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Landslide Susceptibility Mapping and Interpretation in the Upper Minjiang River Basin

Xin Wang,Shibiao Bai

Published 2023 in Remote Sensing

ABSTRACT

To enable the accurate assessment of landslide susceptibility in the upper reaches of the Minjiang River Basin, this research intends to spatially compare landslide susceptibility maps obtained from unclassified landslides directly and the spatial superposition of different types of landslide susceptibility map, and explore interpretability using cartographic principles of the two methods of map-making. This research used the catalogs of rainfall and seismic landslides to select nine background factors that affect the occurrence of landslides through correlation analysis, including lithology, NDVI, elevation, slope, aspect, profile curve, curvature, land use, and distance to faults, to assess rainfall and seismic landslides susceptibility, respectively, by using a WOE-RF coupling model. Then, an evaluation of landslide susceptibility was conducted by merging rainfall and seismic landslides into a dataset that does not distinguish types of landslides; a comparison was also made between the landslide susceptibility maps obtained through the superposition of rainfall and seismic landslide susceptibility maps and unclassified landslides. Finally, confusion matrix and ROC curve were used to verify the accuracy of the model. It was found that the accuracy of the training set, testing set, and the entire data set based on the WOE-RF model for predicting rainfall landslides were 0.9248, 0.8317, and 0.9347, and the AUC area were 1, 0.949, and 0.955; the accuracy of the training set, testing set, and the entire data set for seismic landslides prediction were 0.9498, 0.9067, and 0.8329, and the AUC area were 1, 0.981, and 0.921; the accuracy of the training set, testing set, and the entire data set for unclassified landslides prediction were 0.9446, 0.9080, and 0.8352, and the AUC area was 0.9997, 0.9822, and 0.9207. Both the confusion matrix and the ROC curve indicated that the accuracy of the coupling model was high. The southeast of the line from Mount Xuebaoding to Lixian County is a high landslide prone area and, through the maps, it was found that, spatially, the extremely high susceptibility area of seismic landslides is located at a higher elevation than the rainfall landslides; this was found by extracting the extremely high susceptibility zones of both. It was also found that the results of the two methods of discerning landslide susceptibility were significantly different. As for the background factor, the distribution of the areas occupied by the same landslide occurrence class was not the same according to the two methods, which indicates the necessity of conducting relevant research on distinguishing landslide types.

PUBLICATION RECORD

  • Publication year

    2023

  • Venue

    Remote Sensing

  • Publication date

    2023-10-13

  • Fields of study

    Geology, Computer Science, Environmental Science

  • Identifiers
    DOI 10.3390/rs15204947
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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