## 4 Conclusions  
Soil erosion is a universal concern that mainly impacts the environment and different habitats. It is crucial to assess soil erosion rates and map the spatial distribution of soil erosion in order to plan conservation and management practices. The current study proved that the RUSLE model can be efficiently applied at the catchment scale to estimate soil erosion rates through the integration of GIS and remote sensing techniques. The highest soil loss rates were found in sparsely vegetated areas. The high erosion values depicted for natural grassland and pasture areas can be attributed to the increasing levels of livestock and inappropriate grazing practices, which further intensify the soil degradation. The RUSLE can, therefore, be replicated in other catchments in Chania in particular and in Crete island in general for the estimation and delimitation of erosion-prone areas. Although relatively modest data are required for the RUSLE, the soil erosion rates are often overestimated for low rainfall events, while they are underestimated for high rainfall events [56]. This variation can be explained by the fact that runoff is incorporated within  
Figure 24: Priority levels of the sub-catchments based on mean soil erosion: (**a**) scenario 1; (**b**) scenario 2; (**c**) scenario 5; (**d**) scenario 6.  
the R-factor. Additionally, the RUSLE assesses soil loss only for sheet and rill erosion, but it does not account other types of erosion (gully erosion, channel erosion, bank erosion, or extreme events such as landslides) [25; 57]. The landscape metric analysis showed that most of the estimated landscape metrics had a significant correlation with the soil erosion rate, which was assessed using the RUSLE, with R-factor values derived from in situ data and C-factor values derived from NDVI data. It can be concluded that the landscape pattern has a positive impact on soil erosion. Regarding the morphometric parameter analysis, the prioritization results at the catchment scale seemed to be consistent with the result for the RUSLE implemented with R-factor values derived from in situ data and C-factor values derived from literature and NDVI data, respectively. At the sub-catchment scale, the morphometric parameter analysis seemed to be consistent with the results for the RUSLE implemented using C-factor values derived from NDVI data and R-factor values derived from GPM and in situ data, respectively. The overall evaluation of the three approaches (RUSLE model, landscape metrics, and morphometric parameters) showed that the RUSLE model implemented with R-factor values derived from in situ data and C-factor values derived from NDVI data, was the most relevant scenario, which was correlated with both the landscape metric and morphometric parameter analyses. In this scenario, the mean soil erosion rate in the study area was about 3.24 \(\mathrm{t\,ha^{-1}\,year^{-1}}\). The current study revealed the fact that the integrated three-fold approach of soil erosion modeling, landscape fragmentation study, and estimation of morphometric parameters can complement each other in the efficient monitoring of soil loss at the catchment scale. In most of the relevant studies, the researchers implemented the specific approaches separately. In our study, we highlighted the fact that the simultaneous application of these different approaches in the GIS environment can offer a new option for soil erosion modelers, who usually focus only on applying empirical models and not on investigating the land use and morphological regimes within the study areas. In addition, this approach is considered to be extremely useful in cases where in situ soil loss data are not available, because landscape and morphometric approaches can effectively support the final results, as estimated from models such as the RUSLE. Conceptualization, I.B., D.D.A., C.K. ; methodology, I.B., D.D.A., C.K. ; software, I.B. ; validation, I.B., D.D.A., C.K. ; formal analysis, I.B; investigation, I.B. ; resources, D.D.A. ; data curation, I.B.