Journal
LANDSLIDES
Volume 15, Issue 9, Pages 1851-1865Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s10346-018-0986-0
Keywords
Automatic landslide mapping; Pixel-based approach; Feature sensitivity
Funding
- Wroclaw University of Environmental and Life Sciences [D220/0001/17]
Ask authors/readers for more resources
The automatic detection of landslides after major events is a crucial issue for public agencies to support disaster response. Pixel-based approaches (PBAs) are widely used in the literature for various applications. However, the accuracy of PBAs in the case of automatic landslide mapping (ALM) is affected by several issues. In this study, we investigated the sensitivity of ALM using PBA through digital terrain models (DTMs). The analysis, carried out in a study area of Poland, consisted of the following steps: (1) testing the influence of selected DTM resolutions for ALM, (2) assessing the relevance of diverse landslide morphological indicators for ALM, and (3) assessing the sensitivity to landslide features for a selected size of moving window (kernel) calculations for ALM. Ultimately, we assessed the performance of three classification methods: maximum likelihood (ML), feed-forward neural network (FFNN), and support vector machine (SVM). This broad analysis, as combination of grid cell resolution, surface derivatives calculation, and performance classification methods, is the challenging aspect of the research. The results of almost 500 experimental tests provide valuable guidelines for experts performing ALM. The most important findings indicate that feature sensitivity in the case of kernel size increases with coarser DTM resolution; however, the peak of the optimal feature performance for the selected study area and landslide type was demonstrated for a resolution of 20m. Another finding indicated that in combining a set of topographic variables, the optimal performance was acquired for a DTM resolution of 30m and the support vector machine classification. Moreover, the best performance of the identification is represented for SVM classification.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available