Affiliations: [a] Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Sri Venkateswara University, Tirupati, India | [b] Department of Electronics and Communication Engineering, Erstwhile Sree Vidyanikethan Engineering College, Mohan Babu University, Tirupati, India
Correspondence:
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Corresponding author: Venkata Sudhakar C, Department of Electronics and Communication Engineering, Sri Venkateswara University College of Engineering, Sri Venkateswara University, Tirupati 517502, India. E-mail: [email protected]://orcid.org/0000-0002-0205-4470.
Abstract: Limestone mining is a significant economic activity in India, accounting for around 10% of the GDP however, it has certain negative environmental consequences. The objective of this study is to determine the spatial distribution area of captive limestone mines using remote sensing datasets, spectral index, and machine learning algorithms and compare their area estimation with industrial field survey reports for the financial year 2019. The study area includes a limestone resource area of 2226.16 ha with an excavation area of 487.10 ha in 2019. In the present research, we used a high-resolution Sentinel-2A satellite dataset to map and compute the active mining area by implementing the Normalised Vegetation Index (NDVI), Iterative Self-Organizing Data Analysis Technique (ISODATA), K-Nearest Neighbours (KNN), and Random Forest (RF) algorithms in the QGIS 3.18 software tool. The RF classifier estimated a limestone mine area of 379.57 ha with user accuracy (UA) of 97.25% and producer accuracy (PA) of 99.18% with a kappa coefficient value of 0.957. The mine area was estimated at 417.47 ha with a UA of 98.99% and PA of 99.10% and kappa value of 0.947 of the KNN method, The NDVI method estimated 469.92 ha with a UA of 93.63% and PA of 92.04% and kappa value 0.685. This research confirmed that the RF classifier well performed in classification with overall accuracy (OA) of 95.79% to KNN (OA of 94.78%), NDVI (OA of 79.84%) classifiers, and ISODATA poor in classification with OA of 64.16%. This research assists limestone mine owners and environmental engineers in making environmentally sustainable decisions, eco-friendly mine design, and monitoring.
