21 Jul 2020
21 Jul 2020
The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations
- 1Gamma Remote Sensing, 3073 Gümligen, Switzerland
- 2Max Planck Institute for Biogeochemistry, Hans Knöll Strasse 10, 07745 Jena, Germany
- 3Departamento de Ciências e Engenharia do Ambiente, DCEA, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
- 4Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands
- 5Plant Production Systems Group, Wageningen University & Research, P.O. Box 430, 6700 AK, Wageningen, The Netherlands
- 6Centre for Crop Systems Analysis, Wageningen University & Research, P.O. Box 430, 6700 AK, Wageningen, The Netherlands
- 7European Commission, Joint Research Centre, Ispra, Italy
- 8National Centre for Earth Observation (NCEO), University of Sheffield, Sheffield, S3 7RH, United Kingdom
- 9Centre for Landscape and Climate Research, School of Geography, Geology and the Environment, University of Leicester, United Kingdom
- 10National Centre for Earth Observation, University of Leicester, United Kingdom
- 11International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria
- 12Center of Forest Ecology and Productivity of the Russian Academy of Sciences, Profsoyuznaya 84/32/14, Moscow, 117997, Russia
- 13Institute of Ecology and Geography, Siberian Federal University, 660041, Krasnoyarsk, 79 Svobodny Prospect, Russia
- 14Laboratory of Ecophysiology of Permafrost Systems, V.N. Sukachev Institute of Forest of the Siberian Branch of Russian Academy of Sciences – separated department of the KSC SB RAS, Krasnoyarsk, 660036, Russia
- 15Tokyo Denki University, School of Science and Engineering, Division of Architectural, Civil and Environmental Engineering
- 16Remote Sensing Technology Center of Japan, Tokyu Reit Toranomon Bldg, 3f, 3-17-1 Toranomon, Minato-Ku, Tokyo, 105-0001, Japan
- 17Image Processing Laboratory (IPL), Universitat de València, València, Spain
- 18Numerical Terradynamic Simulation Group (NTSG), University of Montana, Missoula, USA
- 19University of Zagreb, Faculty of Forestry, Department of Forest Inventory and Management, Svetosimunska cesta 25, 10000, Zagreb, Croatia
- 20Biological Institute, Tomsk State University, 634050 Tomsk, Russia
- 21Konrad Lorenz Institute for Evolution and Cognition, 3400 Klosterneuburg, Austria
- 22School of Environment, Education and Development/Department of Geography, University of Manchester, Oxford Road, M13 9PL Manchester, United Kingdom
- 23Guyana Forestry Commission, 1 Water Street, Kingston, Georgetown, Guyana
- 24Laboratoire Évolution et Diversité Biologique, UMR 5174 (CNRS/IRD/UPS), 31062 Toulouse Cedex 9, France
- 25Department of Forestry and Natural Resources, Purdue University
- 26A Rocha International, Cambridge, United Kingdom
- 27The RSPB Centre for Conservation Science, Bedfordshire, United Kingdom
- 28University of Edinburgh, School of GeoSciences, Crew Building, The King's Buildings, Edinburgh, EH9 3FF, United Kingdom
- 29Department of Geography and Environmental Sciences, University of Dundee, United Kingdom
- 30Faculty of Science,University Brunei Darussalam, Jln Tungku Link, Gadong, BE1410, Brunei Darussalam
- 31Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, 01100 Viterbo, Italy
- 32CAVElab – Computational and Applied Vegetation Ecology, Department of Environment, Ghent University, Coupure Links 653, 9000 Gent, Belgium
- 33World Resources Institute, Indonesia
- 34School of Natural Sciences, Bangor University, United Kingdom
- 1Gamma Remote Sensing, 3073 Gümligen, Switzerland
- 2Max Planck Institute for Biogeochemistry, Hans Knöll Strasse 10, 07745 Jena, Germany
- 3Departamento de Ciências e Engenharia do Ambiente, DCEA, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
- 4Laboratory of Geo-Information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands
- 5Plant Production Systems Group, Wageningen University & Research, P.O. Box 430, 6700 AK, Wageningen, The Netherlands
- 6Centre for Crop Systems Analysis, Wageningen University & Research, P.O. Box 430, 6700 AK, Wageningen, The Netherlands
- 7European Commission, Joint Research Centre, Ispra, Italy
- 8National Centre for Earth Observation (NCEO), University of Sheffield, Sheffield, S3 7RH, United Kingdom
- 9Centre for Landscape and Climate Research, School of Geography, Geology and the Environment, University of Leicester, United Kingdom
- 10National Centre for Earth Observation, University of Leicester, United Kingdom
- 11International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria
- 12Center of Forest Ecology and Productivity of the Russian Academy of Sciences, Profsoyuznaya 84/32/14, Moscow, 117997, Russia
- 13Institute of Ecology and Geography, Siberian Federal University, 660041, Krasnoyarsk, 79 Svobodny Prospect, Russia
- 14Laboratory of Ecophysiology of Permafrost Systems, V.N. Sukachev Institute of Forest of the Siberian Branch of Russian Academy of Sciences – separated department of the KSC SB RAS, Krasnoyarsk, 660036, Russia
- 15Tokyo Denki University, School of Science and Engineering, Division of Architectural, Civil and Environmental Engineering
- 16Remote Sensing Technology Center of Japan, Tokyu Reit Toranomon Bldg, 3f, 3-17-1 Toranomon, Minato-Ku, Tokyo, 105-0001, Japan
- 17Image Processing Laboratory (IPL), Universitat de València, València, Spain
- 18Numerical Terradynamic Simulation Group (NTSG), University of Montana, Missoula, USA
- 19University of Zagreb, Faculty of Forestry, Department of Forest Inventory and Management, Svetosimunska cesta 25, 10000, Zagreb, Croatia
- 20Biological Institute, Tomsk State University, 634050 Tomsk, Russia
- 21Konrad Lorenz Institute for Evolution and Cognition, 3400 Klosterneuburg, Austria
- 22School of Environment, Education and Development/Department of Geography, University of Manchester, Oxford Road, M13 9PL Manchester, United Kingdom
- 23Guyana Forestry Commission, 1 Water Street, Kingston, Georgetown, Guyana
- 24Laboratoire Évolution et Diversité Biologique, UMR 5174 (CNRS/IRD/UPS), 31062 Toulouse Cedex 9, France
- 25Department of Forestry and Natural Resources, Purdue University
- 26A Rocha International, Cambridge, United Kingdom
- 27The RSPB Centre for Conservation Science, Bedfordshire, United Kingdom
- 28University of Edinburgh, School of GeoSciences, Crew Building, The King's Buildings, Edinburgh, EH9 3FF, United Kingdom
- 29Department of Geography and Environmental Sciences, University of Dundee, United Kingdom
- 30Faculty of Science,University Brunei Darussalam, Jln Tungku Link, Gadong, BE1410, Brunei Darussalam
- 31Department for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, 01100 Viterbo, Italy
- 32CAVElab – Computational and Applied Vegetation Ecology, Department of Environment, Ghent University, Coupure Links 653, 9000 Gent, Belgium
- 33World Resources Institute, Indonesia
- 34School of Natural Sciences, Bangor University, United Kingdom
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−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).
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Maurizio Santoro et al.
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SC1: 'Forest biomass', Andrii Bilous, 10 Nov 2020
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AC1: 'Reply on SC1', Maurizio Santoro, 13 Jan 2021
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AC1: 'Reply on SC1', Maurizio Santoro, 13 Jan 2021
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SC2: 'Spatial autocorrelations in the AGB data', Maksym Matsala, 14 Nov 2020
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AC5: 'Reply on SC2', Maurizio Santoro, 18 Jan 2021
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AC5: 'Reply on SC2', Maurizio Santoro, 18 Jan 2021
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SC3: 'Can polarimetric SAR data help further improve biomass estimation from space?', Ake Rosenqvist, 02 Dec 2020
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AC2: 'Reply on SC3', Maurizio Santoro, 13 Jan 2021
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AC2: 'Reply on SC3', Maurizio Santoro, 13 Jan 2021
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SC4: 'Biomass Expansion Factors', Richard Lucas, 04 Dec 2020
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AC6: 'Reply on SC4', Maurizio Santoro, 18 Jan 2021
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AC6: 'Reply on SC4', Maurizio Santoro, 18 Jan 2021
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SC5: 'Selection of global biomass products', Richard Lucas, 04 Dec 2020
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AC3: 'Reply on SC5', Maurizio Santoro, 13 Jan 2021
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AC3: 'Reply on SC5', Maurizio Santoro, 13 Jan 2021
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SC6: 'Future reference', Jan Joseph Dida, 04 Dec 2020
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AC4: 'Reply on SC6', Maurizio Santoro, 13 Jan 2021
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AC4: 'Reply on SC6', Maurizio Santoro, 13 Jan 2021
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SC7: 'Accuracy and validation', Mihai Tanase, 04 Dec 2020
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AC7: 'Reply on SC7', Maurizio Santoro, 18 Jan 2021
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AC7: 'Reply on SC7', Maurizio Santoro, 18 Jan 2021
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SC8: 'Comment / Query', Peter Bunting, 04 Dec 2020
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AC8: 'Reply on SC8', Maurizio Santoro, 18 Jan 2021
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AC8: 'Reply on SC8', Maurizio Santoro, 18 Jan 2021
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SC9: 'Possible regional comparison citation', Seth Spawn, 04 Feb 2021
Maurizio Santoro et al.
Data sets
GlobBiomass - global datasets of forest biomass Santoro, M. https://doi.org/10.1594/PANGAEA.894711
Maurizio Santoro et al.
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Cited
5 citations as recorded by crossref.
- Framework for Accounting Reference Levels for REDD+ in Tropical Forests: Case Study from Xishuangbanna, China G. Liu et al. 10.3390/rs13030416
- The Timber Footprint of the German Bioeconomy—State of the Art and Past Development V. Egenolf et al. 10.3390/su13073878
- Apparent ecosystem carbon turnover time: uncertainties and robust features N. Fan et al. 10.5194/essd-12-2517-2020
- Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach P. Bispo et al. 10.3390/rs12172685
- Modeling Forest Aboveground Carbon Density in the Brazilian Amazon with Integration of MODIS and Airborne LiDAR Data X. Jiang et al. 10.3390/rs12203330