###### Abstract
Global navigation satellite systems (GNSS), such as the global positioning system (GPS), have been effective for tracking and monitoring animal behaviour [1; 28; 29; 30], while advances in remote sensing (RS) technologies offer ever-increasing opportunities to assess and characterize land surface conditions over time and scale [12; 31]. In particular, space-borne RS sensors can be used to obtain landscape-wide snapshots at intervals ranging from months to minutes [32]. The recent availability of high temporal (5-day interval) and high spatial (10-60 m) resolution imagery from the Sentinel-2 satellite constellation [33] opened up new opportunities for monitoring temporal changes in vegetation conditions. Sentinel-2 imagery has been used since 2015 for applications ranging from vegetation classification [34; 35; 36; 37; 38], vegetation condition monitoring [36], yield estimation [39; 40; 41], mapping soil organic carbon in croplands [42; 43; 44], nitrogen status of crops [45; 46] and monitoring water quality of inland waters [47]. Indices representing vegetation \"vigour\" or \"greenness\" are commonly used in Earth observation of vegetation. They have, for instance, been successfully used to map and monitor photosynthetically active and healthy vegetation [2], differentiate between weeds, crops and bare soil [48], and determine the chlorophyll content and plant biomass [49; 50]. Commonly used indices include the normalized difference vegetation index (NDVI) [51], the green normalized difference vegetation index (GNDVI) [52] and the soil adjusted vegetation index (SAVI) [53], but the efficacy of a range of other indices has also been demonstrated [54; 55; 56; 57]. Low-resolution (\(\geq\)250 m) NDVI data have been found to be useful for indicating grazing land productivity and evaluating vegetation activity across regional grazing systems [58; 59; 60; 61]. Vegetation indices have been combined with GPS collar data to monitor animal movement patterns, habitat use and grazing preferences [1; 12; 62; 63]. Using NDVI and GPS data, [PERSON] et al. [1] and [PERSON] et al. [12] showed that changes in grazing vegetation conditions can be correlated with animal movement and grazing preferences. Apart from these pioneering studies, relatively little research has been done on using RS data to inform grazing management. Ground-based behavioural sampling assessments often lack continuous observations over extended periods and normally only last a few days, are often limited to daylight hours and weather conditions could play a significant role [64; 65; 66]. For instance, observations could be affected by wind or rain where animals tend to lie down during adverse weather conditions. It is important to assess behavioural patterns across all four seasons of a year. For instance, animals' grazing and movement patterns on cold winter mornings are different compared to those of hot summer months. Due to limited funds, time, and observers, many studies are only carried out in one or maybe two seasons [64; 67]. Another limitation of in situ (ground-based) observations is that observer proximity could alter the animals' grazing behaviour [65; 68]. Therefore, techniques and technologies that can bridge the gap between ground-based assessments and longitudinal observations offered by remotely sensed data are of great importance for current and future monitoring purposes [69]. This study aims to evaluate the use of remotely sensed vegetation indices for rangeland management in semi-arid areas sensitive to overgrazing and degradation. GPS measurements and Getis-Ord Gi* hotspot analysis were used to map statistically significant hotspots of animal movement (i.e., gazing). The hotspots were then statistically compared to eight commonly used vegetation indices, extracted from Sentinel-2 imagery, to determine how growth vigour relates to animal movement.