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Evaluation of ISODATA Clustering Algorithm for Surface Gold Mining Using Satellite Data

The regular mapping and monitoring of surface mining areas are highly important due to their potential negative impacts on the surrounding environment. In this research, the sentinel remote sensing multispectral data with Blue, Green, Red, and Near-Infrared was acquired, preprocessed and enhanced using Histogram Equalization Techniques. Sixteen different ISODATA Clustering Algorithm scenarios were designed with variations in Maximum Iterations and Convergence Threshold parameter values, in order to correctly map the surface gold mining areas. The results showed that the lower values of Maximum Iterations and higher values of Convergence Threshold yielded the most correctly classified surface gold mining map. The methodology developed in this research is not only robust but also cost and time effectively by using satellite remote sensing images in order to monitor and map the surface gold mining activities in the regions particularly in the dense forest covered areas.