The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations
<p>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-g...
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Copernicus Publications
2021-08-01
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Online Access: | https://essd.copernicus.org/articles/13/3927/2021/essd-13-3927-2021.pdf |
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author |
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 |
spellingShingle |
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 The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations Earth System Science Data |
author_facet |
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 |
author_sort |
M. Santoro |
title |
The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations |
title_short |
The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations |
title_full |
The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations |
title_fullStr |
The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations |
title_full_unstemmed |
The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations |
title_sort |
global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations |
publisher |
Copernicus Publications |
series |
Earth System Science Data |
issn |
1866-3508 1866-3516 |
publishDate |
2021-08-01 |
description |
<p>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 <span class="inline-formula">ha</span>. 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 <span class="inline-formula">>250</span> <span class="inline-formula">Mg ha<sup>−1</sup></span>, where the retrieval was effectively based on a single radar
observation. With a total global AGB of 522 <span class="inline-formula">Pg</span>, our estimate of the
terrestrial biomass pool in forests is lower than most estimates published
in the literature (426–571 <span class="inline-formula">Pg</span>). 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 <span class="inline-formula">%</span> 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 <a href="https://doi.org/10.1594/PANGAEA.894711">https://doi.org/10.1594/PANGAEA.894711</a>
(Santoro, 2018).</p> |
url |
https://essd.copernicus.org/articles/13/3927/2021/essd-13-3927-2021.pdf |
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doaj-b9b0c7e5831246388da2cc296b648be42021-08-11T13:49:37ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162021-08-01133927395010.5194/essd-13-3927-2021The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observationsM. Santoro0O. Cartus1N. Carvalhais2N. Carvalhais3D. M. A. Rozendaal4D. M. A. Rozendaal5D. M. A. Rozendaal6V. Avitabile7A. Araza8S. de Bruin9M. Herold10S. Quegan11P. Rodríguez-Veiga12P. Rodríguez-Veiga13H. Balzter14H. Balzter15J. Carreiras16D. Schepaschenko17D. Schepaschenko18D. Schepaschenko19M. Korets20M. Shimada21T. Itoh22Á. Moreno Martínez23Á. Moreno Martínez24J. Cavlovic25R. Cazzolla Gatti26P. da Conceição Bispo27P. da Conceição Bispo28N. Dewnath29N. Labrière30J. Liang31J. Lindsell32J. Lindsell33E. T. A. Mitchard34A. Morel35A. M. Pacheco Pascagaza36A. M. Pacheco Pascagaza37C. M. Ryan38F. Slik39G. Vaglio Laurin40H. Verbeeck41A. Wijaya42S. Willcock43Gamma Remote Sensing, 3073 Gümligen, SwitzerlandGamma Remote Sensing, 3073 Gümligen, SwitzerlandMax Planck Institute for Biogeochemistry, Hans Knöll Strasse 10, 07745 Jena, GermanyDepartamento de Ciências e Engenharia do Ambiente, DCEA, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, 2829-516 Caparica, PortugalLaboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the NetherlandsPlant Production Systems Group, Wageningen University and Research, P.O. Box 430, 6700 AK Wageningen, the NetherlandsCentre for Crop Systems Analysis, Wageningen University and Research, P.O. Box 430, 6700 AK Wageningen, the NetherlandsJoint Research Centre, European Commission, Ispra, ItalyLaboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the NetherlandsLaboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the NetherlandsLaboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the NetherlandsNational Centre for Earth Observation (NCEO), University of Sheffield, Sheffield, S3 7RH, UKCentre for Landscape and Climate Research, School of Geography, Geology and the Environment, University of Leicester, LE1 7RH, UKNational Centre for Earth Observation (NCEO), Leicester, LE1 7RH, UKCentre for Landscape and Climate Research, School of Geography, Geology and the Environment, University of Leicester, LE1 7RH, UKNational Centre for Earth Observation (NCEO), Leicester, LE1 7RH, UKNational Centre for Earth Observation (NCEO), University of Sheffield, Sheffield, S3 7RH, UKInternational Institute for Applied Systems Analysis, Schlossplatz 1, 2361 Laxenburg, AustriaCenter of Forest Ecology and Productivity, Russian Academy of Sciences, Profsoyuznaya 84/32/14, 117997 Moscow, RussiaInstitute of Ecology and Geography, Siberian Federal University, 79 Svobodny Prospect, 660041 Krasnoyarsk, RussiaLaboratory 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, RussiaTokyo Denki University, School of Science and Engineering, Division of Architectural, Civil and Environmental Engineering, Ishizaka, Hatoyama, Hiki, Saitama, 350-0394, JapanRemote Sensing Technology Center of Japan, Tokyu Reit Toranomon Bldg, 3f, 3-17-1 Toranomon, Minato-Ku, Tokyo, 105-0001, JapanImage Processing Laboratory (IPL), Universitat de València, València, SpainNumerical Terradynamic Simulation Group (NTSG), University of Montana, Missoula, MT, USADepartment of Forest Inventory and Management, Faculty of Forestry and Wood Technology, University of Zagreb, Svetosimunska cesta 23, 10000 Zagreb, CroatiaBiological Institute, Tomsk State University, 634050 Tomsk, RussiaCentre for Landscape and Climate Research, School of Geography, Geology and the Environment, University of Leicester, LE1 7RH, UKDepartment of Geography, School of Environment, Education and Development, University of Manchester, Oxford Road, M13 9PL Manchester, UKGuyana Forestry Commission, 1 Water Street, Kingston, Georgetown, GuyanaLaboratoire Évolution et Diversité Biologique, UMR 5174 (CNRS/IRD/UPS), 31062 Toulouse CEDEX 9, FranceDepartment of Forestry and Natural Resources, Purdue University, 715 W State St, West Lafayette, IN 47907, USAA Rocha International, Cambridge, UKThe RSPB Centre for Conservation Science, Bedfordshire, UKSchool of GeoSciences, University of Edinburgh, Crew Building, The King's Buildings, Edinburgh, EH9 3FF, UKDepartment of Geography and Environmental Sciences, University of Dundee, Dundee, UKCentre for Landscape and Climate Research, School of Geography, Geology and the Environment, University of Leicester, LE1 7RH, UKDepartment of Geography, School of Environment, Education and Development, University of Manchester, Oxford Road, M13 9PL Manchester, UKSchool of GeoSciences, University of Edinburgh, Crew Building, The King's Buildings, Edinburgh, EH9 3FF, UKFaculty of Science, University Brunei Darussalam, Jln Tungku Link, Gadong, BE1410, Brunei Darussalam amma Remote Sensing, 3073 Gümligen, SwitzerlandDepartment for Innovation in Biological, Agro-Food and Forest Systems (DIBAF), University of Tuscia, 01100 Viterbo, ItalyCAVElab – Computational and Applied Vegetation Ecology, Department of Environment, Ghent University, Coupure Links 653, 9000 Gent, BelgiumDepartment of Research, Data and Innovation, World Resources Institute Indonesia (WRI Indonesia), Wisma PMI, 3rd Floor, Jl. Wijaya I/63, Kebayoran Baru, South Jakarta, IndonesiaSchool of Natural Sciences, Bangor University, Bangor, Gwynedd, UK<p>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 <span class="inline-formula">ha</span>. 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 <span class="inline-formula">>250</span> <span class="inline-formula">Mg ha<sup>−1</sup></span>, where the retrieval was effectively based on a single radar observation. With a total global AGB of 522 <span class="inline-formula">Pg</span>, our estimate of the terrestrial biomass pool in forests is lower than most estimates published in the literature (426–571 <span class="inline-formula">Pg</span>). 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 <span class="inline-formula">%</span> 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 <a href="https://doi.org/10.1594/PANGAEA.894711">https://doi.org/10.1594/PANGAEA.894711</a> (Santoro, 2018).</p>https://essd.copernicus.org/articles/13/3927/2021/essd-13-3927-2021.pdf |