## 4 Discussion
### Impact of Spatial Resolution  
Vegetation indices such as NDVI are widely employed in rangeland management, but often used at relatively low (e.g., 250-1000 m) spatial resolutions to assess vegetation quality at landscape and regional scales [101; 102]. To better understand the impact of using such low-resolution data for modelling grazing behaviour (and stock carrying capacity calculations), we statistically compared the observed grazing behaviour in the study area to a range of NDVI datasets extracted at different resolutions. The results showed that the relationship between the grazing behaviour (Gi* z-scores) and NDVI was strongest at 100 m resolution, but that the model fit deteriorated significantly as resolutions were reduced to beyond 100 m (Table 3). This is explained by the importance of drainage lines in our study area as sheep tend to congregate in such areas, as well as the fact that the average width of the drainage lines in the study area is 102 m. While the higher (e.g., 10 m) resolution imagery represents larger amounts of variability, the patchy nature of the vegetation [70] reduced the statistical relationships between grazing behaviour and NDVI. This finding has far-reaching implications as it shows that NDVI (and likely other indices) derived from imagery such as MODIS (250 m) and Sentinel-3 (300 m) will not adequately represent grazing conditions and behaviour in semi-arid environments such as the Upper Karoo [70]. Conversely, the results show high (e.g., 10-30 m) resolution imagery is not necessarily optimal for modelling grazing conditions as such high-resolution imagery is more sensitive to localized variations such as rocky outcrops or bare patches. However, the differences between using 10 m, 20 m, 30 m 50 m and 100 m imagery were marginal.  
There exists a need to use RS data to advise sustainable management intervention strategies to prevent overgrazing from taking place [1]. From this study, it was shown that it can be achieved by having a good understanding of the spatiotemporal heterogeneity of vegetation across rangelands and within grazing camps. Modelling grazing hotspots across rangelands allows for the identification of areas that need to be fenced off, to protect certain palatable vegetation areas and ultimately prevent the loss of biodiversity and rangeland productivity. Such measures could increase the profitability of livestock farming by increasing stock numbers in areas with less variation in grazing hotspots and decreasing stock numbers in areas that are prone to overgrazing (as can be identified using NDVI imagery). Remotely sensed NDVI is ideal for monitoring vegetation attributes, such as plant density and -vigour, and for predicting grazing patterns of livestock [23]. It is cost effective, can be generated on a timely basis, and can be applied to monitor conditions throughout the year and over extensive periods. Clearly, it is important for supporting -ranged management and to prevent the degradation of delicate environments such as the Upper Karoo.  
It is important to note that the dynamics of grazing behaviour of animals change over spatial scales [103; 104]. In less arid environments where stocking rates are higher and the rainfall is more reliable, the effect of selective grazing in drainage areas may be less. More work is needed to better understand why the drainage lines in our study area had such a large influence on grazing patterns. The correlation between the remotely sensed vegetation conditions and livestock production potential should be investigated in future studies. Finally, replicating this study in similar semi-arid environments, but with different vegetation types (e.g., the succulent Karoo- and savanna biomes) will add much value to the current research.