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

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This study developed GIS-based landslide susceptibility maps for Malaysia using remote sensing data and statistical models. The spatial multi-criteria evaluation (SMCE) model achieved 96% accuracy, outperforming others for hazard mitigation.

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Area of Science:

  • Geosciences
  • Environmental Science
  • Remote Sensing

Background:

  • Landslides pose significant risks in mountainous regions like Cameron Highlands, Malaysia.
  • Accurate landslide susceptibility mapping is crucial for effective hazard mitigation and land-use planning.
  • Integration of Geographic Information System (GIS) and remote sensing data offers powerful tools for environmental assessment.

Purpose of the Study:

  • To generate landslide susceptibility maps for the Cameron Highlands area, Malaysia.
  • To compare the predictive performance of three GIS-based statistical models: Analytical Hierarchy Process (AHP), Weighted Linear Combination (WLC), and Spatial Multi-Criteria Evaluation (SMCE).
  • To identify key contributing factors for landslide occurrence in the study area.

Main Methods:

  • Extraction of ten relevant factors (slope, aspect, soil, lithology, NDVI, land cover, distance to drainage, precipitation, distance to fault, distance to road) from SAR, SPOT 5, and WorldView-1 imagery.
  • Development of a landslide inventory map with 92 locations using aerial photographs, AIRSAR, WorldView-1 images, and field surveys.
  • Application and validation of AHP, WLC, and SMCE models using 80% of the landslide inventory for training and 20% for validation, employing R-index and ROC analysis.

Main Results:

  • The SMCE model demonstrated the highest prediction accuracy (96%), followed by AHP (91%) and WLC (89%).
  • The study identified significant relationships between landslide occurrences and the ten selected environmental and topographical factors.
  • Generated landslide susceptibility maps provide a detailed spatial assessment of potential landslide-prone areas.

Conclusions:

  • The SMCE model is the most effective for landslide susceptibility mapping in the study area.
  • The developed landslide susceptibility maps are valuable tools for regional planning and landslide hazard mitigation strategies.
  • The methodology highlights the utility of integrating GIS and remote sensing for effective geological hazard assessment.