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Remote Sens. 2019, 11(1), 86; https://doi.org/10.3390/rs11010086

Multi-Temporal Analysis of Forest Fire Probability Using Socio-Economic and Environmental Variables

1
Department of Environmental Science and Ecological Engineering, Korea University, Seoul 02841, Korea
2
Potsdam Institute for Climate Impact Research, 14412 Potsdam, Germany
3
Institute of Life Science and Natural Resources, Korea University, Seoul 02841, Korea
4
Department of Land and Water Environment Research, Korea Environment Institute (KEI), Sejong 30147, Korea
5
Center for Climate Technology Cooperation, Green Technology Center, Seoul 04554, Korea
6
Young Researchers and Elites Club, Khorramabad Branch, Islamic Azad University, Khorramabad 6817816645, Iran
*
Author to whom correspondence should be addressed.
Received: 31 October 2018 / Revised: 25 December 2018 / Accepted: 1 January 2019 / Published: 6 January 2019
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Abstract

As most of the forest fires in South Korea are related to human activity, socio-economic factors are critical in estimating their probability. To estimate and analyze how human activity is influencing forest fire probability, this study considered not only environmental factors such as precipitation, elevation, topographic wetness index, and forest type, but also socio-economic factors such as population density and distance from urban area. The machine learning Maximum Entropy (Maxent) and Random Forest models were used to predict and analyze the spatial distribution of forest fire probability in South Korea. The model performance was evaluated using the receiver operating characteristic (ROC) curve method, and models’ outputs were compared based on the area under the ROC curve (AUC). In addition, a multi-temporal analysis was conducted to determine the relationships between forest fire probability and socio-economic or environmental changes from the 1980s to the 2000s. The analysis revealed that the spatial distribution was concentrated in or around cities, and the probability had a strong correlation with variables related to human activity and accessibility over the decades. The AUC values for validation were higher in the Random Forest result compared to the Maxent result throughout the decades. Our findings can be useful for developing preventive measures for forest fire risk reduction considering socio-economic development and environmental conditions. View Full-Text
Keywords: forest fire; probability; disaster risk reduction; Maxent; socio-economic; multi-temporal analysis; spatial analysis forest fire; probability; disaster risk reduction; Maxent; socio-economic; multi-temporal analysis; spatial analysis
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Kim, S.J.; Lim, C.-H.; Kim, G.S.; Lee, J.; Geiger, T.; Rahmati, O.; Son, Y.; Lee, W.-K. Multi-Temporal Analysis of Forest Fire Probability Using Socio-Economic and Environmental Variables. Remote Sens. 2019, 11, 86.

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