UAV-Based Soil Erosion Assessment in Mediterranean Agricultural Orchards

Abstract

Unmanned Aerial Vehicle (UAV) imagery has become an important tool for erosion monitoring, but little is known about its application in Mediterranean agricultural systems such as vineyards and olive groves. In this study, drone flights were conducted in vineyards and olive groves where mulch and biochar treatments had been applied. Digital terrain models (DTMs) and orthomosaics were constructed using a photogrammetry workflow, and model error was determined via global positioning system (GPS) transects. Erosion was assessed using Digital elevation models of Difference (DoD) and compared with field-based erosion plot measurements. Explanatory variables for erosion (soil roughness, slope length, steepness, vegetation cover) were derived from DTMs and orthomosaics and were evaluated in a multiple linear regression model. Although direct measurement of erosion from the DoDs was difficult, this was primarily influenced by the unexpectedly low erosion rates during the study period, and the high root mean square error (RMSE) of the DTMs. Significant differences in DTM-derived variables were found between study areas, and especially between areas with organic and integrated management, even though treatments showed similar patterns. The multiple linear regression model demonstrated strong explanatory power, accounting for a large part of the variation in measured erosion using the UAV-derived variables (R2 = 0.81). Slope and slope length were the most important predictors of erosion together with the interaction between these two variables. The results suggest that soil erosion in the study areas was mostly determined by topographic and management factors, rather than the applied treatments. This study highlights the value of UAV imagery in advancing the understanding of erosion processes in Mediterranean agricultural systems, while also identifying the challenge of accurately measuring erosion from DoDs under conditions of low erosion rates.