Spatial mapping of the extents and volumes of solid mine waste at Samrudha Resources Mine, Kenya: a GIS and remote sensing approach

Abstract

Monitoring and estimating solid mine waste produced during mining operations at a spatial–temporal scale plays a fundamental role in waste management and mitigation of environmental impacts. Iron ore mining and processing results in waste production that may cause environmental degradation, therefore, the need to estimate their volumes and extents. This research aims at mapping and estimating the areal extents and the volumes of solid mine waste produced during iron ore mining and processing. Contours were generated from control points with X, Y, Z information and interpolated to create a Triangular Irregular Network (TIN), which was rasterized to create a 3D Digital Elevation Model (DEM) that was used in volume estimation. Maximum-Likelihood Classification method (MLC) was used for classification at an accuracy of 74% to estimate the areal extents of the solid mine wastes, with a Kappa Coefficient of 0.65. Solid mine waste approximately covered an area of 591,100 m2 and a volume of 2694,670.55 Metric Tonnes. This research presents a fast and accurate method of mapping and estimating the areal extents and volume of solid waste dumped during mining and processing operations.