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Remote Sensing
Volume 14
Issue 6
10.3390/rs14061306
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Article

Susceptibility Prediction of Post-Fire Debris Flows in Xichang, China, Using a Logistic Regression Model from a Spatiotemporal Perspective

by
Tao Jin
1,
Xiewen Hu
1,*,
Bo Liu
1,
Chuanjie Xi
1,
Kun He
1,
Xichao Cao
1,
Gang Luo
1,
Mei Han
2,
Guotao Ma
1,3,
Ying Yang
1 and
Yan Wang
1
1
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
2
School of Mathematics, Southwest Jiaotong University, Chengdu 611756, China
3
School of Engineering, University of Warwick, Library Road, Coventry CV4 7AL, UK
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(6), 1306; https://doi.org/10.3390/rs14061306
Submission received: 29 December 2021 / Revised: 1 March 2022 / Accepted: 6 March 2022 / Published: 8 March 2022
(This article belongs to the Special Issue Geospatial Techniques for Landslides and Erosion Studies: Data Capture, Monitoring, Analysis and Modelling)
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Abstract

The post-fire debris flow (PFDF) is a commonly destructive hazard that may persist for several years following the wildfires. Susceptibility mapping is an effective method for mitigating hazard risk. Yet, the majority of susceptibility prediction models only focus on spatial probability in the specific period while ignoring the change associated with time. This study improves the predictive model by introducing the temporal factor. The area burned by the 30 March 2020 fire in Xichang City, China is selected as an illustrative example, and the susceptibility of the PFDF was predicted for different periods of seven months after the wildfires. 2214 hydrological response events, including 181 debris flow events and 2033 flood events from the 82 watersheds are adopted to construct the sample dataset. Seven conditioning factors consist of temporal factors and spatial factors are extracted by the remote sensing interpretation, field investigations, and in situ tests, after correlation and importance analysis. The logistic regression (LR) is adopted to establish prediction models through 10 cross-validations. The results show that the susceptibility to PFDF has significantly reduced over time. After two months of wildfire, the proportions of very low, low, moderate, high, and very high susceptibility are 1.2%, 3.7%, 24.4%, 23.2%, and 47.6%, respectively. After seven months of wildfire, the proportions of high and very high susceptibility decreased to 0, while the proportions of very low to medium susceptibility increased to 35.4%, 35.6%, and 28.1%, respectively. The reason is that the drone seeding of grass seeds and artificial planting of trees accelerated the natural recovery of vegetation and soil after the fire. This study can give insight into the evolution mechanism of PFDF over time and reflect the important influence of human activity after the wildfire.
Keywords: post-fire debris flow; logistic regression; occurrence probability; susceptibility; watershed recovery; spatiotemporal evolution post-fire debris flow; logistic regression; occurrence probability; susceptibility; watershed recovery; spatiotemporal evolution

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MDPI and ACS Style

Jin, T.; Hu, X.; Liu, B.; Xi, C.; He, K.; Cao, X.; Luo, G.; Han, M.; Ma, G.; Yang, Y.; et al. Susceptibility Prediction of Post-Fire Debris Flows in Xichang, China, Using a Logistic Regression Model from a Spatiotemporal Perspective. Remote Sens. 2022, 14, 1306. https://doi.org/10.3390/rs14061306

AMA Style

Jin T, Hu X, Liu B, Xi C, He K, Cao X, Luo G, Han M, Ma G, Yang Y, et al. Susceptibility Prediction of Post-Fire Debris Flows in Xichang, China, Using a Logistic Regression Model from a Spatiotemporal Perspective. Remote Sensing. 2022; 14(6):1306. https://doi.org/10.3390/rs14061306

Chicago/Turabian Style

Jin, Tao, Xiewen Hu, Bo Liu, Chuanjie Xi, Kun He, Xichao Cao, Gang Luo, Mei Han, Guotao Ma, Ying Yang, and et al. 2022. "Susceptibility Prediction of Post-Fire Debris Flows in Xichang, China, Using a Logistic Regression Model from a Spatiotemporal Perspective" Remote Sensing 14, no. 6: 1306. https://doi.org/10.3390/rs14061306

APA Style

Jin, T., Hu, X., Liu, B., Xi, C., He, K., Cao, X., Luo, G., Han, M., Ma, G., Yang, Y., & Wang, Y. (2022). Susceptibility Prediction of Post-Fire Debris Flows in Xichang, China, Using a Logistic Regression Model from a Spatiotemporal Perspective. Remote Sensing, 14(6), 1306. https://doi.org/10.3390/rs14061306

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