Abstract

RAMJEAWON, Manish; DEMLIE, Molla; TOUCHER, Michele L.  and  VAN RENSBURG, Susan Janse. Analysis of three decades of land cover changes in the Maputaland Coastal Plain, South Africa. Koedoe [online]. 2020, vol.62, n.1, pp.1-12. ISSN 2071-0771.  http://dx.doi.org/10.4102/koedoe.v62i1.1642.

The northern half of the Maputaland Coastal Plain (MCP) of South Africa has undergone significant change in land-use over the last five decades, including afforestation, agriculture and rural settlements. To understand the extent of land-use changes that took place in the northern half of the MCP from 1986 to 2019 and its efficacy, various Landsat satellite images that are freely available were processed, analysed and interpreted. The cloud-based Google Earth Engine (GEE) platform was used to determine the land-use changes. The random forest classification algorithm available within GEE was used to classify the Landsat 5 Thematic Mapper, Landsat 7 Enhanced Thematic Mapper Plus and Landsat 8 Operational Land Imager images for 1986, 1991, 1996, 2001, 2006, 2011, 2016 and 2019. The development of plantations was tracked, which indicated that forestry increased by more than 100% between 1986 and 2019. Over the same period, surface water bodies and wetlands decreased by 36.1% and 49.1%, respectively. In addition to forestry, climate had a major impact on water resources in the MCP. Given that the MCP is a predominately groundwater-driven system, the impact of increased plantations on groundwater is an area that requires more investigation. This will improve the understanding of water resources in the area. CONSERVATION IMPLICATIONS: The management, protection and conservation of water resources within protected areas are entwined with land-use decisions and planning outside of its boundaries. The rapid change in land-use experienced outside of protected areas and its impact on water resources disregard boundaries and may transgress on protected areas

Keywords : Google Earth Engine; Landsat imageries; land-use change; remote sensing; north-eastern South Africa; water resources; random forest classification; land cover.

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