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Monitoring Mining Disturbance and Restoration over RBM Site in South Africa Using LandTrendr Algorithm and Landsat Data

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dc.contributor.author Dlamini, Lubanzi Z. D.
dc.contributor.author Xulu, Sifiso
dc.date.accessioned 2020-01-27T07:53:26Z
dc.date.available 2020-01-27T07:53:26Z
dc.date.issued 2019
dc.identifier.citation Dlamini, L.Z. and Xulu, S., 2019. Monitoring Mining Disturbance and Restoration over RBM Site in South Africa Using LandTrendr Algorithm and Landsat Data. Sustainability, 11(24), p.6916. en_US
dc.identifier.issn 2071-1050
dc.identifier.uri https://doi.org/10.3390/su11246916
dc.identifier.uri http://hdl.handle.net/10530/1987
dc.description Peer reviewed article published under Journals Sustainability Volume 11 Issue 24 en_US
dc.description.abstract Considering the negative impact of mining on ecosystems in mining areas, the South African government legislated the Mineral and Petroleum Resources Development Act (No. 28 of 2002), to compel mining companies to restore the land affected by mining. Several studies have used remotely sensed data to observe the status and dynamics of surface mines. Advances in remote sensing along the cloud-based Google Earth Engine (GEE) now promise an enhanced observation strategy for improved monitoring of mine environments. Despite these advances, land rehabilitation at Richards Bay Minerals (RBM) is mainly restricted to field-based approaches which are unable to reveal seamless patterns of disturbance and restoration. Here, we illustrate the value of the trajectory-based LandTrendr algorithm in conjunction with GEE for mine rehabilitation studies. Our automated method produced disturbance and recovery patterns (1984–2018) over the RBM site. The study revealed that RBM has progressively been mining different portions of the mineral-rich coastal area after which restoration was undertaken. The duration of mining over each site ranged from 2 to 6 years. The LandTrendr outputs correspond with independent reference datasets that were classified with an overall accuracy of 99%; it captures mine-induced disturbance efficiently and offers a practical tool for mine restoration management. en_US
dc.language.iso en en_US
dc.publisher MDPI en_US
dc.subject restoration en_US
dc.subject mining en_US
dc.subject Landsat en_US
dc.subject Google Earth Engine en_US
dc.subject coastal dune forest en_US
dc.subject Richards Bay Minerals en_US
dc.subject KwaZulu-Natal; South Africa en_US
dc.title Monitoring Mining Disturbance and Restoration over RBM Site in South Africa Using LandTrendr Algorithm and Landsat Data en_US
dc.type Article en_US


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