Earth System Science Data (Aug 2021)
The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations
- M. Santoro,
- O. Cartus,
- N. Carvalhais,
- N. Carvalhais,
- D. M. A. Rozendaal,
- D. M. A. Rozendaal,
- D. M. A. Rozendaal,
- V. Avitabile,
- A. Araza,
- S. de Bruin,
- M. Herold,
- S. Quegan,
- P. Rodríguez-Veiga,
- P. Rodríguez-Veiga,
- H. Balzter,
- H. Balzter,
- J. Carreiras,
- D. Schepaschenko,
- D. Schepaschenko,
- D. Schepaschenko,
- M. Korets,
- M. Shimada,
- T. Itoh,
- Á. Moreno Martínez,
- Á. Moreno Martínez,
- J. Cavlovic,
- R. Cazzolla Gatti,
- P. da Conceição Bispo,
- P. da Conceição Bispo,
- N. Dewnath,
- N. Labrière,
- J. Liang,
- J. Lindsell,
- J. Lindsell,
- E. T. A. Mitchard,
- A. Morel,
- A. M. Pacheco Pascagaza,
- A. M. Pacheco Pascagaza,
- C. M. Ryan,
- F. Slik,
- G. Vaglio Laurin,
- H. Verbeeck,
- A. Wijaya,
- S. Willcock
Affiliations
- M. Santoro
- Gamma Remote Sensing, 3073 Gümligen, Switzerland
- O. Cartus
- Gamma Remote Sensing, 3073 Gümligen, Switzerland
- N. Carvalhais
- Max Planck Institute for Biogeochemistry, Hans Knöll Strasse 10, 07745 Jena, Germany
- N. Carvalhais
- Departamento de Ciências e Engenharia do Ambiente, DCEA, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
- D. M. A. Rozendaal
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands
- D. M. A. Rozendaal
- Plant Production Systems Group, Wageningen University and Research, P.O. Box 430, 6700 AK Wageningen, the Netherlands
- D. M. A. Rozendaal
- Centre for Crop Systems Analysis, Wageningen University and Research, P.O. Box 430, 6700 AK Wageningen, the Netherlands
- V. Avitabile
- Joint Research Centre, European Commission, Ispra, Italy
- A. Araza
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands
- S. de Bruin
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands
- M. Herold
- Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands
- S. Quegan
- National Centre for Earth Observation (NCEO), University of Sheffield, Sheffield, S3 7RH, UK
- P. Rodríguez-Veiga
- Centre for Landscape and Climate Research, School of Geography, Geology and the Environment, University of Leicester, LE1 7RH, UK
- P. Rodríguez-Veiga
- National Centre for Earth Observation (NCEO), Leicester, LE1 7RH, UK
- H. Balzter
- Centre for Landscape and Climate Research, School of Geography, Geology and the Environment, University of Leicester, LE1 7RH, UK
- H. Balzter
- National Centre for Earth Observation (NCEO), Leicester, LE1 7RH, UK
- J. Carreiras
- National Centre for Earth Observation (NCEO), University of Sheffield, Sheffield, S3 7RH, UK
- D. Schepaschenko
- International Institute for Applied Systems Analysis, Schlossplatz 1, 2361 Laxenburg, Austria
- D. Schepaschenko
- Center of Forest Ecology and Productivity, Russian Academy of Sciences, Profsoyuznaya 84/32/14, 117997 Moscow, Russia
- D. Schepaschenko
- Institute of Ecology and Geography, Siberian Federal University, 79 Svobodny Prospect, 660041 Krasnoyarsk, Russia
- M. Korets
- Laboratory of Ecophysiology of Permafrost Systems, V.N. Sukachev Institute of Forest of the Siberian Branch of the Russian Academy of Sciences – separated department of the KSC SB RAS, 660036 Krasnoyarsk, Russia
- M. Shimada
- Tokyo Denki University, School of Science and Engineering, Division of Architectural, Civil and Environmental Engineering, Ishizaka, Hatoyama, Hiki, Saitama, 350-0394, Japan
- T. Itoh
- Remote Sensing Technology Center of Japan, Tokyu Reit Toranomon Bldg, 3f, 3-17-1 Toranomon, Minato-Ku, Tokyo, 105-0001, Japan
- Á. Moreno Martínez
- Image Processing Laboratory (IPL), Universitat de València, València, Spain
- Á. Moreno Martínez
- Numerical Terradynamic Simulation Group (NTSG), University of Montana, Missoula, MT, USA
- J. Cavlovic
- Department of Forest Inventory and Management, Faculty of Forestry and Wood Technology, University of Zagreb, Svetosimunska cesta 23, 10000 Zagreb, Croatia
- R. Cazzolla Gatti
- Biological Institute, Tomsk State University, 634050 Tomsk, Russia
- P. da Conceição Bispo
- Centre for Landscape and Climate Research, School of Geography, Geology and the Environment, University of Leicester, LE1 7RH, UK
- P. da Conceição Bispo
- Department of Geography, School of Environment, Education and Development, University of Manchester, Oxford Road, M13 9PL Manchester, UK
- N. Dewnath
- Guyana Forestry Commission, 1 Water Street, Kingston, Georgetown, Guyana
- N. Labrière
- Laboratoire Évolution et Diversité Biologique, UMR 5174 (CNRS/IRD/UPS), 31062 Toulouse CEDEX 9, France
- J. Liang
- Department of Forestry and Natural Resources, Purdue University, 715 W State St, West Lafayette, IN 47907, USA
- J. Lindsell
- A Rocha International, Cambridge, UK
- J. Lindsell
- The RSPB Centre for Conservation Science, Bedfordshire, UK
- E. T. A. Mitchard
- School of GeoSciences, University of Edinburgh, Crew Building, The King's Buildings, Edinburgh, EH9 3FF, UK
- A. Morel
- Department of Geography and Environmental Sciences, University of Dundee, Dundee, UK
- A. M. Pacheco Pascagaza
- Centre for Landscape and Climate Research, School of Geography, Geology and the Environment, University of Leicester, LE1 7RH, UK
- A. M. Pacheco Pascagaza
- Department of Geography, School of Environment, Education and Development, University of Manchester, Oxford Road, M13 9PL Manchester, UK
- C. M. Ryan
- School of GeoSciences, University of Edinburgh, Crew Building, The King's Buildings, Edinburgh, EH9 3FF, UK
- F. Slik
- Faculty of Science, University Brunei Darussalam, Jln Tungku Link, Gadong, BE1410, Brunei Darussalam amma Remote Sensing, 3073 Gümligen, Switzerland
- G. Vaglio Laurin
- Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, 01100 Viterbo, Italy
- H. Verbeeck
- CAVElab – Computational and Applied Vegetation Ecology, Department of Environment, Ghent University, Coupure Links 653, 9000 Gent, Belgium
- A. Wijaya
- Department of Research, Data and Innovation, World Resources Institute Indonesia (WRI Indonesia), Wisma PMI, 3rd Floor, Jl. Wijaya I/63, Kebayoran Baru, South Jakarta, Indonesia
- S. Willcock
- School of Natural Sciences, Bangor University, Bangor, Gwynedd, UK
- DOI
- https://doi.org/10.5194/essd-13-3927-2021
- Journal volume & issue
-
Vol. 13
pp. 3927 – 3950
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 live biomass (AGB; dry mass) stored in forests 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 the 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 identified major biases compared to inventory data, up to 120 % of the inventory value in dry tropical forests, in the subtropics 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 change estimates. The dataset is available at https://doi.org/10.1594/PANGAEA.894711 (Santoro, 2018).