scholarly journals Temporal Variation in Water Induced Soil Erosion by RUSLE Model Using Remote Sensing and GIS

10.29007/271c ◽  
2018 ◽  
Author(s):  
Aditi Bhadra ◽  
H. Lalramnghaki ◽  
L. G. Kiba ◽  
Arnab Bandyopadhyay
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Temporal Variation ◽  
Soil Loss ◽  
Temporal Variations ◽  
Equation Model ◽  
Remote Sensing And Gis ◽  
Rainfall Erosivity ◽  
Erosion Risk ◽  
Modis Ndvi

Soil erosion by various agents is one of the major threats of land degradation throughout the world. Revised Universal Soil Loss Equation model integrated with remote sensing and GIS was employed to assess soil erosion in the Mago basin of Arunachal Pradesh, India for a period of ten years (2004–2013). The rainfall erosivity (R-factor) was calculated using ten years rainfall data. ASTER DEM of 30 m resolution was used to generate the LS-factor map. Soil map and soil samples were analyzed to generate soil erodibility (K) map. MODIS NDVI images were used to obtain C-factor maps. The average annual soil loss was estimated and spatial and temporal variations of annual soil erosion were analyzed. The largest portion of the snow or glacier free area was observed under slight erosion and the rest of the area under moderate to very severe erosion risk zones. The temporal variation in the area under slight soil erosion showed a decreasing trend. Increasing trends were observed over the years in areas under moderate to very severe soil erosion classes. The average soil loss by water for each year crossed permissible soil loss limit of 12 t ha-1 year-1 except for the year 2006.

scholarly journals Terrain Characterization and Soil Erosion Risk Assessment for Watershed Prioritization Using Remote Sensing and GIS (A case study of Nawagaon Maskara Rao Watershed, Saharanpur, India)

2009 ◽  
Vol 23 (1) ◽  
pp. 86
Author(s):  
Beny Harjadi
Keyword(s):  
Remote Sensing ◽  
Risk Assessment ◽  
Land Use ◽  
Soil Erosion ◽  
Soil Conservation ◽  
Soil Loss ◽  
Remote Sensing And Gis ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Conservation Measures

Soil erosion is crucial problem in India where more than 70% of land in degraded. This study is to establish conservation priorities of the sub watersheds across the entire terrain, and suggest suitable conservation measures. Soil conservation practices are not only from erosion data both qualitative SES (Soil Erosion Status) model and quantitative MMF (Morgan, Morgan and Finney) model erosion, but we have to consider LCC (Land Capability Classification) and LULC (Land Use Land Cover). Study demonstrated the use of RS (Remote Sensing) and GIS (Geographic Information System) in soil erosion risk assessment by deriving soil and vegetation parameters in the erosion models. Sub-watersheds were prioritized based on average soil loss and the area falls under various erosion risk classes for conservation planning. The annual rate of soil loss based on MMF model was classified into five soil erosion risk classes for soil conservation measures. From 11 sub watersheds, for the first priority of the watershed is catchment with the small area and the steep slope. Recommendation for steep areas (classes VI, VII, and VIII) land use allocation should be made to maintain forest functions.

scholarly journals Soil Erosion Assessment Using the RUSLE Model and Geospatial Techniques (Remote Sensing and GIS) in South-Central Niger (Maradi Region)

Water ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 3511
Author(s):  
Mohamed Adou Sidi Almouctar ◽  
Yiping Wu ◽  
Fubo Zhao ◽  
Jacqueline Fifame Dossou
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Soil Loss ◽  
Management Strategies ◽  
Equation Model ◽  
Rainfall Erosivity ◽  
Slope Length ◽  
Erosion Rates ◽  
South Central ◽  
Input Variables

A systematic method, incorporating the revised universal soil loss equation model (RUSLE), remote sensing, and the geographic information system (GIS), was used to estimate soil erosion potential and potential area in the Maradi region of south-central Niger. The spatial trend of seasonal soil erosion was obtained by integrating remote sensing environmental variables into a grid-based GIS method. RUSLE is the most commonly used method for estimating soil erosion, and its input variables, such as rainfall erosivity, soil erodibility, slope length and steepness, cover management, and conservation practices, vary greatly over space. These factors were calculated to determine their influence on average soil erosion in the region. An estimated potential mean annual soil loss of 472.4 t/ac/year, based on RUSLE, was determined for the study area. The potential erosion rates varied from 14.8 to 944.9 t/ac/year. The most eroded areas were identified in central and west-southern areas, with erosion rates ranging from 237.1 to 944.9 t/ac/year. The spatial erosion maps can serve as a useful reference for deriving land planning and management strategies and provide the opportunity to develop a decision plan for soil erosion prevention and control in south-central Niger.

scholarly journals Applications of remote sensing and GIS for watershed characterization and soil loss assessment of tons watershed in Dehradun, Garhwal Himalaya

2020 ◽  
Vol 1 (1) ◽  
pp. 56-67
Author(s):  
P.C. Chanyal ◽  
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Watershed Management ◽  
Soil Loss ◽  
Remote Sensing And Gis ◽  
Garhwal Himalaya ◽  
Rainfall Erosivity ◽  
Loss Assessment ◽  
Factor C

Watershed characterization is the most important part of watershed management which includes soil loss, soil loss assessment indicates the amount of soil loss or erosion in ton/hectare/ year through applying to Geospatial techniques as Remote sensing and GIS. The agricultural land is being lost by manmade as well natural whereas manmade or anthropogenic factor accelerates erosion of soil. It is a worldwide phenomenon leading to loss of decrease of water table availability for plants, increases runoff from the more impermeable subsoil, and loss of nutrients from the soil. Watershed management and assessment of soil loss are most helpful for planning and batter management in a watershed and planning units. Remote sensing and GIS along with the satellite image-based model approach provides a scientific, quantitative, and applied result. It can compute a consistent outcome of soil erosion and sediment yield for a wide range of areas under all climatic circumstances. Revised Universal Soil Loss Equation (RUSLE) apply to soil loss, which is integrated with Remote Sensing and GIS in Tons watershed lies between 77°56’05” E to 78°01’01” East longitude and 30° 21’05” N to 30°26’51” North latitude, having 97.02 km2 area (9,702 hectares) under the sub-tropical climatic region of Uttarakhand. The present case study based on computational with software and geospatial technologies results come i.e. A = is the computed soil loss per unit area, R = is the rainfall erosivity, K = is the soil erodibility factor, L = is the slope-length factor, C = is the cover and management factor, P = is the support practice factor. The rainfall erosivity (R=87.5 + 0.375 × R), C P is under range 0.006-0.8, Soil Erosion Risk range is slight to High 51.40% and 0.85% total area of the study region. Average annual soil loss ton/ha/year indicated in different land-use classification as lowest soil loss found in River bed (0.17 ton/ha/year) and highest shown in the open forest (56.58 ton/ha/year) in 2016. The study area comes under a low probability zone and partially comes under a moderate and moderate-high zone. The case study can be highly recommended and will help to implementation of management of soil loss and soil conservation practice in the Tons watershed as well as Himalayan regions. Keywords: RUSLE, Tons Watershed, Soil Loss, Remote Sensing & GIS, Garhwal Himalaya.

Integration of Remote Sensing and GIS for Evaluating Soil Erosion Risk in Northwestern Zhejiang, China

2012 ◽  
Vol 78 (9) ◽  
pp. 935-946 ◽  
Author(s):  
Jianqin Huang ◽  
Dengsheng Lu ◽  
Jin Li ◽  
Jiasen Wu ◽  
Shiquan Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Erosion ◽  
Remote Sensing And Gis ◽  
Erosion Risk ◽  
Soil Erosion Risk

scholarly journals Predicting the Impact of Future Land Use and Climate Change on Potential Soil Erosion Risk in an Urban District of the Harare Metropolitan Province, Zimbabwe

2021 ◽  
Vol 13 (21) ◽  
pp. 4360
Author(s):  
Andrew K. Marondedze ◽  
Brigitta Schütt
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Soil Erosion ◽  
Soil Loss ◽  
Rainfall Erosivity ◽  
Climate Scenarios ◽  
Residential Areas ◽  
Erosion Risk ◽  
Soil Erosion Risk ◽  
Loss Rates

Monitoring urban area expansion through multispectral remotely sensed data and other geomatics techniques is fundamental for sustainable urban planning. Forecasting of future land use land cover (LULC) change for the years 2034 and 2050 was performed using the Cellular Automata Markov model for the current fast-growing Epworth district of the Harare Metropolitan Province, Zimbabwe. The stochastic CA–Markov modelling procedure validation yielded kappa statistics above 80%, ascertaining good agreement. The spatial distribution of the LULC classes CBD/Industrial area, water and irrigated croplands as projected for 2034 and 2050 show slight notable changes. For projected scenarios in 2034 and 2050, low–medium-density residential areas are predicted to increase from 11.1 km2 to 12.3 km2 between 2018 and 2050. Similarly, high-density residential areas are predicted to increase from 18.6 km2 to 22.4 km2 between 2018 and 2050. Assessment of the effects of future climate change on potential soil erosion risk for Epworth district were undertaken by applying the representative concentration pathways (RCP4.5 and RCP8.5) climate scenarios, and model ensemble averages from multiple general circulation models (GCMs) were used to derive the rainfall erosivity factor for the RUSLE model. Average soil loss rates for both climate scenarios, RCP4.5 and RCP8.5, were predicted to be high in 2034 due to the large spatial area extent of croplands and disturbed green spaces exposed to soil erosion processes, therefore increasing potential soil erosion risk, with RCP4.5 having more impact than RCP8.5 due to a higher applied rainfall erosivity. For 2050, the predicted wide area average soil loss rates declined for both climate scenarios RCP4.5 and RCP8.5, following the predicted decline in rainfall erosivity and vulnerable areas that are erodible. Overall, high potential soil erosion risk was predicted along the flanks of the drainage network for both RCP4.5 and RCP8.5 climate scenarios in 2050.

Soil loss monitoring of vineyards in the Gerecse Hills (Hungary), using UAV technology

2020 ◽  
Author(s):  
Tünde Takáts ◽  
Gáspár Albert
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Equation Model ◽  
Surface Model ◽  
Rainfall Erosivity ◽  
Weather Impact ◽  
Significant Difference ◽  
Innovation And Technology ◽  
Definition Of ◽  
Wine Region

<p>The northern loess-covered part of the Gerecse belongs to the Ászár-Neszmély Wine Region, and is highly frequented by soil erosion. One of the largest vinery in the region recognized the problem and already makes efforts to cope with the natural degradation, but the exact measure of soil loss, and thus its cost, is yet unknown. In this project three vineyards were selected in the vicinity of Dunaszentmiklós village. Previous studies identified the most erosion-sensitive locations using satellite images, but to specify the soil erosion between the rows of grape vines, high resolution images were collected with UAV (Unmanned Aerial Vehicle). The images were used to create the digital surface model (DSM) and the orthophoto of the areas by means of photogrammetric analysis. The final resolution in which the soil loss was defined is 10 cm.</p><p>Since the summer of 2019 data have been collected in seasonal measurements. We used the USLE (Universal Soil Loss Equation) model [1] to determine the soil loss and its precise location. The focus was on the definition of the C (crop management) and the R (rainfall erosivity) factors because these change from season to season. The effect of the change of land cover as the summer turned into autumn was remarkable from the aspect of soil erosion. A similar change was observed in the weather impact: in this period more rain fell during the summer than in the autumn. According to the USLE model in the study area the rate of the soil loss was twice as high during the summer as in the autumn.</p><p>The vinery do its best to prevent soil erosion. One of their effective method is to sow grass among the vines. In this study a hypothetic model was also created to prove the importance of their method in the scale of the erosion. The most significant difference between the results of the model and the reality was observed in the summertime. Based on the hypothetic model the soil loss would be 3.5 times more if they would not take care of sowing grass in the vineyard.</p><p>The project was supported by the ÚNKP-19-2 New National Excellence Program of the Ministry for Innovation and Technology (from part of T. Takáts),  the Thematic Excellence Program, Industry and Digitization Subprogram, NRDI Office, project no. ED_18-1-2019-0030 (from part of G. Albert), and the  Hilltop vinery.</p><p>Data Sources:</p><p>Precipitation data from the OMSZ Hungarian Meteorological Service and the K factor (soil erodibility) from Pásztor et. al. [2] MTA ATK TAKI.</p><p>References:</p><p>[1] Wischmeier, W. H., & Smith, D. D. (1978). Predicting rainfall erosion losses- a guide to conservation planning. USA: USDA, Science and Education Administration.</p><p>[2] Pásztor, L., Waltner, I., Centeri, C., Belényesi, M., & Takács, K. (2016). Soil erosion of Hungary assessed by spatially explicit modelling. Journal of Maps, 1-8.</p>

scholarly journals Time Scale Effects and Interactions of Rainfall Erosivity and Cover Management Factors on Vineyard Soil Loss Erosion in the Semi-Arid Area of Southern Sicily

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 978 ◽  
Author(s):  
Giorgio Baiamonte ◽  
Mario Minacapilli ◽  
Agata Novara ◽  
Luciano Gristina
Keyword(s):  
Soil Erosion ◽  
Soil Loss ◽  
Scale Effects ◽  
Management Practice ◽  
Temporal Analysis ◽  
Temporal Scale ◽  
Rainfall Erosivity ◽  
Erosion Risk ◽  
Best Management ◽  
The Impact

Several authors describe the effectiveness of cover crop management practice as an important tool to prevent soil erosion, but at the same time, they stress on the high soil loss variability due to the interaction of several factors characterized by large uncertainty. In this paper the Revised Universal Soil Loss Equation (RUSLE) model is applied to two Sicilian vineyards that are characterized by different topographic factors; one is subjected to Conventional Practice (CP) and the other to Best Management Practice (BMP). By using climatic input data at a high temporal scale resolution for the rainfall erosivity (R) factor, and remotely sensed imagery for the cover and management (C) factor, the importance of an appropriate R and C factor assessment and their inter and intra-annual interactions in determining soil erosion variability are showed. Different temporal analysis at ten-year, seasonal, monthly and event scales showed that results at events scales allow evidencing the interacting factors that determine erosion risk features which at other temporal scales of resolution can be hidden. The impact of BMP in preventing soil erosion is described in terms of average saved soil loss over the 10-year period of observation. The evaluation of soil erosion at a different temporal scale and its implications can help stakeholders and scientists formulate better soil conservation practices and agricultural management, and also consider that erosivity rates are expected to raise for the increase of rainfall intensity linked to climate change.

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