Detecting and monitoring early post-fire sliding phenomena using uav–sfm photogrammetry and t-lidar-derived point clouds

Abstract

Soil changes, including landslides and erosion, are some of the most prominent post-fire effects in Mediterranean ecosystems. Landslide detection and monitoring play an essential role in mitigation measures. We tested two different methodologies in five burned sites with different characteristics in Central Greece. We compared Unmanned Aerial Vehicles (UAV)-derived high-resolution Digital Surface Models and point clouds with terrestrial Light Detection and Ranging (LiDAR)-derived point clouds to reveal new cracks and monitor scarps of pre-existing landslides. New cracks and scarps were revealed at two sites after the wildfire, measuring up to 27 m in length and up to 25 ± 5 cm in depth. Pre-existing scarps in both Kechries sites appeared to be active, with additional vertical displacements ranging from 5–15 ± 5 cm. In addition, the pre-existing landslide in Magoula expanded by 8%. Due to vegetation regrowth, no changes could be detected in the Agios Stefanos pre-existing landslide. This high-spatial-resolution mapping of slope deformations can be used as landslide precursor, assisting prevention measures. Considering the lack of vegetation after wildfires, UAV photogrammetry has great potential for tracing such early landslide indicators and is more efficient for accurately recording soil changes.