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.

Measuring Annual Sedimentation through High Accuracy UAV-Photogrammetry Data and Comparison with RUSLE and PESERA Erosion Models

“…Additionally, point clouds and UAV-SfM methods are the baseline for the production of 3D models for surface reconstruction, surface change detection, and soil erosion monitoring [44,82,83,115]. The 3D point-to-point analysis leads to high-accuracy results in several scientific applications regarding surface change detection [45,50,116,117]. This multi-vector analysis denotes the complexity of the microsurface, which cannot be accurately represented by 2.5D DEM data and the interpolation method [83].…”

“…This multi-vector analysis denotes the complexity of the microsurface, which cannot be accurately represented by 2.5D DEM data and the interpolation method [83]. The M 3 C 2 algorithm that we selected as the baseline for surface change detection is currently used in a broad field of geo-research applications, such as landslides, flood events, post-fire erosion, and forests [44,45,[118][119][120]. In this study, we managed to achieve a cm-accuracy of the microsurface reconstruction, leading to high-accuracy sediment pathway and distribution delineation.…”

“…At smaller scales, soil surface (or volume) differencing through the successive recording of topographic changes is a different pathway to locate and quantify soil loss rates. The latter utilizes Terrestrial Laser Scanning (TLS) [44][45][46][47]-also known as terrestrial Light Detection and Ranging (t-LiDAR)-and Unmanned Aerial Vehicle (UAV) photogrammetry [48][49][50][51][52][53].…”

Multi-Temporal PSI Analysis and Burn Severity Combination to Determine Ground-Burned Hazard Zones

“…with H i (hazard index) as the outcome, d t as the reclassified deformation trend, b s as the reclassified burn severity, and NAD as the normalized absolute difference between d t and b s , as shown in Equation (3).…”

“…It is well known that the potential for severe soil erosion increases in a post-fire event since plant material is totally destroyed leading to soil stability degradation and connecting with major post-fire effects such as landslides, deformation, and soil changes [2,3]. The post-fire soil erosion has notably intensified across the extensive Mati settlement area, resulting in the onset of deformation phenomena.…”