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The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations.

Authors :
Santoro, Maurizio
Cartus, Oliver
Carvalhais, Nuno
Rozendaal, Danaë
Avitabilie, Valerio
Araza, Arnan
Bruin, Sytze de
Herold, Martin
Quegan, Shaun
Veiga, Pedro Rodríguez
Balzter, Heiko
Carreiras, João
Schepaschenko, Dmitry
Korets, Mikhail
Shimada, Masanobu
Itoh, Takuya
Martínez, Álvaro Moreno
Cavlovic, Jura
Gatti, Roberto Cazzolla
Bispo, Polyanna da Conceição
Source :
Earth System Science Data Discussions; 7/21/2020, p1-38, 38p
Publication Year :
2020

Abstract

The terrestrial forest carbon pool is poorly quantified, in particular in regions with low forest inventory capacity. By combining multiple satellite observations of synthetic aperture radar (SAR) backscatter around the year 2010, we generated a global, spatially explicit dataset of above-ground forest biomass (dry mass, AGB) with a spatial resolution of 1 ha. Using an extensive database of 110,897 AGB measurements from field inventory plots, we show that the spatial patterns and magnitude of AGB are well captured in our map with the exception of regional uncertainties in high carbon stock forests with AGB > 250 Mg ha<superscript>−1</superscript> where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 Pg, our estimate of the terrestrial biomass pool in forests is lower than most estimates published in literature (426-571 Pg). Nonetheless, our dataset increases knowledge on the spatial distribution of AGB compared to the global Forest Resources Assessment (FRA) by the Food and Agriculture Organization (FAO) and highlights the impact of a country's national inventory capacity on the accuracy of the biomass statistics reported to the FRA. We also reassessed previous remote sensing AGB maps, and identify major biases compared to inventory data, up to 120 % of the inventory value in dry tropical forests, in the sub-tropics and temperate zone. Because of the high level of detail and the overall reliability of the AGB spatial patterns, our global dataset of AGB is likely to have significant impacts on climate, carbon and socio-economic modelling schemes, and provides a crucial baseline in future carbon stock changes estimates. The dataset is available at: https://doi.pangaea.de/10.1594/PANGAEA.894711 (Santoro, 2018). [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
18663591
Database :
Complementary Index
Journal :
Earth System Science Data Discussions
Publication Type :
Academic Journal
Accession number :
144700959
Full Text :
https://doi.org/10.5194/essd-2020-148