Browsing by Author "Shraban Sarkar"

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A note on boundaries in atlas maps
(Geological Society of India, 2014) K.N. Prudhvi Raju; Manish Kumar Pandey; Shraban Sarkar
[No abstract available]
Demographic and morphological changes in Kolkata, India during 1951-2014
(European Association of Geographers, 2019) Pralip Kumar Narzary; Ambarish Kumar Rai; Shraban Sarkar; Sulochana Shekhar; Dinabandhu Mahata
Kolkata, one of the oldest cities in India has been the destination for migrants. The influx of population over the time caused demographic and morphological vicissitudes. Hence, the present study aims to analyse the changes in demographic structure, city sprawl, and spatiotemporal changes in land use of Kolkata city during 1951 to 2014. Population data are taken from the Census of India and spatial data from the Landsat satellite image. The geographical area of the city has expanded by more than two folds during 1951-1991. Significant alteration in age-sex structure is noticeable; sex ratio improved from mere 593 to 908, the urban settlement has sprawled from mere 23% to 63% of the city area, whereas the greenery has dwindled to just 2.8%. The most substantial impact of urban sprawl is seen on agricultural/fallow land. © Association of European Geographers.
Deterministic approach for susceptibility assessment of shallow debris slide in the Darjeeling Himalayas, India
(Elsevier B.V., 2016) Shraban Sarkar; Archana K. Roy; Priyankar Raha
High magnitude rainfall triggers numerous shallow debris slides in the Darjeeling Himalayas causing widespread damage to the environment, loss of life and property. Thin soil cover and steep topography make the region vulnerable to debris slides. The objective of the present study is to assess the susceptibility of the eastern part of Darjeeling Himalayas (covering about 330 km2) to shallow debris slides through the functional relationship of hillslope hydrology and mechanical properties of slope materials. Deterministic approach-based shallow landsliding stability (SHALSTAB) model following Mohr-Coulomb failure law was adopted to assess landslide susceptibility. Topographical parameters were derived from 8-m resolution Cartosat-1 digital elevation model (DEM) and mechanical properties of soil were obtained from an analysis of 15 soil samples. For slope stability assessment, the topographical and soil parameters were put into three different scenarios - (i) assuming the surface entirely free from vegetation (Model-1), (ii) involving the role of vegetation root cohesion (Model-2) and (iii) surcharge of vegetation, buildings and other structures along with root cohesion (Model-3). These predictive models were used to classify the area into various susceptibility classes with specific amounts of critical rainfall (Qc). The result shows that 28%, 9% and 10% of the study area come under unconditionally unstable class in the three models, respectively. About 22% land in Model-1 and 42% in each Model-2 and Model-3 come under unconditionally stable class. Protective capacity of roots against debris slide played a significant role in Model-2 and Model-3. Performance of models was validated by comparison of observed-predicted landslide areas and the area under the receiver operating characteristic (ROC) curve. It is found that the overall success rate of all the three models is relatively low (56.60% to 64.50%). Thus, it may be concluded that the SHALSTAB model in assessing landslide should either not be used at all at a regional level in the Himalayas or be used only with great caution along with additional field and lab data. © 2015 Elsevier B.V.
Landslide susceptibility assessment using Information Value Method in parts of the Darjeeling Himalayas
(2013) Shraban Sarkar; Archana K. Roy; Tapas R. Martha
Landslide susceptibility is the likelihood of a landslide occurrence in an area predicted on the basis of local terrain conditions. Since last few years, researchers have attempted to analyse the probability of landslide occurrences and introduced different methods of landslide susceptibility assessment. The objective of this paper is to assess the landslide susceptibility in parts of the Darjeeling Himalayas using a relatively simple bivariate statistical technique. Seven factor layers with 24 categories, responsible for landslide occurrences in this area, are prepared from Cartosat and Resourcesat - 1 LISS-IV MX data. Each category was given a weight using the Information Value Method. Weighted sum of these values were used to prepare a landslide susceptibility map. The result shows that 8% area was predicted for high, 32% for moderate and remaining 60% for low landslide susceptibility zones. The high value (0.89) of the area under the receiver operating characteristic curve showed the high accuracy of the prediction model. © 2013 Geological Society of India.
Soil depth estimation through soil-landscape modelling using regression kriging in a Himalayan terrain
(2013) Shraban Sarkar; Archana K. Roy; Tapas R. Martha
Soil formation depends upon several factors such as parent material, soil biota, topography and climate. It is difficult to use conventional soil survey methods for mapping the depth of soil in complex mountainous terrains. In this context, the present study aimed to estimate the soil depth for a large area (330.35 km2) using different geo-environmental factors through a soil-landscape regression kriging (RK) model in the Darjeeling Himalayas. RK with seven predictor variables such as elevation, slope, aspect, general curvature, topographic wetness index, distance from the streams and land use, was used to estimate the soil depth. While topographic parameters were derived from an 8-m resolution digital elevation model, the ortho-rectified Cartosat-1 satellite image was used to prepare the land use map. Soil depth measured at 148 sites within the study area was used to calibrate and validate the RK model. The result showed that the RK model with the seven predictors could explain 67% spatial variability of soil depth with a prediction variance between 0.23 and 0.42 m at the test site. In the regression analysis, land use (0.133) and slope (-0.016) were identified as significant determinants of soil depth. The prediction map showed higher soil depth in south-facing slopes and near valleys in comparison to other areas. Mean, mean absolute and root mean-square errors were used to access the reliability of the prediction, which indicated a goodness-of-fit of the RK model. © 2013 © Taylor & Francis.