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Dr 

Mbatha, Nkanyiso

Faculty: Faculty of Science, Agriculture and Engineering
Department: Geography and Environmental Studies
Research Interest(s): Atmospheric modeling, Earth remote sensing, Atmospheric chemistry and physics, Data science, Signal processing.
Active Research Project(s): To be added
Active Community Engagement: To be added
Biography: Nkanyiso Mbatha joined the Department of Geography at the University of Zululand in 2016. His academic interests lie in atmospheric chemistry and atmospheric physics. He teaches physical geography modules from first year to honors level, including SGES111: Introduction to Physical and Environmental Geography; SGES222: Hydrometeorology; SGES321: Atmospheric Processes and Pollution; SGES341: Climate Dynamics, Weather Variability, and Prediction; and SGES502: Applied Climatology. In addition to his teaching responsibilities, Nkanyiso collaborates with the Clean Air Association of Richards Bay on the analysis of air pollution data. He is currently collaborating with researchers from several institutions, including the University of KwaZulu-Natal, the University of Venda, and the University of Réunion Island, France. He supervises a number of Honours and Master’s students engaged in research within these fields.
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20211
Author: Phungula, Philani T. ×
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    PublicationJournal Article
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    Multi year mapping of disturbance and reclamation patterns over Tronox` Hillendale mine, South Africa with DBEST and Google Earth Engine
    2021
     | MDPI
    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 force 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.
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