Application of geospatial indices for mapping land cover/use change detection in a mining area

Abstract

Mining contributes significantly to the GDP of South Africa but it also comes with some adverse impacts on the environment. In this study, remote sensing was used to quantify land-use/cover changes in the Welkom – Virginia Goldfields. The aim was to analyse Landsat images with a 5-year interval from 1988 to 2018 using geospatial indices: Global Environmental Monitoring Index (GEMI), the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Soil Index (NDSI) and the Normalized Difference Water Index (NDWI) to discriminate different land cover types. Supervised classification with the maximum likelihood method was used to classify the images into appropriate classes. Findings revealed different land-use changes with fluctuations in values for each index with an overall accuracy of the classified images ranging from 88% to 96% respectively. Hence these indices are reliable for mapping and monitoring land use/cover changes in mining areas over a large extent.