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Waste rock dumping is a major problem for opencast mine. The key approach to waste rock dump management is its re-use in the form of aggregates, m-sand, etc. which is also the basis of circular economy. The application of multispectral satellite data is considered to be one of the best approaches to characterise the mineral content of the waste rock dumps. In this research, multispectral data from Landsat 9 (OLI) were used to determine the sensitive bands for muscovite schist and gneissic rocks present in the hanging wall and footwall faces of an open-cast manganese mine in India. The behaviour of the spectral signature curves and their association with the surface reflectance values were used to identify these sensitive bands. XRF and XRD were used to identify the minerals of the rocks. The spectral signature reflectance curves obtained from the multispectral data of the identified minerals were compared with the USGS spectral libraries to determine these sensitive bands (bands 4, 5, 6, and 7) for rocks. Applying the band ratio technique, formulae representing the optimal band ratios were developed for mineral mapping. Methods like object-oriented principal component (PC) and independent component (IC) analyses were applied to determine the lithology based on spectral characteristics. False colour composite images in the RGB band combinations (PC4-PC3-PC1) and (IC4-IC2-IC1) were utilised for precise mineral mapping of the area. The methods were also validated with the waste rock dumps of a neighbouring manganese mine having similar geological setting. [ABSTRACT FROM AUTHOR]

This paper focuses on forest health risk (FHR) assessment and prediction in the mining-affected forest region using AHP model based on multi-criteria analysis in a GIS platform. We considered a total twenty-eight (twenty two present and six predicted) causative parameters including climate, natural or geomorphological, forestry, topographical, environmental, and anthropogenic. The assessment results of FHR show that of the total existing forest area, 2.85% area under very high, 13.63% high, 31.98% moderate, 32.68% low, and 18.87% are under very low categories. According to the assessment and prediction FHR results, the very high-risk classes were found at mines surrounding forest compartments. The sensitivity analysis showed that some factors were more sensitive to FHR. The correlation results showed a negative relationship between FHR and distance from mines and foliar dust concentration. This work will provide a basic guideline for effective planning and management in forestry studies for the mining-affected region. [ABSTRACT FROM AUTHOR]