Wildfire Susceptibility Assessment in Southern China: A Comparison of Multiple Methods

Yinxue Cao; Ming Wang; Kai Liu

doi:10.1007/s13753-017-0129-6

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Article Navigation > International Journal of Disaster Risk Science > 2017 > 8(2): 164-181

Yinxue Cao, Ming Wang, Kai Liu. Wildfire Susceptibility Assessment in Southern China: A Comparison of Multiple Methods[J]. International Journal of Disaster Risk Science, 2017, 8(2): 164-181. doi: 10.1007/s13753-017-0129-6

Citation:

Yinxue Cao, Ming Wang, Kai Liu. Wildfire Susceptibility Assessment in Southern China: A Comparison of Multiple Methods[J]. International Journal of Disaster Risk Science, 2017, 8(2): 164-181. doi: 10.1007/s13753-017-0129-6

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Wildfire Susceptibility Assessment in Southern China: A Comparison of Multiple Methods

doi: 10.1007/s13753-017-0129-6

Yinxue Cao¹,
Ming Wang^{1
,
,},
Kai Liu¹

State Key Laboratory of Earth Surface Processes and Resource Ecology/Academy of Disaster Reduction and Emergency Management, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

Funds:

This work has been supported by the international partnership program of Chinese Academy of Sciences (Grant # 131551KYSB20160002) and the National Natural Science Foundation of China (Grants # 41671503 and 41621061).

Available Online: 2021-04-26

Abstract

Abstract

Wildfire is a primary forest disturbance. A better understanding of wildfire susceptibility and its dominant influencing factors is crucial for regional wildfire risk management. This study performed a wildfire susceptibility assessment using multiple methods, including logistic regression, probit regression, an artificial neural network, and a random forest (RF) algorithm. Yunnan Province, China was used as a case study area. We investigated the sample ratio of ignition and nonignition data to avoid misleading results due to the overwhelming number of nonignition samples in the models. To compare model performance and the importance of variables among the models, the area under the curve of the receiver operating characteristic plot was used as an indicator. The results show that a cost-sensitive RF had the highest accuracy (88.47%) for all samples, and 94.23% accuracy for ignition prediction. The identified main factors that influence Yunnan wildfire occurrence were forest coverage ratio, month, season, surface roughness, 10 days minimum of the 6 h maximum humidity, and 10 days maxima of the 6 h average and maximum temperatures. These seven variables made the greatest contributions to regional wildfire susceptibility. Susceptibility maps developed from the models provide information regarding the spatial variation of ignition susceptibility, which can be used in regional wildfire risk management.
- China,
- Random forest,
- Variable importance rank,
- Wildfire susceptibility,
- Yunnan forest

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References(46)

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