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In recent years, forest fires have increased in terms of frequency, extent and intensity, especially in Mediterranean countries. Climate characteristics and anthropogenic disturbances lead forest environments to display high vulnerability to wildfires, with their sustainability being threatened by the loss of vegetation, changes on soil properties, and increased soil loss rates. Moreover, wildfires are a great threat to property and human life, especially in Wildland-Urban Interface (WUI) areas. In light of the impacts and trends mentioned above, this study aims to assess the impact of the Mati, Attika wildfire on soil erosion. The event caused 102 fatalities, inducing severe consequences to the local infrastructure network; economy; and natural resources. As such, the Revised Universal Soil Loss Equation (RUSLE) was implemented (pre-; post-fire) at the Rafina, Attika watershed encompassing the Mati WUI. Fire severity was evaluated based on the Normalized Burn Ratio (NBR). This index was developed utilizing innovative remotely sensed Earth Observation data (Sentinel-2). The high post-fire values indicate the fire's devastating effects on vegetation loss and soil erosion. A critical “update” was also made to the CORINE Land Cover (CLC) v. 2018, by introducing a new land use class namely “Urban Forest”, in order to distinguish the WUI configuration. Post-fire erosion rates are notably higher throughout the study area (4.53–5.98 t ha−1 y−1), and especially within the WUI zone (3.75–18.58 t ha−1 y−1), while newly developed and highly vulnerable cites now occupy the greater Mati area. Furthermore, archive satellite data (Landsat-5) revealed how the repeated (historical) wildfires have ultimately impacted vegetation recovery and erosional processes. To our knowledge this is the first time that RUSLE is used to simulate soil erosion at a WUI after a fire event, at least at a Mediterranean basin. The realistic results attest that the model can perform well at such diverse conditions, providing a solid basis for soil loss estimation and identification of high-risk erosion areas.
Efthimiou et al. (Mon,) studied this question.