Articles | Volume 13, issue 11
https://doi.org/10.5194/essd-13-5353-2021
© 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-13-5353-2021
© 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
| 
18 Nov 2021
Data description paper |
| 18 Nov 2021
| 18 Nov 2021
Refined burned-area mapping protocol using Sentinel-2 data increases estimate of 2019 Indonesian burning
David L. A. Gaveau
CORRESPONDING AUTHOR
TheTreeMap, Bagadou Bas, 46600 Martel, France
Adrià Descals
CREAF, Centre de Recerca Ecològica i Aplicacions Forestals,
E08193 Bellaterra (Cerdanyola de Vallès), Catalonia, Spain
Mohammad A. Salim
TheTreeMap, Bagadou Bas, 46600 Martel, France
Douglas Sheil
Forest Ecology and Forest Management Group, Wageningen University and Research, P.O. Box 47, 6700 AA, Wageningen, the Netherlands
Sean Sloan
Department of Geography, Vancouver Island University, Nanaimo, BC,
Canada
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Cited
26 citations as recorded by crossref.
- Reconstructing 34 Years of Fire History in the Wet, Subtropical Vegetation of Hong Kong Using Landsat A. Chan et al. 10.3390/rs15061489
- Burned area semantic segmentation: A novel dataset and evaluation using convolutional networks T. Ribeiro et al. 10.1016/j.isprsjprs.2023.07.002
- Comparison of convolutional neural network and support vector machine for identification of forest types and burned areas B. Li et al. 10.1117/1.JRS.18.014531
- Mechanistic Analysis and Numerical Simulation of the 2021 Post‐Fire Debris Flow in Xiangjiao Catchment, China C. Ouyang et al. 10.1029/2022JF006846
- Sentinel-2 sampling design and reference fire perimeters to assess accuracy of Burned Area products over Sub-Saharan Africa for the year 2019 D. Stroppiana et al. 10.1016/j.isprsjprs.2022.07.015
- Mapping forest fire severity using bi-temporal unmixing of Sentinel-2 data - Towards a quantitative understanding of fire impacts K. Pfoch et al. 10.1016/j.srs.2023.100097
- Agriculture, Development and Sustainability in the Covid-19 Era A. Halimatussadiah et al. 10.1080/00074918.2022.2056935
- Enhancing Fire Monitoring Method over Peatlands and Non-Peatlands in Indonesia Using Visible Infrared Imaging Radiometer Suite Data A. Indradjad et al. 10.3390/fire7010009
- Assessing Sumatran Peat Vulnerability to Fire under Various Condition of ENSO Phases Using Machine Learning Approaches L. Prasetyo et al. 10.3390/f13060828
- Mono-temporal and multi-temporal approaches for burnt area detection using Sentinel-2 satellite imagery (a case study of Rokan Hilir Regency, Indonesia) N. Afira & A. Wijayanto 10.1016/j.ecoinf.2022.101677
- Multi-decadal trends and variability in burned area from the fifth version of the Global Fire Emissions Database (GFED5) Y. Chen et al. 10.5194/essd-15-5227-2023
- Reimagine fire science for the anthropocene J. Shuman et al. 10.1093/pnasnexus/pgac115
- Assessing space-based smoldering peatland in the tropics with atmospheric products from multi-sensor satellites P. Sofan et al. 10.1007/s40808-023-01793-4
- FIREMAP: Cloud-based software to automate the estimation of wildfire-induced ecological impacts and recovery processes using remote sensing techniques J. Fernández-Guisuraga et al. 10.1016/j.ecoinf.2024.102591
- Unprecedented fire activity above the Arctic Circle linked to rising temperatures A. Descals et al. 10.1126/science.abn9768
- Slowing deforestation in Indonesia follows declining oil palm expansion and lower oil prices D. Gaveau et al. 10.1371/journal.pone.0266178
- Building a small fire database for Sub-Saharan Africa from Sentinel-2 high-resolution images E. Chuvieco et al. 10.1016/j.scitotenv.2022.157139
- Mapping sugarcane residue burnt areas in smallholder farming systems using machine learning approaches K. PNVR & V. Bandaru 10.1016/j.atech.2023.100347
- A Hybrid Convolutional Neural Network and Random Forest for Burned Area Identification with Optical and Synthetic Aperture Radar (SAR) Data D. Sudiana et al. 10.3390/rs15030728
- Fire frequency, intensity, and burn severity in Kalimantan’s threatened Peatland areas over two Decades A. Schmidt et al. 10.3389/ffgc.2024.1221797
- Wetscapes: Restoring and maintaining peatland landscapes for sustainable futures R. Temmink et al. 10.1007/s13280-023-01875-8
- Single-Temporal Sentinel-2 for Analyzing Burned Area Detection Methods: A Study of 14 Cases in Republic of Korea Considering Land Cover D. Lee et al. 10.3390/rs16050884
- Global biomass burning fuel consumption and emissions at 500 m spatial resolution based on the Global Fire Emissions Database (GFED) D. van Wees et al. 10.5194/gmd-15-8411-2022
- Assessing Burned Areas in Sikkim, India through Satellite Mapping K. SHARMA et al. 10.17475/kastorman.1394888
- Madagascar's burned area from Sentinel-2 imagery (2016–2022): Four times higher than from lower resolution sensors V. Fernández-García et al. 10.1016/j.scitotenv.2024.169929
- Declining severe fire activity on managed lands in Equatorial Asia S. Sloan et al. 10.1038/s43247-022-00522-6
25 citations as recorded by crossref.
- Reconstructing 34 Years of Fire History in the Wet, Subtropical Vegetation of Hong Kong Using Landsat A. Chan et al. 10.3390/rs15061489
- Burned area semantic segmentation: A novel dataset and evaluation using convolutional networks T. Ribeiro et al. 10.1016/j.isprsjprs.2023.07.002
- Comparison of convolutional neural network and support vector machine for identification of forest types and burned areas B. Li et al. 10.1117/1.JRS.18.014531
- Mechanistic Analysis and Numerical Simulation of the 2021 Post‐Fire Debris Flow in Xiangjiao Catchment, China C. Ouyang et al. 10.1029/2022JF006846
- Sentinel-2 sampling design and reference fire perimeters to assess accuracy of Burned Area products over Sub-Saharan Africa for the year 2019 D. Stroppiana et al. 10.1016/j.isprsjprs.2022.07.015
- Mapping forest fire severity using bi-temporal unmixing of Sentinel-2 data - Towards a quantitative understanding of fire impacts K. Pfoch et al. 10.1016/j.srs.2023.100097
- Agriculture, Development and Sustainability in the Covid-19 Era A. Halimatussadiah et al. 10.1080/00074918.2022.2056935
- Enhancing Fire Monitoring Method over Peatlands and Non-Peatlands in Indonesia Using Visible Infrared Imaging Radiometer Suite Data A. Indradjad et al. 10.3390/fire7010009
- Assessing Sumatran Peat Vulnerability to Fire under Various Condition of ENSO Phases Using Machine Learning Approaches L. Prasetyo et al. 10.3390/f13060828
- Mono-temporal and multi-temporal approaches for burnt area detection using Sentinel-2 satellite imagery (a case study of Rokan Hilir Regency, Indonesia) N. Afira & A. Wijayanto 10.1016/j.ecoinf.2022.101677
- Multi-decadal trends and variability in burned area from the fifth version of the Global Fire Emissions Database (GFED5) Y. Chen et al. 10.5194/essd-15-5227-2023
- Reimagine fire science for the anthropocene J. Shuman et al. 10.1093/pnasnexus/pgac115
- Assessing space-based smoldering peatland in the tropics with atmospheric products from multi-sensor satellites P. Sofan et al. 10.1007/s40808-023-01793-4
- FIREMAP: Cloud-based software to automate the estimation of wildfire-induced ecological impacts and recovery processes using remote sensing techniques J. Fernández-Guisuraga et al. 10.1016/j.ecoinf.2024.102591
- Unprecedented fire activity above the Arctic Circle linked to rising temperatures A. Descals et al. 10.1126/science.abn9768
- Slowing deforestation in Indonesia follows declining oil palm expansion and lower oil prices D. Gaveau et al. 10.1371/journal.pone.0266178
- Building a small fire database for Sub-Saharan Africa from Sentinel-2 high-resolution images E. Chuvieco et al. 10.1016/j.scitotenv.2022.157139
- Mapping sugarcane residue burnt areas in smallholder farming systems using machine learning approaches K. PNVR & V. Bandaru 10.1016/j.atech.2023.100347
- A Hybrid Convolutional Neural Network and Random Forest for Burned Area Identification with Optical and Synthetic Aperture Radar (SAR) Data D. Sudiana et al. 10.3390/rs15030728
- Fire frequency, intensity, and burn severity in Kalimantan’s threatened Peatland areas over two Decades A. Schmidt et al. 10.3389/ffgc.2024.1221797
- Wetscapes: Restoring and maintaining peatland landscapes for sustainable futures R. Temmink et al. 10.1007/s13280-023-01875-8
- Single-Temporal Sentinel-2 for Analyzing Burned Area Detection Methods: A Study of 14 Cases in Republic of Korea Considering Land Cover D. Lee et al. 10.3390/rs16050884
- Global biomass burning fuel consumption and emissions at 500 m spatial resolution based on the Global Fire Emissions Database (GFED) D. van Wees et al. 10.5194/gmd-15-8411-2022
- Assessing Burned Areas in Sikkim, India through Satellite Mapping K. SHARMA et al. 10.17475/kastorman.1394888
- Madagascar's burned area from Sentinel-2 imagery (2016–2022): Four times higher than from lower resolution sensors V. Fernández-García et al. 10.1016/j.scitotenv.2024.169929
1 citations as recorded by crossref.
Latest update: 24 Apr 2024
Short summary
Severe burning struck Indonesia in 2019. Drawing on new satellite imagery, we present and validate new 2019 burned-area estimates for Indonesia.
We show that > 3.11 million hectares (Mha) burned in 2019, double the official estimate from the Indonesian Ministry of Environment and Forestry. Our relatively more accurate estimates have important implications for carbon-emission calculations from forest and peatland fires in Indonesia.
Severe burning struck Indonesia in 2019. Drawing on new satellite imagery, we present and...
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