*4.1. LULC Mapping and Dynamics*

The present study generated the LULC maps for three periods using the SVM algorithm. We used this machine learning algorithm, because many studies have already been conducted successfully with this and obtained highly accurate results. Thanh Noi and Kappas [54] applied SVM, RF, and K-nearest neighbour for LULC classification and found that SVM obtained the highest overall accuracy compared to the other two models. Similarly, Singh et al. [55] used SVM to classify the LULC for Pichavaram forest on the southeast coast of India. They found that 89–94% of kappa statistics for the LULC maps of 1991, 2000, and 2009 show the higher performances of SVM classifiers. On the other hand, Rana and Suryanarayana [56] utilised the maximum likelihood, RF, and SVM algorithms to classify LULC for the Vishwamitri watershed in Vadodara, India, found that SVM outperformed other models. Therefore, it can be stated that many previous studies obtained highly accurate results for LULC classification using SVM. While, in the present study, we

also obtained 90–94% accuracy for the LULC of 2001, 2011, and 2021, this suggests that the generated LULC maps are highly accurate and reliable. The results of the LULC dynamics show that the LULC transformation in English Bazar occurred rapidly from 2001 to 2021. The predominance of the built-up area replacing agricultural land characterised these transformations. Other studies have reported the increasing percentage of the built-up area in the English Bazar and West Bengal state [11,46,59]. The water bodies, especially in the southern part of the study area, created a cascading pattern (shape) from 2001 to 2021. The area under it decreased from 2001 to 2011 and then increased again from 2011 to 2021. The area under water bodies declined from 2001 to 2011 because of its conversion to agricultural land by filling up water bodies [56]. From 2001 to 2021, the area under the water bodies increased because of the widespread floods of 2017, which converted many low-lying areas into water bodies because of permanent water-logging conditions [56]. The area under barren land has decreased significantly. It has been converted into agriculture and built up because of the paucity of space for increasing the population and mango farming, which is the mainstay of the economy for Malda District [46]. The findings show that changes in the agricultural land, vegetation, water bodies, and the built-up area had the most significant influence on the landscape heterogeneities [60]. Land use change aided the transformation of the landscape, especially for the rise of the built-up area. Additionally, the conversion of vegetation and agricultural land to a built-up area, and barren land converted into agricultural land, implies that the intensity of human activities affects land use change, landscape fragmentation, and ecological change [62].
