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  Multi-temporal analysis of forest fire probability using socio-economic and environmental variables

Kim, S. J., Lim, C.-H., Kim, G. S., Lee, J., Geiger, T., Rahmati, O., Son, Y., Lee, W.-K. (2019): Multi-temporal analysis of forest fire probability using socio-economic and environmental variables. - Remote Sensing, 11, 1, 86.
https://doi.org/10.3390/rs11010086

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 Creators:
Kim, S. J.1, Author
Lim, C.-H.1, Author
Kim, G. S.1, Author
Lee, J.1, Author
Geiger, Tobias2, Author              
Rahmati, O.1, Author
Son, Y.1, Author
Lee, W.-K.1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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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.

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 Dates: 2019
 Publication Status: Finally published
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.3390/rs11010086
PIKDOMAIN: RD3 - Transformation Pathways
eDoc: 8339
Organisational keyword: RD2 - Climate Resilience
Organisational keyword: RD3 - Transformation Pathways
Research topic keyword: Adaptation
Research topic keyword: Ecosystems
Research topic keyword: Climate impacts
Model / method: Nonlinear Data Analysis
Regional keyword: Asia
 Degree: -

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Title: Remote Sensing
Source Genre: Journal, SCI, Scopus, p3, OA
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Pages: - Volume / Issue: 11 (1) Sequence Number: 86 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals426