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To prepare a landslide susceptibility map of Shivkhola watershed, one of the landslide prone parts of Darjeeling Himalaya, remote sensing and GIS tools were used to integrate 10 landslide triggering parameters: lithology, slope angle, slope aspect, slope curvature, drainage density, upslope contributing area (UCA), lineament, settlement density, road contributing area (RCA), and land use and land cover (LULC). The Analytical Hierarchy Process (AHP) was applied to derive factor weights using MATLAB with reasonable consistency ratio (CR). The frequency ratio (FR) model was used to derive class frequency ratio or class weights that indicate the relative importance of individual classes for each factor. The weighted linear combination (WLC) method was used to determine the landslide susceptibility index value (LSIV) on a GIS platform, by incorporating both factor weights and class weights. The Shiv-khola watershed is classified into five landslide susceptibility zones. The overall classification accuracy is 99.22 and Kappa Statistics is 0.894.

Accurate assessment of the spatial variability of soil properties is key component of the agriculture ecosystem and environment modeling. The main objective of the present study is to measure the soil properties and their spatial variability. A combination of conventional analytical methods and geostatistical methods were used to analyze the data for spatial variability. In November 2014 a total of 32 soil samples were collected in the field through random sampling in Medinipur Sadar block of Paschim Medinipur district in West Bengal (India). Soil properties of pH, electric conductivity (EC), phosphorus (P), potassium (K), and organic carbon (OC) were estimated using the standard analytical methods. A classical ordinary kriging (OK) interpolation was used for direct visualization of soil properties. The spatial distribution of EC, pH, and OC in soil are influenced by structural factors, such as climate, parent material, topography, soil properties and other natural factors. The semivariograms of the six soil properties were fit with exponential curve and root mean square error value is near about zero (0). Finally, spatial distribution and correlation between OC and other soil properties is shown by overlay of maps in GIS environment. The present study suggest that the OK interpolation can directly reveal the spatial distribution of soil properties and the sample distance in this study is sufficient for interpolation.

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.

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