The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-4/W9
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W9, 181–188, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-W9-181-2018
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W9, 181–188, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-W9-181-2018

  30 Oct 2018

30 Oct 2018

EXTRACTION OF ELEMENT AT RISK FOR LANDSLIDES USING REMOTE SENSING METHOD

R. C. Hasan1, Q. A. Rosle2, M. A. Asmadi3, and N. A. Mohd Kamal4 R. C. Hasan et al.
  • 1Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia
  • 2Minerals and Geoscience Department (Selangor/Wilayah Persekutuan), Malaysia
  • 3Faculty of Built Environment and Geoinformation, Universiti Teknologi Malaysia, Johor, Malaysia
  • 4Malaysian-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia

Keywords: Landslides, hazard, risk, element at risk, LiDAR, remote sensing

Abstract. One of the most critical steps towards landslide risk analysis is the determination of element at risk. Element at risk describes any object that could potentially fail or exposed to hazards during disaster. Without quantification of element at risk information, it is difficult to estimate risk. This paper aims at developing a methodology to extract and quantity element at risk from airborne Light Detection and Ranging (LiDAR) data. The element at risk map produced was then used to construct exposure map which describes the amount of hazard for each element at risk involved. This study presented two study sites at Kundasang and Kota Kinabalu in Sabah with both areas have experienced major earthquake in June 2015. The results show that not all the features can be automatically extracted from the LiDAR data. For example, automatic extraction process could be done for building footprint and building heights, but for others such as roads and vegetation areas, a manual digitization is still needed because of the difficulties to differentiate between these features. In addition to this, there were also difficulties in identifying attribute for each feature, for example to separate between federal roads with state and unpaved roads. Therefore, for large area hazard and risk mapping, the authors suggested that an automatic process should be investigated in the future to reduce time and cost to extract important features from LiDAR data.