Multi-Year Mapping of Disturbance and Reclamation Patterns over Tronox’s Hillendale Mine, South Africa with DBEST and Google Earth Engine

Sifiso Xulu, Philani T. Phungula, Nkanyiso Mbatha and Inocent Moyo
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Sifiso Xulu: Department of Geography, University of the Free State, Phuthaditjhaba 9869, South Africa
Philani T. Phungula: Department of Geography and Environmental Studies, University of Zululand, KwaDlangezwa 3886, South Africa
Nkanyiso Mbatha: Department of Geography and Environmental Studies, University of Zululand, KwaDlangezwa 3886, South Africa
Inocent Moyo: Department of Geography and Environmental Studies, University of Zululand, KwaDlangezwa 3886, South Africa

Land, 2021, vol. 10, issue 7, 1-17

Abstract: This study was devised to examine the pattern of disturbance and reclamation by Tronox, which instigated a closure process for its Hillendale mine site in South Africa, where they recovered zirconium- and titanium-bearing minerals from 2001 to 2013. Restoring mined-out areas is of great importance in South Africa, with its ominous record of almost 6000 abandoned mines since the 1860s. In 2002, the government enacted the Mineral and Petroleum Resources Development Act (No. 28 of 2002) to enforce extracting companies to restore mined-out areas before pursuing closure permits. Thus, the trajectory of the Hillendale mine remains unstudied despite advances in the satellite remote sensing technology that is widely used in this field. Here, we retrieved a collection of Landsat-derived normalized difference vegetation index (NDVI) within the Google Earth Engine and applied the Detecting Breakpoints and Estimating Segments in Trend (DBEST) algorithm to examine the progress of vegetation transformation over the Hillendale mine between 2001 and 2019. Our results showed key breakpoints in NDVI, a drop from 2001, reaching the lowest point in 2009–2011, with a marked recovery pattern after 2013 when the restoration program started. We also validated our results using a random forests strategy that separated vegetated and non-vegetated areas with an accuracy exceeding 78%. Overall, our findings are expected to encourage users to replicate this affordable application, particularly in emerging countries with similar cases.

Keywords: mining; restoration; Landsat; breakpoint; DBEST; GEE; Tronox; Hillendale mine (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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