| dc.description.abstract | The migration of individuals from rural regions to urban areas leads to a growing need for
housing, infrastructure, and services, which drives social, economic, and environmental
changes. This study uses Landsat Data to generate LULC maps across multiple timeframes.
Utilizing satellite images acquired from the United States Geological Survey (USGS), the
research employs various techniques, including image processing, classification and accuracy testing to analyze dynamic shifts in five land classes. The study’s methodology involves
using a Quantum Geographic Information System (QGIS) for LULC classification, vector
layer intersection for refining results, and change detection through pivot tables and zonal
statistics. The findings provide insights into spatial patterns and temporal trends within the
district. The data indicates that forest areas consistently occupy most of the land, although
gradual decline is observed, particularly between 2020 and 2023. Agricultural and barren
lands show fluctuating trends, with agricultural land experiencing a notable decrease in
specific years due to urbanization pressures. Built-up areas (BUA) have seen continuous
growth, reflecting the district’s shifts towards urban development, which can be linked
to its proximity to the Central Business District (CBD), which leads to land conversion.
The data underscores the need for sustainable land management practicing balance amidst
pressures from rapid urban growth. | en_US |
