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Abstract
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 20 m. Another finding indicated that in combining a set of topographic variables, the optimal performance was acquired for a DTM resolution of 30 m and the support vector machine classification. Moreover, the best performance of the identification is represented for SVM classification.
Highlights
Landslide inventories provide a fundamental data source for landslide susceptibility, hazard, and risk assessment (Petschko et al 2016)
General settings of the study area This study focused on a selected region of the Flysch Carpathian Mountains in Poland, known for its frequent occurrence of landslides
Performance of specific morphological indicators To assess the significance of topographic features, separate maximum likelihood (ML) classifications were performed for particular digital terrain models (DTMs) derivatives
Summary
Landslide inventories provide a fundamental data source for landslide susceptibility, hazard, and risk assessment (Petschko et al 2016). Landslide inventory maps are produced by conventional (consolidated) and innovative (remote sensing) methods (Guzzetti et al 2012). Broad overviews concerning landslide investigations can be found in Guzzetti et al (2012) and Scaioni et al (2014). Visual interpretations of stereoscopic aerial photography and geomorphological field reconnaissance are widely used. Conventional map-generating techniques require expert knowledge, are highly subjective, and have limited reproducibility (Dou et al 2015). Semiautomated or automated approaches exploiting remote sensing (RS) data can overcome such issues (Dou et al 2015)
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