## 2 Materials and Methods
### Identifying Potential Change  
Machine learning classification of Sentinel 2 imagery was used to identify areas of potential Renosterveld loss between 2016 and 2020, supervised using a manually created set of training data points from across the full extent of the study area. The year 2016 was chosen as the starting year for monitoring as it is the first full year for which both Sentinel 2 and PlanetScope data are extensively available. Sites with stable Renosterveld (no loss  
Figure 1: The Overberg district in South Africa. The **left panel** shows the natural extent of lowlands Renosterveld in the district and the **right panel** the remaining natural and degraded fragments. between February 2016 and January 2020), stable agriculture (cultivated by February 2016) and Renosterveld loss (loss of Renosterveld between February 2016 and January 2020) were identified using very high resolution Google Earth imagery and 50 cm resolution aerial photography [32]. Any area under cultivation by February 2016 was considered stable agriculture, as it is assumed impossible for agriculture to revert to natural land cover over this time frame [33; 34; 35]. Points were chosen to cover a variety of agricultural land use types using a regional map of crop types [36]. Points for stable Renosterveld were located in areas with apparently intact Renosterveld in January 2020. It is impossible to confirm that sites were pristine, primary Renosterveld from remotely sensed imagery alone. However, the presence of shrubs and heterogeneous plant composition characteristic of Renosterveld stands in stark contrast to the monocultures or bare soil characteristic of agricultural land, and can thus be discerned with confidence by a trained eye. The reliability of Renosterveld identification from aerial imagery alone was confirmed by multiple ad hoc field excursions. Locations of loss of Renosterveld between February 2016 and January 2020 were created from identifying areas where the presence of Renosterveld could be confirmed in February 2016 and the presence of non-natural land cover could be confirmed in January 2020. The location of sites where change was suspected to have occurred between these dates was provided by local conservation partners. The final dataset of labelled sites consists of 2177 sites for stable Renosterveld, 2336 sites for stable agriculture, and 909 site of Renosterveld loss. QGIS 3.10 and Google Earth were used for image interpretation and digitization. Input features were derived from Sentinel 2 L1C data from 6 February 2016 and 11 January 2020. Top-of-atmosphere Sentinel 2 data were used as surface reflectance maps and are not globally available for the entire Sentinel 2 record on Google Earth Engine. The entire study region falls within a single Sentinel 2 track (sensing orbit number 121) and thus images from a single date could be used for the beginning and end of the monitoring period, ensuring relatively homogeneous atmospheric conditions over the region. Dates were selected based on the availability of favourable atmospheric conditions and the absence of clouds, while mid- to late-summer was preferred due to the strong visual contrast  
Figure 2: Outline of the methodology used for **(A)** accurate mapping of Renosterveld loss and **(B)** obtaining dates for the occurrence of change events. The output from a random forest classification outlines an area of potential Renosterveld loss (**A1**). Very-high resolution Google Earth imagery confirms the presence of Renosterveld on 6 May 2017 (**A2**) and its absence on 6 December 2017 (**A3**). With the help of very-high resolution imagery an accurate outline of the areas lost is obtained (**A4**). The time-series of PlanetScope imagery confirms 15 May 2017 as the last day that the presence of Renosterveld can be confirmed (**B1**). Vegetation removal is ongoing on 16 May 2017, suggested by the presence of smoke (**B2**). By 19 May 2017, no Renosterveld remains (**B3**), and this is confirmed by later imagery from 2 June 2017 (**B4**).