D4 Ground Data Document -...

DUE GlobBiomass

D4

Ground Data Document

Prepared for European Space Agency (ESA-ESRIN)

In response to ESRIN/Contract No. 4000113100/14/I_NB

Prepared by

Wageningen University and Research Centre, Laboratory of Geoinformation

Science and Remote Sensing, The Netherland

December 2015

GlobBiomass /6

Vol. 0.1 Wageningen University and Research Centre

D4 - GDD Date 16-Dec-15

Revision History

Deliverable D4, GDD

Work Package 2000

Due date KO+12

Authors Valerio Avitabile, Maurizio Santoro

Distribution FSU: Christiane Schmullius, Evelin Matejka

ESA: Frank Martin Seifert; Nathalie Boisard

Reason for change

Issue

Revision

Date

Release 1

Version 01

GlobBiomass /6



Contents

1. Key concepts ................................................................................................................................ 4

2. Methods to select the reference data (QA/QC) .......................................................................... 6

2.1. Reference field plots................................................................................................................ 6

2.1.1. Metadata screening ............................................................................................................. 6

2.1.2. Data harmonization ............................................................................................................. 6

2.1.3. Data screening ..................................................................................................................... 7

2.2. Reference biomass maps ......................................................................................................... 8

2.2.1. Metadata screening ............................................................................................................. 8

2.2.2. Data harmonization ............................................................................................................. 8

2.2.3. Data screening ..................................................................................................................... 8

3. The Ground Database (v.01) ..................................................................................................... 10

Appendix I .......................................................................................................................................... 13

Metadata of the ground reference datasets ..................................................................................... 13

Metadata of the reference biomass maps ........................................................................................ 15

Appendix II ......................................................................................................................................... 17

Metadata tables for new reference data .......................................................................................... 17

References ......................................................................................................................................... 22

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1. Key concepts

Type of reference data. Ground data are an essential component of the GlobBiomass project and

one of the key parameters that determines the quality and accuracy of the final products. In this

document, ground data refer to a set of biomass reference data that can be used for the calibration

and validation of the GlobBiomass products. Biomass data that can be used as a reference are

primarily field observations (forest inventory plots) but also remotely sensed data that are of greater

quality than the map data are considered acceptable for use as reference data (Stehman, 2009), and

these may include biomass estimates derived from airborne LiDAR data or extracts from reliable

high-resolution biomass maps (Avitabile et al., 2015). In addition, national and sub-national biomass

statistics are also considered for map assessment and inter-comparison with existing estimates.

Scope and content. The Ground Database has the primary scope to support the validation of the

GlobBiomass global products. The Ground Database should be considered as a living database and

the current version will be expanded within the time frame of the GlobBiomass project to

incorporate additional biomass datasets as soon as they become available. In the first project year

data acquisition was prioritized in the tropical region where uncertainties are higher, and in the

second project year further reference data will be acquired in the boreal region.

Source of reference data. Since acquiring biomass reference data is a very time consuming and costly

task, dedicated large-scale field campaigns with global representativeness are not feasible in the

project timeframe and essentially are out of scope of the GlobBiomass project. Hence, the Ground

Database mostly relies on existing biomass reference data, such as ground datasets provided as in-

kind contributions by various science and user groups. In situ data were obtained by cooperation

with the organizations identified as GlobBiomass user group indicated in the Statement of Work,

existing networks as Forestplots (Lopez-Gonzalez et al., 2009, 2011), research groups and national

forest organizations. Specifically, relevant forest database were accessed through existing

collaborations with international organizations as the CIFOR (www.cifor.org), initiatives as the

GEOCARBON project (www.geocarbon.net) and the Biomass Geo-Wiki platform (http://biomass.geo-

wiki.org/), and research networks as the European ICP Forests (http://icp-forests.net/), Fluxnet

(http://fluxnet.ornl.gov/), the RAINFOR network (www.rainfor.org), the GEM network

(http://gem.tropicalforests.ox.ac.uk/) and the Sustainable Landscape Brazil network (http://

geoinfo.cnpm.embrapa.br/geonetwork/srv/eng/main.home). In addition, ground observations and

local biomass maps, including sub-national or national datasets, were acquired through direct

collaborations with the data owner, such as the national forest authorities, forestry companies as

well as various research organizations, universities and governmental institutions.

QA/QC. The collection of existing data was only the first task for the creation of the Ground

Database. Since existing biomass ground data had been acquired using a variety of methods and

standards, specific procedures for quality assurance and quality control (QA/QC) of the available data

were specifically designed and implemented. The QA/QC procedures include criteria for acquiring,

screening, harmonizing and upscaling existing biomass data in order to produce a quality reference

dataset, the Ground Database. Firstly, the biomass data were screened on the basis of their

metadata to assess if their characteristics and quality are appropriate for the validation of the

GlobBiomass products. After selecting the observations that qualify to be used as reference data, the

biomass estimations were harmonized to the same biomass compartment, measurement unit and

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geographic reference system in order to be directly comparable with the GlobBiomass maps. Then,

the reference data need to be upscaled to match the resolution of the GlobBiomass maps. The

upscaling process aims to harmonize the spatial resolutions of the two datasets and to further select

(second screening) only the observations that are representative of the map units (pixel). However,

this process could not be implemented in the present Ground Database because at this stage of the

project the spatial resolution and grid alignment of the GlobBiomass products are not yet defined.

Hence, the Ground Database contains the reference data in their original spatial resolution, and the

upscaling and screening processes will be implemented during the second project year. It is noted

that the data screening will remove a substantial amount of available data but will ensure the quality

of the selected data. The QA/QC procedures are in accordance with the quality criteria and the

processing steps to select and process the reference data provided in the GlobBiomass Validation

Protocol (Deliverable D5). In order to maintain comparability among the various GlobBiomass

products, the procedures for data selection, screening and processing should be consistent

throughout the project lifetime. After the complete data processing for QA/QC, the availability and

representativeness of in situ reference data will be assessed and data gaps will be identified. The

identification of areas with insufficient ground data will indicate where efforts to obtain additional

field data (existing but not yet accessible, or to be acquired) should be focused in the future.

Data policy. Each biomass reference dataset has a specific data policy that defines the possibilities

for data sharing within and outside the GlobBiomass consortium. While the aim is to produce an

open and freely available Ground Database, restrictions are necessarily applied to the biomass data

acquired under a non-disclosure agreement with the data owner. Ad example, biomass reference

data in the areas of interest of the GlobBiomass regional products were acquired by the respective

regional teams, and in most cases they are not included in the present Ground Database unless the

respective data policy allow their free and open access. In the cases where the original ground data

or reference maps are accessible only to a GlobBiomass project partner but are not available for

open sharing, derivative products as aggregated and processed data may be obtained and

distributed, given the consent of the data owner. In the cases where biomass data exist but cannot

be shared with any GlobBiomass partner even under a non-disclosure agreement (such as for most

national forest inventory datasets), it will be explored the possibility that the validation of the

GlobBiomass products is performed directly by the data owners.

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2. Methods to select the reference data (QA/QC)

The Ground Database currently comprises individual tree-based field data and high-resolution

biomass maps. The field data include biomass estimates derived from field measurement of tree

parameters and allometric equations. The biomass maps include high-resolution (≤ 100 m) datasets

derived from satellite data using empirical models calibrated and validated using local ground

observations and, in some cases, airborne LiDAR measurements. Given the variability of procedures

used to acquire and produce the various datasets, they were screened according to a set of quality

assurance and quality control (QA/QC) criteria to select only the most reliable biomass estimates.

The QA/QC for field data and for biomass maps are provided in the respective paragraphs below, and

the complete description of the metadata of the reference data, including their characteristics and

sources (references), are provided in Appendix I.

2.1. Reference field plots

The reference field data consists of ground measurements from forest inventory plots for which

accurate geolocation and biomass estimates were available. The QA/QC of the data includes three

steps: a preliminary screening based on the plot metadata, a harmonization procedure, and a

secondary screening based on high resolution satellite data or derived products.

2.1.1. Metadata screening

The field plot reference data were screened according to the following quality criteria:

• Plot coordinates acquired with GPS

• Ground measurements acquired on or after the year 2000

• Variable estimated is aboveground biomass (AGB) density of all living trees with diameter at

breast height (DBH) ≥ 0-10 cm

• Allometric model used for biomass estimation is appropriate to the forest type to which is

applied and use sufficient input parameters (dbh and wood density and/or height)

Since the taxonomic identities of trees strongly indicate wood density, and hence stand-level

biomass, plots were usually selected if tree biomass was estimated using at least tree diameter and

wood density as input parameters. Datasets were excluded if they did not conform to these

requirements or did not provide clear information on the biomass pool measured, the tree

parameters measured in the field, the allometric model applied, the year of measurement or the plot

geolocation and extent.

2.1.2. Data harmonization

The plot data were harmonized in terms of reference system by converting them to the geographic

reference system WGS-84. Next, the datasets providing aboveground carbon density were converted

to biomass units using the same coefficients used for their original conversion from biomass to

carbon.

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Lastly, the plot data need to be harmonized with the GlobBiomass maps in terms of spatial

resolution. This is achieved by averaging the biomass plot values located within the same map unit

(pixel) if there is more than one plot per pixel, or by directly attributing the plot biomass to the

respective pixel if there is only one plot per pixel (the plot representativeness is evaluated in the next

step, described in the paragraph 2.1.3). Field plots not fully located within one pixel are attributed to

the map cell where the majority of the plot area (i.e., the plot centroid) is located. This step will be

performed when the spatial resolution and grid of GlobBiomass products are defined. In addition,

when a considerable time period (> 5 years) occurred between the acquisition of the ground

observations and the GlobBiomass maps, and reliable data on growth rates are available, the

reference data may also be harmonized in terms of temporal resolution by applying annual biomass

increment rates to correct for the temporal difference between the two datasets.

2.1.3. Data screening

The representativeness of the plot data to the GlobBiomass map units (pixels) to which are applied

need to be evaluated, and further screen and discard the ground data not representative of the map

cells in terms of biomass density. This step includes two sub-steps that consider both the spatial and

temporal representativeness, and it will be performed when the spatial resolution and grid of

GlobBiomass products are defined.

Firstly, the field plots need to be screened for the spatial representativeness, which assess if the

biomass estimates of the field plots are applicable to the larger pixel area. The spatial

representativeness will be evaluated on the basis of the homogeneity of the tree cover within the

pixel, considered as a proxy of biomass density. This homogeneity can be determined in two ways:

through visual interpretation of high-resolution images provided on the Google Earth platform, or via

automated analysis of the variability of tree cover, as provided by the Landsat Vegetation Continuous

Field (VCF), within the map pixels. If the tree cover is not homogeneous over the pixel area, the plots

located within the pixel will be discarded. It is noted that, while the visual analysis allows to consider

also the homogeneity of the tree crown (related to the image texture) and other context-specific

information that may allow for a better data screening, this manual procedure may not feasible for

large datasets, such as national forest inventory data including thousands of field plots, due to time

and cost constrains.

Secondly, the field plots need to be further screened at individual level for temporal mismatch. This

screening is aimed to verify that no change processes have occurred between the plot measurement

and reference year of the GlobBiomass map, which is related to the acquisition year of the remote

sensing data. This screening can be performed in two ways: by visual analysis of high-resolution

images provided on the Google Earth platform acquired in the period between the acquisition of the

plots and the biomass maps, or via automated analysis of existing forest change datasets, as the

Global Forest Change dataset (Hansen et al., 2013). If subsequent high resolution reference images

or existing datasets indicate that forest change processes such as deforestation or forest regrowth

occurred in the period between the field measurement and the reference years of the GlobBiomass

maps, the corresponding plots will be discarded.

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2.2. Reference biomass maps

The reference biomass maps consist of high-resolution local or national biomass maps published in

the scientific literature. As indicated above, remotely sensed data that are of greater quality than the

map data are considered acceptable for use as reference data (Stehman, 2009), and these may

include biomass estimates extracted from reliable high-resolution biomass maps (Avitabile et al.,

2015). Maps providing biomass estimates grouped in classes were not included in this section since

the class values represent the mean biomass over large areas and do not allow for a pixel-by-pixel

comparison with the GlobBiomass maps. The quality of the reference biomass maps is evaluated

using a procedure similar to that applied for the field plots, using a two 2-step screening and a

harmonization procedure.

2.2.1. Metadata screening

The reference biomass maps were screened and included in the Ground Database only when the

following quality criteria were fulfilled:

High spatial resolution (≤ 100 m)

Calibrated with local ground data and/or airborne LiDAR data, estimating aboveground biomass (AGB) of all living trees with diameter at breast height (DBH) ≥ 0-10 cm

• Map reference year on or after the year 2000

Map produced with a sound methodology and published in the scientific literature

2.2.2. Data harmonization

The plot data were harmonized in terms of reference system by converting them from the native

projection to the geographic reference system WGS-84. Next, the datasets providing aboveground

carbon density were converted to biomass units using the same coefficients used for their original

conversion from biomass to carbon.

Lastly, the reference maps need to be harmonized with the GlobBiomass maps by aggregating the

map to the same spatial resolution, averaging the biomass estimates of the reference maps located

within the same pixels of the GlobBiomass maps (assuming that the GlobBiomass maps have lower

spatial resolution than the reference maps). This step will be performed when the spatial resolution

and grid of GlobBiomass products are defined. In addition, when a considerable time period (> 5

years) occurred between the acquisition of the reference maps and the GlobBiomass maps, and

reliable data on growth rates are available, the reference data may also be harmonized in terms of

temporal resolution by applying annual biomass increment rates to correct for the temporal

differences between the two datasets.

2.2.3. Data screening

Compared to the reference field plots, even reference biomass maps covering small areas can

provide much larger amounts of reference units (pixels) that can outweigh the role of plot data in the

calibration or validation of the GlobBiomass products. For this reason, and considering that not all

pixel-based biomass estimates in a reference map have the same accuracy, only the cells with largest

confidence (i.e., lowest uncertainty) will be selected from the reference maps and used as reference

GlobBiomass /6



data. When the reference maps are based on empirical models, the map cells with greatest

confidence are assumed to be those in correspondence of the training data (field plots and/or LiDAR

data). If the location of the training data is unknown, pixels can be extracted from the maps using the

uncertainty layers to prioritize cells with lower uncertainty, or can be extracted randomly if no

uncertainty information are available.

For biomass maps using only radar or optical sensors as source of remote sensing data whose signals

saturate above a certain biomass density value (between 100 and 200 Mg/ha, depending on the

sensor properties and vegetation structure), only pixels well below such threshold should be

considered as reference data. Furthermore, in order to compile a reference database representative

of the area of interest and well-balanced among the various reference datasets (field plots and

biomass maps), the amount of reference data extracted from the biomass maps should be

proportional to their area and not greater than the amount of samples provided by the field datasets

representing a similar area.

As for the reference field plots, the reference pixels need to be further screened at individual level

for temporal mismatch. This screening is aimed to verify that no change processes have occurred

between the reference year of the reference biomass maps and the GlobBiomass products, which

depend on the acquisition year of the remote sensing data used to develop the respective maps. This

screening can be performed in two ways: by visual analysis of high resolution images provided on the

Google Earth platform acquired in the period between the reference year of the reference and

GlobBiomass maps, or via automated analysis of existing forest change datasets, as the Global Forest

Change dataset (Hansen et al., 2013). If subsequent high-resolution reference images or existing

datasets indicate that forest change processes such as deforestation or forest regrowth occurred in

the period between the reference and GlobBiomass maps, the corresponding reference pixels will be

discarded.

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3. The Ground Database (v.01)

The biomass reference dataset in the current Version 01 (December 2015) consists of 22 ground

datasets providing 11,268 reference plots and 10 reference biomass maps calibrated by field

observations and, in 4 cases, airborne LiDAR data. The plot data are distributed as follows: 2,435 in

Africa, 522 in South America, 4,296 in Central America 3,720 in Asia and 295 in North America and

Eurasia (Fig. 1, Table 1). The amount of reference pixels to be extracted from the high-resolution

biomass maps will be defined according to the criteria indicated above when the GlobBiomass maps

will be available. National and sub-national biomass statistics are not currently included in the

Ground Database but they will be considered for map assessment and inter-comparison with

existing estimates especially in areas where plot or local maps are not available. As indicated above,

the Ground Database is a living database and it will expand to include additional biomass reference

datasets as soon as they become available. In the first project year (2015) data acquisition was

prioritized in the tropical region, and in the second project year (2016) further reference data will

be acquired in the boreal region.

Each reference dataset is identified in this document and in the Ground Database by a unique ID,

and a complete description of the metadata and the respective literature references are provided in

Annex I. The metadata information that should be provided for the new reference datasets, namely

field plots (in situ data), reference biomass maps and regional (sub-national and national) statistics,

are provided in Annex II.

The Ground Database in its version 1 is delivered as an Excel file providing the coordinates (Latitude

and Longitude, in decimal degrees, with reference system WGS84) and the ID of each reference

dataset for the plot data. Due to the current limitations and restrictions to data sharing defined by

the data owners of most reference datasets, the Ground Database currently available to the

GlobBiomass consortium contains only the location of the plot reference data, while the

information on biomass density is restricted until the data owners allow the data sharing openly or

within the GlobBiomass consortium.

While it is expected that the number of reference plots and maps contained in the Ground Database

will expand considerably during the duration of the project, it should also be considered that the

amount of reference data available for validation of the GlobBiomass maps will be reduced by the

data screening, aimed to discard reference data not representative of the map cells due to spatial

and temporal ‘mismatches’, and by aggregating the data at lower resolution (e.g., two or more plots

located within the same pixels will be averaged in one reference data point). It is expected that

ground observations will be discarded more often in areas characterized by fragmented or

heterogeneous vegetation cover and high biomass spatial variability. In such contexts, reference

data are more likely to be acquired from the reference biomass maps.

Table 1: Summary description of the biomass reference field plots, for each reference dataset

ID Continent Country/Region Plots

AFR1 Africa DRC 1,157

AFR2 Africa Sierra Leone 609

AFR3 Africa Central Africa 269

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AFR4 Africa Ethiopia 119

AFR5 Africa Ghana 74

AFR6 Africa Tanzania 24

AFR7 Africa DRC 20

AFR8 Africa Guinea-Bissau 112

AFR9 Africa Mozambique 51

TOTAL AFRICA 2,435

SAM1 S. America Amazon basin 287

SAM2 S. America Brazil 124

SAM3 S. America Guyana 111

TOTAL S. AMERICA 522

CAM1 C. America Mexico 4,296

TOTAL C. AMERICA 4,296

ASI1 Asia Vietnam 3,197

ASI2 Asia Laos 122

ASI3 Asia Sabah 104

ASI4 Asia Indonesia 82

ASI5 Asia SE Asia 132


ASI7 Asia SE Asia 25


TOTAL ASIA 3,720

BOR1 N. America, Eurasia N. America, Europe, Russia 295

TOTAL N. AMERICA, EURASIA 295

TOTAL WORLD 11,268

Table 2: summary description of the reference maps for each reference dataset

Code Continent Country/Location Extent Year (map) Resolution (m)

AFR10 Africa Uganda National 1999-2003 30

AFR11 Africa Madagascar (North) Local 2010 100

AFR12 Africa Mozambique (Gorongosa) Local 2007 50

AFR13 Africa Cameroon (Mbam Djerem) Local 2007 100

AFR14 Africa Cameroon (Adamawa) Local 2007-2010 25

AFR15 Africa Guinea-Bissau National 2008 50

SAM4 S. America Peru National NA 100

SAM5 S. America Colombia (Amazon) Sub-nat. 2010 100

CAM1 C. America Mexico National 2007 30

CAM2 C. America Panama National 2008 - 2012 100

AUS1 Australia Queensland Local 2009 50

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Figure 1: Overview of the biomass reference plots available in the Ground Database v.01

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Appendix I

Metadata of the ground reference datasets

ID Continent Country / Location Extent Vegetation type(s) Year(s) N. plots Area - Range (ha)

Area - Mean (ha)

Min. DBH (cm)

Reference

AFR1 Africa DRC / Lukenie Local Forest (concession) 2007-2010 1157 0.5 0.5 10 Hirsch et al., 2013

AFR2 Africa Sierra Leone / Gola Local Forest 2005-2007 609 0.125 0.125 10 Lindsell and Klop, 2013

AFR3 Africa Tropical Africa Regional Forest (Intact) 1984 - 2012 269 0.2 - 10 1.2 10 Lewis et al., 2013

AFR4 Africa Ethiopia / Kafa Local Forest - Woodland 2011-2013 119 0.126 0.126 5 De Vries et al., 2012

AFR5a Africa Ghana / Ankasa Local Forest 2012 34 0.05 0.05 10 Vaglio Laurin et al., 2013

AFR5b Africa Ghana / Bia Boin, Dadieso Local Forest 2012-2013 40 0.16 0.16 5 Pirotti et al., 2014

AFR6 Africa Tanzania / Eastern Arc Mountain

Local Forest 2007-10 24 0.08 - 1 0.66 10 Willcock et al., 2014

AFR7 Africa DRC / Yangambi Local Forest (Intact) 2011-2012 20 1 1 10 Kearsley et al., 2013

AFR8 Africa Guinea-Bissau National Forest, Savanna, Mangrove

2007-2008 112 0.125 0.125 5 Carreiras et al., 2012

AFR9 Africa Mozambique / Lugela Local Savanna 2011 51 0.125 0.125 5 Carreiras et al., 2013

SAM1 S. America Amazon Regional Forest 1956-2013 287 0.25 - 9 1 10 Mitchard et al., 2014; Lopez-Gonzalez et al., 2014

SAM2 S. America Brazil National Forest 2009-2013 124 0.16 - 1 0.42 5 - 10 Embrapa, 2014

SAM3 S. America Guyana Local Forest 2010-2011 111 0.126 0.126 5 Brown et al., 2014

CAM1 C. America Mexico National Forest 2004-2008 4296 1 1 7.5 de Jong, 2013

ASI1a Asia Vietnam / Quang Nam Province Forest 2007-2009 3035 0.05 0.05 6 Avitabile et al., 2014

ASI1b Asia Vietnam / Quang Nam Province Forest 2011-2012 162 0.01 - 0.126 0.08 5 Avitabile et al., 2014

ASI2 Asia Laos / Xe Pian Local Forest 2011-2012 122 0.1 - 0.126 0.11 5 WWF and OBf, 2013

ASI3 Asia Indonesia / Sabah Local Forest (concession) 2005-2008 104 0.5 - 1.5 1 10 Morel et al., 2011

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ASI4 Asia Indonesia / Riau Local Forest 2009-2010 82 0.015 0.015 5 Wijaya et al., 2015

ASI5 Asia India, China, Indonesia Local Forest (Intact) circa-2010 132 0.25 - 20 1.5 10 Slik et al., 2013, 2014

ASI6 Asia Malaysia (Sarawak), Indonesia (C. Kalimantan)

Regional Forest (Intact) 2013-2014 25 0.25 - 1 0.57 10 Qie et al., unpublished

ASI7 Asia Indo-Pacific Regional Mangrove 2008-2009 25 0.015 0.015 5 Donato et al., 2011

ASI8 Asia Indonesia (Kalimantan) Local Mangrove 2008-2009 33 0.015 0.015 5 Murdiyarso et al., 2010 (a)

BOR1 Boreal N. America, Eurasia Regional Forest 1964-20074 383 (b)

NA NA NA Luyssaert et al. (2007)

(a) The metadata for this dataset are provided also in Taberima et al. (2014), Amira (2008) and Kauffman and Donato (2012); (b)

This dataset has not been screened yet

ID Parameter measured (

a)

Tree Height Allometric equation Parameters of allom. eq. (

a)

Plot type Permanent plot

AFR1 Dbh, Sp, Hei not used Chave (2005) Moist Dbh, wd, hei For. Inv. No

AFR2 Dbh, Sp, Hei local eq. Chave (2005) Moist Dbh, wd, hei For. Inv. No

AFR3 Dbh, Sp Feldpausch (2012) Chave (2005) Moist Dbh, wd, hei Res. plots Yes

AFR4 Dbh, Sp not used Chave (2005) Wet dbh, wd Res. plots No

AFR5a Dbh, Sp, Hei measured for all trees Chave (2005) Moist Dbh, wd, hei Res. plots No

AFR5b Dbh, Sp, Hei measured for all trees Chave (2005) Moist Dbh, wd, hei Res. plots No

AFR6 Dbh, Sp Feldpausch (2012) Chave (2005) Moist Dbh, wd, hei Res. plots Yes

AFR7 Dbh, Sp, Hei stand-specific eq. Chave (2005) Moist Dbh, wd, hei Res. plots Yes

AFR8 Dbh, Sp, Hei Measured Chave (2005) Dry Dbh,wd, hei For. Inv. No

AFR9 Dbh, Sp not used Ryan (2011), Chidumayo (1997), Chave (2005) Dry, Brown (1989) Dbh, wd For. Inv. No

SAM1 Dbh, Sp Feldpausch (2012) Chave (2005) Moist Dbh, wd, hei Res. plots Yes

SAM2 Dbh, Sp, Hei measured for all trees Chave (2005) Moist Dbh, wd, hei For. Inv. No

SAM3 Dbh, Sp not used Chave (2005) Moist dbh, wd For. Inv. No

CAM1 Dbh, Sp, Hei measured for all trees Urquiza-Haas et al. (2007) Dbh, wd, hei For. Inv. Yes

ASI1a Dbh, Sp, Hei local eq. Chave (2005) Moist Dbh, wd, hei For. Inv. No

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ASI1b Dbh, Sp not used Chave (2005) Moist dbh, wd Res. plots No

ASI2 Dbh, Sp not used Chave (2005) Dry/Moist dbh, wd Res. plots No

ASI3 Dbh, Sp, Hei stand-specific eq. Chave (2005) Moist Dbh, wd, hei Res. plots No

ASI4 Dbh, Sp not used Komiyama et al. (2008), Chave (2005) Moist dbh, wd Res. plots No

ASI5 Dbh, sp Feldpausch (2012) Chave (2005) Dry/Moist/Wet Dbh, wd, hei Res. plots No

ASI6 Dbh, sp Feldpausch (2012) Chave (2005) Moist Dbh, wd, hei Res. plots Yes

ASI7 Dbh, sp not used Komiyama et al. (2008) dbh, wd Res. plots No

ASI8 Dbh, Sp not used Komiyama et al. (2008) dbh, wd Res. plots No

BOR1 NA NA NA NA NA NA (a)

Dbh is Diameter at Breast Height, Sp is species, Hei is Height, wd is wood density

Metadata of the reference biomass maps

ID Continent Country/Location Extent Vegetation types

(a)

Year (map) Resolution (m)

Accuracy dataset

RMSE (Mg/ha)

R2 RS data Reference

AFR10 Africa Uganda National For – Wood – Sav

1999-2003 30 Validation 13 0.81 Landsat, LC Avitabile et al., 2012

AFR11 Africa Madagascar (North) Local Forest 2010 100 Calibration 42 0.88 Landsat, LiDAR Asner et al., 2012b

AFR12 Africa Mozambique (Gorongosa)

Local Wood – Sav 2007 50 Validation 20 0.49 ALOS PALSAR Ryan et al., 2012

AFR13 Africa Cameroon (Mbam Djerem)

Local For - Sav 2007 100 Calibration 29 NA ALOS PALSAR Mitchard et al., 2011

AFR14 Africa Cameroon (Adamawa) Local Savannah 2007-2010 25 Validation 32 NA ALOS PALSAR Mermoz et al., 2014

AFR15 Africa Guinea-Bissau National For – Sav - Mangrove

2008 50 Validation 27 0.90 ALOS PALSAR Carreiras et al., 2012

AFR16 Africa Mozambique (Lugela) Local Savanna 2010 90 Validation 5 0.90 ALOS PALSAR Carreiras et al., 2013

SAM4 S. America Peru National For – Wood – Grass

NA 100 Calibration 55 0.82 Landsat, LiDAR Asner et al., 2014

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SAM5 S. America Colombia (Amazon) Regional Forest 2010 100 Validation 58 NA Landsat, LiDAR Asner et al., 2012a

CAM1 C. America Mexico National Forest 2007 30 Validation 28 0.52 Landsat, ALOS Cartus et al., 2014

CAM2 C. America Panama National For – Wood – Grass

2008 - 2012 100 Validation 45 0.62 Landsat, LiDAR Asner et al., 2013

AUS1 Australia Australia (Queensland) Local For - Wood – Sav

2009 50 NA NA NA ALOS Lucas et al., 2010

(a) Forest (For), Woodland (Wood), Savannah (Sav), Grassland (Grass)

ID N. plots Years (Plots) Plot size - Range (ha)

Plot size - Mean (ha)

Min. DBH (cm)

Parameter measured

Tree Height Allometric equation Parameters of allometric eq.

Plot type

AFR10 2527 1995-2005 0.25 0.25 3 Dbh, Sp, Crown measured Drichi (2003) Dbh, wd, crown For. Inv.

AFR11 19 NA 0.28 0.28 0 Dbh, Sp, Hei local eq. Chave (2005) Wet Dbh, wd, hei Res. plots

AFR12 96 2006-2009 0.1 - 2.2 0.63 5 Dbh not used Ryan et al. (2011) dbh Res. plots

AFR13 25 2007 0.2 - 1 0.6 10 Dbh, Sp, Hei local eq. Chave (2005) Dry/Moist/Wet Dbh, wd, hei Res. plots

AFR14 21 2012 1 1 5-10 Dbh, Sp, Hei measured Chave (2005) Dbh, wd, (hei) Res. plots

AFR15 112 2007-2008 0.125 0.125 5 Dbh, Sp, Hei Measured Chave (2005) Dry Dbh, wd, Hei For. Inv.

AFR16 51 2011 0.125 0.125 5 Dbh, Sp not used Ryan (2011), Chidumayo (1997), Chave (2005) Dry, Brown (1989)

Dbh, wd For. Inv.

SAM4 272 NA 0.3 - 1 0.33 NA Dbh, Sp, Hei local eq. Chave et al. (2014) Dbh, wd, hei For. Inv.

SAM5 11 NA 0.28 0.28 10 Dbh, Sp local eq. Chave (2005) Moist Dbh, wd, hei Res. plots

CAM1 16906 2004 - 2007 1 1 7.5 Dbh, Sp, Hei measured National species-specific eq. Dbh, wd, hei For. Inv.

CAM2 228 NA 0.1 - 0.36 0.25 10 Dbh, Sp, Hei local eq. Chave (2005) Dry/Moist/Wet Dbh, wd, hei Res. plots

AUS1 2781 2007-2010 variable NA 5 Dbh, Sp, Hei measured See Lucas et al. (2010) See Lucas et al., 2010

For. Inv. and Res. plots

Appendix II

Metadata tables for new reference data

In situ data

Field Comment

ID (internal) Unique ID to identify the dataset (S1, S2 ... etc.)

Name or acronym of dataset If the dataset has an name originally, insert it here (e.g. NFI

Sweden)

Location/ Coverage

(country, province or coordinates)

For small-scale dataset the center coordinate is sufficient. For

large-scale datasets, the name of the country, province or

equivalent is sufficient.

Variable (biomass/GSV) Variable provided: Aboveground biomass (biomass) or Growing

stock volume (GSV)

Sampling unit (plot, stand) Indicate whether the sampling unit is a plot, a stand, or a

different sampling unit

Vegetation type(s) Identify the main vegetation type(s) (e.g., tropical moist forest,

woodland, savannah)

Year(s) Year(s) or date when the data were measured in the field

Number of sampling units Total number of sampling units

Size of sampling units (ha) Size of sampling units in hectares (indicate if another unit is

used)

Minimum DBH Minimum Diameter at Breast Height (DBH) of the trees

measured in the sampling units

Parameter measured (DBH, species, height) Tree parameters measured in the field

Tree Height (measured / estimated) Was the height of each tree measured or estimated using an

equation/model?

Allometric equation (reference) Provide reference to the allometric equation (or other model)

used to estimate biomass or GSV (e.g., Chave et al., 2005 for

moist forest)

Parameters of allometric eq. (DBH, wood

density, height) Provide the parameters used to estimate biomass or volume

Permanent plot? (Yes/No) Are the sampling units permanent (repeatedly measured)?

GlobBiomass /6



Reference (if possible) Add a reference if the data is published. Also add a reference if

the data is described in a paper or report.

Stored where

(ftp, website, hard-disk, etc.)

Location of dataset (internet source or the project's internal

storage facility)

Owner or produced by Who generated the dataset (needed for acknowledgments)

Policy of use Public, available within Globbiomass, or private

Point of contact (within Globbiomass) Name of person within Globbiomass responsible for sharing the

dataset

Remarks Any additional information or remarks on the dataset

GlobBiomass /6



Reference maps

Field Comment

ID (internal) Unique ID to identify the dataset (M1, M2 ... etc.)

Name or acronym of dataset If the dataset has an name originally, insert it here (e.g. kNN

Sweden)

Location/ Coverage

(country, province or coordinates)

For small-scale dataset the center coordinate is sufficient. For

large-scale datasets, the name of the country, province or

equivalent is sufficient.

Variable (biomass/GSV) Variable provided: Aboveground biomass (biomass) or Growing

stock volume (GSV)

Variable

(biomass, GSV, height) Forest variable (biomass-related only)

Vegetation type(s) Identify the main vegetation type(s) (e.g., tropical moist forest,

woodland, savannah)

Year(s) Year(s) or date to which the map refers to

Spatial resolution (m) Spatial resolution (cell size) of the map, in meter

Validated (yes/no) Flag value to indicate if the product is validated

RS data (e.g. Landsat, ALOS, GLAS) Indicate the main Remote Sensing data (e.g. Landsat, ALOS,

GLAS) used to produce the map (if applicable)



Stored where

(ftp, website etc.)


storage facility)


Policy of use Public, within Globbiomass or private


dataset



Additional information on the ground reference data used to calibrate the map

GlobBiomass /6



Number of sampling units Total number of sampling units (plots)

Year(s) Year(s) or date when the data were measured in the field

Size of sampling units (ha) Size of sampling units in hectares (indicate if another unit is

used)

Minimum DBH Minimum Diameter at Breast Height (DBH) of the trees

measured in the sampling units

Allometric equation (reference) Provide reference to the allometric equation (or other model)

used to estimate biomass or GSV (e.g., Chave et al., 2005 for

moist forest)

GlobBiomass /6



Regional statistics

Field Comment

ID (internal) Unique ID to identify the dataset (R1, R2 ... etc.)

Name or acronym of dataset If the dataset has an name originally, insert it here (e.g. NFI

Sweden statistics)

Variable

(biomass, GSV, height) Forest variable (biomass-related only)

Reporting unit (forestry unit, administrative

unit)

Indicate whether the statistics are provided for inventory units,

administrative units (e.g. province, county etc.) etc.

Number of sampling units Total number of sampling units

Coverage

(country, continent) Name of the country or the continent (or global)



Stored where

(ftp, website etc.)


storage facility)


Policy of use Public, within Globbiomass or private


dataset


GlobBiomass /6



References

Amira S (2008) An estimation of Rhizophora apiculata biomass in mangrove forest in Batu Ampar

Kubu Raya Regency, West Kalimantan. Undergraduate Thesis, Bogor Agricultural University,

Indonesia

Asner GP, Clark JK, Mascaro J et al. (2012a) High-resolution mapping of forest carbon stocks in the

Colombian Amazon. Biogeosciences, 9, 2683–2696.

Asner GP, Clark JK, Mascaro J et al. (2012b) Human and environmental controls over aboveground

carbon storage in Madagascar. Carbon Balance and Management, 7, 2.

Asner GP, Mascaro J, Anderson C et al. (2013) High-fidelity national carbon mapping for resource

management and REDD+. Carbon balance and management, 8, 7.

Asner GP, Knapp DE, Martin RE et al. (2014a) Targeted carbon conservation at national scales with

high-resolution monitoring. Proceedings of the National Academy of Sciences, 111, E5016–E5022.

Avitabile V, Baccini A, Friedl MA, Schmullius C (2012) Capabilities and limitations of Landsat and land

cover data for aboveground woody biomass estimation of Uganda. Remote Sensing of Environment,

117, 366–380.

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.

http://leutra.geogr.uni-jena.de/vgtbRBIS/metadata/start.php

Avitabile V, Herold M, Heuvelink GBM, Lewis SL, Phillips OL, Asner GP, et al. (2015) An integrated

pan-tropical biomass map using multiple reference datasets. Global Change Biology, Accepted

Author Manuscript. doi:10.1111/gcb.13139.

Brown S, Goslee K, Casarim F, Harris NL, Petrova S (2014) Sampling Design and Implementation Plan

for Guyana’s REDD+ Forest Carbon Monitoring System (FCMS): Version 2. Submitted by Winrock

International to the Guyana Forestry Commission. Guyana Forestry Commission GFC 02/08/2010

Addendum.

Carreiras JM, Vasconcelos MJ, Lucas RM (2012) Understanding the relationship between

aboveground biomass and ALOS PALSAR data in the forests of Guinea-Bissau (West Africa) Remote

Sensing of Environment, 121:426-42.

Carreiras J, Melo JB, Vasconcelos MJ. Estimating the above-ground biomass in Miombo Savanna

woodlands (Mozambique, East Africa) using L-band synthetic aperture radar data (2013) Remote

Sensing, 5(4):1524-48.

Cartus O, Kellndorfer J, Walker W, Franco C, Bishop J, Santos L, Michel-Fuentes JM (2014) A National,

Detailed Map of Forest Aboveground Carbon Stocks in Mexico. Remote Sensing, 6, 5559–5588.

DeVries B, Avitabile V, Kooistra L, Herold M (2012) Monitoring the impact of REDD+ implementation

in the Unesco Kafa biosphere reserve, Ethiopia. Proceedings of the “Sensing a Changing World”

Workshop (2012). http://www.wageningenur.nl/upload_mm/9/d/c/f80b6db7-9c3c-4957-8717-

6fdc6e46e60f_deVries.pdf

GlobBiomass /6



Donato DC, Kauffman JB, Murdiyarso D, Kurnianto S, Stidham M, Kanninen M (2011) Mangroves

among the most carbon-rich forests in the tropics. Nature Geoscience, 4, 293–297.

EMBRAPA (2014) Sustainable Landscape Brazil. http://geoinfo.cnpm.embrapa.br/geonetwork/srv/

eng/main.home

Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV,

Goetz SJ, Loveland TR, Kommareddy A. (2013) High-resolution global maps of 21st-century forest

cover change. Science, 342(6160):850-3.

Hirsh F, Jourget JG, Feintrenie L, Bayol N, Atyi RE (2013) REDD+ pilot project in Lukenie. Center for

International Forestry Research (CIFOR), Jakarta, Indonesia, CIFOR Working Paper, 2013, 111, pp 48.

Kauffman JB and Donato DC (2012) Protocols for the measurement, monitoring and reporting of

structure, biomass and carbon stocks in mangrove forests. Working Paper 86. CIFOR, Bogor,

Indonesia.

Kearsley E, de Haulleville T, Hufkens K et al. (2013) Conventional tree height-diameter relationships

significantly overestimate aboveground carbon stocks in the Central Congo Basin. Nature

communications, 4, 2269.

Lewis SL, Sonké B, Sunderland T et al. (2013) Above-ground biomass and structure of 260 African

tropical forests. Philosophical transactions of the Royal Society of London. Series B, Biological

sciences, 368, 20120295.

Lindsell JA, Klop E (2013) Spatial and temporal variation of carbon stocks in a lowland tropical forest

in West Africa. Forest Ecology and Management, 289, 10–17.

Lopez-Gonzalez G, Lewis SL, Burkitt M, Baker TR and Phillips OL (2009) ForestPlots.net Database.

www.forestplots.net. Date of extraction [09,09,13]

Lopez-Gonzalez G, Lewis SL, Burkitt M and Phillips OL (2011) ForestPlots.net: a web application and

research tool to manage and analyse tropical forest plot data. Journal of Vegetation Science 22: 610–

613. doi: 10.1111/j.1654-1103.2011.01312.x

Lopez-Gonzalez G, Mitchard ETA, Feldpausch TR et al. (2014) Amazon forest biomass measured in

inventory plots. Plot Data from "Markedly divergent estimates of Amazon forest carbon density from

ground plots and satellites." doi: 10.5521/FORESTPLOTS.NET/2014_1

Lucas R, Armston J, Fairfax R et al. (2010) An evaluation of the ALOS PALSAR L-band backscatter –

above ground biomass relationship over Queensland, Australia. IEEE Journal of Selected Topics in

Earth Observations and Remote Sensing, 3(4): 576-593.

Luyssaert S, Inglima I, Jung M, Richardson AD, Reichstein M, Papale D, Piao SL, Schulze ED, Wingate L,

Matteucci G, Aragao LE (2007) CO2 balance of boreal, temperate, and tropical forests derived from a

global database. Global change biology, 1;13(12):2509-37.

Mermoz S, Le Toan T, Villard L, Réjou-Méchain M, Seifert-Granzin J (2014) Biomass assessment in the

Cameroon savanna using ALOS PALSAR data. Remote Sensing of Environment, 155, 109-119.

GlobBiomass /6



Mitchard ET, Saatchi SS, Lewis SL et al. (2011) Measuring biomass changes due to woody

encroachment and deforestation/degradation in a forest-savanna boundary region of central Africa

using multi-temporal L-band radar backscatter. Remote Sensing of Environment, 115, 2861–2873.

Mitchard ET, Feldpausch TR, Brienen RJW et al. (2014) Markedly divergent estimates of Amazon

forest carbon density from ground plots and satellites. Global Ecology and Biogeography, 23, 935–

946.

Morel AC, Saatchi SS, Malhi Y et al. (2011) Estimating aboveground biomass in forest and oil palm

plantation in Sabah, Malaysian Borneo using ALOS PALSAR data. Forest Ecology and Management,

262, 1786–1798.

Murdiyarso D, Donato D, Kauffman JB, Kurnianto S, Stidham M, Kanninen M (2010) Carbon storage in

mangrove and peatland ecosystems: a preliminary account from plots in Indonesia. CIFOR Working

Paper no. 48. 35pp. Bogor – Indonesia.

Pirotti F, Laurin G, Vettore A, Masiero A, Valentini R (2014) Small Footprint Full-Waveform Metrics

Contribution to the Prediction of Biomass in Tropical Forests. Remote Sensing, 9576–9599.

Qie L, Phillips OL, Lewis SL, Banin L et al. (unpublished) Tropical Forests in the Changing Earth System

Borneo forest dataset.

Ryan CM, Hill T, Woollen E et al. (2012) Quantifying small-scale deforestation and forest degradation

in African woodlands using radar imagery. Global Change Biology, 18, 243–257.

Slik JWF, Paoli G, Mcguire K et al. (2013) Large trees drive forest aboveground biomass variation in

moist lowland forests across the tropics. Global Ecology and Biogeography, 22, 1261–1271.

Stehman SV (2009) Model-assisted estimation as a unifying framework for estimating the area of

land cover and land-cover change from remote sensing. Remote Sensing of Environment, 113(11),

2455-2462.

Vaglio Laurin G, Chen Q, Lindsell J, Coomes D, Cazzolla-Gatti R, Grieco E, Valentini R (2013) Above

ground biomass estimation from lidar and hyperspectral airbone data in West African moist forests.

EGU General Assembly Conference Abstracts, 15, 6227.

Wijaya A, Liesenberg V, Susanti A, Karyanto O, Verchot LV (2015) Estimation of Biomass Carbon

Stocks over Peat Swamp Forests using Multi-Temporal and Multi-Polarizations SAR Data. Proceeding

of the 36th International Symposium on Remote Sensing of Environment, 11-15 May 2015, Berlin,

Germany.

Willcock S, Phillips OL, Platts PJ et al. (2014) Quantifying and understanding carbon storage and

sequestration within the Eastern Arc Mountains of Tanzania, a tropical biodiversity hotspot. Carbon

balance and management, 9, 2.

WWF and ÖBf (2013) Xe Pian REDD+ project document. Gland, Switzerland.

http://www.leafasia.org/sites/default/files/public/resources/WWF-REDD-pres-July-2013-v3.pdf

GlobBiomass /6



Wageningen (The Netherlands), 16.12.2015

…………………………………………

Prof. Dr. Martin Herold

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