Landslidescan

Landslidescan is a term used to describe a systematic approach to detecting, mapping, and monitoring landslides by integrating data from various scanning and sensing technologies. The concept emphasizes rapid identification of new landslides, assessment of their extent and potential for escalation, and ongoing surveillance of known landslides.

Data sources commonly included in landslidescan projects include satellite synthetic aperture radar (InSAR) for ground deformation, optical satellite imagery for change detection, aerial LiDAR and UAV photogrammetry for detailed elevation models, and field surveys for validation.

The typical workflow involves data acquisition, preprocessing and co-registration, change detection or deformation analysis, landslide delineation, volume estimation, slope stability assessment, and hazard mapping. Machine learning and rule-based classifiers are often employed to distinguish landslide features from stable terrain, vegetation, and non-landslide soil movements.

Applications include hazard assessment and risk mitigation, land-use planning, post-disaster rapid response, and monitoring of vulnerable slopes near infrastructure. Landslidescan supports authorities and researchers in prioritizing interventions and allocating resources based on observed deformation patterns and mapped extents.

Limitations include data gaps due to cloud cover or revisit frequency, complex terrain causing geometric distortions, subsidence versus landslide discrimination challenges, and the need for ground truth validation. Results depend on sensor resolution, temporal frequency, and processing choices, which can influence detection accuracy and timeliness.

See also: Landslide monitoring, Remote sensing, InSAR, LiDAR, Hazard mapping.