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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/9912

Title: Monitoring the extent of reclamation of small scale mining areas using artificial neural networks
Authors: Abaidoo, Christian Aboagye
Issue Date: 17-Jan-2017
Abstract: Small scale mining is widespread mainly in developing and underdeveloped countries. It causes environmental degradation although it is a source of livelihood for several people. Reclamation is needed to restore mined areas to an acceptable condition. Artificial neural networks (ANN) are also being used recently for soil analysis, land use/land cover analysis, etc. due to the increased availability of Landsat data. There have been studies on various aspects of small scale mining, reclamation and artificial neural networks but this research focused on using artificial neural networks to monitor reclamation activities in small scale mining areas. Data used for analysis included Landsat satellite images of study area (2007, 2011 and 2016), ground truth data and shapefile of the study area. Two ANN classification methods, Unsupervised Self – Organized Mapping (SOM) and Supervised Multilayer Perceptron (MLP), were used for the classification of the satellite images. Normalized Difference Vegetation Index (NDVI) change maps and class mask maps were also generated in order to help confirm where actual change and to what extent it had occurred. Results of the study indicated disturbance and revegetation in the study area within the 9 –year period. The Barelands/mined areas class increased by 60.4% and a decrease in the vegetation class by 18.7% from 2007 to 2011. NDVI maps, NDVI change maps, class mask maps and maps showing reclaimed areas, disturbed and undisturbed areas together with statistical information obtained from the classification results, confirmed the extent to which the reclamation activities had gone. There was evidence of revegetation from 2011 to 2016 with the Barelands/Mined Area class decreasing by 51.7% and the vegetation class increasing by 3.9%. There was also an increase in the settlement class by 87.3%. The research concludes that the application of ANN be strongly encouraged for image classification and mine reclamation monitoring activities and studies in the country
Description: A thesis submitted to The Department of Geomatic Engineering, Kwame Nkrumah University of Science and Technology in partial fulfilment of the requirements for the degree of Master of Science in Geomatic Engineering, 2016
URI: http://hdl.handle.net/123456789/9912
Appears in Collections:College of Engineering

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