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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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  • Lubanzi Z. D. Dlamini

    (Department of Geography and Environmental Studies, University of Zululand, KwaDlangezwa 3886, South Africa)

  • Sifiso Xulu

    (Department of Geography and Environmental Studies, University of Zululand, KwaDlangezwa 3886, South Africa)

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.

Suggested Citation

  • Lubanzi Z. D. Dlamini & Sifiso Xulu, 2019. "Monitoring Mining Disturbance and Restoration over RBM Site in South Africa Using LandTrendr Algorithm and Landsat Data," Sustainability, MDPI, vol. 11(24), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:24:p:6916-:d:294355
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    References listed on IDEAS

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    1. Zhang, Airong & Moffat, Kieren, 2015. "A balancing act: The role of benefits, impacts and confidence in governance in predicting acceptance of mining in Australia," Resources Policy, Elsevier, vol. 44(C), pages 25-34.
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    1. Quansheng Li & Feiyue Li & Junting Guo & Li Guo & Shanshan Wang & Yaping Zhang & Mengyuan Li & Chengye Zhang, 2023. "The Synergistic Effect of Topographic Factors and Vegetation Indices on the Underground Coal Mine Utilizing Unmanned Aerial Vehicle Remote Sensing," IJERPH, MDPI, vol. 20(4), pages 1-23, February.
    2. Sifiso Xulu & Philani T. Phungula & Nkanyiso Mbatha & Inocent Moyo, 2021. "Multi-Year Mapping of Disturbance and Reclamation Patterns over Tronox’s Hillendale Mine, South Africa with DBEST and Google Earth Engine," Land, MDPI, vol. 10(7), pages 1-17, July.

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