Detection of tropical landslides using airborne lidar data and multi imagery: a case study in Genting Highland, Pahang
The landslide geomorphological system in a tropical region is complex, and its understanding often depends on the completeness and correctness of landslide inventorization. In mountainous regions, landslides pose a significant impact and are known as an important geomorphic process in shaping major...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Physics Publishing
2014
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/52332/1/I.Khamsin2014_Detectionoftropicallandslides.pdf http://eprints.utm.my/id/eprint/52332/ http://dx.doi.org/10.1088/1755-1315/18/1/012033 |
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| Summary: | The landslide geomorphological system in a tropical region is complex, and its understanding often depends on the completeness and correctness of landslide inventorization. In mountainous regions, landslides pose a significant impact and are known as an important geomorphic process in shaping major landscape in the tropics. A modern remote sensing based approach has revolutionized the landslide investigation in a forested terrain. Optical satellite imagery, aerial photographs and synthetic aperture radar images are less effective to create reliable tropical DTMs for landslide recognition, and even so in the forested equatorial regions. Airborne laser scanning (ALS) data have been used to construct the digital terrain model (DTM) under dense vegetation, but its reliability for landslide recognition in the tropics remains surprisingly unknown. The present study aims at providing better insight into the use of airborne laser scanning (ALS) data. For the bare-earth extraction, several prominent filtering algorithms and surface interpolation methods, i.e. progressive TIN densitification, morphological, and command prompt from Lastool are evaluated in a qualitative analysis, aiming at removing non-ground points while preserving important landslide features. As a result, a large landslide can be detected using OOA. Small landslides remain unrecognized. Three out of five landslides can be detected, with a 60 percent overall accuracy |
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