Modeling of potential gully erosion hazard using geo-spatial technology at Garbheta block, West Bengal in India

Abstract

The gully erosion is the most serious environmental problem in West Bengal in India. Present study focused on delineation the gully affected areas and characterization of geo-environmental factor in the gully affected region to prevent future problems. Ground investigation and geo-spatial data along with bivariate statistical approach were employed to identity the most crucial factors among lithology, dynamic and slope inclination, landuse, aspect, plan curvature, stream power index, topographical wetness index and length-slope factor and also understand the most dominant class of each factor associated the gully erosion in the area under study. All the information were integrated into geographical information system platform and categorized in zones of very high, high, moderate, and low gully erosion susceptibility. Weight index overlay method is used to validate the gully proneness map. Results showed land use factor (barren land and waste land), slope ([20°), topographical wetness index values ([1.2), length-slope index ([4.00), fragments of pebbles, boulder and gravels, older alluvium and lateritic soil play important roles in gully processes. Model validation indicated that the resulting map of areas prone to gully erosion has a prediction accuracy of 88.25 %. The methodology adopted for gully erosion proneness mapping can be exercised in other gully vulnerability areas that could be an excellent approach to defend the natural resources and progress in the land use conservation.

Soil erosion risk mapping using RUSLE model on jhargram sub-division at West Bengal in India

“…The Landsat4-5 Thematic Mapper data (Spatial resolution-30 m; Path/Row-139/44 and 139/45) was used for preparing land use/land cover map derived from United State Department of the Interior, United State Geological Survey (http://earthexplorer.usgs.gov/). Supervised classification system with maximum likelihood classifier (MLC) algorithm of ERDAS IMAGINE v.8.5 was used for digital classification of satellite data (Shit et al 2015). The maximum likelihood classification algorithm assumes that spectral values of training pixels are statistically distributed according to a multivariate normal (Gaussian) probability density function.…”

“…Topographic factors like, slope length factor (L) and slope steepness factor (S) mainly reflect the effect of surface topography on erosion (Yildirim 2012;Shit et al 2015). Slope length is defined as the horizontal distance from the point of origin of overland flow to the point where either the slope gradient decreases enough that deposition begins, or runoff is concentrated in a defined channel (Renard et al 1997;Wischmeier and Smith 1978).…”

Multivariate Logistic Regression Model for Soil Erosion Susceptibility Assessment under Static and Dynamic Causative Factors

“…The static CFs have been identified to trigger soil erosion and applied by many researchers in susceptibility studies. For instance, drainage density [8,[24][25][26], lineament density [8,27,28], LS-factor [9,[29][30][31][32][33], K-factor [34][35][36], and NDVI [25,28,37] were implemented in those studies. However, some dynamic CFs such as R-factor, LST and SMI are rarely considered despite their roles in the soil erosion process.…”

Soil crack morphology analysis using image processing techniques

“…1). The Rangamati badlands, cut in upper lateritic and mudstone, occur on steep slopes developed between Quaternary pediments and have a restricted vertical extension with a maximum local relief of up to 50 m. They are relatively young and have only development since the dissection of one of the younger surfaces of the Rangamati badland (Shit et al 2014(Shit et al , 2015. On these slopes there is a full range of erosional forms from discontinuous gullies to deep linear gullies.…”