EXTRACTION OF ELEMENT AT RISK FOR LANDSLIDES USING REMOTE SENSING METHOD

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Asmadi, M. A.^3¹  Mohd Kamal, N. A.^4²  Rosle, Q. A.^2³  Hasan, R. C.^1⁴ 

[1] Faculty of Built Environment and Geoinformation, Universiti Teknologi Malaysia, Johor, Malaysia^3;Malaysian-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia^4;Minerals and Geoscience Department (Selangor/Wilayah Persekutuan), Malaysia^2;Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia^1

关键词: Landslides;    hazard;    risk;    element at risk;    LiDAR;    remote sensing;   

DOI  :  10.5194/isprs-archives-XLII-4-W9-181-2018

学科分类：地球科学(综合)

来源: Copernicus Publications

【 摘 要 】

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.

【 授权许可】

CC BY