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A GIS procedure for automatically calculating the USLE LS factor on topographically complex landscape units
Abstract
A computer algorithm to calculate the USLE and RUSLE LS-factors over a two-dimensional landscape is presented. When compared to a manual method, both methods yield broadly similar results in terms of relative erosion risk mapping. However there appear to be important differences in absolute values. Although both method-yield similar slope values, the use of the manual method leads to an underestimation of the erosion risk because the effect of flow convergence is not accounted for. The computer procedure has the obvious advantage that it can easily be linked to GIS software. If data on land use and soils are available, specific K, C and P-values can be assigned to each land unit so that predicted soil losses can then be calculated using a simple overlay procedure. The algorithm leaves the user the choice to consider land units as being hydrologically isolated or continuous. A comparison with soil data showed a reasonably good agreement between the predicted erosion risk and the intensity of soil truncation observed in the test area.
... According to Karydas and Panagos [8], as a gravitytriggered process, erosion is influenced by terrain according to the gradient, the length, and the site-specific shape of the slope. The G2 has adopted the Desmet and Grovers [50] formula for quantitative estimation of terrain influence (T, denoted as LS by RUSLE): ...
... The soil erodibility factor (S) is calculated most often with Wischmeier's nomogram or its algebraic form [49,50]. The present paper uses the method proposed by Stone and Hilborn, improved with a correction coefficient regarding organic matter (OMC) [39]. ...
Photogrammetric and remote sensing studies have a wide range of applications. They are popular data sources for various purposes, including spatial analysis, site and object surveys, and environmental studies. Research aimed at developing an optimal and reliable erosion model requires a specialized and comprehensive approach due to the numerous natural and anthropogenic factors that must be considered. Physical and chemical characteristics of soils change according to land use practices, terrain topography, and prevailing meteorological conditions. Such diverse issues require detailed and specific investigations. Data for these studies can be obtained through spatial analyses based on remote sensing and photogrammetric data as well as thematic cartographic studies. We present a method for acquiring and processing various types of geospatial data using Geographic Information System tools to generate the individual parameters required in the G2 erosion model. This study was conducted in an area covering 36.3 km2, corresponding to the Odra River watershed in the Silesian Voivodeship, Poland. This work employed publicly available high-resolution thematic layers, such as high-resolution layers from the Copernicus Land Monitoring Service and Sentinel-2. Methods such as remote sensing, GIS analysis, and normalization of available parameters were used to determine various parameters of the G2 erosion model. This effort yielded a high-resolution erosion map, facilitating the accurate determination of the model parameters at any given location on the site.
... The steeper and longer the slope, the higher the erosion risk (Wischmeier & Smith, 1978). The formula used was developed by Desmet and Govers (1996). Topography plays a significant role in erosion and landslide. ...
Soil erosion significantly impacts land quality and water resources. This paper focuses on estimating spatiotemporal changes in land-use/land-cover patterns and soil erosion in the Aghien lagoon watershed in Côte d'Ivoire. The study utilized Landsat imagery from 2016 and 2020. Images were classified into categories using supervised classification with the maximum likelihood algorithm. The Universal Soil Loss Equation (USLE) model was applied in a GIS environment to quantify potential soil erosion risk. The areas of bare soil/habitats and crops/fallow increased by 2,981 ha (37.8%) and 2,642 ha (17.58%), respectively, between 2016 and 2020. High soil losses were observed on the slopes of rivers and valleys adjacent to the Aghien lagoon, which are naturally predisposed to erosion due to the steepness, length, and inclination of the slopes. Average soil loss values were 60.65 t/ha/year in 2016 and 47.64 t/ha/year in 2020. Areas with very low and low soil loss values covered 34,441.52 ha (94.36%) in 2016 and 34,956.76 ha (95.77%) in 2020. Conversely, areas with high and very high soil losses were minimal, accounting for 0.95% and 0.60% of the watershed in 2016 and 2020, respectively. Moderate soil erosion contributed most to soil loss, affecting 1,712.19 ha (4.69%) in 2016 and 1,305.77 ha (3.58%) in 2020.
... Te maximum limit for the slope length parameter (L) is 122, as a literature place it anywhere between 122 and 333 [48]. ...
In the highland areas of Ethiopia, farming communities are struggling with severe soil erosion, which is putting significant strain on ecosystems and food production capabilities. Many smallholder farmers in the basin cultivate fragile soils on steep slopes, often without proper management practices, leading to substantial soil loss, ecosystem degradation, and economic setbacks. The study aimed to assess the impact of soil–water conservation (SWC) practices on controlling soil erosion and enhancing soil nutrients in the Abay Basin. Utilizing Landsat 5 TM, Landsat 7 ETM+, and Sentinel 2 sensors available through Google Earth Engine (GEE), the study analyzed historical and recent land use and land cover changes (LULCC). The revised universal soil loss equations (RUSLE) model within the Integrated Valuation of Ecosystem Services and Trade-Offs (InVEST) application was used for the analysis. The findings indicated that the mean soil loss values were 33.2 tons/ha in 2007, 48.6 tons/ha in 2012, 32.1 tons/ha in 2017, and 31.8 tons/ha in 2022. The higher mean soil loss in 2012 was attributed to significant human intervention in the sample watersheds after 2012, which impacted natural ecosystems. The widespread adoption of sustainable land management (SLM) systems was hindered by factors such as the labor-intensive nature of the technologies, difficulty in identifying the best SWC technologies, and a lack of continuous follow-up and maintenance for physical SWC technologies. The study emphasized that these challenges were largely due to a lack of commitment and weak community participation during the planning and implementation stages. Therefore, it is crucial to enhance the involvement of residents and nongovernmental organizations in conservation efforts. The study concluded that expanding the scope and scale of local community participation is essential for future SWC interventions.
... Após o cálculo da média mensal do índice de erosão, foi feito a somatória entre os valores obtidos para obter-se o Fator R de erosividade da chuva através da Equação 3 (erosividade (R)). O cálculo do fator L em ambiente SIG, é utilizado a equação 4 propostas por Desmet & Govers (1996) seguindo a metodologia de Foster & Wischmeier (1974), cujo método calcula para cada pixel a declividade, direção do fluxo e a quantidade de fluxo acumulado a montante do pixel. ...
Estudos de erosão em bacias hidrográficas podem auxiliar nas tomadas de decisões e promover medidas que respaldem a gestão sustentável, manejo das atividades e mitigação do fenômeno, evitando o comprometimento dos usos múltiplos potenciais. Neste sentido, este trabalho tem como objetivo estimar a perda de solo na bacia do Pirajibu-Mirim, Sorocaba-SP. Foram utilizadas ferramentas de análise espacial (álgebra de mapas) para calcular a perda de solo utilizando-se da Equação Universal da Perda de Solos (EUPS). Os resultados indicaram que os anos de 2003 e 2012 apresentaram os maiores valores de perdas de solo acima de 200 Mg.ha־¹.ano־¹. As perdas de solo nessa bacia estiveram associadas as classes com maior declividade e o tipo de uso e ocupação do solo. Portanto, a análise geral da perda de solo na bacia permitiu concluir que aproximadamente metade da área possuí perdas de solo significativo.
... The LS factor represents the degree to which topographic relief changes influence the physical processes of water erosion [82], and it was calculated using the method developed by Desmet and Govers [83]. ...
Accurately delineating sediment export dynamics using high-quality vegetation factors remains challenging due to the spatio-temporal resolution imbalance of single remote sensing data and persistent cloud contamination. To address these challenges, this study proposed a new framework for estimating and analyzing monthly sediment inflow to rivers in the cloud-prone Minjiang River Basin. We leveraged multi-source remote sensing data and the Continuous Change Detection and Classification model to reconstruct monthly vegetation factors at 30 m resolution. Then, we integrated the Chinese Soil Loss Equation model and the Sediment Delivery Ratio module to estimate monthly sediment inflow to rivers. Lastly, the Optimal Parameters-based Geographical Detector model was harnessed to identify factors affecting sediment export. The results indicated that: (1) The simulated sediment transport modulus showed a strong coefficient of determination (R2 = 0.73) and a satisfactory Nash–Sutcliffe efficiency coefficient (0.53) compared to observed values. (2) The annual sediment inflow to rivers exhibited a spatial distribution characterized by lower levels in the west and higher in the east. The monthly average sediment value from 2016 to 2021 was notably high from March to July, while relatively low from October to January. (3) Erosive rainfall was a decisive factor contributing to increased sediment entering the rivers. Vegetation factors, manifested via the quantity (Fractional Vegetation Cover) and quality (Leaf Area Index and Net Primary Productivity) of vegetation, exert a pivotal influence on diminishing sediment export.
... The LS factor represents the morphometric and hydorlogic-related parameters that are involved in soil erosion phenomena (e.g., slope, aspect, flow accumulation). For this study, the procedure proposed by Elnashar et al. [46] was followed (using Equations (6) and (8)-(10)) and Table 5 from Wischmeier et al. [18], Desmet et al. [48], and Renard et al. [19]. ...
The Massarosa wildfire, which occurred in July 2022 in Northwestern Tuscany (Italy), burned over 800 hectares, leading to significant environmental and geomorphological issues, including an increase in soil erosion rates. This study applied the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil erosion rates with a multi-temporal approach, investigating three main scenarios: before, immediately after, and one-year post-fire. All the analyses were carried out using the Google Earth Engine (GEE) platform with free-access geospatial data and satellite images in order to exploit the cloud computing potentialities. The results indicate a differentiated impact of the fire across the study area, whereby the central parts suffered the highest damages, both in terms of fire-related RUSLE factors and soil loss rates. A sharp increase in erosion rates immediately after the fire was detected, with an increase in maximum soil loss rate from 0.11 ton × ha−1 × yr−1 to 1.29 ton × ha−1 × yr−1, exceeding the precautionary threshold for sustainable soil erosion. In contrast, in the mid-term analysis, the maximum soil loss rate decreased to 0.74 ton × ha−1 × yr−1, although the behavior of the fire-related factors caused an increase in soil erosion variability. The results suggest the need to plan mitigation strategies towards reducing soil erodibility, directly and indirectly, with a continuous monitoring of erosion rates and the application of machine learning algorithms to thoroughly understand the relationships between variables.
... The K factor is a function of soil properties, and it was estimated through the approach of Williams (1995) by considering the soil organic carbon content and the proportion of sand, silt, and clay derived from the African Soil Information System (AfSIS) database (Hengl et al. 2015). The LS factor was derived from the SRTM-DEM by using the approach of Desmet and Govers (1996). The C and P factors were assigned to different land use units following the value proposed by Yang et al. (2003) as indicated in (Table 3) was evaluated from the literature and weighted following field observation in reference to the approach applied in the same region by Adidja et al. (2016) and Eisenberg and Muvundja (2020). ...
Land degradation is a major issue for attainment of sustainable development in Eastern DR Congo. This study aims to assess and model the spatial pattern of land degradation vulnerability (LDV) in this region through the application of the Analytical Hierarchy Process (AHP) and geospatial techniques by using the Kalehe territory as a case study. LDV has been defined as the resultant of three components: the exposure to erosion risk, the sensitivity associated with biophysical factors, and the adaptability associated with socioeconomic factors of vulnerability. These factors were weighted using the EasyAHP plugin and multisource open spatial data to obtain the LDV model. This model was validated using the frequency ratio of physical land degradation features. The LDV map was obtained through the application of the weighted overlay technique in QGIS. The results indicated that about 32% of the territory is prone to high to very high vulnerability, representing the hotspot of LDV. The developed model has an overall accuracy of 77.8% in predicting the area with high to very high LDV. Thus, it can be used during land conservation planning to identify the priority areas for implementation of landscape restoration initiatives at the territorial level in Eastern DR Congo.
... (2) The LS factor represents the slope length (L) and gradient (S), which can be calculated using the Digital Elevation Model (DEM); this process is detailed in Desmet and Govers (1996). ...
Human activities have significant influence on soil erosion in karst areas. The spatial and temporal evolution of soil erosion in Guizhou Province was evaluated using the Revised Universal Soil Loss Equation (RUSLE), which revealed an increasing trend in the initial data analysis for the soil erosion modulus. To disclose the impact of human activities on regional soil erosion, the soil erosion in 2000, 2010, and 2020 was analyzed. The results show the following: (1) The average values of the soil erosion modulus in the study area for 2000, 2010, and 2020 were 4.479, 4.945, and 5.806 t·hm⁻²·a⁻¹, respectively; when considering human activities without the influence of rainfall erosivity, these values were 4.679, 4.963, and 4.799 t·hm⁻²·a⁻¹. The influence of human activities on soil erosion is gradually becoming a positive force. (2) According to the Spearman regression analysis, the top four factors related to soil erosion in 2000 and 2010 were soil loss risk (E, 0.721 and 0.737), anti-erosion factors (Pr, − 0.236 and − 0.221), rock exposure rate (0.222 and 0.279), and altitude (0.210 and 0.195). In 2020, the top four factors were Pr (0.725), land surface temperature (LST, 0.268), NDVI (− 0.232), and E (0.186). In the first two stages, soil erosion is closely related to natural factors, while in 2020, soil erosion is more closely related to human activities. (3) The geographically weighted regression (GWR) showed the highest range of regression coefficients for Pr (150), followed by E and NDVI (25), rock exposure rate (10), and land surface temperature (LST) (1.5). The rainfall erosivity is increasing annually as a consequence of global climate change. This rise in rainfall erosivity has resulted in a corresponding increase in soil erosion in the study area, which obscures the positive impact of human activities in the reduction of soil erosion.
Global sensitivity analysis (GSA) of the revised universal soil loss equation (RUSLE) factors is in its infancy but is crucial to rank the importance of each factor in terms of its non-linear impact on the soil erosion rate. Hence, the goal of this study was to perform a GSA of each factor of RUSLE for a soil erosion assessment in southern Bahia, Brazil. To meet this goal, three non-linear topographic factor (LS factor) equations alternately implemented in RUSLE, coupled with geographic information system (GIS) software and a variogram analysis of the response surfaces (VARSs), were used. The results showed that the average soil erosion rate in the Pardo River basin was 25.02 t/ha/yr. In addition, the GSA analysis showed that the slope angle which is associated with the LS factor was the most sensitive parameter, followed by the cover management factor (C factor) and the support practices factor (P factor) (CP factors), the specific catchment area (SCA), the sheet erosion (m), the erodibility factor (K factor), the rill (n), and the erosivity factor (R factor). The novelty of this work is that the values of parameters m and n of the LS factor can substantially affect this factor and, thus, the soil loss estimation.
The article discusses the various types of surveyed digital terrain models the points of which are assumed to describe a surface approximating the true earth surface; these models being differentiated by the surveying process only. The suitability of the various model types as an input for computer assisted cartographic utilisation is discussed and judged from the point of view of a relief cartographer with a geodetic, photogrammetric and geomorphological background. The pure mathematical analysis of digital terrain models is rejected in principle and a spatially-geometrical approach recommended. The necessity for mathematical interpolation of secondary DTMs based on surveyed values as input for computer assisted cartographic products is doubted and the opinion is expressed that cartographic andlor cartometric evaluation should be based directly on the approximation surface described by the surveyed values irrespective of the geometric disposition.
Soil loss and runoff were measured from concave, uniform, and convex slopes subjected to simulated rainfall. Fluorescent glass particles and microrelief measurements were used to determine soil movement patterns in the slopes. For slopes of equal average steepness, a concave shape greatly reduced the total sediment loss in comparison with that from either a uniform or conves slope. Average slope steepness was not a good indicator of soil delivery past a given point except for a uniform slope. In general, soil losses from irregular slopes depend on the steepness of a short section of that slope immediately above the point of measurement.
Measurements carried out in central Belgium were designed to calculate the parameters of the USLE in Europe. There are important differences between the C-values proposed for the US and those estimated in western Europe.-K.Clayton
Soil loss was measured from straight slopes of 3·5, 7, 10·5, 14 and 18% and from convex and concave slopes with average steepnesses of 3·5, 7, 10·5 and 14%, on plots, 3 m long and 2 m wide, constructed from a sandy loam soil of the Cottenham Series, and subjected to simulated rainfall of 53 mm/h intensity for 30 min. Linear, power and quadratic functions are fitted to the data to express empirically the relationship between soil loss and slope steepness and the results are compared with those of other workers. The relationship is best described by a second-order parabolic function and significant differences exist at the 5% level or better between the functions obtained for convex, straight and concave slopes. The parabolic equations in current use for soil loss prediction would appear to be inadequate because they do not take account of slope curvature.