Khamsin, I. and Abdul Rahman, Muhammad Zulkarnain and Razak, Khamarrul Azahari and Rizal, S. (2014) Detection of tropical landslides using airborne lidar data and multi imagery: a case study in Genting Highland, Pahang. 8th International Symposium of the Digital Earth (ISDE8), 18 (1). ISSN 1755-1315
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Official URL: http://dx.doi.org/10.1088/1755-1315/18/1/012033
Abstract
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
Item Type: | Article |
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Uncontrolled Keywords: | forestry, laser applications, optical radar, satellite imagery, surface analysis, tropics |
Subjects: | T Technology |
Divisions: | Malaysia-Japan International Institute of Technology |
ID Code: | 52332 |
Deposited By: | Siti Nor Hashidah Zakaria |
Deposited On: | 01 Feb 2016 03:52 |
Last Modified: | 17 Sep 2018 04:01 |
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