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.

采矿业为南非的GDP做出了巨大贡献，但同时也对环境产生了一些不利影响。在这项研究中，遥感技术用于量化Welkom – Virginia Goldfields地区的土地利用/覆盖变化。目的是使用地理空间指数分析1988年至2018年的5年间隔的Landsat影像：全球环境监测指数（GEMI），归一化差异累积指数（NDBI），归一化差异植被指数（NDVI），归一化土壤指数（NDSI）和归一化水指数（NDWI）用来区分不同的土地覆被类型。使用最大似然法的监督分类将图像分类为适当的类别。调查结果揭示了不同土地利用方式的变化，每种指标的值都有波动，分类图像的总体准确度分别为88％至96％。因此，这些指标在很大程度上可用于制图和监测矿区的土地利用/覆盖变化。