...................................................................................................................... 18

4.3. Analysis ....................................................................................................................... 19 4.3.1. Exploratory analysis of sample data ............................................................................................ 19 4.3.2. Model based prediction ............................................................................................................... 21 4.3.3. Spatial aggregation of uncertainties ............................................................................................ 21

5. Map inter-comparison .............................................................................................. 22

6. Expert assessment .................................................................................................... 24

7. References ............................................................................................................... 25

APPENDIX 1 .................................................................................................................... 26

APPENDIX 2 .................................................................................................................... 29


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LIST OF TABLES

Table 1. Uncertainty metrics. ................................................................................................................................. 11

Table 2. Estimation methods for the parameters of the uncertainty model. ........................................................ 14


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LIST OF FIGURES

Figure 1. Validation framework including four components (left) to achieve the validation objectives (right). 8

Figure 2. Example curvilinear relationship between bias and AGB values for Australia from GlobBiomass. 14

Figure 3. Distribution of inventory years of AGB plot data detailed in Appendix 1. 17

Figure 4. Spatial distribution of AGB plots collected up to January 2019 (red). 17

Figure 5. Overview of data harmonization steps. 19

Figure 6. Example of GlobBiomass AGB against harmonized plot AGB at 0.1 degree grid-spacing. 20

Figure 7. Comparison of different global biomass maps against harmonized plot data. 22

Figure 8. Screenshot of a prototype analysis tool provided with the R package for expert assessment. 24


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SYMBOLS AND ACRONYMS

AGB

(A)ATSR

Above ground biomass

(Advanced) Along Track Scanning Radiometer

ALOS Advanced Land Observing Satellite

ASAR Advanced Synthetic Aperture Radar

AOD Aerosol Optical Depth

ATBD

BEF

Algorithm Theoretical Basis Document

Biomass Expansion Factor

CCI Climate Change Initiative

CCI-Biomass Climate Change Initiative – Biomass

CEOS Committee on Earth Observation Satellites

CEOS-WGCV

CI

Committee on Earth Observing Satellites Working Group on Calibration and Validation

Confidence Interval

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


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JAXA Japan Aerospace Exploration Agency

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

MSE

Maximum Value Composite

Mean Squared Error

NASA National Aeronautics and Space Administration

NDVI Normalized Difference Vegetation Index

NSIDC National Snow and Ice Data Centre

PALSAR Phased Array type L-band Synthetic Aperture Radar

PFT

PI

Plant Function Type

Prediction Interval

PSD Product Specification Document

PUG Product User Guide

PVASR Product Validation and Algorithm Selection Report

PVP

RMSE

Product Validation Plan

Root Mean Squared Error

S1, S2 Sentinel-1, Sentinel-2

SAR

SD

Synthetic Aperture Radar

Standard Deviation

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

Var

WD

United States Geological Survey

Variance

Wood Density

WGS84 World Geodetic System 84

WSM Wide Swath Mode

WWF World Wildlife Fund


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1. Introduction This Validation Plan aims to provide a common framework for assessing and reporting the accuracy of the CCI Biomass products, namely the 2010, 2017/2018 and 2018/2019 global biomass maps as well as the corresponding uncertainty layers, and to assess user appreciation of these products. Elaboration of the plan and the forthcoming validation itself run in parallel with ongoing CEOS cal/val development, which provides opportunities for co-creation of the CEOS cal/val procedure. We further build on results of the Globbiomass project (Avitabile et al., 2015; Rozendaal, Santoro, Schepaschenko, Avitabile, & Herold, 2017).

The framework consists of four main components that jointly lead to the achievement of the validation objectives, as shown in Figure 1:

• Uncertainty assessment at different aggregation levels (individual pixels, pixel blocks, regions)

• Independent validation

• Maps inter-comparison

• Expert assessment

Figure 1. Validation framework including four components (left) to achieve the validation objectives (right).

This product validation plan is developed in line with the current draft of the new CEOS LPV protocol for biomass from space calibration and validation. The new CEOS protocol contains a dedicated section on using existing plot data as reference for the validation of larger area biomass maps, assuming they are properly screened, processed and harmonized, to allow for comparison with large area biomass map predictions. It is recognized that different users such as national inventory experts, global climate modelers, local project implementers, etc. all have specific needs when it comes to biomass estimation


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and uncertainty requirements with respect to spatial resolution, geographic extent, timing, thematic content and definitions, and type and standards of uncertainty reporting. The Biomass-CCI project and its climate users are particularly interested in also getting an idea of the spatial accuracy of the map in addition to the more standard accuracy analysis done for biomass map validation exercises. This requires an effort to include a large number of plot-data sources covering all major geographical regions and forest types. The main data sources of forest biomass information include National Forest Inventories (NFIs), research forest plot networks and operational monitoring stations established for forestry, ecology or environmental purposes. Given the variety of data sources, the reference data are also estimates and therefore affected by errors that should be taken into account when using them as reference data.

During the first CCI Biomass User Workshop, the climate, carbon cycle and REDD+ communities expressed the need for global unbiased biomass estimates accompanied by uncertainty information at spatial resolutions ranging from below the targeted 1 ha resolution up to 1-degree cells (for climate modelling) or countries (for REDD+) (Quegan & Ciais, 2018). Hence CCI Biomass product validation should explicitly address estimation of systematic differences between reference and map biomass and spatial uncertainty assessment at different aggregation levels, as indicated in Figure 1.

Methodologically, the plan is to select above-ground biomass (AGB) plot data based on a set of quality criteria and to harmonize them with the map to be validated. After harmonization, systematic differences between reference and map biomass is modelled by regression analysis and the variance of remaining differences between the biomass map and plot data is assessed and partitioned into several components, including a map uncertainty component. The latter is derived from the uncertainty layer provided with CCI Biomass maps, which is updated to conform with the harmonized AGB plot data. Next, it is used for model-based prediction of AGB uncertainty, given the map, at pixel level and at aggregated level, as required by the user communities. This model-based approach implies a strong link between uncertainty assessment and independent validation, which is indicated by the topmost double-headed arrow in Figure 1.

The lower double-headed arrows in Figure 1 indicate that for the Maps Inter-comparison and the Expert Assessment a common set of harmonization and analysis methods will be used. For the expert assessment, these will be provided in the form of an R-package that will allow the climate change community (both within and outside project) to assess biomass maps based on their own reference data, without the need to upload those data.

The plan also entails expanding the database of plot data available for biomass map validation. An obvious reason for this is that the CCI Biomass project delivers multi-year products (2010, 2017/2018 and 2018/2019), each requiring adequate reference data for validation.


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2. Concepts

2.1. Definitions

Since above ground biomass (AGB) is a continuous variable, AGB error is defined as the difference between our representation of reality (a mapped AGB value) and reality (a true AGB value). We can only know error at some locations, if at all, because we rely on scarce reference AGB values (from plots) which are only estimates of true AGB. Therefore, we will mostly refer to differences between mapped AGB values and reference AGB. Additionally, we may lack actual reference values and may have to rely on proxy data, which introduces a further source of uncertainty.

Bias is a systematic difference between predicted biomass on the map and true biomass. Map producers are interested in bias for every possible value of biomass. Therefore, the population that matters for determining bias is the ensemble of predictions obtained for any fixed true biomass value. Bias is likely to vary over the range of biomass values.

Systematic deviation refers to a systematic difference between predicted biomass (on the map) and reference biomass obtained from plot data. Only if plot data (which themselves are estimates) are unbiased, systematic deviation would equal bias.

Uncertainty is an acknowledgement of error: we are aware that our representation differs from reality, but we are only able to model the distribution of error (expressed by a probability distribution or statistics of the distribution), rather than the error itself. This is a common situation, because if we knew error, we would simply correct for it and reduce the error to zero.

Fuzzy set theory (Zadeh, 1965) describes uncertainty of another kind, i.e., that of imprecisely defined and overlapping classes with partial membership grades. Though different, it is often confused with the probabilistic kind of uncertainty referred to above (De Bruin, 2000). The concept of rough sets (Pawlak, 1982) also allows for overlapping classes but without partial membership grades. In our analyses we avoid any such class overlap and take a probabilistic view of uncertainty.

2.2. Uncertainty metrics

Table 1 lists the uncertainty metrics used in this Product Validation Plan (PVP), as well as their definitions, where E is the expected value, Z denotes a random variable, 𝜇 is the mean of Z, Y is a vector

of n reference values, and �̂� is a vector of n predicted values (i.e., CCI-Biomass predictions).


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Table 1. Uncertainty metrics.

Acronym Name Description Definition

Var Variance Measure of spread of a random

variable 𝑉𝑎𝑟(𝑍) = E[(𝑍 − 𝜇)2]

SD Standard deviation Measure of spread of a random

variable 𝑆𝐷(𝑍) = √𝑉𝑎𝑟(𝑍)

d Residual Difference between predicted and

reference value 𝑑𝑖 = �̂�𝑖 − 𝑦𝑖

MSD Mean squared difference Average squared difference

between predicted and reference

values

𝑀𝑆𝐷 =1

𝑛∑ (�̂�𝑖 − 𝑦𝑖)2

𝑛

𝑖=1

RMSD Root mean squared

difference

Square root of MSD 𝑅𝑀𝑆𝐷 = √𝑀𝑆𝐷

CI Confidence interval Measure of uncertainty associated

with a sample population estimate

(e.g. 𝜇); intervals covering

individual observations are

commonly referred to as prediction

intervals.

Estimated range of values

which is likely to include an

unknown property.

PI Prediction interval Measure of uncertainty associated

with the prediction of single

observations

Estimated range in which a

new observation falls, with a

certain probability, given an

existing model


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3. Uncertainty assessment

3.1. Sources of uncertainty in biomass maps

The sources of uncertainty in AGB maps are described in (Quegan et al., 2017) and in deliverable D2.3 (Santoro & Cartus, 2019). The sources can be summarized as follows:

• SAR backscatter uncertainty, including uncertainty caused by radiometric and calibration inaccuracies, thermal noise and speckle, forest backscatter model parameter uncertainty, SAR pre-processing (geometric transformation, resampling, digital elevation model (DEM) accuracy and slope induced effects).

• Uncertainty inflation caused by the rescaling of growing stock volume (GSV) predictions (from BIOMASAR-C) from coarse to finer resolution.

• Uncertainty about wood density (WD), which is used in the conversion of GSV to AGB.

• Uncertainty about the biomass expansion factor (BEF) that parameterises the allometric relationship between stem mass and whole above-ground mass, including branches and leaves.

3.2. Approach for assessing uncertainty of biomass maps

Again, reference is made to (Quegan et al., 2017) where section 1.4.5. describes the uncertainty propagation methods used for the GlobBiomass product. In the first year of the CCI Biomass project, similar approaches are used (Santoro & Cartus, 2019), which can be summarized as follows:

• A first order Taylor series method is used to derive the variance of GSV prediction errors for two SAR sensors (Envisat ASAR and ALOS PALSAR).

• Rescaling (enhancing resolution) is assumed to inflate the standard deviation of the GSV prediction error at coarse scale by the inverse square root of the ratio of the pixel areas (Eq. 1-33 in (Quegan et al., 2017)).

• The variance of the integrated GSV product is computed by the inverse variance weighting method.

• Uncertainty propagation through the product AGB = WD * BEF * GSV is again assessed by a first order Taylor series method.

• Standard deviation of the AGB prediction error aggregated to 100m resolution is computed by averaging the standard deviations at the original 25m resolution, i.e., assuming perfect correlation of errors at 25m resolution. If the errors of the 25 m product were perfectly correlated and had equal variance, the variance of the 100 m product would be identical to the variance of the 25 m product

3.3. Uncertainty propagation map - plot comparison

3.3.1. Definition of the error model

As mentioned in section 2.1, the AGB plot data are themselves not error-free and therefore comparisons between AGB maps and AGB plot data will be accompanied by an uncertainty analysis. The first step in


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such analysis is definition of the error model. We propose an additive model expressing the unknown residual between an AGB prediction and plot AGB at location x (denoted as D(x)) as the sum of five components:

𝐷(𝑥) = 𝑀(𝑥) + 𝑃𝑙𝑡(𝑥) + 𝑃𝑜𝑠(𝑥) + 𝑆(𝑥) + 𝐻(𝑥), (1)

where M(x) is the map biomass error at location x, Plt(x) is the plot measurement error which includes a plot size effect (Réjou-Méchain, Tanguy, Piponiot, Chave, & Hérault, 2017), Pos(x) is a positional error component, S(x) is a within-pixel sampling error component and H(x) is an error introduced at a data harmonization step (section 4.2.1). The sampling error, S(x), arises because the AGB plot size (on average 0.15 ha for the data acquired up to January 2019, see Appendix 1) is usually small compared to the AGB map pixel (~1 ha). Even a pixel completely covered by forest will have variation at ground level, and the AGB plot samples only part of it. Pixel areas partly covered with forest undergo a harmonization procedure as explained in section 4.2.1. Note that M(x), Plt(x), Pos(x), S(x) and H(x) are random variables whose values are unknown but can be described by probability distributions (Heuvelink, 2005).

Assuming the error terms in Equation (1) are mutually uncorrelated, the variance of the sum of these errors equals the sum of their variances:

𝑉𝑎𝑟(𝐷(𝑥)) = 𝑉𝑎𝑟(𝑀(𝑥)) + 𝑉𝑎𝑟(𝑃𝑙𝑡(𝑥)) + 𝑉𝑎𝑟(𝑃𝑜𝑠(𝑥)) + 𝑉𝑎𝑟(𝑆(𝑥)) + 𝑉𝑎𝑟(𝐻(𝑥)) (2)

The map error M(x) is modelled as the sum of a deterministic trend 𝑡(𝑥) representing systematic differences between map and reference biomass at location x and A(x) representing random deviations:

𝑀(𝑥) = 𝑡(𝑥) + 𝐴(𝑥) (3)

The deterministic trend t(x) will be modelled as a function of spatially exhaustive covariates such as mapped AGB at location x, continent and/or ecological zone. Since systematic deviation is modelled by t(x) we assume A(x) to have zero mean.

We consider spatial correlation of A(∙), because errors in the AGB maps can be spatially correlated. However, because our aim is map validation rather than map updating, we will not use this spatial correlation for locally updating the expected value and variance of A(x) using nearby plot locations (by kriging) but rather for purposes of assessing joint AGB uncertainty when aggregating map data to larger support units, such as pixel blocks, countries or other regions.

3.3.2. Identification of the error model

Table 2 provides an overview of the planned approaches for estimating the parameters of the uncertainty model described above. First results —not shown here— confirm an anticipated inverse relationship between 𝑉𝑎𝑟(𝑃𝑙𝑡(𝑥)) and plot size, while t(x) is often positive when the predicted AGB value is small (i.e., low AGB values in the map tend to exceed the reference AGB) and negative when they are large (i.e., high AGB values in the map tend to be less than the reference AGB), as suggested by the red trend line in Figure 2. Trends in t(x) such as that shown in Figure 2 will be assessed for different continents or ecological zones, under the assumption that the reference plot data are unbiased (for each biomass value) but with dispersion.

Note that Figure 2 also suggests an increasing variance of the residuals with higher mapped AGB values. It will be investigated whether this indeed is the case and —if so— whether it is properly captured by the uncertainty layer of the CCI Biomass products.


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Table 2. Estimation methods for the parameters of the uncertainty model.

Parameter Estimation approach

t(x) Expected residuals between 𝑚𝑎𝑝𝐴𝐺𝐵(𝑥) and harmonized (section 4.2.1) plot AGB

(𝑝𝑙𝑜𝑡′𝐴𝐺𝐵(𝑥)). The latter are assumed unbiased. Trend t(x) is modelled as a function of

mapped AGB, continent and ecological zone by regression analysis. Note that t(x) is

regarded as a deterministic function.

𝑉𝑎𝑟(𝐴(𝑥)) Square of the SD of the prediction error accompanying the CCI Biomass maps.

𝑉𝑎𝑟(𝑃𝑙𝑡(𝑥)) For a subset of plots having individual tree measurements (~4200 plots, January 2019),

Réjou-Méchain et al. (2017) biomass R-package will be used. For other plots lacking such

data 𝑉𝑎𝑟(𝑃𝑙𝑡(𝑥)) will be predicted by a random forest model trained on the subset, map

AGB, plot size, general and specific eco-zones and continent as explanatory variables.

𝑉𝑎𝑟(𝑃𝑜𝑠(𝑥)) Monte Carlo sampling of map AGB using the distribution of positional error based on GPS

accuracy and uncertainty about the location of the reported geolocation with respect to plot

geometry as characterized by plot size. Potentially (to speed-up calculations), Monte Carlo

sampling will be circumvented by weighted sampling over a plot size dependent kernel.

This error component is expected to be of minor importance.

𝑉𝑎𝑟(𝑆(𝑥)) Use literature and/or measurements of within-plot AGB variability over large plots

(preferably ≥ 1ha) acquired by LIDAR in cooperation with WP1 (see Santoro & Cartus,

2019). Pixel areas containing both forest and non-forest fractions are handled in a data

harmonization step.

𝑉𝑎𝑟(𝐻(𝑥)) Variance of mathematical operations applied to random variables in the harmonization

steps. Details in section 4.2.1.

Figure 2. Example curvilinear relationship between systematic deviation (map – reference) and AGB values for Australia from GlobBiomass. The green dashed horizontal line indicates

zero deviation (for orientation purposes). The sharp and straight edge at the top of the point cloud is due to non-negative reference data.


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The uncertainty layer of the CCI Biomass maps and the other uncertainties considered in Equation 2 acknowledge that we expect Var(D(x)) to vary over space (i.e., it is heteroscedastic). In other words, we assume that at some locations, larger deviations of AGB from the mapped AGB are more likely to occur than at other locations, likely as a function of mapped AGB. To make such locations comparable and at the same time evaluate our error model, we will assess standardized residuals rather than the residuals themselves.

The proposed approach is as follows. For all locations x, the local deviations t(x) are added to the map AGB. We denote the result by 𝑚𝑎𝑝′

𝐴𝐺𝐵(𝑥). For all plot locations, standardized residuals or zscore(x) are

computed by Equation (4):

𝑧𝑠𝑐𝑜𝑟𝑒(𝑥) = 𝑚𝑎𝑝′

𝐴𝐺𝐵(𝑥)−𝑝𝑙𝑜𝑡′𝐴𝐺𝐵(𝑥)

√𝑉𝑎𝑟(𝐷(𝑥)), (4)

where 𝑝𝑙𝑜𝑡′𝐴𝐺𝐵

(𝑥) denotes the harmonized plot AGB value at location x (see section 4.2.1). We next

compute the mean and standard deviation of 𝑧𝑠𝑐𝑜𝑟𝑒(𝑥) and conclude our trend model is incorrect if the mean 𝑧𝑠𝑐𝑜𝑟𝑒 substantially deviates from zero or if there is a spatial pattern in 𝑧𝑠𝑐𝑜𝑟𝑒(∙). We further conclude that we incorrectly assessed uncertainty if the standard deviation of 𝑧𝑠𝑐𝑜𝑟𝑒(𝑥) deviates substantially from one. In the former case, the trend t(x) will be adjusted by considering other explanatory variables or another (order of the) trend model. In the latter case, the uncertainty component attributed to the map 𝑉𝑎𝑟(𝑀(𝑥)) will be adjusted by considering a potential trend, an additive or multiplicative adjustment factor or some combination of these. We also consider doing separate analyses for different strata representing continents or ecological zones. The best option will be obtained by iterating over several models for Var(M(x)) and repeated evaluations of Equation (4). The adjusted variance is denoted 𝑉𝑎𝑟(𝑀(𝑥))′. After finalizing the model, it will be available for each location on the AGB map, which serves our purpose.

Next, we compute variograms of 𝑧𝑠𝑐𝑜𝑟𝑒(𝑥), using the method of moments and subsequent fitting of a valid variogram model (Diggle & Ribeiro Jr., 2007; Goovaerts, 1997; Pebesma, 2004).We force the variogram models to have unit total sill and invert them to obtain correlograms, which by construction (because we fit a valid variogram model) are positive semidefinite. Correlograms are preferred over variograms or covariograms because the former facilitate modelling spatial correlation under heteroscedasticity. The correlograms will be computed per continent or per ecological zone, if there are enough plot data to do so. Otherwise, stationarity over similar regions will be assumed.

3.3.3. Model-based uncertainty prediction

The use of the uncertainty models for predicting uncertainty at pixel level and over spatial aggregates such as pixel blocks or countries is described in sections 4.3.2 and 4.3.3.


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4. Independent validation

4.1. Characteristics of the validation dataset

4.1.1. Sources of reference data

We build upon the GlobBiomass reference database. Table 16 in (Rozendaal et al., 2017) lists the broad variety of plot data used for GlobBiomass map validation. In the current project similar data will be used, whilst in cooperation with WP1 we will add data sources to allow reliable validation of the 2010, 2017/2018 and 2018/2019 CCI Biomass products. Appendices 1 and 2, taken from the Data Access Requirement Document (DARD) document (Lucas, Bunting, Siggins, Kay, & Santoro, 2018), provide lists of already used and potential sources of validation data. The current data agreements will have to be renewed and new agreements will have to be established.

The following criteria were used by (Rozendaal et al., 2017) and they are also proposed for CCI Biomass product validation:

• Plot coordinates need to be recorded with at least 4 decimals precision (for decimal degrees) or similar if expressed in degrees, minutes and seconds.

• The plot inventory date is allowed to be at most ten years separated from the CCI Biomass map reference year. More recent plot data will be collected to allow reliable validation of the 2017/2018 and 2018/2019 CCI Biomass products (see Figure 3).

• Plots that have been deforested according to the (Hansen et al., 2013) tree cover loss product between the year of the inventory and the year of the global biomass map are excluded from the analysis.

• The inventory should at least include trees with stem diameter of 10 cm and above.

• Data used in creating the map cannot be used for independent validation and vice versa.

4.1.2. Sampling design

We rely on AGB plot data that are not specifically produced for validation purposes but that are rather collected within the context of national forest inventories and similar efforts at local to regional scale. This has several consequences, which are summarised as follows:

• The populations of the CCI Biomass products and those of the inventories differ. CCI Biomass concerns forest biomass over the entire globe (including areas without forest), whereas forest inventories only concern forested areas within countries or regions. Moreover, Large portions of the world inluding Southeast Asia, Africa, the dry tropics and Siberia have very little or no plot data at all (see Figure 4).

• The sampling frames are different: CCI Biomass concerns forest biomass discretised in ~100m × 100m pixels (including non-forested area) while the inventories employ non-uniformly sized and typically small plots (on average 0.15 ha for the AGB plot data referred to in Appendix 1) within forested areas.

• Regionally, the AGB plot locations may have been chosen by probability sampling but large areas of the world are not included in the AGB plot sample (see first bullet). That is because in these


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areas there are no forest inventories or because institutions or authorities are unwilling to share inventory data.

• The wide variety of sampling designs included in the AGB plot dataset produces a complex amalgamated sample.

Given the above, our approach is to consider the AGB plot data with its mix of plot sizes and local sampling designs as an opportunistic sample (also referred to as an ad hoc sample by other authors). Such sampling invalidates conventional statistical inference methods, which are typically preferred since they do not depend on model assumptions.

Rather, a model-based approach is proposed here, with the model parameters estimated from the plot data along with other data sources (see section 3.3.2). Absence of plot data in large portions of the world forces us to apply model parameters (trend models of systematic deviation and correlograms) estimated for ecological zones or continents in areas where they cannot be verified but which are assumed to have similar characteristics.

Figure 3. Distribution of inventory years of AGB plot data detailed in Appendix 1.

Figure 4. Spatial distribution of AGB plots collected up to January 2019 (red).


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4.2. Response design

For AGB product validation, the response design encompasses all steps leading to the assessment of differences between map and plot AGB (cf. (Olofsson et al., 2014). The plot data used in our comparison may have been surveyed at a different time than the map to be assessed, they typically differ in spatial support (i.e., the area covered by individual plots) from the AGB map pixels and they measure different spatial entities (forest biomass over a pixel area versus forest biomass within a forest plot). Therefore, data harmonization is needed prior to the analysis of differences.

4.2.1. Data harmonization

Differences in acquisition time of AGB plot data and the reference year of the AGB map (the latter is assumed to apply to the entire map) are harmonized using specific growth rates listed in (IPCC, 2019). The growth rate table in IPCC (2019) also reports different types of uncertainty estimates, such as confidence intervals, which will be translated into variances using distribution information which at the time of writing was not yet available. If such information is unavailable, assumptions will have to be made. The variances are propagated to D(x) as described in section 3.3.1 and Table 2. Temporal adjustment by growth rates is applied up to a difference of ten years between the inventory date and the map reference year. Plots having a larger temporal difference are discarded in the analyses.

Recall that the AGB plot data and the map have distinct sampling populations (see section 4.1.2) in terms of both different spatial support and the inclusion of non-forested areas within map pixels. Harmonization of these differences is attempted by multiplying the temporally adjusted plot AGB by forest fraction. This forest fraction is computed by putting a 10% threshold on the tree cover product (Hansen et al., 2013) corresponding to the CCI Biomass map reference year. This will be done both at pixel level and over larger aggregated blocks. When more than one AGB plot lies within a pixel or block, the average of the adjusted AGB per plot is used. For aggregates, a minimum number of plots per pixel block is set (e.g. 5). The uncertainty introduced by this harmonization step will be accounted for in D(x) (see section 3.3.1 and H(x) in Table 2). Figure 5 shows a flow diagram of the data harmonization steps.


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Figure 5. Overview of data harmonization steps (blue boxes denote inputs, yellow boxes intermediate results, orange circles processing steps and green boxes inputs for validation).

4.3. Analysis

4.3.1. Exploratory analysis of sample data

Exploratory data analysis includes plotting harmonized reference data against mapped AGB at varying resolution so as to visually assess goodness of fit for different ecological zones, continents and aggregation levels (see example in Figure 6). We will further model bias as a function of biomass by regression analysis. Note that plots such as Figure 6 are particularly useful for map producers; map users would rather see the axes reversed, with whiskers on harmonized plot AGB values for varying AGB levels on the map.


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Figure 6. Example of GlobBiomass AGB against harmonized plot AGB at 0.1 degree grid-spacing. The size of the dots indicates the number of cells per bin; whiskers denote

interquartile ranges. Only cells with at least 5 plots (Appendix 1) are included.


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4.3.2. Model based prediction

Because of the opportunistic sampling design, validation beyond the sampled locations will have to rely on model-based prediction (McRoberts, 2014). Systematic differences between predicted biomass (on the map) and reference biomass are modelled by trend, t(x), using a regression equation that is fitted to the harmonized plot data, possibly after stratification on continents or ecological zones. The expected mean of that trend at non-sampled locations is predicted using the fitted model (see section 3.3.2 and Figure 2). The associated residual uncertainty at pixel support is represented by the adjusted map uncertainty, 𝑉𝑎𝑟(𝑀(𝑥))′ (see section 3.3.2).

Depending on how data are used, biomass map users such as climate modellers and REDD+ communities may be interested in uncertainties over larger support units, such as square pixel blocks or countries (Quegan & Ciais, 2018). Aggregation of biomass predictions and measurements over larger spatial units often results in a partial cancelling out of random prediction errors and measurement errors. Note that this does not hold for systematic error or bias. Therefore, aggregation is expected to improve the precision of map and harmonized plot data if both map and multi-plot data are averaged over larger spatial units.

We will assess systematic deviations between map and reference biomass with trend models for different levels of spatial aggregation to allow users to assess at what aggregation level the map can be used for their needs. Bias over aggregates is computed as the difference between averaged map pixels over the spatial support units and averaged harmonized plot data over those units. Assessment of the uncertainties in biomass residuals is addressed in the next section.

4.3.3. Spatial aggregation of uncertainties

Spatially uncorrelated errors tend to cancel out when aggregating over larger spatial units, but this effect is less pronounced when errors are spatially correlated. We model this effect using the correlograms introduced in section 3.3.2. From the correlogram and the distance matrix for all pixel pairs, xi, xj contained in a support unit, a correlation matrix P is computed, with elements 𝜌𝑖,𝑗, where i

and j (1… n) refer to the n pixels within the spatial unit. Hence, the diagonal of the matrix where i = j is filled with ones representing the correlation of pixels with themselves. Off-diagonal, partial correlations between pixels within the spatial unit are listed. The elements 𝜌𝑖,𝑗 are multiplied by the corresponding

adjusted standard deviations of the map error, √𝑉𝑎𝑟(𝑀(𝑥𝑖))′, √𝑉𝑎𝑟(𝑀(𝑥𝑗))′ which produces a

covariance matrix, Σ, with elements, 𝜎𝑖,𝑗. The variance of the map error over the support unit is then

predicted by summing the elements of Σ and division by n2 (Equation 6):

𝑉𝑎𝑟(𝑎𝑔𝑔𝑟) = 1

𝑛2∑ ∑ 𝜎𝑖,𝑗

𝑛𝑗=1

𝑛𝑖=1 (6)

The use of correlograms (section 3.3.2) rather than variograms is motivated by the possibility to deal with heteroscedasticity.


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5. Map inter-comparison This task consists of the comparison of the CCI Biomass maps with other existing products covering a given geographic extent, as well as comparison of map bias based on AGB reference data (cf. section 4.3.1). The comparison aims to complement product validation with the following information: evaluation of consistency between different products; identification of areas with larger disagreements and assessment of whether these areas need further study; assessment of strengths and weaknesses of different datasets based on the analysis of the data and methods used to produce the maps; increase awareness and acceptance of CCI Biomass products within the international community.

The map inter-comparison involves the following steps. Firstly, datasets to be compared (i.e., regional or global maps) are identified and acquired. Secondly, the datasets are harmonized with CCI Biomass maps in terms of spatial and temporal support (see section 4.2.1) as well as thematic content (e.g., biomass unit). Thirdly, the following comparison metrics are computed at pixel level and at aggregated grid spacing (e.g., 0.1 degree):

1. Comparison statistics, global and over continents and ecological zones:

• Mean (absolute) difference

• Root Mean Square Difference

• Linear correlation

2. Comparison maps:

• Difference maps

• Relative difference maps, using the CCI Biomass maps as reference

3. Comparison plots of mapped data:

• Scatterplots or whisker plots such as exemplified in Figure 6.

• Histograms and cumulative distributions

4. Comparison plots of mapped data against harmonized AGB plot data, such as exemplified in

Figure 7.

Figure 7. Comparison of different global biomass maps against harmonized plot data (source:


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see Appendix 1).


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6. Expert assessment The Expert Assessment is an essential quality control and feedback mechanism, aimed at assessing the users’ acceptance of CCI Biomass products, evaluating their quality and limitations from the users’ perspective, and obtaining recommendations for improvements. The output of the user assessment consists of an Expert Survey report.

The user assessment is performed using standard questionnaires, which are produced for each CCI Biomass product and will be sent to users within and outside the project consortium. The questionnaires aim to assess:

• User satisfaction

• Product usability

• Delivery system (timing, delivery method, naming, format, etc.)

• Product quality and limitations related to spatial and temporal resolution

• Applicability of the products for climate modelling

• Need of capacity building (optional)

• Future data and product requirements To support users in assessing the CCI Biomass products using their own data, an R-package is being developed that provides easy access to the analysis methods described earlier in this validation plan. Figure 8 shows a screenshot of a prototype of a tool provided in the package.

Figure 8. Screenshot of a prototype analysis tool provided with the R package for expert


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assessment.

7. References Avitabile, V., Balzter, H., de Bruin, S., Carreiras, J., Carvalhais, N., Quegan, S., . . . Rodriguez-Veiga, P.

(2015). DUE GlobBiomass - Validation Protocol. Retrieved from http://globbiomass.org/wp-content/uploads/DOC/Deliverables/D5/GlobBiomass_D5_VP_V01.pdf

De Bruin, S. (2000). Querying probabilistic land cover data using fuzzy set theory. International Journal of Geographical Information Science, 14(4), 359-372. doi:10.1080/13658810050024287

Diggle, P. J., & Ribeiro Jr., P. J. (2007). Model-based Geostatistics. New York: Springer. Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. New York: Oxford University Press. Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A., Tyukavina, A., . . . Townshend,

J. R. G. (2013). High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 342(6160), 850-853. doi:10.1126/science.1244693

Heuvelink, G. B. M. (2005). Propagation of error in spatial modelling with GIS. In P. A. Longley, M. F. Goodchild, D. J. Maguire, & D. W. Rhind (Eds.), Geographical Information Systems: Principles, Techniques, Management and Applications, 2nd Edition (pp. 85-95). Hoboken, NJ: Wiley.

IPCC. (2019). Table 4.9, Chapter 4, Volume 4 (AFOLU), 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (under review). In.

Lucas, R., Bunting, P., Siggins, A., Kay, H., & Santoro, M. (2018). CCI BIOMASS Data Access RequirementsDocument Year 1.

McRoberts, R. E. (2014). Post-classification approaches to estimating change in forest area using remotely sensed auxiliary data. Remote Sensing of Environment, 151, 149-156. doi:10.1016/j.rse.2013.03.036

Olofsson, P., Foody, G. M., Herold, M., Stehman, S. V., Woodcock, C. E., & Wulder, M. A. (2014). Good practices for estimating area and assessing accuracy of land change. Remote Sensing of Environment, 148, 42-57. doi:10.1016/j.rse.2014.02.015

Pawlak, Z. (1982). Rough sets. International Journal of Computer & Information Sciences, 11(5), 341-356. doi:10.1007/BF01001956

Pebesma, E. J. (2004). Multivariable geostatistics in S: the gstat package. Computers & Geosciences, 30(7), 683-691. doi:10.1016/j.cageo.2004.03.012

Quegan, S., & Ciais, P. (2018). CCI BIOMASS User Requirement Document Year 1 Quegan, S., Rauste, Y., Bouvet, A., Carreiras, J., Cartus, O., Carvalhais, N., . . . Santoro, M. (2017). DUE

GlobBiomass Global Biomass Map Algorithm Theoretical Basis Document (incorporating the Design Justification File). Retrieved from http://globbiomass.org/products/global-mapping/

Réjou-Méchain, M., Tanguy, A., Piponiot, C., Chave, J., & Hérault, B. (2017). biomass: an r package for estimating above-ground biomass and its uncertainty in tropical forests. Methods in Ecology and Evolution, 8(9), 1163-1167. doi:doi:10.1111/2041-210X.12753

Rozendaal, D. M. A., Santoro, M., Schepaschenko, D., Avitabile, V., & Herold, M. (2017). DUE GlobBiomass D17 Validation Report. Retrieved from http://globbiomass.org/products/global-mapping/

Santoro, M., & Cartus, O. (2019). End to end ECV uncertainty budget - Year 1 Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 38-353.

http://globbiomass.org/wp-content/uploads/DOC/Deliverables/D5/GlobBiomass_D5_VP_V01.pdf

http://globbiomass.org/wp-content/uploads/DOC/Deliverables/D5/GlobBiomass_D5_VP_V01.pdf

http://globbiomass.org/products/global-mapping/




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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.

APPENDIX 1. Validation sites used in CCI Biomass where further measurements are being acquired (Lucas et al., 2018).

ID CONTINE

NT

COUNTRY/

REGION

LOCATIO

N

EXT-

ENT

1

VEGETATI

ON

TYPE(S) 2

YEAR(S) N.

PLOTS

PLOT

SIZE (ha)

PERMANE

NT PLOT REFERENCE CONTACT

PERSON

DATA

ACCESS

3

AFR

1 Africa DRC Lukenie L

F

(concession) 2007-2010 1157 0.5 No

Hirsch et al.,

2013 N. Bayol

AO -

MoU

required

AFR

2 Africa Sierra Leone

Gola

Forest L F 2005-2007 609 0.125 No

Lindsell and

Klop, 2013 C. Tayleur AO

AFR

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

De Vries et

al., 2012 V. Avitabile

AO -

GBI

AFR

5 Africa Ghana Ankasa L F 2012 34 0.05 No

Vaglio Laurin

et al., 2013

G. Vaglio

Laurin AO

AFR

5 Africa Ghana

Bia Boin,

Dadieso L F 2012-2013 40 0.16 No

Pirotti et al.,

2014

G. Vaglio

Laurin AO

AFR

6 Africa Tanzania

Eastern

Arc

Mountain

L F 2007-2010 24 0.08 - 1 Yes Willcock et

al., 2014 S. Willcock AO

AFR

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

Kearsley et al.,

2013 H. Verbeeck AO

AFR

8 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

AFR

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

Carreiras et

al., 2013 J. Carreiras

AO -

GBI

AFR

10 Africa Cameroon

Mbam

Djerem

National

Park

L F - S 2007 24 0.2 - 1 No Mitchard et

al., 2011 E. Mitchard AO

AFR

11 Africa Uganda Uganda N F - W - S 2000-2005 897 0.25 Yes Drichi, 2003 V. Avitabile

AO -

GBI

AFR

12 Africa Uganda Budongo L F 2008 114 0.16 No

Avitabile et

al., 2012 V. Avitabile

AO -

GBI


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Remote Sensing AG.

AFR

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

Mitchard et

al., 2009 E. Mitchard AO

AFR

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

Ryan et al.,

2012 C. Ryan AO

AFR

15 Africa Madagascar

Madagasca

r N F 2007-2013 1003

max. 0.13

or 0.28 ha

(4)

No / Not

clear

Vieilledent et

al. 2016

G.

Vieilledent FA

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

CA

M1 C. America Mexico Mexico N F 2004-2008 4296 0.16 Yes de Jong, 2013 B. de Jong AO

NA

M1 N. America Alaska Alaska N F 2002-2014 605 0.04 Yes

Liang et al.,

2015

Jingjing

Liang AO

NA

M2 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;

Avitabile et

al., in review

V. Avitabile AO -

GBI

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


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Remote Sensing AG.

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

al., in review

V. Avitabile AO -

GBI

AUS

1 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

Hengeveld FA

EU3 EU The

Netherlands

The

Netherland

s

N F 2012-2013 3100 5-20m

radius 5 Yes (50%)

Schelhaas et

al. 2014,

Geerten

Hengeveld 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-Shrubland, W-Woodland

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

4 Variable, dependent on tree size

5 Density dependent


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Remote Sensing AG.

APPENDIX 2 Potentially useful validation sites for CCI Biomass, used in WP1 (Lucas et al., 2018).

REGION COUNTRY SITE NAME CODE COORDINATOR PRIMARY CONTACT LAT. LON. YEAR_ESTA

BLISHED

PLOT_

No

AREA_

UNIT

ALS TLS

Africa Cameroon Dja DJA AfriTRON SL Lewis and B Sonke 3.1502 12.9997 2005 18 1 no no

Africa Cameroon Korup KRP ForestGEO D Kenfack 5.0739 8.8547 1996 1 50 no no

Africa CAR M'Baiki MBK TmFO B Herault and P Sist 4.5000 18.6000 1982 10 4 no no

Africa

Congo-

Brazzaville

Nouabale-

Ndoki NBL AfriTRON

SL Lewis, C Clark and

J Poulsen 2.1858 16.3240 2010 41 1 no no

Africa Cote d'Ivoire La Tene LTN CIRAD B Herault 6.6667 -5.5000 1978 25 4 no no

Africa DRC Ituri ITR ForestGEO D Kenfack 1.4368 28.5826 1994 4 10 no no

Africa DRC Malebo MLB WWF (?) JF Bastin -2.4977 16.5061 2012 21 1 yes no

Africa DRC Salonga SLN AfriTRON SL Lewis -1.7044 20.5437 2008 16 1 NA no

Africa DRC Yangambi YNG AfriTRON

E Kearsley and H

Verbeek 0.8338 24.5084 2012 20 1 NA NA

Africa Gabon Lope LOP AfriTRON SL Lewis -0.2004 11.5886 2016 3 & 11 0.5 & 1 yes yes

Africa Gabon Mabounie MBN AMAP N Barbier -0.7610 10.5565 2012 12 1 yes NA

Africa Gabon Mondah MND NASA S Saatchi 0.5653 9.3515 2016 19 1 yes yes

Africa Gabon Rabi RAB ForestGEO D Kenfack -1.9250 9.8812 2010 1 25 yes yes

Africa Ghana Ankasa ANK AfriTRON Y Malhi and SL Lewis 5.2864 -2.6923 2011 3-5 1 yes yes

Africa Mozambique Gorongosa GRN Edinburgh E Mitchard -18.7667 34.5000 2005-2007* 15 1 no NA

Africa Nigeria Ngel Nyaki NGL ForestGEO D Kenfack 7.0680 11.0566 2014 1 20 no no

America Bolivia La Chonta CHN TmFO

M Pena Claros and P

Sist -15.7830 -62.9170 2000 12 4 no no

America Brazil Caxiuana CXN

RAINFOR and

TEAM OL Phillips -1.7483 -51.4858 1994 10 1 no no

America Brazil

Chico

Mendes CHM RAINFOR

S Cerruto Ribeiro and

OL Phillips -10.6505 -68.5003 1991 8 1 no no

America Brazil

Ilha do

Cardoso ILH ForestGEO S Davies -25.0955 -47.9573 2004 1 10 no no

America Brazil Manaus MNS

ForestGEO +

RAINFOR

S Davies + AA de

Oliveira -2.4417 -59.7858 2004 1 + >10 25 + 1 no no

America Brazil

Nova

Xavantina NVX RAINFOR

B Marimon and O

Phillips -14.7099 -52.3499 2008 8 1 no no


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Remote Sensing AG.

America Brazil Paragominas PRG TmFO P Sist -3.6670 -48.1670 1993 3 24.5 NA NA

America Colombia Amacayacu AMC ForestGEO S Davies -3.8091 -70.2678 2006 1 25 no no

America Colombia Choco CHC NASA S Saatchi 4.0565 -77.0665 2013 45 & 15 0.25 &1 yes no

America Colombia La Planada LPL ForestGEO S Davies 1.1558 -77.9935 1997 1 25 no no

America Costa Rica La Selva LSL OTS J Kellner 10.4205 -84.0124 1997 18 & 3 0.5 & 4 yes yes

America Costa Rica

Osa

Peninsula OSP Wien University F Hofhansl 8.7013 -83.5418 2012* 20 1 yes yes

America

French

Guiana BAFOG BAF Guyafor

C Bedeau and G

Derroire 5.4905 -53.9871 1968 4 4 no no

America

French

Guiana

Montagne

Tortue MNT Guyafor

C Bedeau and G

Derroire 4.2214 -52.4119 2002 3 4-10 yes NA

America

French

Guiana Nouragues NRG RAINFOR J Chave 4.0689 -52.6827 1992-1993 11 0.5-12 yes yes

America

French

Guiana Organabo ORG Guyafor

C Bedeau and G

Derroire 5.4716 -53.4781 1997 6 4 no no

America

French

Guiana Paracou PRC TmFO G Derroire 5.2669 -52.9311 1984-1986 16 & 1

6.25 &

25 yes yes

America Panama

Barro

Colorado

Island BCI ForestGEO S Davies 9.1543 -79.8461 1981 1 50 yes NA

America Peru

Allpahuayo /

Jenaro

Herrera AJH RAINFOR

T Baker and OL

Phillips -3.9493 -73.4295 1991-2005 15 0.5-1.5 yes no

America Peru Tambopata TMB RAINFOR OL Phillips -12.8344 -69.2816 1983 9 1 yes yes

Asia-

Pacific Australia

Litchfield

Savanna LTC TERN J Armston -13.1491 130.7868 2012 2 1 yes yes

Asia-

Pacific Australia

Robson

Creek RBS TERN J Armston -17.1201 145.6323 2009 1 25 yes NA

Asia-

Pacific India

Karnataka_Y

ellapur KRN AMAP M Réjou-Méchain 14.9650 74.7122 2014-2015 20 1 yes NA

Asia-

Pacific Indonesia Malinau MLN TmFO P Sist 2.8670 116.6670 1998-1999 24 1 no no

Asia-

Pacific Indonesia STREK STR TmFO P Sist 2.0000 117.2500 1989-1990 18 4 no no


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Remote Sensing AG.

Asia-

Pacific

Malaysia-

Borneo

Danum

Valley DNM ForestGEO + Leeds

DFRP Burslem and S

Davies + OL Phillips 5.1019 117.6880 2010 1 + 3 50 + 1 yes NA

Asia-

Pacific

Malaysia-

Borneo Lambir LMB ForestGEO S Davies 4.1865 114.0170 1991 1 52 no no

Asia-

Pacific

Malaysia-

Borneo Sepilok SPL

Aberdeen, Leeds

and Cambridge

D Coomes, DFRP

Burslem, OL Phillips

and R Nilus 5.8581 117.9483 2000-2001 9 & 1 4 & 2 yes NA

Asia-

Pacific

Malaysia-

Penisular Pasoh PSH

ForestGEO +

TmFO S Davies + P Sist 2.9820 102.3130 1986 1 + 1 50 + 6 no no

Asia-

Pacific

Papua New

Guinea Wanang WNN ForestGEO

V Novotny and Stuart

Davies -5.2500 145.2670 2009 1 50 no no

Asia-

Pacific Taiwan Fushan FSH ForestGEO S Davies 24.7614 121.5550 2004 1 25 yes no

Asia-

Pacific Thailand Doi Inthanon DIN ForestGEO S Davies 18.5833 98.4333 1997 1 15 no no

Asia-

Pacific Thailand

Huai Kha

Khaeng HKK ForestGEO S Davies 15.6324 99.2170 1992 1 50 no no

Asia-

Pacific Thailand Mo Singto MSN

ForestGEO +

AMAP

S Davies + M Réjou-

Méchain 14.4333 101.3500 2010

1 + ca.

10 30 + 1 yes NA

* Year census mentioned in the literature. Does not necessarily correspond to year established.

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