## 4 Discussion  
The vegetation monitored in this study is of exceptionally high conservation value, the density of species threatened with extinction is almost unmatched globally, and every remnant of natural habitat is of enormous value in ensuring the continued survival of the flora and fauna of the Renosterveld (11; 19; 41; 42). The loss of 478 hectares of intact Renosterveld between February 2016 and January 2020 shows that the current approach to conservation in  
Figure 5: The spatial distribution of Renosterveld loss observed in the Overberg district between February 2016 and January 2020. Point size indicates the size of loss events in both panels, whereas colour indicates size (**left**), and the date of loss (**right**). Figure 6: Hectares of Renosterveld lost (**left**) and number of loss events (**right**), defined as spatially contiguous polygons, by month and year over the study period. the region is failing in its goal of preventing further loss of what remains of the Renosterveld. The ability to monitor the daily progression of land cover change in this region demonstrated here provides information with which to better understand the drivers of Renosterveld loss and the potential for improved enforcement of existing protections. Large inter- and intra-annual variability in rates of loss suggests that actors responsible for land cover change are doing so in response to external factors. Accurate dating of individual events aids in diagnosing these factors and potential future mitigation [43]. For law enforcement and others involved in efforts to prevent further loss, the ability to continuously monitor ongoing change will serve as a strong deterrent, and aid in the deployment of existing resources. Until recently, frequent monitoring of land cover change at high (<30 m ) spatial resolution has either been impossible or prohibitively expensive. This has precluded the detailed study of land cover change dynamics in highly fragmented and non-forest ecosystems. With the growing time series of data freely available from Sentinel 2 and the unprecedented temporal resolution of the Planet labs Dove constellation it has become possible to reconstruct accurate timelines of land cover change and continuously monitor ongoing change in many of the world ecosystems [22; 44]. The approach applied here produced a detailed description of the patterns of land cover change in space and time, allowing 50% of events to be dated within 2 weeks of their occurrence, and the loss of fragments as small as 300 m\({}^{2}\) to be detected in a highly fragmented, low-biomass shrubland ecosystem. A significant amount of expert knowledge and manual image interpretation is required to locate and date land cover change events in this ecosystem. However, screening of areas in which change is likely through the rapid creation of a simple training dataset and application of standard classification algorithms greatly improved the efficiency of this task. This map of Renosterveld change events with associated dates provides a unique dataset with the potential to automate monitoring in the region. Existing approaches to continuous monitoring and detection of change almost exclusively apply an unsupervised approach to detecting land cover change (e.g., [21; 22; 23; 24]). Abrupt changes in time-series' of vegetation activity are interpreted as land cover change in this framework. Natural processes that may cause surface reflectance to change, such as natural wildfire or treefall, will be missclassified. Using a dataset in which only land cover change events caused by anthropogenic processes are included to train an algorithm in a supervised manner may separate the signal of unnatural disturbance from that of natural disturbance. The additional knowledge of the date of each event enables training algorithms that will be robust to seasonal changes in vegetation, and hence can be applied continuously as new data become available rather than annually [45; 46].