Remote Sensing-Based 3D Assessment of Landslides: A Review of the Data, Methods, and Applications

Abstract

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.

Geospatial Analysis and Mapping of Regional Landslide Susceptibility: A Case Study of Eastern Tennessee, USA

“…This approach directly achieves the optimal modeling of landslide susceptibility, removing the complicated stepwise regression and significant variable selection procedures required when many potential variables are directly input into logistic regression. Using remote sensing applications for landslide identification and risk assessment could be another type of approach to mapping landslide susceptibility [41][42][43], which was beyond the scope of this study because this study did not use any remote sensing data.…”

Mechanics of Rainfall-Induced Landslides after a Prolonged Dry Period Based on Laboratory Tests and Numerical Models Incorporating Soil-Water Characteristic Curves

“…Geosciences 2024, 14, x FOR PEER REVIEW 3 of 24 stabilization measures are vital for slope integrity [21,22]. Continuous monitoring and realtime assessments are crucial for managing construction and environmental risks [19,23].…”

“…Rainfall intensity and soil properties impact slope stability, highlighting the importance of predictive modeling [19,20]. Setting rainfall thresholds and using stabilization measures are vital for slope integrity [21,22]. Continuous monitor-ing and real-time assessments are crucial for managing construction and environmental risks [19,23].…”

Development of the SiAlong Platform to Improve Digital Literacy on Landslide Disasters among Generation Z in Semarang City

“…Many of these studies leverage GIS technology or other mapping platforms. Various previous research efforts have concentrated on the use of GIS and remote sensing technologies combined with other innovative technologies for assessing and predicting landslide disasters [68][69][70][71][72][73] and designing early warning systems for landslide disasters [74][75][76].…”