Modelling topographic potential for erosion and deposition using GIS

Itasova, Helena M; Hofierka, Jaroslav; Zlocha, Maros; Iverson, Louis R.

doi:10.1080/026937996137918

Cited by 142 publications

(206 citation statements)

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“…Direct measurement of slope gradient and slope length is feasible for small plots and parcels (Wischmeier and Smith 1978); however at the catchment scale the use of a Digital Elevation Model (DEM) is more suited (Mitasova et al 1996). DEMs of the three catchment sites were constructed by (1) digitizing all contours (20 m interval) from the official 1:50,000 maps of Claveria of the National Mapping and Resource Information Authority of the Philippines, and (2) interpolating a raster DEM from the digital contour data using spline interpolation.…”

Section: Study Areasmentioning

confidence: 99%

Modeling the impacts of agroforestry systems on the spatial patterns of soil erosion risk in three catchments of Claveria, the Philippines

Delgado

Canters

2011

Agroforest Syst

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Agroforestry is one of the preferred landuse options for smallholder farms in tropical landscapes due to its ability to increase land productivity and protect soil from erosion. We investigated the impacts of agroforestry and traditional monocropping systems on the spatial patterns of soil erosion risk in three catchment areas of Claveria, the Philippines, using WaTEM/SEDEM, a spatially distributed soil erosion model. The model predicts soil loss in catchments based on the Revised Universal Soil Loss Equation (RUSLE) by taking into account the influences of rainfall, soil erodibility, vegetation cover and 2-dimensional variations in landscape structure. The predicted soil erosion rates were transformed into risk values in order to identify areas with higher risk for erosion. Model results indicate a large spatial variability in soil erosion risk patterns, with higher risks occurring on slopes greater than 8% on land under non-agroforestry use. The soil erosion risk maps were used to formulate site-specific agroforestry recommendations for future landscape amelioration plans.

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Section: Study Areasmentioning

confidence: 99%

Modeling the impacts of agroforestry systems on the spatial patterns of soil erosion risk in three catchments of Claveria, the Philippines

Delgado

Canters

2011

Agroforest Syst

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“…The ground slope data can be used to generate an erosion risk map of the bare soil areas (Nobrega et al 2006). By using Geographical Information Systems (GIS) techniques, a slope data can be produced accurately and quickly based on topographic maps and DEM (Mitasova et al 1996). Sazbo et al (1998) andBojie et al (1995) indicated that the GIS techniques can be successfully used to develop erosion risk maps based on DEM, slope, aspect, and land use data.…”

Section: Introductionmentioning

confidence: 98%

“…Huang and Cai (2007) conducted a study where GIS based stochastic model was developed to simulate land use change in Shiqian County, Southwestern China. In a more recent study, Zhijun et al (2009) RS technology and GIS techniques can be efficiently used to provide the spatial distributions of soil erosion, which is very important to determine the areas with high erosion risks and to prevent soil erosion incidents in such areas (Mitasova et al 1996). In this study, remotely sensed data and elevation data were used to extract and classify bare soil erosion risk areas for a study area selected from Hatila Valley Natural Protected Area in northeastern Turkey.…”

Section: Introductionmentioning

confidence: 99%

Using high resolution images and elevation data in classifying erosion risks of bare soil areas in the Hatila Valley Natural Protected Area, Turkey

Eroğlu

Cakir

Sivrikaya

et al. 2009

Stoch Environ Res Risk Assess

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Soil erosion is one of the most important environmental problems. In the case of small scale areas where soil properties and climate have relatively uniform characteristics, vegetation cover and topography (i.e. ground slope) are the main factors that affect the amount of soil erosion. Lack of vegetation cover on bare soil areas, including forest road side slopes, especially in mountainous regions with steep slopes, may significantly increase the erosion rate. Determining and classifying erosion risks in such areas can help preventing environmental impacts. In this study, remotely sensed data and elevation data were used to extract and classify bare soil erosion risk areas for a study area selected from Hatila Valley Natural Protected Area in northeastern Turkey. High resolution IKONOS imagery was used to apply land use classification in ER-DAS Imagine 9.0. To generate erosion risk map of the bare soil areas, classified image was superimposed on top of slope map, generated based on a Digital Elevation Model (DEM) in ArcGIS 9.2. The results indicated that 1. 43, 5.85, 34.62, 53.16, and 4.94% of the bare soil areas in the study area were under very low, low, medium, high, and very high erosion risks, respectively. The overall classification accuracy of 82.5% indicated the potential of the proposed methodology.

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“…The spatial differences in the levels of soil erosion in Taita Hills by using mean annual rainfall to compute the rainfall intensities with intensity (Knijft et al, 1999) and a thresholds of 40mm and above was deemed erosive in the computation, soil erodibility (Harison et al, 2012); a combine of soil texture, available water content and gravel, study area population (KNBS 2010). The slope generated from DEM as detailed in (Morgan R.P.C, 1978& Mitasova et al, 1996 as the determinants used in the modeling. The slope factor was a combine of slope length and steepness; LS factor, (Mitasova et al, 1996) and the vegetation index derived from the NDVI and the land use for the study area.…”

Section: V) Soil Erosion Mappingmentioning

confidence: 99%

“…The slope generated from DEM as detailed in (Morgan R.P.C, 1978& Mitasova et al, 1996 as the determinants used in the modeling. The slope factor was a combine of slope length and steepness; LS factor, (Mitasova et al, 1996) and the vegetation index derived from the NDVI and the land use for the study area.…”

Section: V) Soil Erosion Mappingmentioning

confidence: 99%

Land Suitability Assessment For Effective Crop Production, a Case Study of Taita Hills, Kenya

Boitt

Mundia

Pellikka

2015

JAI

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A B S T R A C TAgriculture is the backbone of Kenya's economy. Agriculture in Kenya is characterized by low productivity due to low external inputs, lack of good farming practices, soil erosion, and other losses. In most farming regions of the country, agriculture depends entirely on rainfall which sometimes is scarce. The problem of selecting the correct land for the cultivation of certain crops is a long-standing and mainly empirical issue. The objective of this study is to extrapolate and generate a crop suitability map showing areas suitable for agricultural activities in Taita Hills in Kenya. It utilizes the information on environmental condition, altitude, rainfall and other relevant parameters of the case study where the variability of rainfall and recurrent droughts have a great impact on the lives of people whose livelihood is mainly dependent on subsistence agriculture. The methods used include development of elevation models, watershed mapping, climate variability mapping, soil erosion mapping that incorporates the revised universal soil loss empirical (RUSSLE) model and multi-criteria evaluation analysis. The analysis was done using the sum weighted overlay analysis of soil erodibility, slopes, vegetation index and rainfall availability in the modeling. Four categories were achieved and mapped out: most suitable, more suitable, less suitable and least suitable. The research implies that there can be both suitable areas and unsuitable areas for crops in Taita Hills. The study helps farmers to be aware of the environmental conditions of their agricultural land and the impacts that may arise due to varying climate conditions on their cropping patterns.

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Modelling topographic potential for erosion and deposition using GIS

Cited by 142 publications

References 0 publications

Modeling the impacts of agroforestry systems on the spatial patterns of soil erosion risk in three catchments of Claveria, the Philippines

Modeling the impacts of agroforestry systems on the spatial patterns of soil erosion risk in three catchments of Claveria, the Philippines

Using high resolution images and elevation data in classifying erosion risks of bare soil areas in the Hatila Valley Natural Protected Area, Turkey

Land Suitability Assessment For Effective Crop Production, a Case Study of Taita Hills, Kenya

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