Evaluating the effect of biological practices on the soil loss by remote sensing-based RUSLE model in Chikan and Morzian watershed
The main objective of this study was to evaluate the impact of biological watershed management measures on soil loss using the RUSLE model, remote sensing, and geographic information systems.
The study area, Chikan and Morzian watershed, is one of the sub-basins of the Darudzen dam basin. In this study, using the RUSLE model and based on remote sensing data, soil loss was estimated over 29 years at 4 time points (1992, 2003, 2017, and 2021). In order to estimate the amount of soil erosion, the map of rain erosion factors (R), soil erodibility (K), topography (LS), vegetation cover (C), and protection factor (P) at the catchment level was first prepared using the relevant instructions in the RUSLE model. In order to study the effect of biological watershed management measures, assuming that the other factors of the model remain constant during the study period, the changes in factor C under the effect of biological measures were studied.
The results of the C-factor estimations showed that this factor was 0.83±0.29, 0.80±0.28, 0.76±0.28 and 0.76±0.28 in the years 1992, 2003, 2017, and 2021, respectively. The average amount of soil erosion in the Chikan and Morzian basins during 1992, 2003, 2017, and 2021 was calculated as 16.88±20.19, 16.32±19.52, 15.41±18.29, and 15.80±18.82, respectively.
The results indicated that the implementation of biological operations during this period decreased soil erosion. In conclusion, the implementation of biological operations is a useful and reliable measure to reduce soil erosion in degraded watersheds. However, other functions of this operation, such as fodder production, run-off reduction, recreational value, etc., can be added to its soil protection function.
One of the most innovative and practical aspects of the research is using remote sensing techniques to study the amount of soil loss over time under the influence of biological watershed management measures.
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