Abstract
Soil erosion is a severe and continuous environmental problem caused mainly by natural factors, which can be enhanced by anthropogenic activities. The morphological relief with relatively steep slopes, the dense drainage network, and the Mediterranean climate are some of the factors that render the Paleochora region (South Chania, Crete, Greece) particularly prone to soil erosion in cases of intense rainfall events. In this study, we aimed to assess the correlation between soil erosion rates estimated from the Revised Universal Soil Loss Equation (RUSLE) and the landscape patterns and to detect the most erosion-prone sub-basins based on an analysis of morphometric parameters, using geographic information system (GIS) and remote sensing technologies. The assessment of soil erosion rates was conducted using the RUSLE model. The landscape metrics analysis was carried out to correlate soil erosion and landscape patterns. The morphometric analysis helped us to prioritize erosion-prone areas at the sub-basin level. The estimated soil erosion rates were mapped, showing the spatial distribution of the soil loss for the study area in 2020. For instance, the landscape patterns seemed to highly impact the soil erosion rates. The morphometric parameter analysis is considered as a useful tool for delineating areas that are highly vulnerable to soil erosion. The integration of three approaches showed that there is are robust relationships between soil erosion modeling, landscape patterns, and morphometry.
Highlights
Soil erosion is one of the most important worldwide issues, which is caused mainly by natural factors and can be accelerated by anthropogenic activities [1], exceeding in some cases the amount of soil regeneration [2]
The main aim of this paper is to evaluate the relationship between soil erosion rates estimated from the Revised Universal Soil Loss Equation (RUSLE) model and the landscape patterns and to draw conclusions regarding the most erosion-prone areas or sub-basins based on morphometric parameter analysis by combining remote sensing and geographic information system technologies with in situ data
The RUSLE model was integrated with geographic information system (GIS) and remote sensing techniques to conduct cell-by-cell calculations of the mean annual soil loss rate (t ha−1 year−1), and to identify and map soil erosion risk areas in the Paleochora region
Summary
Soil erosion is one of the most important worldwide issues, which is caused mainly by natural factors (wind, water, and snow) and can be accelerated by anthropogenic activities [1], exceeding in some cases the amount of soil regeneration [2]. In some parts of the Mediterranean region, erosion is reaching an irreversible level, while in other places no more soil is left. As soil formation is a slow process, any soil erosion exceeding 1 t ha−1 yr−1 can be considered irreversible within a time span of 50–100 years [3]. Due to the intensification of olive culture and improper agricultural management practices by the olive farmers, significant land degradation and reductions in soil fertility have been noticed [4]. The above-mentioned inappropriate agricultural management, in combination with the Mediterranean climate and the topography, can seriously enhance soil erosion. Spatial and quantitative assessments of soil erosion contribute to soil erosion control and conservation practices
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