Harnessing AI for Environmental Monitoring: An Integrated Remote Sensing Study of Kanigiri region, Andhra Pradesh

AI techniques, particularly image classification, were leveraged in this paper to explore the dynamics of greenness and temperature in Kanigiri, Prakasam district, a region severely impacted by drought and reliant on rainfall and surface water for agriculture. The study utilized remotely sensed satellite data from 2017, 2019, and 2021, comprehensively analyzing vegetation indices, temperature fluctuations, rainfall patterns, and land use classifications. The findings revealed substantial transformations in vegetation cover and temperature, attributed partly to the implementation of farm ponds under the Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA). These water conservation activities have significantly lowered temperatures by $3-4^{\circ} \mathrm{C}$ and expanded crop areas by 10-15 sq.km over the study period. By integrating remote sensing and image classification methods, this research underscored the potential of advanced computational techniques in enhancing our understanding of environmental changes and their impacts on agricultural sustainability.