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. A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps. Remote Sensing of Environment, 272:112917, 16 p.

Url - jeu de données - Entrepôt autre : https://doi.org/10.6084/m9.figshare.18393689.v1

Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Géographie-Aménagement-Urbanisme-Architecture

Résumé : 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.

Mots-clés Agrovoc : biomasse aérienne des arbres, cartographie des fonctions de la forêt, couverture végétale, modélisation environnementale, inventaire forestier, télédétection, incertitude statistique, exactitude

Mots-clés libres : Aboveground biomass, Carbon cycle, Map validation, Uncertainty assessment, Remote Sensing

Classification Agris : K01 - Foresterie - Considérations générales
U30 - Méthodes de recherche

Champ stratégique Cirad : CTS 5 (2019-) - Territoires

Agences de financement européennes : European Commission

Programme de financement européen : H2020

Projets sur financement : (EU) Observation-based system for monitoring and verification of greenhouse gases

Auteurs et affiliations

• Araza Arnan, Wageningen University and Research Centre (NLD) - auteur correspondant
• de Bruin Sytze, Wageningen University (NLD)
• Herold Martin, Wageningen University (NLD)
• Quegan Shaun, University of Sheffield (GBR)
• Labriere Nicolas, CNRS (FRA)
• Rodriguez-Veiga Pedro, University of Leicester (GBR)
• Avitabile Valerio, European Commission-Joint Research Centre (ITA)
• Santoro Maurizio, Gamma Remote Sensing (CHE)
• Mitchard Edward T.A., University of Edinburgh (GBR)
• Ryan Casey M., University of Edinburgh (GBR)
• Phillips Oliver L., University of Leeds (GBR)
• Willcock Simon, Bangor University (GBR)
• Verbeeck Hans, Ghent University (BEL)
• Carreiras João M.B., University of Sheffield (GBR)
• Hein Lars, Wageningen University and Research Centre (NLD)
• Schelhaas Mart-Jan, Wageningen University (NLD)
• Pacheco-Pascagaza Ana Maria, University of Leicester (GBR)
• da Conceição Bispo Polyanna, University of Manchester (GBR)
• Laurin Gaia Vaglio, Università della Tuscia (ITA)
• Vieilledent Ghislain, CIRAD-BIOS-UMR AMAP (FRA) ORCID: 0000-0002-1685-4997
• Slik J.W. Ferry, Universiti Brunei Darussalam (BRN)
• Wijaya Arief, WRI (USA)
• Lewis Simon L., University College London (GBR)
• Morel Alexandra, University of Dundee (GBR)
• Liang Jingjing, Purdue University (USA)
• Sukhdeo Hansrajie, Guyana Forestry Commission (GUF)
• Schepaschenko Dmitry, International Institute for Applied System Analysis (AUS)
• Cavlovic Jura, University of Zagreb (HRV)
• Gilani Hammad, Institute of Space Technology (PAK)
• Lucas Richard, Aberystwyth University (GBR)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/600256/)

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