Satellite Image Based Spatio-Temporal Variation Assessment in Captive Limestone Mines for Long-Term Viability

Abstract

Land use and Land cover mapping are crucial applications for planning and disaster mitigation as well as prevention. Such mapping is possible via satellite imagery. Every pixel in the training set must be labelled in order to employ machine learning techniques combined with image analysis for such land cover mapping. This paper mainly concentrates on the mining sector for assessing sustainable land environmental management plans. Google Earth mapping is one of the most efficient, economical, and reliable methods for better understanding of spatial and temporal changes of limestone mining area with less human efforts. High-resolution Google Earth images are used in assessing the temporal changes of the Limestone mine area in Kadapa district from 2011 to 2020. Google Earth Pro open-source software was used to extract images of captive limestone mines, which were then clipped using a polygon tool to aid in the creation of a land-use region occupied by the active mining area. It was found that the active mining area variation rate is in the Dalmia Cement Limestone mine is 308.49% which is the highest compared to Bharathi Cement Limestone Mine mining area variation rate (240%), which is second, and the lowest mining area variation rate is found in the Niduzuvvi Limestone Block-1 area, which is 11.67%. Based on the overall impact of limestone mining in the study area, it is recommended that all stakeholders especially mine and cement plant owners; pay close attention to the area’s land environmental issues. The proposed technique is used for quality assessment in terms of prediction accuracy. The system so developed is observed to outperform existing systems implemented with the benchmarks counterparts.