A Multiscale Approach to Predicting Soil Erosion on Cropland Using Empirical and Physically Based Soil Erosion Models in a Geographic Information System

Soil Erosion ◽  
2000 ◽  
pp. 109-134 ◽  
Author(s):  
V. Wickenkamp ◽  
R. Duttmann ◽  
T. Mosimann
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Soil Erosion ◽  
Geographic Information ◽  
Multiscale Approach ◽  
Erosion Models ◽  
Physically Based

scholarly journals Aplikasi Sistem Informasi Geografi (Gis) untuk Pemetaan dan Simulasi Erosi Tanah

2018 ◽  
Vol 22 (1) ◽  
pp. 60
Author(s):  
Mujiyo Mujiyo ◽  
Sumani Sumani ◽  
Joko Winarno
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Soil Erosion ◽  
Geographic Information ◽  
Gis Analysis ◽  
Land Area ◽  
The Difference ◽  
Arcview Gis ◽  
Method R ◽  
Erosion Intensity

<p>The aims of the research are to use the technology of the geographic information system for mapping and simulation of the soil erosion, and to know the difference betweet actual and potential erosion on the spesific land use type. The area of the research is Jumapolo District, Karanganyar Regency. USLE equation method (R=R.K.L.S.C.P) was used to predict the soil erosion intensity. The steps of the research are (1) pre-survey, prepared equipments for suvey, (2) survey, was done by exploring predertemined land map unit, and (3) labs, analysis of the soil samples, the rainfall data and teh ArcView GIS. Analysis was done using rating based on Zachar (1982) in order to know the difference of the soil erosion intensity.</p>The results of the research are <em>first, </em>geographic information system is useful to mapping and simulation of the soil erosion, especially to calculate data that numerous and difficult, to overlay, to layout the map (or the other spasial data) and the statistic of land area, <em>second, </em>Jumapolo District has variability of the soil erosion intensity i.e. very slight 25,77 ha (0,62% of the agriculture land area), slight 815,71 ha (20,39%), moderate 1.094,69 ha (25,49%), severe 1.150,58 ha (33,09%), very severe 827,25 ha (18,88%) and catastrophic 243,69 ha (1,53%) and <em>third, </em>the change of the land unit type and the conservation technic can change the soil erosio  intensity. Simulation process result that land area which have soil erosion intensity severe, very severe and catastrophic changes become slight and moderate, therefore the new compotitions of the soil erosion intensity are very slight 25,77 ha (0,62%), slight 1.665,48 ha (40,06%), moderate 2.466,43 ha (59,32%), and each severe, very severe and catastrophic 0 ha (0%).

REVISITING THE METHODOLOGICAL DEVELOPMENT IN SOIL EROSION RESEARCH

Ensemble ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 145-165
Author(s):  
Tanmoy Sarkar ◽  
◽  
Tapas Pal ◽  
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Soil Erosion ◽  
Geographic Information ◽  
Methodological Development ◽  
Advantages And Disadvantages ◽  
The Future ◽  
Soil Erosion By Water

Soil erosion (by water) is a major land degradation process that may threat the Sustainable Development Goals (SDG) by its negative impact on environment and human well-being. Soil erosion research demands scientific methods, tools and techniques to assess soil erosion with more accuracy and reliability. Soil erosion research has had experienced crude field-based techniques in early twentieth century to model-based approaches since the 1970s and very recent machine learning and artificial intelligence models to predict soil erosion susceptibility and risk. The paper aims to review the trend in methodological development in soil erosion by water through time. The brief background of different approaches, their relative advantages and disadvantages are reviewed. Depending on the time of establishment and wide application the approaches are classified and represented as erosion plot/runoff approach, erosion pin technique followed by environmental tracer method and model approach in combination with Remote Sensing (RS) and Geographic Information System (GIS). Recent advancement in artificial intelligence and application of statistical techniques have a great potential to contribute in soil erosion research by identifying various degrees of susceptibility in large scale and also to quantify the erosion rate with high accuracy. The Remote sensing (RS) and Geographic Information System (GIS) contribute to develop regional scale data base with exploration of real time data and spatial analysis. The combination of RS & GIS and process-based models must be more effective than the traditional soil erosion model in the context of prediction with greater reliability and validity. The future research on soil erosion is better to focus on the theoretical analysis and development of erosion prediction model with more quantitative refinement and to model the future.

scholarly journals Impact of land-use and land-cover change on soil erosion using the RUSLE model and the geographic information system: a case of Temeji watershed, Western Ethiopia

2021 ◽  
Author(s):  
Mitiku Badasa Moisa ◽  
Daniel Assefa Negash ◽  
Biratu Bobo Merga ◽  
Dessalegn Obsi Gemeda
Keyword(s):  
Land Use ◽  
Information System ◽  
Geographic Information System ◽  
Soil Erosion ◽  
Land Cover ◽  
Water Conservation ◽  
Soil Loss ◽  
Geographic Information ◽  
Lulc Change ◽  
The Impact

Abstract The impact of land-use land-cover (LULC) change on soil resources is getting global attention. Soil erosion is one of the critical environmental problems worldwide with high severity in developing countries. This study integrates the Revised Universal Soil Loss Equation model with a geographic information system to estimate the impacts of LULC conversion on the mean annual soil loss in the Temeji watershed. In this study, LULC change of Temeji watershed was assessed from 2000 to 2020 by using 2000 Landsat ETM+ and 2020 Landsat OLI/TIRS images and classified using supervised maximum likelihood classification algorithms. Results indicate that the majority of the LULC in the study area is vulnerable to soil erosion. High soil loss is observed when grassland and forest land were converted into cultivated land with a mean soil loss of 88.8 and 86.9 t/ha/year in 2020. Results revealed that about 6,608.5 ha (42.8%) and 8,391.8 ha (54.4%) were categorized under severe classes in 2000 and 2020, respectively. Accordingly, the soil loss severity class is directly correlated with the over-exploitation of forest resources and grasslands for agricultural purposes. These results can be useful for advocacy to enhance local people and stakeholder's participation toward soil and water conservation practices.

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