- Title
- Landslide Susceptibility Prediction Considering Neighborhood Characteristics of Landslide Spatial Datasets and Hydrological Slope Units Using Remote Sensing and GIS Technologies
- Creator
- Huang, Faming; Tao, Siyu; Li, Deying; Lian, Zhipeng; Catani, Filippo; Huang, Jinsong; Li, Kailong; Zhang, Chuhong
- Relation
- Remote Sensing Vol. 14, Issue 18, no. 4436
- Publisher Link
- http://dx.doi.org/10.3390/rs14184436
- Publisher
- MDPI AG
- Resource Type
- journal article
- Date
- 2022
- Description
- Landslides are affected not only by their own environmental factors, but also by the neighborhood environmental factors and the landslide clustering effect, which are represented as the neighborhood characteristics of modelling spatial datasets in landslide susceptibility prediction (LSP). This study aims to innovatively explore the neighborhood characteristics of landslide spatial datasets for reducing the LSP uncertainty. Neighborhood environmental factors were acquired and managed by remote sensing (RS) and the geographic information system (GIS), then used to represent the influence of landslide neighborhood environmental factors. The landslide aggregation index (LAI) was proposed to represent the landslide clustering effect in GIS. Taking Chongyi County, China, as example, and using the hydrological slope unit as the mapping unit, 12 environmental factors including elevation, slope, aspect, profile curvature, plan curvature, topographic relief, lithology, gully density, annual average rainfall, NDVI, NDBI, and road density were selected. Next, the support vector machine (SVM) and random forest (RF) were selected to perform LSP considering the neighborhood characteristics of landslide spatial datasets based on hydrologic slope units. Meanwhile, a grid-based model was also established for comparison. Finally, the LSP uncertainties were analyzed from the prediction accuracy and the distribution patterns of landslide susceptibility indexes (LSIs). Results showed that the improved frequency ratio method using LAI and neighborhood environmental factors can effectively ensure the LSP accuracy, and it was significantly higher than the LSP results without considering the neighborhood conditions. Furthermore, the Wilcoxon rank test in nonparametric test indicates that the neighborhood characteristics of spatial datasets had a great positive influence on the LSP performance.
- Subject
- landslide susceptibility prediction; neighborhood environmental factors; landslide aggregation index; machine learning models; remote sensing; SDG 17; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1489311
- Identifier
- uon:52677
- Identifier
- ISSN:2072-4292
- Language
- eng
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