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

Sea Jin Kim, Chul Hee Lim, Gang Sun Kim, Jongyeol Lee, Tobias Geiger, Omid Rahmati, Yo Whan Son, Woo-Kyun Lee

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish
Article number86
JournalRemote Sensing
Volume11
Issue number1
DOIs
Publication statusPublished - 2019 Jan 1

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temporal analysis
forest fire
human activity
entropy
spatial distribution
accessibility
environmental change
population density
economic development
environmental factor
urban area
environmental conditions
socioeconomics

Keywords

  • Disaster risk reduction
  • Forest fire
  • Maxent
  • Multi-temporal analysis
  • Probability
  • Socio-economic
  • Spatial analysis

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

Cite this

Multi-temporal analysis of forest fire probability using socio-economic and environmental variables. / Kim, Sea Jin; Lim, Chul Hee; Kim, Gang Sun; Lee, Jongyeol; Geiger, Tobias; Rahmati, Omid; Son, Yo Whan; Lee, Woo-Kyun.

In: Remote Sensing, Vol. 11, No. 1, 86, 01.01.2019.

Research output: Contribution to journalArticle

Kim, Sea Jin ; Lim, Chul Hee ; Kim, Gang Sun ; Lee, Jongyeol ; Geiger, Tobias ; Rahmati, Omid ; Son, Yo Whan ; Lee, Woo-Kyun. / Multi-temporal analysis of forest fire probability using socio-economic and environmental variables. In: Remote Sensing. 2019 ; Vol. 11, No. 1.
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