Articles | Volume 13, issue 5
https://doi.org/10.5194/essd-13-1925-2021
© 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-13-1925-2021
© 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
| 
08 May 2021
Data description paper |
| 08 May 2021
| 08 May 2021
MOSEV: a global burn severity database from MODIS (2000–2020)
Esteban Alonso-González
Instituto Pirenaico de Ecología, Spanish Research Council
(IPE-CSIC), Zaragoza, 50059, Spain
Ecology, Department of Biodiversity and Environmental Management, Faculty of Biological and Environmental Sciences, University of León, León, 24071, Spain
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23 citations as recorded by crossref.
- Evaluation of pre- and post-fire flood risk by analytical hierarchy process method: a case study for the 2021 wildfires in Bodrum, Turkey O. Yilmaz et al. 10.1007/s11355-023-00545-x
- Building patterns and fuel features drive wildfire severity in wildland-urban interfaces in Southern Europe V. Fernández-García et al. 10.1016/j.landurbplan.2022.104646
- Large-scale burn severity mapping in multispectral imagery using deep semantic segmentation models X. Hu et al. 10.1016/j.isprsjprs.2022.12.026
- Space-Based Earth Observations on Hotspots of Atmospheric NO2 over India Using Google Earth Engine: An Open-Source Cloud Platform A. Bandary et al. 10.1007/s12524-024-02071-1
- 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
- Refining historical burned area data from satellite observations V. Fernández-García & C. Kull 10.1016/j.jag.2023.103350
- Multi-source tri-environmental conceptual framework for fire impact analysis Z. Li et al. 10.1007/s44212-024-00063-7
- Impact of large kernel size on yield prediction: a case study of corn yield prediction with SEDLA in the U.S. Corn Belt A. Terliksiz & D. Altilar 10.1088/2515-7620/ad27fa
- Using Pre-Fire High Point Cloud Density LiDAR Data to Predict Fire Severity in Central Portugal J. Fernández-Guisuraga & P. Fernandes 10.3390/rs15030768
- Characterizing spatial burn severity patterns of 2016 Chimney Tops 2 fire using multi-temporal Landsat and NEON LiDAR data T. Park & S. Sim 10.3389/frsen.2023.1096000
- Characterizing Fire-Induced Forest Structure and Aboveground Biomass Changes in Boreal Forests Using Multitemporal Lidar and Landsat T. Feng et al. 10.1109/JSTARS.2024.3400218
- Molecular insights and impacts of wildfire-induced soil chemical changes A. Lopez et al. 10.1038/s43017-024-00548-8
- Response of active layer thickening to wildfire in the pan-Arctic region: Permafrost type and vegetation type influences X. Jiang et al. 10.1016/j.scitotenv.2023.166132
- Widespread and systematic effects of fire on plant–soil water relations M. Baur et al. 10.1038/s41561-024-01563-6
- Constructing a Comprehensive National Wildfire Database from Incomplete Sources: Israel as a Case Study E. Guk et al. 10.3390/fire6040131
- Global Patterns and Dynamics of Burned Area and Burn Severity V. Fernández-García & E. Alonso-González 10.3390/rs15133401
- A global forest burn severity dataset from Landsat imagery (2003–2016) K. He et al. 10.5194/essd-16-3061-2024
- Higher burn severity stimulates postfire vegetation and carbon recovery in California L. Qiu et al. 10.1016/j.agrformet.2023.109750
- Predicting potential wildfire severity across Southern Europe with global data sources V. Fernández-García et al. 10.1016/j.scitotenv.2022.154729
- Assessing Fire Regimes in the Paraguayan Chaco: Implications for Ecological and Fire Management C. Vidal-Riveros et al. 10.3390/fire7100347
- MOSEV: a global burn severity database from MODIS (2000–2020) E. Alonso-González & V. Fernández-García 10.5194/essd-13-1925-2021
- Widespread and systematic effects of fire on plant–soil water relations M. Baur et al. 10.1038/s41561-024-01563-6
- Mapping Soil Burn Severity at Very High Spatial Resolution from Unmanned Aerial Vehicles D. Beltrán-Marcos et al. 10.3390/f12020179
20 citations as recorded by crossref.
- Evaluation of pre- and post-fire flood risk by analytical hierarchy process method: a case study for the 2021 wildfires in Bodrum, Turkey O. Yilmaz et al. 10.1007/s11355-023-00545-x
- Building patterns and fuel features drive wildfire severity in wildland-urban interfaces in Southern Europe V. Fernández-García et al. 10.1016/j.landurbplan.2022.104646
- Large-scale burn severity mapping in multispectral imagery using deep semantic segmentation models X. Hu et al. 10.1016/j.isprsjprs.2022.12.026
- Space-Based Earth Observations on Hotspots of Atmospheric NO2 over India Using Google Earth Engine: An Open-Source Cloud Platform A. Bandary et al. 10.1007/s12524-024-02071-1
- 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
- Refining historical burned area data from satellite observations V. Fernández-García & C. Kull 10.1016/j.jag.2023.103350
- Multi-source tri-environmental conceptual framework for fire impact analysis Z. Li et al. 10.1007/s44212-024-00063-7
- Impact of large kernel size on yield prediction: a case study of corn yield prediction with SEDLA in the U.S. Corn Belt A. Terliksiz & D. Altilar 10.1088/2515-7620/ad27fa
- Using Pre-Fire High Point Cloud Density LiDAR Data to Predict Fire Severity in Central Portugal J. Fernández-Guisuraga & P. Fernandes 10.3390/rs15030768
- Characterizing spatial burn severity patterns of 2016 Chimney Tops 2 fire using multi-temporal Landsat and NEON LiDAR data T. Park & S. Sim 10.3389/frsen.2023.1096000
- Characterizing Fire-Induced Forest Structure and Aboveground Biomass Changes in Boreal Forests Using Multitemporal Lidar and Landsat T. Feng et al. 10.1109/JSTARS.2024.3400218
- Molecular insights and impacts of wildfire-induced soil chemical changes A. Lopez et al. 10.1038/s43017-024-00548-8
- Response of active layer thickening to wildfire in the pan-Arctic region: Permafrost type and vegetation type influences X. Jiang et al. 10.1016/j.scitotenv.2023.166132
- Widespread and systematic effects of fire on plant–soil water relations M. Baur et al. 10.1038/s41561-024-01563-6
- Constructing a Comprehensive National Wildfire Database from Incomplete Sources: Israel as a Case Study E. Guk et al. 10.3390/fire6040131
- Global Patterns and Dynamics of Burned Area and Burn Severity V. Fernández-García & E. Alonso-González 10.3390/rs15133401
- A global forest burn severity dataset from Landsat imagery (2003–2016) K. He et al. 10.5194/essd-16-3061-2024
- Higher burn severity stimulates postfire vegetation and carbon recovery in California L. Qiu et al. 10.1016/j.agrformet.2023.109750
- Predicting potential wildfire severity across Southern Europe with global data sources V. Fernández-García et al. 10.1016/j.scitotenv.2022.154729
- Assessing Fire Regimes in the Paraguayan Chaco: Implications for Ecological and Fire Management C. Vidal-Riveros et al. 10.3390/fire7100347
3 citations as recorded by crossref.
- MOSEV: a global burn severity database from MODIS (2000–2020) E. Alonso-González & V. Fernández-García 10.5194/essd-13-1925-2021
- Widespread and systematic effects of fire on plant–soil water relations M. Baur et al. 10.1038/s41561-024-01563-6
- Mapping Soil Burn Severity at Very High Spatial Resolution from Unmanned Aerial Vehicles D. Beltrán-Marcos et al. 10.3390/f12020179
Latest update: 24 Dec 2024
Short summary
We present the first global burn severity database (MOSEV database), which is based on Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance and burned area products. The database inludes monthly scenes with the dNBR, RdNBR and post-burn NBR spectral indices at 500 m spatial resolution from November 2000 onwards. Moreover, in this work we show that there is a close relationship between the burn severity metrics included in MOSEV and the same ones obtained from Landsat-8.
We present the first global burn severity database (MOSEV database), which is based on Moderate...
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