In these studies, rainfall was identified as the crucial climatic factor influencing vegetation production and was closely correlated with NDVI's seasonal variability [22]. Consequently, rainfall is a crucial predictor of vegetation production. Different applications have been used to monitor vegetation dynamics based on multi-temporal Advanced Very High-Resolution Radiometer (AVHRR) data [8]. In the past few decades, the increasing availability of long-term satellite time-series data has significantly benefited landscape monitoring and LD research [24]. However, whereas the accessibility of NDVI data from among others, the National Oceanic and Atmospheric Administration--Advanced Very High Resolution Radiometer (NOAA-AVHRR), has enabled intensive research on LD [25], the results are often dominated by erratic rainfall, associated changes in seasonality and drastic land cover or land use changes [8], which makes human and rainfall induced LD indistinguishable. The residual trends (RESTEND) method is widely used to distinguish human-induced from rainfall-induced LD [8; 26; 27; 28]. The RESTEND method can be used to reveal the spatial patterns of LD drivers at cell resolution. The method is based on the premise that water is the most limiting factor to vegetation productivity and there is a strong correlation between vegetation productivity and climatic variables in arid and semi-arid ecosystems [8]. Since vegetation productivity in drylands peak in high rainfall years, it is possible to factor out the effects of rainfall to understand the impacts of human-induced activities on the overall vegetation condition [8]. The RESTEND model reveals that human-induced LD can be detected when the significant effects of climate are removed from the long-term NDVI trends [8]. Land Degradation affects food security, regional economic development, and resource conservation strategies, necessitating urgency for LD monitoring systems. Approportioning various types of factors affecting vegetation degradation is central to socio-economic and environmental research in drylands and management [8]. However, there is still a dearth in literature that distinguishes LD arising from anthropogenic and climatic drivers i.e., rainfall. It is also evident from various LD studies that there is no agreement on the existence,location and degree of LD [8]. Hence, studies often end discussing the degree or even the reality of degradation [9; 29]. There is an urgent need for an objective, measurable, repetitive, systematicand spatially and temporally explicit assessments of LD [30]. The long-term temporal and spatial reduction of vegetation productivity trend in drylands is regarded as an effective indicator of LD [31]. Even though previous studies measured and quantified degradation in drylands and semi-arid regions using vegetation greenness, climate change concerns, and its complexities have increased. With vegetation growth depending on rainfall in these ecosystems [32], NDVI trends in these areas tend to have varieties in magnitude and direction. Therefore, for meaningful mapping of consistent permanent LD, precipitation contribution has to be distinguished from human drivers. Moreover, in South Africa, previous and current policies have resulted in complex driving forces of LD. In the former homelands of South Africa, rural communal districts are experiencing challenges regarding land use conflicts due to dual administration systems that is evident in GSDM [12]. Thus, this study contributes to building a measure and understanding of LD by: (1) apportioning human and anthropogenic drivers of LD, thus identifying priority areas for specific sustainable land management (SLM) interventions; (2) assessing LD in rural communal districts under traditional leadership, which have not been fully studied by analysing different LD for various LULC units that has not been detected in a systematic way on a long-term and landscape scale.