TY - JOUR
T1 - Landslide susceptibility mapping of Penang Island, Malaysia, using remote sensing and multi-geophysical methods
AU - Husainy, Siti Nurkhalidah
AU - Bery, Andy Anderson
AU - Abir, Ismail Ahmad
AU - Lestari, Wien
AU - Akingboye, Adedibu Sunny
N1 - Publisher Copyright:
© 2023, Universidad Nacional de Colombia. All rights reserved.
PY - 2023/8/16
Y1 - 2023/8/16
N2 - Malaysia is one of the countries in the world that experiences landslides yearly due to natural events and human activities. Penang Island is Malaysia’s second most developed state and the largest by population. It is prone to landslides with devastating environmental impacts. Hence, the need to characterize its near-surface soil-rock conditions. This study uses remotely sensed data, with frequency ratio (FR) analysis, to identify landslide-prone areas based on different categories of landslide causative factors. To further understand the conditions and hy-drodynamics of the soil-rock profiles causing landslides, electrical resistivity tomography and seismic refraction tomography were carried out at a landslide-suspected section in the study area. Also, satellite-derived Bouguer gravity anomaly modeling was performed to map the varied gravity anomalies associated with landslide-trigge-ring factors in lithologic units. The multi-geophysical models offer strongly correlated results with the remote sensing causative factor maps and the landslide susceptibility index (LSI) map. The likelihood of landslides oc-curring in the area, as suggested by the area under curve modeling of LSI data, yielded a high predicted success rate of 83.47%. Hence, prospective landslides were identified in the hilly and elevated sections, while the less susceptible sections were identified on flat reliefs. Landslides may also be triggered, for instance, at steep sections with varied contractive soil bodies and shallow structures. Most importantly, leveraging the LSI map would help the necessary agencies forestall and mitigate future landslide occurrences in the area.
AB - Malaysia is one of the countries in the world that experiences landslides yearly due to natural events and human activities. Penang Island is Malaysia’s second most developed state and the largest by population. It is prone to landslides with devastating environmental impacts. Hence, the need to characterize its near-surface soil-rock conditions. This study uses remotely sensed data, with frequency ratio (FR) analysis, to identify landslide-prone areas based on different categories of landslide causative factors. To further understand the conditions and hy-drodynamics of the soil-rock profiles causing landslides, electrical resistivity tomography and seismic refraction tomography were carried out at a landslide-suspected section in the study area. Also, satellite-derived Bouguer gravity anomaly modeling was performed to map the varied gravity anomalies associated with landslide-trigge-ring factors in lithologic units. The multi-geophysical models offer strongly correlated results with the remote sensing causative factor maps and the landslide susceptibility index (LSI) map. The likelihood of landslides oc-curring in the area, as suggested by the area under curve modeling of LSI data, yielded a high predicted success rate of 83.47%. Hence, prospective landslides were identified in the hilly and elevated sections, while the less susceptible sections were identified on flat reliefs. Landslides may also be triggered, for instance, at steep sections with varied contractive soil bodies and shallow structures. Most importantly, leveraging the LSI map would help the necessary agencies forestall and mitigate future landslide occurrences in the area.
KW - Electrical resistivity tomography (ERT)
KW - Landslide susceptibility mapping
KW - Penang Island
KW - Remote sensing
KW - Satellite-derived gravity mapping
KW - Seismic refraction tomography (SRT)
UR - http://www.scopus.com/inward/record.url?scp=85168594323&partnerID=8YFLogxK
U2 - 10.15446/esrj.v27n2.107274
DO - 10.15446/esrj.v27n2.107274
M3 - Article
AN - SCOPUS:85168594323
SN - 1794-6190
VL - 27
SP - 93
EP - 107
JO - Earth Sciences Research Journal
JF - Earth Sciences Research Journal
IS - 2
ER -