Articles | Volume 16, issue 6
https://doi.org/10.5194/essd-16-3061-2024
© Author(s) 2024. 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-16-3061-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
01 Jul 2024
Data description paper |
| 01 Jul 2024
| 01 Jul 2024
A global forest burn severity dataset from Landsat imagery (2003–2016)
Kang He
Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
Eversource Energy Center, University of Connecticut, Storrs, CT 06269, USA
Xinyi Shen
School of Freshwater Sciences, University of Wisconsin, Milwaukee, WI 53204, USA
Emmanouil N. Anagnostou
CORRESPONDING AUTHOR
Department of Civil and Environmental Engineering, University of Connecticut, Storrs, CT 06269, USA
Eversource Energy Center, University of Connecticut, Storrs, CT 06269, USA
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Kang He, Qing Yang, Xinyi Shen, Elias Dimitriou, Angeliki Mentzafou, Christina Papadaki, Maria Stoumboudi, and Emmanouil N. Anagnostou
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~820 km2 of agricultural land is inundated in central Greece due to storm Daniel flood. A detailed analysis revealed that the crop most affected by the flooding was cotton; the inundated area of more than 282 km2 comprised ~30 % of the total area planted with cotton in central Greece. In terms of livestock, we estimated more than 14,000 ornithoids and 21,500 sheep and goats were affected. Consequences for agriculture and animal husbandry in Greece are expected to be severe.
Kang He, Xinyi Shen, Cory Merow, Efthymios Nikolopoulos, Rachael V. Gallagher, Feifei Yang, and Emmanouil N. Anagnostou
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A framework combines a fire severity classification with a regression model to predict an indicator of fire severity derived from Landsat imagery (difference Normalized Burning Ratio (dNBR)) is proposed. The results show that the proposed predictive technique is capable of providing robust fire severity prediction information, which can be used for forecasting seasonal fire severity and, subsequently, impacts on biodiversity and ecosystems under future projected climate conditions.
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This study depicts the flood-affected areas in western Europe in July 2021 and particularly the agriculture land that was under flood inundation. The results indicate that the total inundated area over western Europe is about 1920 km2, of which 1320 km2 is in France. Around 64 % of the inundated area is agricultural land. We expect that the agricultural productivity in western Europe will have been severely impacted.
Kang He, Qing Yang, Xinyi Shen, Elias Dimitriou, Angeliki Mentzafou, Christina Papadaki, Maria Stoumboudi, and Emmanouil N. Anagnostou
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~820 km2 of agricultural land is inundated in central Greece due to storm Daniel flood. A detailed analysis revealed that the crop most affected by the flooding was cotton; the inundated area of more than 282 km2 comprised ~30 % of the total area planted with cotton in central Greece. In terms of livestock, we estimated more than 14,000 ornithoids and 21,500 sheep and goats were affected. Consequences for agriculture and animal husbandry in Greece are expected to be severe.
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This study comprehends and predicts the socioeconomic effects of floods in the High Mountain Asia (HMA) region. We proposed a machine-learning strategy for mapping flood damages. We predicted the Lifeyears Index (LYI), which quantifies the financial cost and loss of life caused by floods, using variables including climate, geomorphology, and population. The study's overall goal is to offer useful information on flood susceptibility and subsequent risk mapping in the HMA region.
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A framework combines a fire severity classification with a regression model to predict an indicator of fire severity derived from Landsat imagery (difference Normalized Burning Ratio (dNBR)) is proposed. The results show that the proposed predictive technique is capable of providing robust fire severity prediction information, which can be used for forecasting seasonal fire severity and, subsequently, impacts on biodiversity and ecosystems under future projected climate conditions.
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Short summary
Forest fire risk is expected to increase as fire weather and drought conditions intensify. To improve quantification of the intensity and extent of forest fire damage, we have developed a global forest burn severity (GFBS) database that provides burn severity spectral indices (dNBR and RdNBR) at a 30 m spatial resolution. This database could be more reliable than prior sources of information for future studies of forest burn severity on the global scale in a computationally cost-effective way.
Forest fire risk is expected to increase as fire weather and drought conditions intensify. To...
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