Articles | Volume 13, issue 8
https://doi.org/10.5194/essd-13-3927-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-3927-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
11 Aug 2021
Data description paper |
| 11 Aug 2021
The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations
Gamma Remote Sensing, 3073 Gümligen, Switzerland
Oliver Cartus
Gamma Remote Sensing, 3073 Gümligen, Switzerland
Nuno Carvalhais
Max Planck Institute for Biogeochemistry, Hans Knöll Strasse 10, 07745 Jena, Germany
Departamento de Ciências e Engenharia do Ambiente, DCEA, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal
Danaë M. A. Rozendaal
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands
Plant Production Systems Group, Wageningen University and Research,
P.O. Box 430, 6700 AK Wageningen, the Netherlands
Centre for Crop Systems Analysis, Wageningen University and Research, P.O. Box 430, 6700 AK Wageningen, the Netherlands
Valerio Avitabile
Joint Research Centre, European Commission, Ispra, Italy
Arnan Araza
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands
Sytze de Bruin
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands
Martin Herold
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University and Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands
Shaun Quegan
National Centre for Earth Observation (NCEO), University of Sheffield, Sheffield, S3 7RH, UK
Pedro Rodríguez-Veiga
Centre for Landscape and Climate Research, School of Geography,
Geology and the Environment, University of Leicester, LE1 7RH, UK
National Centre for Earth Observation (NCEO), Leicester, LE1 7RH,
UK
Heiko Balzter
Centre for Landscape and Climate Research, School of Geography,
Geology and the Environment, University of Leicester, LE1 7RH, UK
National Centre for Earth Observation (NCEO), Leicester, LE1 7RH,
UK
João Carreiras
National Centre for Earth Observation (NCEO), University of Sheffield, Sheffield, S3 7RH, UK
Dmitry Schepaschenko
International Institute for Applied Systems Analysis, Schlossplatz 1, 2361 Laxenburg, Austria
Center of Forest Ecology and Productivity, Russian Academy of Sciences, Profsoyuznaya 84/32/14, 117997 Moscow, Russia
Institute of Ecology and Geography, Siberian Federal University, 79 Svobodny Prospect,
660041 Krasnoyarsk, Russia
Mikhail 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
Masanobu Shimada
Tokyo Denki University, School of Science and Engineering, Division of Architectural, Civil and Environmental Engineering, Ishizaka, Hatoyama, Hiki, Saitama, 350-0394, Japan
Takuya Itoh
Remote Sensing Technology Center of Japan, Tokyu Reit Toranomon Bldg, 3f, 3-17-1 Toranomon, Minato-Ku, Tokyo, 105-0001, Japan
Álvaro Moreno Martínez
Image Processing Laboratory (IPL), Universitat de València,
València, Spain
Numerical Terradynamic Simulation Group (NTSG), University of
Montana, Missoula, MT, USA
Jura Cavlovic
Department of Forest Inventory and Management, Faculty of Forestry and Wood Technology, University of Zagreb, Svetosimunska cesta 23,
10000 Zagreb, Croatia
Roberto Cazzolla Gatti
Biological Institute, Tomsk State University, 634050 Tomsk, Russia
Polyanna da Conceição Bispo
Centre for Landscape and Climate Research, School of Geography,
Geology and the Environment, University of Leicester, LE1 7RH, UK
Department of
Geography, School of Environment, Education and Development, University of Manchester, Oxford Road, M13 9PL Manchester, UK
Nasheta Dewnath
Guyana Forestry Commission, 1 Water Street, Kingston, Georgetown,
Guyana
Nicolas Labrière
Laboratoire Évolution et Diversité Biologique, UMR 5174
(CNRS/IRD/UPS), 31062 Toulouse CEDEX 9, France
Jingjing Liang
Department of Forestry and Natural Resources, Purdue University, 715 W State St, West Lafayette, IN 47907, USA
Jeremy Lindsell
A Rocha International, Cambridge, UK
The RSPB Centre for Conservation Science, Bedfordshire, UK
Edward T. A. Mitchard
School of GeoSciences, University of Edinburgh, Crew Building, The
King's Buildings, Edinburgh, EH9 3FF, UK
Alexandra Morel
Department of Geography and Environmental Sciences, University of
Dundee, Dundee, UK
Ana Maria Pacheco Pascagaza
Centre for Landscape and Climate Research, School of Geography,
Geology and the Environment, University of Leicester, LE1 7RH, UK
Department of
Geography, School of Environment, Education and Development, University of Manchester, Oxford Road, M13 9PL Manchester, UK
Casey M. Ryan
School of GeoSciences, University of Edinburgh, Crew Building, The
King's Buildings, Edinburgh, EH9 3FF, UK
Ferry Slik
Faculty of Science, University Brunei Darussalam, Jln Tungku Link,
Gadong, BE1410, Brunei Darussalam
amma Remote Sensing, 3073 Gümligen, Switzerland
Gaia Vaglio Laurin
Department for Innovation in Biological, Agro-Food and Forest Systems
(DIBAF), University of Tuscia, 01100 Viterbo, Italy
Hans Verbeeck
CAVElab – Computational and Applied Vegetation Ecology, Department
of Environment, Ghent University, Coupure Links 653, 9000 Gent, Belgium
Arief 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
Simon Willcock
School of Natural Sciences, Bangor University, Bangor, Gwynedd, UK
Viewed
Total article views: 10,153 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 21 Jul 2020)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 7,394 | 2,660 | 99 | 10,153 | 857 | 132 | 152 |
- HTML: 7,394
- PDF: 2,660
- XML: 99
- Total: 10,153
- Supplement: 857
- BibTeX: 132
- EndNote: 152
Total article views: 6,757 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 11 Aug 2021)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 5,025 | 1,663 | 69 | 6,757 | 579 | 97 | 105 |
- HTML: 5,025
- PDF: 1,663
- XML: 69
- Total: 6,757
- Supplement: 579
- BibTeX: 97
- EndNote: 105
Total article views: 3,396 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 21 Jul 2020)
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 2,369 | 997 | 30 | 3,396 | 278 | 35 | 47 |
- HTML: 2,369
- PDF: 997
- XML: 30
- Total: 3,396
- Supplement: 278
- BibTeX: 35
- EndNote: 47
Viewed (geographical distribution)
Total article views: 10,153 (including HTML, PDF, and XML)
Thereof 9,258 with geography defined
and 895 with unknown origin.
Total article views: 6,757 (including HTML, PDF, and XML)
Thereof 6,327 with geography defined
and 430 with unknown origin.
Total article views: 3,396 (including HTML, PDF, and XML)
Thereof 2,931 with geography defined
and 465 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
24 citations as recorded by crossref.
- Change Detection of Selective Logging in the Brazilian Amazon Using X-Band SAR Data and Pre-Trained Convolutional Neural Networks T. Kuck et al. 10.3390/rs13234944
- Large Area Aboveground Biomass and Carbon Stock Mapping in Woodlands in Mozambique with L-Band Radar: Improving Accuracy by Accounting for Soil Moisture Effects Using the Water Cloud Model Y. Gou et al. 10.3390/rs14020404
- A data-driven estimate of litterfall and forest carbon turnover and the drivers of their inter-annual variabilities in forest ecosystems across China X. Zhao et al. 10.1016/j.scitotenv.2022.153341
- Carbon stock in litter of middle taiga forest ecosystems of Central Siberia O. Sergeeva et al. 10.1088/1755-1315/875/1/012085
- Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring U. Ahmad et al. 10.3390/agronomy12030555
- A global map of root biomass across the world's forests Y. Huang et al. 10.5194/essd-13-4263-2021
- The three major axes of terrestrial ecosystem function M. Migliavacca et al. 10.1038/s41586-021-03939-9
- From Ecosystem Observation to Environmental Decision-Making: Model-Data Fusion as an Operational Tool T. Smallman et al. 10.3389/ffgc.2021.818661
- SMOS L-VOD Retrieved by Level 2 Algorithm and its Correlation With GEDI LIDAR Products C. Vittucci et al. 10.1109/JSTARS.2021.3128022
- Aboveground forest biomass varies across continents, ecological zones and successional stages: refined IPCC default values for tropical and subtropical forests D. Rozendaal et al. 10.1088/1748-9326/ac45b3
- A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images T. Kuck et al. 10.3390/rs13173341
- Does Decentralized and Voluntary Commitment Reduce Deforestation? The Effects of Programa Municípios Verdes M. Moz-Christofoletti et al. 10.1007/s10640-022-00659-0
- Mapping global forest age from forest inventories, biomass and climate data S. Besnard et al. 10.5194/essd-13-4881-2021
- A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps A. Araza et al. 10.1016/j.rse.2022.112917
- Global sensitivities of forest carbon changes to environmental conditions S. Besnard et al. 10.1111/gcb.15877
- Framework for Accounting Reference Levels for REDD+ in Tropical Forests: Case Study from Xishuangbanna, China G. Liu et al. 10.3390/rs13030416
- Carbon Stocks, Species Diversity and Their Spatial Relationships in the Yucatán Peninsula, Mexico J. Hernández-Stefanoni et al. 10.3390/rs13163179
- 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
- Rapid remote monitoring reveals spatial and temporal hotspots of carbon loss in Africa’s rainforests O. Csillik et al. 10.1038/s43247-022-00383-z
- Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach P. Bispo et al. 10.3390/rs12172685
- Fire enhances forest degradation within forest edge zones in Africa Z. Zhao et al. 10.1038/s41561-021-00763-8
- Spatial Optimization and Tradeoffs of Alternative Forest Management Scenarios in Macedonia, Greece P. Palaiologou et al. 10.3390/f12060697
- Modeling Forest Aboveground Carbon Density in the Brazilian Amazon with Integration of MODIS and Airborne LiDAR Data X. Jiang et al. 10.3390/rs12203330
15 citations as recorded by crossref.
- Change Detection of Selective Logging in the Brazilian Amazon Using X-Band SAR Data and Pre-Trained Convolutional Neural Networks T. Kuck et al. 10.3390/rs13234944
- Large Area Aboveground Biomass and Carbon Stock Mapping in Woodlands in Mozambique with L-Band Radar: Improving Accuracy by Accounting for Soil Moisture Effects Using the Water Cloud Model Y. Gou et al. 10.3390/rs14020404
- A data-driven estimate of litterfall and forest carbon turnover and the drivers of their inter-annual variabilities in forest ecosystems across China X. Zhao et al. 10.1016/j.scitotenv.2022.153341
- Carbon stock in litter of middle taiga forest ecosystems of Central Siberia O. Sergeeva et al. 10.1088/1755-1315/875/1/012085
- Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring U. Ahmad et al. 10.3390/agronomy12030555
- A global map of root biomass across the world's forests Y. Huang et al. 10.5194/essd-13-4263-2021
- The three major axes of terrestrial ecosystem function M. Migliavacca et al. 10.1038/s41586-021-03939-9
- From Ecosystem Observation to Environmental Decision-Making: Model-Data Fusion as an Operational Tool T. Smallman et al. 10.3389/ffgc.2021.818661
- SMOS L-VOD Retrieved by Level 2 Algorithm and its Correlation With GEDI LIDAR Products C. Vittucci et al. 10.1109/JSTARS.2021.3128022
- Aboveground forest biomass varies across continents, ecological zones and successional stages: refined IPCC default values for tropical and subtropical forests D. Rozendaal et al. 10.1088/1748-9326/ac45b3
- A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images T. Kuck et al. 10.3390/rs13173341
- Does Decentralized and Voluntary Commitment Reduce Deforestation? The Effects of Programa Municípios Verdes M. Moz-Christofoletti et al. 10.1007/s10640-022-00659-0
- Mapping global forest age from forest inventories, biomass and climate data S. Besnard et al. 10.5194/essd-13-4881-2021
- A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps A. Araza et al. 10.1016/j.rse.2022.112917
- Global sensitivities of forest carbon changes to environmental conditions S. Besnard et al. 10.1111/gcb.15877
9 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
- Carbon Stocks, Species Diversity and Their Spatial Relationships in the Yucatán Peninsula, Mexico J. Hernández-Stefanoni et al. 10.3390/rs13163179
- 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
- Rapid remote monitoring reveals spatial and temporal hotspots of carbon loss in Africa’s rainforests O. Csillik et al. 10.1038/s43247-022-00383-z
- Woody Aboveground Biomass Mapping of the Brazilian Savanna with a Multi-Sensor and Machine Learning Approach P. Bispo et al. 10.3390/rs12172685
- Fire enhances forest degradation within forest edge zones in Africa Z. Zhao et al. 10.1038/s41561-021-00763-8
- Spatial Optimization and Tradeoffs of Alternative Forest Management Scenarios in Macedonia, Greece P. Palaiologou et al. 10.3390/f12060697
- Modeling Forest Aboveground Carbon Density in the Brazilian Amazon with Integration of MODIS and Airborne LiDAR Data X. Jiang et al. 10.3390/rs12203330
Latest update: 05 Jun 2023
Short summary
Forests play a crucial role in Earth’s carbon cycle. To understand the carbon cycle better, we generated a global dataset of forest above-ground biomass, i.e. carbon stocks, from satellite data of 2010. This dataset provides a comprehensive and detailed portrait of the distribution of carbon in forests, although for dense forests in the tropics values are somewhat underestimated. This dataset will have a considerable impact on climate, carbon, and socio-economic modelling schemes.
Forests play a crucial role in Earth’s carbon cycle. To understand the carbon cycle better, we...
