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

Himan Shahabi,Mazlan Hashim

doi:10.1038/srep09899

Himan Shahabi, Mazlan Hashim

Open Access

https://doi.org/10.1038/srep09899

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Journal: Scientific Reports	Publication Date: Apr 22, 2015
Citations: 294	License type: CC BY 4.0

Affiliation: University of Technology Malaysia

Abstract

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.

Highlights

Geoscience and Digital Earth Centre (Geo-DEC), Research Institute for Sustainability and Environment (RISE) Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia
The relationships between the detected landslide locations and these ten related factors were identified by using geographic information system (GIS)-based statistical models including analytical hierarchy process (AHP), weighted linear combination (WLC) and spatial multi-criteria evaluation (SMCE) models
The results identified that most of the landslides detected from AIRSAR data, digital aerial photographs and WorldView-1 satellite images are shallow rotational, and there are a few translational and flow types

Summary

Introduction

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. Landslide susceptibility mapping has been made possible due to the accessibility and variety of remote sensing data and thematic layers as causative factors data using GIS4–6. Most of these landslides are referred to as significant geomorphic processes which usually form an important landscaping aspect in humid tropical mountain surroundings[7]. The SAR technique is highly popular in landslide studies[12]

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