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, Yowhan Son, Woo Kyun Lee

Research output: Contribution to journalArticlepeer-review

68 Citations (Scopus)


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
Issue number1
Publication statusPublished - 2019 Jan 1

Bibliographical note

Funding Information:
Acknowledgments: This study was jointly completed by the request of the UNDP Seoul Policy Centre. This study was supported by the “Climate Change Correspondence Program (Project Number: 2014001310008)” of the Ministry of Environment (MOE) of the Republic of Korea. This work was funded through the framework of the Leibniz Competition (SAW-2016-PIK-1) and by the German Federal Ministry of Education and Research through the funding line “Economics of Climate Change”.

Funding Information:
Funding: This research was funded by the Ministry of Environment (MOE) of the Republic of Korea (Project Number: 2014001310008).

Publisher Copyright:
© 2019 by the authors.


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

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)


Dive into the research topics of 'Multi-temporal analysis of forest fire probability using socio-economic and environmental variables'. Together they form a unique fingerprint.

Cite this