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Article

Assessment of Rainfall-Induced Landslide Distribution Based on Land Disturbance in Southern Taiwan

1
Department of Land Management and Development, Chang Jung Christian University, Tainan 71101, Taiwan
2
Department of Business Administration, Chang Jung Christian University, Tainan 71101, Taiwan
3
Magong Airport Office, Civil Aeronautics Administration, MOTC, Penghu 88593, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Wolfgang Kainz and Hamid Reza Pourghasemi
ISPRS Int. J. Geo-Inf. 2021, 10(4), 209; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040209
Received: 30 January 2021 / Revised: 18 March 2021 / Accepted: 23 March 2021 / Published: 1 April 2021
(This article belongs to the Special Issue Multi-Hazard Spatial Modelling and Mapping)
This study explores the impact of rainfall on the followed-up landslides after a severe typhoon and the relationship between various rainfall events and the occurrence, scale, and regional characteristics of the landslides, including second landslides. Moreover, the influence of land disturbance was evaluated. The genetic adaptive neural network was used in combination with the texture analysis of the geographic information system for satellite image classification and interpretation to analyze land-use change and retrieve disaster records and surface information after five rainfall events from Typhoon Morakot (2009) to Typhoon Nanmadol (2011). The results revealed that except for extreme Morakot rains, the greater the degree of slope disturbance after rain, the larger the exposed slope. Extreme rainfall similar to Morakot strikes may have a greater impact on the bare land area than on slope disturbance. Moreover, the relationship between the bare land area and the index of land disturbance condition (ILDC) is positive, and the ratio of the bare land area to the quantity of bare land after each rainfall increases with the ILDC. With higher effective accumulative rainfall on the slope in the study area or greater slope disturbance, the landslide area at the second landslide point tended to increase. View Full-Text
Keywords: rainfall-induced landslide; second landslide; genetic adaptive neural network; geographic information system rainfall-induced landslide; second landslide; genetic adaptive neural network; geographic information system
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MDPI and ACS Style

Tseng, C.-M.; Chen, Y.-R.; Chang, C.-M.; Chue, Y.-S.; Hsieh, S.-C. Assessment of Rainfall-Induced Landslide Distribution Based on Land Disturbance in Southern Taiwan. ISPRS Int. J. Geo-Inf. 2021, 10, 209. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040209

AMA Style

Tseng C-M, Chen Y-R, Chang C-M, Chue Y-S, Hsieh S-C. Assessment of Rainfall-Induced Landslide Distribution Based on Land Disturbance in Southern Taiwan. ISPRS International Journal of Geo-Information. 2021; 10(4):209. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040209

Chicago/Turabian Style

Tseng, Chih-Ming, Yie-Ruey Chen, Chwen-Ming Chang, Yung-Sheng Chue, and Shun-Chieh Hsieh. 2021. "Assessment of Rainfall-Induced Landslide Distribution Based on Land Disturbance in Southern Taiwan" ISPRS International Journal of Geo-Information 10, no. 4: 209. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10040209

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