Multi-Temporal Analysis of Forest Fire Probability Using Socio-Economic and Environmental Variables. Kim, S. J., Lim, C., Kim, G. S., Lee, J., Geiger, T., Rahmati, O., Son, Y., & Lee, W. 11(1):86+.
Multi-Temporal Analysis of Forest Fire Probability Using Socio-Economic and Environmental Variables [link]Paper  doi  abstract   bibtex   
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.
@article{kimMultitemporalAnalysisForest2019,
  title = {Multi-Temporal Analysis of Forest Fire Probability Using Socio-Economic and Environmental Variables},
  author = {Kim, Sea J. and Lim, Chul-Hee and Kim, Gang S. and Lee, Jongyeol and Geiger, Tobias and Rahmati, Omid and Son, Yowhan and Lee, Woo-Kyun},
  date = {2019},
  journaltitle = {Remote Sensing},
  volume = {11},
  pages = {86+},
  issn = {2072-4292},
  doi = {10.3390/rs11010086},
  url = {https://doi.org/10.3390/rs11010086},
  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.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14677642,elevation,environment-society-economy,environmental-predictors,fire-weather-index,forest-fires,land-cover,maxent,precipitation,probability-vs-possibility,random-forest,regression,remote-sensing,south-korea,standardized-precipitation-index,topographic-wetness-index,urban-areas},
  number = {1}
}
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