Savanna ecosystems are dominated by different densities of grasses and woody plants with inter-annual changes because of dry and wet seasons. They cover half of the African continent and around 20% of the global land surface and are of great significance for ecology (e.g. living environment) and economy (e.g. fuelwood, timber) (Kutsch et al. 2008; Main et al. 2016; Scholes & Walker 1993). The savanna ecosystems of South Africa are shaped by disturbance processes such as droughts, fire and herbivory (Druce et al. 2008; Scholes & Archer 1997; Stevens et al. 2016), as well as anthropogenic impacts like climate change (e.g. increase in atmospheric CO₂) or management actions. It is therefore not surprising that these savannas have been undergoing various changes during the last decades (Buitenwerf et al. 2012; Skowno et al. 2016). Information about changes in woody cover and above-ground biomass (AGB) in national parks (e.g. Kruger National Park [KNP]) is important for park management and conservation efforts, as changes in woody cover are likely to have effects on other ecosystem patterns and processes. For example, an increase in woody cover will lead to a reduction in grass and herbaceous biomass (Berger et al. 2019), which will have cascading effects on herbivores (i.e. favouring browsers to grazers [Smit & Prins 2015]) and fire regimes (i.e. reducing fire frequency [Smit et al. 2012]). Furthermore, woody cover maps are useful for a range of other park management applications. For example, the KNP has used earlier versions of woody cover maps for adjusting rhino census estimates based on visibility (Ferreira et al. 2015) and understanding how woody encroachment may impact the large mammal communities of the park (Smit & Prins 2015). Also, although remote sensing monitoring of woody cover will not be able to replace extensive field monitoring of woody vegetation composition (Kiker et al. 2014), it does provide a cost-effective and wall-to-wall approach for monitoring fractional cover, which is considered as a satellite remote sensing essential biodiversity variable (Pettorelli et al. 2016).

The recent launch of new satellite missions, such as the Copernicus Sentinel fleet of the European Space Agency (ESA), has led to a tremendous increase in the availability of freely available earth observation (EO) data that can be utilised for woody cover-monitoring applications. Sentinel-1A/B, two C-Band Synthetic Aperture Radar (SAR) satellites, have a revisit time of up to 12 days in South Africa and acquire images with a spatial resolution of 10 m (Attema et al. 2009).

Estimation of woody cover by using EO data from different sources across different wavelengths was investigated in various studies (Bucini et al. 2010; Higginbottom et al. 2018; Main et al. 2016; Urbazaev et al. 2015). However, the reproducibility of these applications might be limited because of the utilisation of EO data, which are not systematically acquired or cost intensive (e.g. Japan Aerospace Exploration Agency - Advanced Land Observing Satellite [JAXA’s ALOS], aerial images and so on). The goal of this article was to introduce a workflow to derive woody cover information from freely available Sentinel-1A/B time series and light detection and ranging (LiDAR) data (Smit et al. 2016). This workflow was applied to produce a wall-to-wall woody cover map in different spatial resolutions for the KNP for 2016 and 2017. The estimation of woody cover by using C-Band data has shown great potential in the South African Lowveld region (where the KNP is located) because of deeper penetration of the radar signals into the open savanna vegetation (Mathieu et al. 2013). The workflow is publicly available to allow the continuation of woody cover monitoring, transferability to other savanna regions and future woody cover change mapping.

The woody cover map, derived from Sentinel-1 time series data acquired between 2016 and 2017, is shown in Figure 2. The estimations for woody cover range from 0% to 100%. The highest woody cover is reached in the northern part and the mopane-dominated landscapes on granite in the mid-west of the KNP as well as the mixed thorn and woodland areas in the southern part (30% – 50% and for some regions even higher than 50% on average). Lowest woody cover values were found in the open grasslands on basalt in the eastern part (below 20% on average). These broad-scale spatial patterns indicate that woody cover in KNP is significantly controlled by the underlying geology and the north-to-south rainfall gradient (Venter et al. 2003). Finer-scale contrasts in woody cover are also clearly discernable on the woody cover map. For example, the contrast between the lower woody cover gabbro areas within the higher woody cover granitic areas and the more open sodic areas as opposed to higher woody cover riparian zones are clearly visible.

The RF approach revealed that the Sentinel-1 time series data from the dry season of 2016 were the most important predictors for deriving woody cover information. Furthermore, the cross-polarised scenes between July and September 2016 have been identified to have the highest ranking in the variable importance, followed by the co-polarised scenes of the same time span. This implies that Sentinel-1 predictors representing the dry season are key variables for retrieving woody cover in heterogeneous savanna ecosystems. This is most certainly caused by a lower impact of surface moisture on the C-Band backscatter as well as a better separation between woody plants and grasses during the dry season (Bucini et al. 2010; Heckel et al. 2020; Urbazaev et al. 2015).

The spatial cross-validation, which was performed on the training data, resulted in a root mean squared error (RMSE) of 22.8% for the product with a spatial resolution of 10 m, and improved with spatial averaging to 15.8% for 30 m, 14.8% for 50 m and 13.4% for 100 m as has been found in other radar studies such as Santoro et al. (2013). The spatial cross-validation revealed coefficient of determination values of R² = 0.3 (10 m), R² = 0.5 (30 m), R² = 0.6 (50 m) and R² = 0.6 (100 m), respectively. These error estimates are slightly lower in comparison with the accuracy of other woody cover products with spatial resolution ranging between 50 m and 120 m (Bucini et al. 2010; Higginbottom et al. 2018; Main et al. 2016; Urbazaev et al. 2015), with RMSE of around 10%. In comparison with this study, these products are lacking a spatially weighted cross-validation (except Higginbottom et al. 2018), resulting in modelling accuracies that do not take into account effects of spatial autocorrelation (Brenning 2012). An additional independent validation was carried out by using woody cover estimates based on the LiDAR data from Milan et al. (2018), confirming the results of the spatial cross-validation of the model (Figure 2 – scatterplot in the lower left for the spatial resolution of 10 m). Furthermore, the following RMSE and R² are found for the other spatial resolutions: 30 m (RMSE = 19%, R² = 0.5), 50 m (RMSE = 18%, R² = 0.6) and 100 m (RMSE = 16%, R² = 0.6).

The presented workflow was applied to produce a wall-to-wall woody cover map in different spatial resolutions (e.g. 10 m, 30 m, 50 m and 100 m) for the KNP for 2016 and 2017, which are freely available via a data repository on zenodo.org. The user can choose between different spatial resolutions based on their needs. The woody cover maps provide insightful information for sustainable park management and future planning and conservation-related activities. The overestimation of woody cover below 20% is a typical phenomenon in radar retrievals (Mathieu et al. 2013; Santoro et al. 2011) caused by surface contributions to the signal, such as roughness. Underestimation for woody cover above 60% has been observed likewise in other studies (Bouvet et al. 2018) and is explained by saturation of the C-Band backscatter in dense canopies.

The presented workflow shows the capability of Sentinel-1 radar time series to retrieve fine-scale woody cover estimates in savanna ecosystems at regional scale. We found similar error estimates when comparing the accuracies to other woody cover products (e.g. Bucini et al. 2010; Higginbottom et al. 2018; Main et al. 2016; Urbazaev et al. 2015) at comparable spatial resolutions. This article describes the approach to derive woody cover from Sentinel-1 time series based on open-source software and open-access data to foster the reproducibility of the framework for individual research interests as well as to facilitate the potential of transferring the presented methodology to other savanna ecosystems.

It is worth mentioning that radar data are sensitive to environmental conditions, e.g., surface roughness and surface moisture, which need to be considered in future applications utilising this approach. Moreover, the definition of a height threshold for woody cover estimation, to exclude the non-woody vegetation component, might require an adjustment in other regions. We are currently focussing on comparing woody cover maps of different years to build up a woody cover change-monitoring system, where the mentioned environmental factors are analysed. In the near future, such a monitoring system will likely benefit from open access LiDAR data from the Global Ecosystem Dynamics Investigation (GEDI) (Coyle et al. 2015) on board of the International Space Station (ISS) and holds the potential to become an essential component of park management in the KNP and other national parks in South Africa.