This data repository presents a workflow to derive woody cover information for the Kruger National Park, South Africa, from freely available Sentinel-1 C-Band time series and LiDAR data (modified from Smit et al. 2016) using machine learning (MLR and Ranger in R). The methodology is described in following publication: Urban, M., K. Heckel, C. Berger, P. Schratz, I.P.J. Smit, T. Strydom, J. Baade & C. Schmullius (2020): Woody Cover Mapping in the Savanna Ecosystem of the Kruger National Park Using Sentinel-1 C-Band Time Series Data. Koedoe. In order to derive woody cover percentage information, download all files into one folder and run the R-Files consecutively from 01_ to 04_. Follow the instruction within each of the R-Files, which are written as comments in the programming code. The data repository consist of the following files: R-Files: 1. Script 1: 01_MLR_tune_spatial_final 2. Script 2: 02_MLR_cross_validation_spatial_final 3. Script 3: 03_MLR_RANGER_train_final 4. Script 4: 04_MLR_prediction_woody_cover_final Training dataset - ENVI FILE (layerstack of Sentinel-1 VH and VV backscatter between 2016 and 2017 and the woody cover reference derived from the LiDAR data) : 1. S1_A_VH_VV_16_17_lidar Data for prediction - ENVI FILES (3 example regions in the Kruger National Park): 1. S1_A_VH_VV_16_17_subset_example_Letaba_Rest_Camp 2. S1_A_VH_VV_16_17_subset_example_Lower_Sabie 3. S1_A_VH_VV_16_17_subset_example_Pafuri Final woody cover maps of the Kruger National Park: 1. xx_woody_cover_map_final.rar (contains final maps in 10m, 30m, 50m and 100m spatial resolution as .tif and a QGIS project) References: Smit, I.P.J., Asner, G.P., Govender, N., Vaughn, N.R. & Wilgen, B.W. van, 2016, ‘An examination of the potential efficacy of high-intensity fires for reversing woody encroachment in savannas’, Journal of Applied Ecology, 53(5), 1623–1633.

Acknowledgment: The study was funded by the project South African Land Degradation Monitor (SALDi) sponsored by the German Federal Ministry of Education and Research (BMBF) in the framework of the Science Partnerships for the Assessment of Complex Earth System Processes (SPACES II) under the grant 01LL1701A. It was also partly funded by the European Union (EU) Horizon 2020 Research and Innovation Program under the Grant Agreement No. 640176 (BACI - Towards a Biosphere Atmosphere Change Index). Co-authors received funding from the ARS AfricaE and EMSAfrica research projects, which are part of the SPACES and SPACES II initiatives and funded by the BMBF (grant numbers 01LL1303D and 01LL1801D) and the project Munich Center for Machine Learning (MCML) from the BMBF (grant number 01IS18036A). Substantial scientific and logistical support in the KNP was received through our two SANParks projects ARS Africae EO (ref. no. SCHCC1235) and CO-LD-EMS (ref. no. BAAJ1519). The Carnegie Airborne Observatory (CAO) LiDAR data collection from (Smit et al. 2016) was funded by the Andrew Mellon Foundation. The CAO was supported by the Avatar Alliance Foundation, John D. and Catherine T. MacArthur Foundation, Andrew Mellon Foundation, Mary Anne Nyburg Baker and G. Leonard Baker Jr., and William R. Hearst III. The LiDAR data for independent validation of the woody cover map were provided by G. Heritage (University of Salford, UK) and D. Milan (University of Hull, School of Environmental Sciences, UK).