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

Matori, A.N and Basith, A. and Harahap, I.S.H. (2011) Study of regional monsoonal effects on landslide hazard zonation in Cameron Highlands, Malaysia. [Citation Index Journal]

[thumbnail of ArabJGeo-published.pdf] PDF
ArabJGeo-published.pdf - Published Version
Restricted to Registered users only

Download (1MB)
Official URL:


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.

Item Type: Citation Index Journal
Subjects: T Technology > T Technology (General)
G Geography. Anthropology. Recreation > GE Environmental Sciences
T Technology > TA Engineering (General). Civil engineering (General)
Departments / MOR / COE: Research Institutes > Megacities
Depositing User: Assoc Prof Dr Abd Nassir Matori
Date Deposited: 04 Jun 2011 08:19
Last Modified: 20 Mar 2017 03:18

Actions (login required)

View Item
View Item