Amidst global shifts in land use patterns due to urbanization, this study focuses on the rapid land use and land cover (LULC) changes in Midnapore City during the periods 2003–2014 and 2014–2024. The study employs Landsat 5 and 8 imagery with 30 m spatial resolution which were processed through Maximum Likelihood Classifier (MLC) algorithms. The results were attained through ArcGIS 10.2.2 and ERDAS IMAGINE 2014 software, with ground-truth validation using data from 117, 111, and 116 points for 2024, 2014, and 2003, respectively. For the validation, the kappa coefficient was calculated and achieved 87.3%, 88.1%, and 81.7% for 2024, 2014, and 2003, indicating substantial accuracy. Using statistical measures such as change matrix union, binary logistic regression, and correlation matrix analysis applied to classified LULC outputs and spatial drivers, the research highlights significant transformations in the region. The study reveals significant transformations, notably the conversion of 77% of forest areas and 5% of fallow land to built-up land. The increased rate of agricultural land conversion to built-up areas is evident after 2014, indicating rapid urban growth. These factors led to the reduction of LULC classes possessing substantial ecological value like forests and scrub lands which are becoming more accessible due to the increasing population. The results point out the drastic alteration of these developments and recommend a planning approach responsive to environmental needs for safeguarded ecological impacts. The research highlights the importance of reforestation, preservation of water bodies, and socio-economic surveillance in fostering urban management and sustainable development in Midnapore City.

In this study, we focused on the rapid land use and land cover (LULC) changes in Bankura in 1990, 2000, 2010, and 2024, employing an integrated remote sensing, geospatial, and statistical approach to track land use changes. The supervised classification technique and change detection analysis were applied with the Supper Vector Machine (SVM), Maximum Likelihood (ML), and Random Forest (RF) methods to identify land use classes in various categories like Dense Forest, Open Forest, water body, agricultural land, settlement, barren land, and sand. The Kappa Coefficient was used for the accuracy assessment, which revealed that the overall accuracy of 1990 was 93.33%, 2000 was 93.23%, 2010 was 93.43%, and 2024 was 90%. The analysis revealed a significant increase in built-up land from agricultural and forested areas, with a higher percentage of agricultural land converted to built-up areas observed between 1990 and 2024. During this interval, the built-up land area increased by approximately 13.6%, primarily due to the conversion of agricultural land and forest cover. Agricultural land decreased by 11.45%, while dense forest cover declined by 7.75%, indicating a significant anthropogenic influence on landscape transformation. Our findings underscore the importance of sustainable land use planning, conservation efforts, and policy interventions in mitigating environmental degradation, leveraging the effectiveness of space-based inputs and geospatial techniques. The research emphasizes the need for continuous monitoring and further investigation into socio-economic drivers and environmental consequences to ensure resilient urban management and sustainable development. This reveals the importance of reforestation, preserving water bodies, and developing ecologically sensitive infrastructure. Moreover, the study highlights the importance of sustainable land use planning in mitigating adverse environmental impacts and preserving ecological balance.

The tasar silk production of India’s sericulture industry supports tribal livelihoods and economic sustainability. However, Antheraea mylitta Drury, 1773, the primary species for tasar silk, faces habitat threats due to deforestation, climate change, and anthropogenic pressures. This study evaluates the distribution and habitat suitability of wild tasar silkworm using multi-criteria approach, Geographic Information System (GIS), Remote Sensing (RS), and ecological niche modeling using the MaxEnt algorithm. Field surveys were conducted to collect cocoon samples, and the analysis of environmental parameters and assessment of soil micronutrient influences were also carried out. The MaxEnt model predictions indicate that the Central, Western, and Southern zones of Mayurbhanj, encompassing the Similipal Biosphere Reserve, provide the most suitable habitats. The jackknife test confirmed that these climatic variables collectively contributed 68.7% to the habitat suitability model. This study highlights the impact of habitat fragmentation and deforestation on tasar silkworm populations, emphasizing the need for conservation strategies, sustainable forest management, and afforestation programs. The findings highlight the following key conservation strategies: restoring habitats in Similipal, enforcing anti-deforestation laws, promoting community-led planting of host trees, and adopting climate-resilient silk farming to protect biodiversity and support tribal livelihoods.