Multi-resolution remote sensing data in landslide activity inventory mapping

9th IGRSM International Conference and Exhibition on Geospatial & Remote Sensing

地球科学;计算机科学

Salleh, M.R.M.^1 ; Rahman, M.Z.A.^1 ; Ismail, Z.^2 ; Khanan, M.F.A.^2 ; Razak, K.A.^3

TropicalMap Research Group, Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, UTM Johor Bahru, Johor

81310, Malaysia^1

Photogrammetry and Laser Scanning Research Group, Department of Geoinformation, Faculty of Geoinformation and Real Estate, Universiti Teknologi Malaysia, UTM Johor Bahru, Johor

81310, Malaysia^2

UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia, Jalan Semarak, Kuala Lumpur

54100, Malaysia^3

关键词: Activity inventories;    Geo-spatial data;    Multi-resolution remote sensing;    Remote sensing techniques;    Remote sensing technology;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/169/1/012083/pdf
DOI  :  10.1088/1755-1315/169/1/012083

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

This paper reviews an application of multi-resolution remote sensing data in landslide activity inventory mapping. Landslide activity is an important component for landslide study. On that basis, remote sensing technology become a standard method in getting and deriving the information related to landslide events. The capability of remote sensing technology in acquiring the geospatial data have accelerate the process of landslide activity inventory mapping. A general overview of several remote sensing techniques applied to landslides is given, followed by a review of landslide characteristics and landslide activity inventory mapping. This paper also emphasizes on the role of vegetation anomalies as bio-indicator for landslide activity inventory mapping. Five (5) indicators have been listed together with the findings from previous research. This kind of approach has opening a new perspective of landslide activity inventory mapping which integrating with multi-resolution remote sensing data that significantly increase the effectiveness of any landslide studies.