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Remote sensing-based 3D assessment of landslides: A review of the data, methods, and applications

January 24, 2024
Remote sensing (RS) techniques are essential for studying hazardous landslide events because they capture information and monitor sites at scale. They enable analyzing causes and impacts of ongoing events for disaster management. There has been a plethora of work in the literature mostly discussing (1) applications to detect, monitor, and predict landslides using various instruments and image analysis techniques, (2) methodological mechanics in using optical and microwave sensing, and (3) quantification of surface geological and geotechnical changes using 2D images. Recently, studies have shown that the degree of hazard is mostly influenced by speed, type, and volume of surface deformation. Despite available techniques to process lidar and image/radar-derived 3D geometry, prior works mostly focus on using 2D images, which generally lack details on the 3D aspects of assessment. Thus, assessing the 3D geometry of terrain using elevation/depth information is crucial to determine its cover, geometry, and 3D displacements. In this review, we focus on 3D landslide analysis using RS data. We include (1) a discussion on sources, types, benefits, and limitations of 3D data, (2) the recent processing methods, including conventional, fusion-based, and artificial intelligence (AI)-based methods, and (3) the latest applications.

Publication Year 2024
Title Remote sensing-based 3D assessment of landslides: A review of the data, methods, and applications
DOI 10.3390/rs16030455
Authors Hessah Albanwan, Rongjun Qin, Jung-Kuan (Ernie) Liu
Publication Type Article
Publication Subtype Journal Article
Series Title Remote Sensing
Index ID 70251384
Record Source USGS Publications Warehouse
USGS Organization Center for Geospatial Information Science (CEGIS)
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