Study of regional monsoonal effects on landslide hazard zonation in Cameron Highlands, Malaysia

• Main Authors: Matori, A.N, Basith, A., Harahap, I.S.H.
• Format: Citation Index Journal
• Published: 2011
• Subjects: T Technology (General); GE Environmental Sciences; TA Engineering (General). Civil engineering (General)
• Online Access: http://eprints.utp.edu.my/5688/1/ArabJGeo-published.pdf; http://www.springerlink.com/content/e174p67536759487/; http://eprints.utp.edu.my/5688/

In general, landslides in Malaysia mostly occurred during northeast and southwest periods, two monsoonal systems that bring heavy rain. As the consequence, most landslide occurrences were induced by rainfall. This paper reports the effect of monsoonal-related geospatial data in landslide hazard modeling in Cameron Highlands, Malaysia, using Geographic Information System (GIS). Land surface temperature (LST) data was selected as the monsoonal rainfall footprints on the land surface. Four LST maps were derived from Landsat 7 thermal band acquired at peaks of dry and rainy seasons in 2001. The landslide factors chosen from topography map were slope, slope aspect, curvature, elevation, land use, proximity to road, and river/lake; while from geology map were lithology and proximity to lineament. Landslide characteristics were extracted by crossing between the landslide sites of Cameron Highlands and landslide factors. Using which, the weighting system was derived. Each landslide factors were divided into five subcategories. The highest weight values were assigned to those having the highest number of landslide occurrences. Weighted overlay was used as GIS operator to generate landslide hazard maps. GIS analysis was performed in two modes: (1) static mode, using all factors except LST data; (2) dynamic mode, using all factors including multi-temporal LST data. The effect of addition of LST maps was evaluated. The final landslide hazard maps were divided into five categories: very high risk, high risk, moderate, low risk, and very low risk. From verification process using landslide map, the landslide model can predict back about 13–16% very high risk sites and 70–93% of very high risk and high risk combined together. It was observed however that inclusion of LST maps does not necessarily increase the accuracy of the landslide model to predict landslide sites.