Open AccessAnalysis of Landslide Occurrence using DTM-Based Weighted Overlay: A Case Study in Tropical Mountainous Forest of Cameron Highlands, Malaysia
Author(s) -
Paul Lau Hua Ming,
Azita Ahmad Zawawi
Publication year - 2021
Publication title -
environment and natural resources journal/warasan singwaetlom lae sappayakon tammachat
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.202
H-Index - 5
eISSN - 2408-2384
pISSN - 1686-5456
DOI - 10.32526/ennrj/19/202100069
Subject(s) - landslide , terrain , topographic wetness index , digital elevation model , natural hazard , remote sensing , elevation (ballistics) , sensitivity (control systems) , physical geography , geology , hydrology (agriculture) , cartography , geography , geomorphology , meteorology , geometry , mathematics , geotechnical engineering , electronic engineering , engineering
Landslides are massive natural disasters all around the world. In general, our society is only concerned with the landslides that can cause economic distress and impact human life. Landslides in remote areas such as mountainous forests have often been neglected. Referring to the historical disaster event, forest landslides have vast potential to cause unexpected ecological and social damage. This study reveals the terrain characteristics of the complex mountainous forest area of Cameron Highlands (CH), Malaysia, and demonstrates an approach to evaluate the terrain sensitivity of CH. Terrain assessment can be a powerful tool to prevent or reduce the risk of landslides. In this study, terrain features; elevation, slope gradient, aspect, topography wetness index (TWI), and length-slope factor (LS Factor) were extracted using a Digital Terrain Model (DTM) at 10 m resolution. The selected terrain features were incorporated using weighted overlay analysis to derive a terrain sensitivity map (TSM) using SAGA GIS software. The map identified five types of terrain sensitivity classified as very high sensitivity, high sensitivity, moderate sensitivity, low sensitivity, and very low sensitivity; these areas have a coverage of 0.78 km2, 114.31 km2, 107.50 km2, 102.99 km2, and 0.65 km2, respectively. The findings suggest that the sensitive areas are scattered throughout all of the mountainous forests of CH; thus, this enhanced the risk of landslide. Results showed 79.25% accuracy, which is satisfactory to be a guideline for forest management planning and assist decision making in the respective region.