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Spatial Pattern Analysis of Landslide Using Landscape Metrics and Logistic Regression: a Case Study in Central Taiwan : Volume 7, Issue 3 (11/06/2010)

By Lin, Y.-p.

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Book Id: WPLBN0004012556
Format Type: PDF Article :
File Size: Pages 29
Reproduction Date: 2015

Title: Spatial Pattern Analysis of Landslide Using Landscape Metrics and Logistic Regression: a Case Study in Central Taiwan : Volume 7, Issue 3 (11/06/2010)  
Author: Lin, Y.-p.
Volume: Vol. 7, Issue 3
Language: English
Subject: Science, Hydrology, Earth
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Wu, C., Chu, H., & Lin, Y. (2010). Spatial Pattern Analysis of Landslide Using Landscape Metrics and Logistic Regression: a Case Study in Central Taiwan : Volume 7, Issue 3 (11/06/2010). Retrieved from

Description: Department of Bioenvironmental Systems Engineering, National Taiwan University, 1, Sec. 4, Roosevelt Rd., Da-an District, Taipei City 106, Taiwan, China. The Chi-Chi Earthquake of September 1999 in Central Taiwan registered a moment magnitude MW of 7.6 on the Richter scale, causing widespread landslides. Subsequent typhoons associated with heavy rainfalls triggered the landslides. The study investigates multi-temporal landslide images from spatial analysis between 1996 and 2005 in the Chenyulan Watershed, Taiwan. Spatial patterns in various landslide frequencies were detected using landscapes metrics. The logistic regression results indicate that frequency of occurrence is an important factor in assessing landslide hazards. Low-occurrence landslides sprawl the catchment while the sustained (frequent) landslide areas cluster near the ridge as well as the stream course. From those results, we can infer that landslide area and mean size for each landslide correlates with the frequency of occurrence. Although negatively correlated with frequency in the low-occurrence landslide, the mean size of each landslide is positively related to frequency in the high-occurrence one. Moreover, this study determines the spatial susceptibilities in landslides by performing logistic regression analysis. Results of this study demonstrate that the factors such as elevation, slope, lithology, and vegetation cover are significant explanatory variables. In addition to the various frequencies, the relationships between driving factors and landslide susceptibility in the study area are quantified as well.

Spatial pattern analysis of landslide using landscape metrics and logistic regression: a case study in Central Taiwan

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