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

  • Maurizio Santoro
  • , Oliver Cartus
  • , Nuno Carvalhais
  • , Danae Rozendaal
  • , Valerio Avitabilie
  • , Arnan Araza
  • , Styze de Bruin
  • , Martin Herold
  • , Shaun Quegan
  • , Pedro Rodriguez Veiga
  • , Heiko Baltzer
  • , Joao Carreiras
  • , Dimitry Schepaschenko
  • , Mikhail Korets
  • , Masanobu Shimada
  • , Takuya Itoh
  • , Alvaro Moreno Martinez
  • , Jura Cavlovic
  • , Roberto Cazzolla Gatti
  • , Polyanna da Concecao Bispo
  • Nasheta Dewnath, Nicolas Labriere, Jingjing Liang, Jeremy Lindsell, Edward T.A. Mitchard, Alexandra Morel, Ana Maria Pacheco Pascagaza, Casey M. Ryan, Ferry Slik, Gaia Vaglio Laurin, Hans Verbeeck, Arief Wijaya, Simon Willcock
  • Gamma Remote Sensing, Switzerland
  • Max Planck Institute for Biogeochemistry, Jena
  • Wageningen University & Research
  • European Commission, Joint Research Centre (JRC), Ispra, Italy
  • University of Sheffield
  • University of Leicester
  • International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
  • Russian Academy of Sciences
  • Tokyo Denki University
  • Remote Sensing Technology Center of Japan
  • Universitat de València, València, Spain
  • University of Zagreb, Croatia
  • Tomsk State University, Russia
  • Guyana Forestry Commission
  • Laboratoire Évolution et Diversité Biologique, UMR 5174 (CNRS/IRD/UPS)
  • Purdue University
  • A Rocha International, Cambridge
  • University of Edinburgh
  • University of Dundee
  • University of Brunei Darussalam
  • University of Tuscia
  • Department of Experimental Clinical and Health Psychology, Ghent University, Ghent, Belgium
  • World Resources Institute, Indonesia

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

269 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

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-1 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).
Iaith wreiddiolSaesneg
Tudalennau (o-i)3927-3950
Nifer y tudalennau39
CyfnodolynEarth System Science Data
Cyfrol13
Rhif cyhoeddi8
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 11 Awst 2021

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