A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps
Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the four map epochs. This total becomes closer to the value estimated by the Forest Resources Assessment after every epoch and shows a similar decrease. The framework is applicable to both local and global-scale analysis, and is available at https://github.com/arnanaraza/PlotToMap. Our study therefore constitutes a major step towards improved AGB map validation and improvement.
Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Subjects: | K01 - Foresterie - Considérations générales, U30 - Méthodes de recherche, biomasse aérienne des arbres, cartographie des fonctions de la forêt, couverture végétale, modélisation environnementale, inventaire forestier, télédétection, incertitude statistique, exactitude, http://aims.fao.org/aos/agrovoc/c_1373987680230, http://aims.fao.org/aos/agrovoc/c_1374847637217, http://aims.fao.org/aos/agrovoc/c_25409, http://aims.fao.org/aos/agrovoc/c_9000056, http://aims.fao.org/aos/agrovoc/c_24174, http://aims.fao.org/aos/agrovoc/c_6498, http://aims.fao.org/aos/agrovoc/c_28975, http://aims.fao.org/aos/agrovoc/c_3622b5b8, |
Online Access: | http://agritrop.cirad.fr/600256/ http://agritrop.cirad.fr/600256/1/Araza2022-RSE.pdf |
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K01 - Foresterie - Considérations générales U30 - Méthodes de recherche biomasse aérienne des arbres cartographie des fonctions de la forêt couverture végétale modélisation environnementale inventaire forestier télédétection incertitude statistique exactitude http://aims.fao.org/aos/agrovoc/c_1373987680230 http://aims.fao.org/aos/agrovoc/c_1374847637217 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_9000056 http://aims.fao.org/aos/agrovoc/c_24174 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_28975 http://aims.fao.org/aos/agrovoc/c_3622b5b8 K01 - Foresterie - Considérations générales U30 - Méthodes de recherche biomasse aérienne des arbres cartographie des fonctions de la forêt couverture végétale modélisation environnementale inventaire forestier télédétection incertitude statistique exactitude http://aims.fao.org/aos/agrovoc/c_1373987680230 http://aims.fao.org/aos/agrovoc/c_1374847637217 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_9000056 http://aims.fao.org/aos/agrovoc/c_24174 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_28975 http://aims.fao.org/aos/agrovoc/c_3622b5b8 |
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K01 - Foresterie - Considérations générales U30 - Méthodes de recherche biomasse aérienne des arbres cartographie des fonctions de la forêt couverture végétale modélisation environnementale inventaire forestier télédétection incertitude statistique exactitude http://aims.fao.org/aos/agrovoc/c_1373987680230 http://aims.fao.org/aos/agrovoc/c_1374847637217 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_9000056 http://aims.fao.org/aos/agrovoc/c_24174 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_28975 http://aims.fao.org/aos/agrovoc/c_3622b5b8 K01 - Foresterie - Considérations générales U30 - Méthodes de recherche biomasse aérienne des arbres cartographie des fonctions de la forêt couverture végétale modélisation environnementale inventaire forestier télédétection incertitude statistique exactitude http://aims.fao.org/aos/agrovoc/c_1373987680230 http://aims.fao.org/aos/agrovoc/c_1374847637217 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_9000056 http://aims.fao.org/aos/agrovoc/c_24174 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_28975 http://aims.fao.org/aos/agrovoc/c_3622b5b8 Araza, Arnan de Bruin, Sytze Herold, Martin Quegan, Shaun Labriere, Nicolas Rodriguez-Veiga, Pedro Avitabile, Valerio Santoro, Maurizio Mitchard, Edward T.A. Ryan, Casey M. Phillips, Oliver L. Willcock, Simon Verbeeck, Hans Carreiras, João M.B. Hein, Lars Schelhaas, Mart-Jan Pacheco-Pascagaza, Ana Maria da Conceição Bispo, Polyanna Laurin, Gaia Vaglio Vieilledent, Ghislain Slik, J.W. Ferry Wijaya, Arief Lewis, Simon L. Morel, Alexandra Liang, Jingjing Sukhdeo, Hansrajie Schepaschenko, Dmitry Cavlovic, Jura Gilani, Hammad Lucas, Richard A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps |
description |
Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the four map epochs. This total becomes closer to the value estimated by the Forest Resources Assessment after every epoch and shows a similar decrease. The framework is applicable to both local and global-scale analysis, and is available at https://github.com/arnanaraza/PlotToMap. Our study therefore constitutes a major step towards improved AGB map validation and improvement. |
format |
article |
topic_facet |
K01 - Foresterie - Considérations générales U30 - Méthodes de recherche biomasse aérienne des arbres cartographie des fonctions de la forêt couverture végétale modélisation environnementale inventaire forestier télédétection incertitude statistique exactitude http://aims.fao.org/aos/agrovoc/c_1373987680230 http://aims.fao.org/aos/agrovoc/c_1374847637217 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_9000056 http://aims.fao.org/aos/agrovoc/c_24174 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_28975 http://aims.fao.org/aos/agrovoc/c_3622b5b8 |
author |
Araza, Arnan de Bruin, Sytze Herold, Martin Quegan, Shaun Labriere, Nicolas Rodriguez-Veiga, Pedro Avitabile, Valerio Santoro, Maurizio Mitchard, Edward T.A. Ryan, Casey M. Phillips, Oliver L. Willcock, Simon Verbeeck, Hans Carreiras, João M.B. Hein, Lars Schelhaas, Mart-Jan Pacheco-Pascagaza, Ana Maria da Conceição Bispo, Polyanna Laurin, Gaia Vaglio Vieilledent, Ghislain Slik, J.W. Ferry Wijaya, Arief Lewis, Simon L. Morel, Alexandra Liang, Jingjing Sukhdeo, Hansrajie Schepaschenko, Dmitry Cavlovic, Jura Gilani, Hammad Lucas, Richard |
author_facet |
Araza, Arnan de Bruin, Sytze Herold, Martin Quegan, Shaun Labriere, Nicolas Rodriguez-Veiga, Pedro Avitabile, Valerio Santoro, Maurizio Mitchard, Edward T.A. Ryan, Casey M. Phillips, Oliver L. Willcock, Simon Verbeeck, Hans Carreiras, João M.B. Hein, Lars Schelhaas, Mart-Jan Pacheco-Pascagaza, Ana Maria da Conceição Bispo, Polyanna Laurin, Gaia Vaglio Vieilledent, Ghislain Slik, J.W. Ferry Wijaya, Arief Lewis, Simon L. Morel, Alexandra Liang, Jingjing Sukhdeo, Hansrajie Schepaschenko, Dmitry Cavlovic, Jura Gilani, Hammad Lucas, Richard |
author_sort |
Araza, Arnan |
title |
A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps |
title_short |
A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps |
title_full |
A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps |
title_fullStr |
A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps |
title_full_unstemmed |
A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps |
title_sort |
comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps |
url |
http://agritrop.cirad.fr/600256/ http://agritrop.cirad.fr/600256/1/Araza2022-RSE.pdf |
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dig-cirad-fr-6002562024-01-29T19:01:19Z http://agritrop.cirad.fr/600256/ http://agritrop.cirad.fr/600256/ A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps. Araza Arnan, de Bruin Sytze, Herold Martin, Quegan Shaun, Labriere Nicolas, Rodriguez-Veiga Pedro, Avitabile Valerio, Santoro Maurizio, Mitchard Edward T.A., Ryan Casey M., Phillips Oliver L., Willcock Simon, Verbeeck Hans, Carreiras João M.B., Hein Lars, Schelhaas Mart-Jan, Pacheco-Pascagaza Ana Maria, da Conceição Bispo Polyanna, Laurin Gaia Vaglio, Vieilledent Ghislain, Slik J.W. Ferry, Wijaya Arief, Lewis Simon L., Morel Alexandra, Liang Jingjing, Sukhdeo Hansrajie, Schepaschenko Dmitry, Cavlovic Jura, Gilani Hammad, Lucas Richard. 2022. Remote Sensing of Environment, 272:112917, 16 p.https://doi.org/10.1016/j.rse.2022.112917 <https://doi.org/10.1016/j.rse.2022.112917> A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps Araza, Arnan de Bruin, Sytze Herold, Martin Quegan, Shaun Labriere, Nicolas Rodriguez-Veiga, Pedro Avitabile, Valerio Santoro, Maurizio Mitchard, Edward T.A. Ryan, Casey M. Phillips, Oliver L. Willcock, Simon Verbeeck, Hans Carreiras, João M.B. Hein, Lars Schelhaas, Mart-Jan Pacheco-Pascagaza, Ana Maria da Conceição Bispo, Polyanna Laurin, Gaia Vaglio Vieilledent, Ghislain Slik, J.W. Ferry Wijaya, Arief Lewis, Simon L. Morel, Alexandra Liang, Jingjing Sukhdeo, Hansrajie Schepaschenko, Dmitry Cavlovic, Jura Gilani, Hammad Lucas, Richard eng 2022 Remote Sensing of Environment K01 - Foresterie - Considérations générales U30 - Méthodes de recherche biomasse aérienne des arbres cartographie des fonctions de la forêt couverture végétale modélisation environnementale inventaire forestier télédétection incertitude statistique exactitude http://aims.fao.org/aos/agrovoc/c_1373987680230 http://aims.fao.org/aos/agrovoc/c_1374847637217 http://aims.fao.org/aos/agrovoc/c_25409 http://aims.fao.org/aos/agrovoc/c_9000056 http://aims.fao.org/aos/agrovoc/c_24174 http://aims.fao.org/aos/agrovoc/c_6498 http://aims.fao.org/aos/agrovoc/c_28975 http://aims.fao.org/aos/agrovoc/c_3622b5b8 Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1∘. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1∘ map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the four map epochs. This total becomes closer to the value estimated by the Forest Resources Assessment after every epoch and shows a similar decrease. The framework is applicable to both local and global-scale analysis, and is available at https://github.com/arnanaraza/PlotToMap. Our study therefore constitutes a major step towards improved AGB map validation and improvement. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/600256/1/Araza2022-RSE.pdf text cc_by info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1016/j.rse.2022.112917 10.1016/j.rse.2022.112917 info:eu-repo/semantics/altIdentifier/doi/10.1016/j.rse.2022.112917 info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1016/j.rse.2022.112917 info:eu-repo/semantics/dataset/purl/https://doi.org/10.6084/m9.figshare.18393689.v1 info:eu-repo/grantAgreement/EC/H2020/776810//(EU) Observation-based system for monitoring and verification of greenhouse gases/VERIFY |