OPEN-PIT LIMESTONE MINING AREAS MAPPING AND ASSESSMENT USING RANDOM FOREST ALGORITHM FOR SUSTAINABLE DEVELOPMENT
Limestone mining areas mapping and assessment from space
DOI:
https://doi.org/10.55766/sujst-2024-04-e02388Keywords:
Classification, Google Earth Image, Limestone mine, RF Classifier, satellite ImageryAbstract
Limestone excavation is prone to land use/cover change, which impacts the ecosystem. The objective of this paper is to use a remote sensing dataset and the Quantum Geographic Information System (QGIS) to map and assess active captive limestone mining sites at the Yerraguntla in the YSR Kadapa district, Andhra Pradesh, India, for the year 2019. In this paper, the Limestone mining area was assessed as 379.57 ha with an overall accuracy of 95.79%, user accuracy of 97.25%, producer accuracy of 99.18%, and kappa coefficient of 0.957 by analyzing Sentinel-2A imagery dataset with Random Forest (RF) classifier. The findings were assessed in 2019 using very high-resolution (<1m) Google Earth images (486.47 ha) from Google Earth Pro and industrial field survey reports (487.10 ha). This research helps Limestone mine owners and Environmental engineers implement eco-friendly mining operations and monitor mining progress at low cost with less human efforts for sustainable development.
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