Modelling and Monitoring Land Use: Land Cover Change Dynamics of Cooch Behar District of West Bengal using Multi-Temporal Satellite Data

Land use land cover (LULC) change is an indicator of the sustainability of any region and requires regular monitoring. In measuring and analysing LULC changes, remote sensing (RS) and geographic information systems (GIS) have shown high efficiency. The present study was carried out in Cooch Behar District of West Bengal, India, with the objectives to estimate the area distribution under different LULC, its temporal change and prediction of future area under these LULCs. In order to achieve these objectives, Landsat satellite imagery for three periods, viz. 2001, 2011 and 2021, was used. Six LULC classes were identified using the Maximum likelihood algorithm. The results revealed that there was continuous decrease in natural vegetation from 2001 to 2021, whereas agricultural land and built-up area showed increasing trend. To assess the overall accuracy of the LULC classification, a total 250 reference test pixels were sampled based on a stratified random sampling method. The prediction was modelled by using Cellular Automata and Artificial Neural Network (CA-ANN). Validation of the model was done using Modules for Land Use Change Evaluation (MOLUSCE). Using the trained model along with classified LULC maps of 2011 and 2021, CA further predicted the LULC map of 2031. From the results, it is evident that the area under natural vegetation declined, while built-up area and agricultural land increased. All other classes might face slight changes in their area in future.

Automated summary

This study used remote sensing and geographic information systems to analyze land use and land cover changes in Cooch Behar District, West Bengal, India. The results showed that there was a continuous decrease in natural vegetation from 2001 to 2021, while agricultural land and built-up area showed an increasing trend. The model predicted that there might be slight changes in the area of other land classes in the future.