Assessing Urbanization Dynamics Through Land Use/Land Cover Change Detection in Zunheboto Sadar, Nagaland, India

Abstract

Providing a systematic overview of the environment is crucial for identifying environmental changes and understanding human-related factors and feedback in research on land use and land cover (LULC) changes and the incorporation of LULC data facilitates pinpointing particular areas undergoing changes, discerning the character of these changes, and gaining insights into the evolving nature of the land. Utilizing Remote Sensing & Geographic Information System (GIS) and methodologies, this study aims to examine variation LULC patterns between 2014 and 2023 in Zunheboto Sadar, the highest populated region in the Zunheboto district of Nagaland with area covering about 123 Km2 and altitude encompassing from 642 m to 1989 m above mean sea level. Landsat-8 OLI data was employed to produce a map depicting LULC in the study region. The application of Supervised classification method, specifically the Maximum Likelihood Classifier (MLC), were utilised in producing LULC maps. Through this comprehensive analysis, the research aims to identify specific areas undergoing alterations, discern the nature and extent of these changes, and explore the underlying causes and human responses. Eight classes were delineated namely Jhum cultivation, Moderately Dense Forest, Open Forest, Settlements, Shrub, Terrace Cultivation, Very Dense Forest, and Water Bodies. In the year 2023, Jhum cultivation has decreased by 68.36%, Very Dense Forest by 16.96%, Water Bodies by 6.53%, Shrub by 52.36%, and Terrace Cultivation by 32.83%. Conversely, there is an increase in Moderately Dense Forest by 21.39%, Open Forest by 111.25%, and Settlements by 38.41%. The Kappa accuracy assessment technique was chosen for evaluating the accuracy of the categorized map. The categorized map of 2023 exhibits an Overall Classification Accuracy of 86.50%, coupled with a Kappa Coefficient of 0.84, indicating an almost perfect classification. Similarly, for 2014, the Overall Classification Accuracy stands at 78.50%, with a Kappa Coefficient of 0.75, also falling into the Substantial classification category.