Landslide susceptibility mapping using GIS-based statistical models and Remote sensing data in tropical environment

By Himan Shahabi, Mazlan Hashim. Scientific Reports, 2015

423 citations recorded by the source corpus.

This research presents the results of the GIS-based statistical models for generation of landslide susceptibility mapping using geographic information system (GIS) and remote-sensing data for Cameron Highlands area in Malaysia. Ten factors including slope, aspect, soil, lithology, NDVI, land cover, distance to drainage, precipitation, distance to fault and distance to road were extracted from SAR data, SPOT 5 and WorldView-1 images. The relationships between the detected landslide locations and these ten related factors were identified by using GIS-based statistical models including analytical hierarchy process (AHP), weighted linear combination (WLC) and spatial multi-criteria evaluation (SMCE) models. The landslide inventory map which has a total of 92 landslide locations was created based on numerous resources such as digital aerial photographs, AIRSAR data, WorldView-1 images and field surveys. Then, 80% of the landslide inventory was used for training the statistical models and the remaining 20% was used for validation purpose. The validation results using the Relative landslide density index (R-index) and Receiver operating characteristic (ROC) demonstrated that the SMCE model (accuracy is 96%) is better in prediction than AHP (accuracy is 91%) and WLC (accuracy is 89%) models. These landslide susceptibility maps would be useful for hazard mitigation purpose and regional planning.

Subfields: Global and Planetary Change, Management, Monitoring, Policy and Law

View Paper: https://www.semanticscholar.org/paper/c61445f234293f9b8c84d9b0469a06d169ff4f11

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Citations in the corpus

Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier (2020, background): “where M and N are the number of total flood and nonflood pixels [131].”
New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed (2019, background): “Climate change can change the occurrence and intensity of extreme precipitation events, and hence may a ﬀ ect the occurrence of rainfall-triggered landslides [72–76].”
A novel ensemble approach of bivariate statistical-based logistic model tree classifier for landslide susceptibility assessment (2018, methodology): “…and quantitative in nature, and they include methods such as knowledge-based analysis (analytical hierarchy process) (Pradhan et al. 2011; Shahabi and Hashim 2015; Shirzadi et al. 2017b), heuristic analysis (geomorphic analysis and qualitative map combination) (Abella and Van Westen…”