Analysis of the Influence of DTM Source Data on the LS Factors of the Soil Water Erosion Model Values with the Use of GIS Technology

Abstract

Counteracting soil degradation is one of the strategic priorities for sustainable development. One of the most important current challenges is effective management of available resources. Multiple studies in various aspects of soil water erosion are conducted in many research institutions in the world. They concern, among others, the development of risk estimation models and the use of new data for modelling. The aim of the presented research was a discussion on the impact of the accuracy and detail of elevation data sources on the results of soil water erosion topographic factors modelling. Elevation data for this research were chosen to reflect various technologies of data acquisition, differences in the accuracy and detail of field forms mapping and, consequently, the spatial resolution of the digital terrain models (DTMs). The methodology of the universal soil loss equation USLE/RUSLE was used for the L and S factors modelling and calculation. The research was carried out in three study areas located in different types of geographical regions in Poland: uplands, highlands and lake districts. The proposed methodology consisted of conducting detailed comparative elevation and slope value assessments, calculating the values of topographical factors of the universal soil loss equation: slope length (L) and slope (S) and a detailed analysis of the total LS factors values. An approach to assess LS factors values within homogeneous areas such as agricultural plots has also been proposed. The studies draw the conclusion that the values of topographical factors obtained from various DTM sources were significantly different. It was shown that the choice of the right modelling data has a significant impact on the L and S factors values and, thus, also, on the decision-making process. The conducted research has definitely shown that data of the highest accuracy and detail should be used to study local phenomena (inter alia erosion), even analysing a large area.

Global Digital Elevation Model Comparison Criteria: An Evident Need to Consider Their Application

“…However, slope and aspect also receive attention in numerous papers. Their joint analysis (e.g., [38,40,94,[97][98][99][100]) is usual, but there are also cases in which only one of them is analyzed (e.g., [39,62,101,102]). The analysis of the slope is more frequent than that of the aspect.…”

“…From this information a planimetric accuracy bound of the original source can be inferred, but not the altimetric accuracy because it does not always have a direct relationship with the planimetric accuracy. In some papers, the evaluation of slope and orientation is proposed instead of the elevation (e.g., [38][39][40][41][42]), and the situation regarding the accuracy of the reference data is even more obscure. It is usual to establish conditions or limitations on references.…”

Effects of Micro-Topography and Vegetation on Soil Moisture on Fixed Sand Dunes in Tengger Desert, China

“…Previous research has shown that herbaceous plants in the study area primarily rely on soil moisture within the 0 to 40 cm depth range [ 33 , 34 ]. Furthermore, it has been observed that approximately 80% of the roots of sand-binding shrubs are distributed between 40 and 200 cm, with about 10% of the roots extending between 200 and 300 cm [ 35 , 36 ]. Building upon these findings, this study categorized soil moisture into three layers for analysis: the surface layer (0–40 cm), the middle layer (40–200 cm) and the deep layer (200–300 cm) to investigate its relationship with topographic and vegetation factors.…”

Assessment of the impact of the accuracy of the DMR on the calculation of soil erosion using the USLE and USLE-2D models

“…The lowest mean LS for the DMR 20 is calculated from the W-S, while the highest mean The input data on the relief is very important for the USLE-2D method. When calculating the LS-factor, the resolution of the pixel with the altitude data plays a key role (Fijałkowska, 2021;Michalopoulou et al, 2022). A high resolution (1x1m) resulted in very low LSfactor values in this case.…”