The western part of Crete Island has undergone serious landslide events in the past. The intense rainfalls that took place in the September 2018 to February 2019 period provoked extensive landslide events at the northern part of Chania prefecture, along the motorway A90. Geospatial analysis methods and earth observation data were utilized to investigate the impact of the various physical and anthropogenic factors on landslides and to evaluate landslide susceptibility. The landslide inventory map was created based on literature, aerial photo analysis, satellite images, and field surveys. A very high-resolution Digital Elevation Model (DEM) and land cover map was produced from a dense point cloud and Earth Observation data (Landsat 8), accordingly. Sentinel-2 data were used for the detection of the recent landslide events and offered suitable information for two of them. Eight triggering factors were selected and manipulated in a GIS-based environment. A semi-quantitative method of Analytical Hierarchy Process (AHP) and Weighted Linear Combination (WLC) was applied to evaluate the landslide susceptibility index (LSI) both for Chania prefecture and the motorway A90 in Chania. The validation of the two LSI maps provided accurate results and, in addition, several susceptible points with high landslide hazards along the motorway A90 were detected.

Analysis of two small semi-mountainous catchments in central Evia island, Greece, highlights the advantages of Unmanned Aerial Vehicle (UAV) and Terrestrial Laser Scanning (TLS) based change detection methods. We use point clouds derived by both methods in two sites (S1 & S2), to analyse the effects of a recent wildfire on soil erosion. Results indicate that topsoil’s movements in the order of a few centimetres, occurring within a few months, can be estimated. Erosion at S2 is precisely delineated by both methods, yielding a mean value of 1.5 cm within four months. At S1, UAV-derived point clouds’ comparison quantifies annual soil erosion more accurately, showing a maximum annual erosion rate of 48 cm. UAV-derived point clouds appear to be more accurate for channel erosion display and measurement, while the slope wash is more precisely estimated using TLS. Analysis of Point Cloud time series is a reliable and fast process for soil erosion assessment, especially in rapidly changing environments with difficult access for direct measurement methods. This study will contribute to proper georesource management by defining the best-suited methodology for soil erosion assessment after a wildfire in Mediterranean environments.

Remote sensing techniques, namely Unmanned Aerial Vehicle (UAV) photogrammetry and t-LiDAR (terrestrial Light Detection and Ranging), two well-established techniques, were applied for seven years in a mountainous Mediterranean catchment in Greece (Ilioupoli test site, Athens), following a wildfire event in 2015. The goal was to monitor and quantify soil erosion and sedimentation rates with cm accuracy. As the frequency of wildfires in the Mediterranean has increased, this study aims to present a methodological approach for monitoring and quantifying soil erosion and sedimentation rates in post-fire conditions, through high spatial resolution field measurements acquired using a UAV survey and a t-LiDAR (or TLS—Terrestrial Laser Scanning), in combination with georadar profiles (Ground Penetration Radar—GPR) and GNSS. This test site revealed that 40 m³ of sediment was deposited following the first intense autumn rainfall events, a value that was decreased by 50% over the next six months (20 m³). The UAV–SfM technique revealed only 2 m³ of sediment deposition during the 2018–2019 analysis, highlighting the decrease in soil erosion rates three years after the wildfire event. In the following years (2017–2021), erosion and sedimentation decreased further, confirming the theoretical pattern, whereas sedimentation over the first year after the fire was very high and then sharply lessened as vegetation regenerated. The methodology proposed in this research can serve as a valuable guide for achieving high-precision sediment yield deposition measurements based on a detailed analysis of 3D modeling and a point cloud comparison, specifically leveraging the dense data collection facilitated by UAV–SfM and TLS technology. The resulting point clouds effectively replicate the fine details of the topsoil microtopography within the upland dam basin, as highlighted by the profile analysis. Overall, this research clearly demonstrates that after monitoring the upland area in post-fire conditions, the UAV–SfM method and LiDAR cm-scale data offer a realistic assessment of the retention dam’s life expectancy and management planning. These observations are especially crucial for assessing the impacts in the wildfire-affected areas, the implementation of mitigation strategies, and the construction and maintenance of retention dams.

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.

Model-based soil erosion studies have increased in number, given the availability of geodata and the recent technological advances. However, their accuracy remains rather questionable since the scarcity of field records hinders the validation of simulated values. In this context, this study aims to present a method for measuring sediment deposition at a typical Mediterranean catchment (870 ha) in Greece through high spatial resolution field measurements acquired by an Unmanned Aerial Vehicle (UAV) survey. Three-dimensional modeling is considered to be an emerging technique for surface change detection. The UAV-derived point cloud comparison, applying the Structure-from-Motion (SfM) technique at the Platana sediment retention dam test site, quantified annual topsoil change in cm-scale accuracy (0.02–0.03 m), delivering mean sediment yield of 1620 m³ ± 180 m³ or 6.05 t ha⁻¹yr⁻¹ and 3500 m³ ± 194 m³ or 13 t ha⁻¹yr⁻¹ for the 2020–2021 and 2021–2022 estimation. Moreover, the widely applied PESERA and RUSLE models estimated the 2020–2021 mean sediment yield at 1.12 t ha⁻¹yr⁻¹ and 3.51 t ha⁻¹yr⁻¹, respectively, while an increase was evident during the 2021–2022 simulation (2.49 t ha⁻¹yr⁻¹ and 3.56 t ha⁻¹yr⁻¹, respectively). Both applications appear to underestimate the net soil loss rate, with RUSLE being closer to the measured results. The difference is mostly attributed to the model’s limitation to simulate gully erosion or to a C-factor misinterpretation. To the authors’ better knowledge, this study is among the few UAV applications employed to acquire high-accuracy soil loss measurements. The results proved extremely useful in our attempt to measure sediment yield at the cm scale through UAV-SfM and decipher the regional soil erosion and sediment transport pattern, also offering a direct assessment of the retention dams’ life expectancy.