Rajib Sarkar

Land use and land cover change analysis and prediction are essential for understanding the nature of changes in the environment. Land use modelling of urban space is important for efficient land allocation and management. In the present study, an attempt has been made to simulate the land use and land cover in Bardhhaman Planning Area in West Bengal, India for the years 2028 and 2038. These two years were selected to maintain parity within different land use time frames and also considering the uncertainty of prediction for distance years in comparison to proximus years. For this purpose, land use thematic maps were coupled with different driving factors like road network, existing built-up area, slope and distances from the urban centres. Cellular automata and Markov chain analysis were used to predict spatiotemporal trends based on the land use changes in the past. Landsat 5 (TM), 7 (ETM+) and 8 (OLI & TIRS). Images with decadal gaps from 1988 to 2018 were collected for the study, then genetic transition probabilities from 1998 and 2008 were obtained through the GIS environment. Using the Markov and Cellular automata model, land use and land cover maps were predicted for 2018 using 1998 and 2008 land use data. Validation of the predicted map showed good estimation of output images with 0.83 kappa. Finally the future land use for 2028 and 2038 in Bardhhaman was prepared. The study suggests that the future development of settlements will occur outside the present municipal boundaries and mainly along with the transport and communications corridors. This type of study provides the nature of future growth of urban areas, which is essential for sustainable urban management.

To analyse and characterise land use-land cover (LULC) change, remote sensing (RS) data and geographic information system (GIS) approaches are required. The landscape metrics are also extensively used to explain the structure and pattern of the spatial landscape. This case study has been conducted in the area of the Bhagirathi-Hugli River banks in West Bengal. The study area’s satellite image data from various periods (1989-2017) were used to analyse the spatiotemporal variation of LULC change. The LULC fragmentation was analysed using a total of six landscape measures. Landscape metrics such as Number of Patches (NP), Largest Patch Index (LPI), Landscape Shape Index (LSI), Mean Shape Index (MSI), Contagion (CONTAG), and Shannon’s Diversity Index were used to determine the changes over four different periods. The main features of land use change were the continuous expansion of built-up area (9.30 km²), vegetation (189.14 km²), and the decrease in agricultural fallow land and water bodies from 1989 to 2017. The landscape pattern in the study area has also changed significantly. The landscape metrics indicate that the studied region had a highly fragmented landscape during the study period. The proposed technique is easy to use and can address more than one metric while selecting the appropriate setting can prove problematic.

Water quality is a critical environmental issue because all forms of life depend on the water. The present study primarily focused on the spatiotemporal trends of water quality in a section of the Bhagirathi-Hugli River, West Bengal, using geospatial technology and integrated statistical msssethods. For this purpose, 83 samples of 7 water parameters were analysed and compared them with Indian Standards (IS 2004), Environmental Protection Agency (EPA 2001) and World Health Organization (WHO 1993) for the protection of aquatic life and human consumption. Correlation, box and whisker plots, paired sample t test, water quality index (WQI), cluster analysis (CA) and principal component analysis (PCA) were applied as an integrated multivariate statistical approach to understanding the nature of water quality. Pollution sources were identified by PCA indicating different origins both naturally and anthropogenic sources. The box and whisker plots displayed the significantly spatiotemporal variations and concentration of the variables. The paired sample t test identified that the surface water quality varied significantly between the seasons with significant value p < 0.05. Cluster analysis grouped 83 monitoring sites into 4 clusters to identify the pollution status such as low, moderate, high and very high pollution sites. Principal component analysis confirmed that the first three PCs with eigenvalues are higher than 1 contributing 90.83% of total variability for various parameters. The conductivity, total dissolved solids (TDS), salt and pH were expressively influenced by the anthropogenic effect while the temperature, oxidation-reduction potential (ORP) and dissolved oxygen (DO) were affected by seasonal factors. Results of WQI ranged from 45.04 to 83.79, and an average value was 69.55 with 69% samples representing poor water quality for drinking and domestic purposes. It also indicates that the water quality of rural sites was better than industrial and urban sites in both seasons and also shows that it was better for the duration of the post-monsoon than pre-monsoon.