Geostatistical Vegetation Filtering for Rapid UAV-RGB Mapping of Sudden Geomorphological Events in the Mediterranean Areas
Year: 2025
Type: article
Abstract: The use of UAVs for analyzing soil degradation processes, particularly erosion, has become a crucial tool in environmental monitoring. However, the use of LiDAR (Light Detection and Ranging) or TLS (Terrestrial Lasser Scanner) may not be affordable for many researchers because of the elevated costs and difficulties for cloud processing to present a valuable option for rapid landscape assessment following extreme events like Mediterranean storms. This study focuses on the application of drone-based remote sensing with only an RGB camera in geomorphological mapping. A key objective is the removal of vegetation from imagery to enhance the analysis of erosion and sediment transport dynamics. The research was carried out over a cereal cultivation plot in Málaga Province, an area recently affected by high-intensity rainfalls exceeding 100 mm in a single day in the past year, which triggered significant soil displacement. By processing UAV-derived data, a Digital Elevation Model (DEM) was generated through geostatistical techniques, refining the Digital Surface Model (DSM) to improve topographical change detection. The ability to accurately remove vegetation from aerial imagery allows for a more precise assessment of erosion patterns and sediment redistribution in geomorphological features with rapid spatiotemporal changes.
Source: Drones
Institution: Universidad de Granada
Language: English
Subfield: Geology
Field: Earth and Planetary Sciences
Domain: Physical Sciences
SDG: Life on land
Open Access status: gold
Estimated APC paid: $3,137
Award: G3725025645
OpenAlex ID: W4411329769
