AI Summary & Key Takeaways
AI InsightThe results show that between 1987 and 2020, agricultural area and vegetation with settlement decreased by -11.60 % and -4.34 %, respectively, while dense set-tlement increased by +15.69 % due to significant population growth and overcrowding from neighboring countries.
📌 3 Key Takeaways
Abstract
Mapping, analysis, and monitoring of landuse and landcover in micro region is necessary for sustainable land development, planning and management. The present study is, therefore, aimed to identify the spatio-temporal change of LULC in two central administrative C.D. blocks of North 24 Parganas in West Bengal, India during period 1987-2020. To figure out the essence of the transition, the supervised classification along with post-classification change detection using the 'From'-'To' approach was employed. Furthermore, hotspot analysis has been utilized to identify all of the areas that are the most variable in terms of change potentiality. Besides, cellular automata were also introduced to find out the character of urban growth and future trend of LULC change. The results show that between 1987 and 2020, agricultural area and vegetation with settlement decreased by -11.60 % and -4.34 %, respectively, while dense set-tlement increased by +15.69 % due to significant population growth and overcrowding from neighboring countries. The prediction model also supports this argument. So, the very high and uncontrolled growth of urban settlement in the study area, may become a big challenge for the district authority to control the unplanned urban expansion.