Spatial evaluation of land-use dynamics in gold mining area using remote sensing and GIS technology

Abstract

Gold mining operations generate a range of ecological and environmental impacts that can be measured spatially using geographic information system and remote sensing methods. This study assessed land-use and land-cover dynamics in the gold mining area using geographic information system and remote sensing techniques with the aid of Landsat 5 for years 1984, 1994, 2004 and Landsat 8 for 2014 and 2019 obtained from the United States Geological Survey and Remote Pixel Databases using ArcGIS 10.4 and R programming. The result from the study revealed the current changes and how they have evolved over the years where tailings dam and built-up areas increased from 90.5 to 172.9 km 2 between the year 1984 and 2019, while mine effluent (return water ponds) and water bodies increased from 14.7 to 18.8 km 2 during the same period. The area also experienced increased vegetation from 342.5 to 371.1 km 2 (though fluctuating during the study period) an indication that the area has witnessed revegetation in the area. Results from the study further revealed the vegetation health in the area utilises some vegetation indices such as the Global Environmental Monitoring Index, Normalised Difference Impervious Surface Index and Normalised Difference Moisture Index. Findings from the study show that areas with low index values are susceptible to the impact of mining and other anthropogenic activities, whereas high-index areas connote little or no impact. The outcome of this study provides a cost-effective tool for evaluating environmental impacts in mining areas that can drive policy interventions for remediation in affected areas.

Input imagery, classifiers, and cloud computing: Insights from multi-temporal LULC mapping in the Cambodian Mekong Delta

“…To harness these advantages, a variety of classifiers is available for supervised classification. Among the most frequently used are machine learning algorithms such as Support Vector Machines (SVM), Random Forest (RF), Gradient Tree Boosting (GTB), Maximum Likelihood Classifiers (Orimoloye et al, 2018;Orimoloye & Ololade, 2020), and K Nearest Neighbour (KNN, Talukdar et al, 2020). Furthermore, advanced machine learning approaches, such as artificial neural networks (ANN), convolutional neural networks (CNN), and deep neural networks (DNN) have seen more widespread use in recent years (Jozdani et al, 2019).…”

Land-use/land-cover changes and implications in Southern Ethiopia: evidence from remote sensing and informants

“…Land use/land cover changes were executed by using remote sensing images to verify the ground truth and for monitoring environmental resources. Remote sensing images were employed to map land cover/use changes in a mining area of southwestern of the Witwatersrand Basin, South Africa (Madasa et al, 2021;Orimoloye and Ololade 2020). The investigation of implications of LULC change on temporal variations of land surface temperature was performed using remote sensing (Ogunjobi et al, 2018).…”

Characterizing Khetri copper mine environment using geospatial tools

“…Construction of ore beneficiation units along with roads on forest land results in deforestation and are potential changes observed on the mining landscape. Establishment of tailing dams, waste rocks dumps, effluent treatment plants, water storage ponds and built-up are the visible changes in landscape pattern caused by the mines [19,20]. Spatiotemporal characterization of mining landscape indicates the presence of open pits and waste dumps in place of vegetation cover [21,22].…”