An assessment of land cover changes in Greater Giyani Municipality in South Africa

Abstract

Detecting changes in land cover (LC) on the earth's surface is critical for obtaining continuous and exact information on any given area for any type of development planning. Anthropogenic-caused LC changes are one of the most important drivers of environmental change at any scale. Environmental changes continue to threaten the ecosystem's capacity to perform and offer environmental services that maintain communities' livelihoods. Population expansion, urbanization, high levels of migration, rising household numbers, and mounting development demands are all affecting the Greater Giyani Municipality in Limpopo province of South Africa. As a result of these variables, LC changes have occurred, as well as challenges such as urban sprawl, resource exploitation, and land degradation. However, little is known regarding LC alterations, such as where, when, and at what rate they occur. Understanding the drivers of LC change and how various factors influence LC changes in the research area is also critical. Models that combine and evaluate a variety of LC change elements might help planners make better educated verdicts and achieve a stability between urban growth and environmental maintenance. In South Africa, however, the adoption of these models on a regional scale is quite limited. Models of LC change are useful if their structures are constructed on in-depth understanding of the system being studied and if the results are reliable. This study aimed to evaluate the state of land cover in Greater Giyani Municipality from 2000 to 2020 using remote sensing, as well as the factors that caused the changes, identify classes that were threatened within the municipality, and recommend possible strategies or procedures to address the change challenges. An investigation of LC changes was conducted by integrating a quantitative and qualitative approach to better understand the changes, and their drivers. There was the use of satellite remotely sensed images (Landsat series) to measure which areas were covered by various land cover types, while document analysis, expert opinion in the form of semi-structured interviews with municipality town planners, and observation of the study area constituted qualitative data. Remote sensing and Geographical Information System were used for processing, analysing and presenting data through the ENVI 5.0 software. The Minimum Distance Classifier (MDC) through supervised classification was used to classify images into four land use land cover classes that included Bare Land, Vegetation, Waterbody and Built-up. To help analyse and present LC changes in the area, an adapted Driver-Pressure-State-Impact-Response (DPSIR) framework was used.

The results indicated that all maps attained an overall accuracy (AO) and Kappa coefficient (K) of >80% and 0.8 respectively. The vegetation cover indicated a decline of 5.97% between the years 2000-2007,22,59% between 2007-2014 increase of 3,82% between 2014-2020. The built-up indicated an increase of 0,42% between the years 2000-2007, 3.21% between 2007-2014 and 3,84% between 2014-2020. The bare land indicated an increase of 5,85% between the years 2000-2007, 19,0% between 2007-2014 and a decrease of 8,77% between 2014-2020. The waterbody indicated a decrease of 0,31% between the years 2000-2007, increase of 0,38% between 2007-2014 and 1.1% between 2014-2020. The LC changes in GGM are driven by demographic, technological, political, economic, cultural, and environmental factors. These factors must be considered in future planning policies and regulations to minimize negative environmental consequences while retaining socioeconomic benefits. This study, on the other hand, contributes to a better understanding of the elements that drive LC change and the adoption of the DPSIR framework as a model for LC change at a regional scale in the South African context. Policymakers can utilize the information as a reference to appropriately evaluate the influence that planning policies and other driving factors may partake in future LC trends in the Greater Giyani Municipality.