This dataset contains LiDAR and aerial photograph data for the Sabie, Olifants and Letaba rivers in the Kruger National Park, South Africa. The LiDAR and aerial photograph data were collected for the NERC funded project (NE/K001132/1) 'Evaluating and modelling the impact of extreme events on South African dryland rivers: Cyclone Dando (January 2012)'.

Southern Mapping Geospatial were commissioned in South Africa to undertake the LiDAR flights for the NERC-funded project. Processed data of point cloud, processed contours, and aerial image files were provided. Data were retrieved using an Optech Orion M200 LiDAR which scanned the ground at 70 kHz, and a Rollei AIC with 60 megapixel P65+ and Phase One digital CCD, flown at 1100m from a Cessna 206. The survey concentrated on three 50 km reaches of the Olifants, Sabie and Letaba rivers surveyed on 30th May 2012, nearly 5 months after a large Cyclone driven flood in January 2012.

The folders include: 1) Index - contains a .dgn file to position data in ArcMap, 2) Points - contains sub-folders with the raw LiDAR point cloud data; ground points and vegetation at different heights indicated by the folder label 3) Report - summary report provided by Southern mapping who we commissioned to fly the LiDAR 4) Contour data in dgn and dwg format, 5) Image tiles

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.

Terrestrial LiDAR point clouds and hemispherical photographs from 1 ha plots in southern African woodlands. 15 plots in Bicuar National Park, Angola, and 7 plots in Mtarure Forest Reserve, Tanzania. Scans were located to maximise woodland tree canopy penetration.

Digital terrain data, Image data, LiDAR point cloud, Elevation model, Uncompressed and Compressed.

1. Woody thickening or “bush encroachment†is a growing concern in savannas worldwide and can reportedly be reversed by applying high-intensity fires. Preliminary findings following experimental fires in 2010 and 2013 indicated that woody plant cover declined one year after high-intensity fires, but increased after low-intensity fires. However, the longer-term outcomes of high-intensity fires are largely unknown.
2. To establish longer-term outcomes, we re-assessed sites subjected to Low, Medium and High-intensity fire treatments 10 years after the initial experimental fires. We compared woody vegetation structure in 2010 with that in 2020 using both ground surveys and airborne LiDAR.
3. Ground surveys revealed increases in the number of stems and individual shrubs (< 10 m tall) over 10 years, and decreases in shrub height, with no significant differences between treatments. Large trees (≥ 10 m) declined by about 65% in number due to ongoing high mortality across treatments over 10 y..., Data was collected using both ground surveys as well as airborne LiDAR.,

Two Micro-Pulse Lidar (MPL) systems were deployed to Africa for the SAFARI 2000 experiment. One MPL was setup in Mongu, Zambia and the other was setup in Skukuza, South Africa. The primary focus of MPL work during SAFARI was to study the vertical distribution and optical properties of smoke from biomass burning in the region.

The flexibility of UAV-lidar remote sensing offers a myriad of new opportunities for savanna ecology, enabling researchers to measure vegetation structure at a variety of temporal and spatial scales. However, this flexibility also increases the number of customizable variables, such as flight altitude, pattern, and sensor parameters, that, when adjusted, can impact data quality as well as the applicability of a dataset to a specific research interest. To better understand the impacts that UAV flight patterns and sensor parameters have on vegetation metrics, we compared 7 lidar point clouds collected with a Riegl VUX-1LR over a 300 x 300 m area in the Kruger National Park, South Africa. We varied the altitude (60 m above ground, 100 m, 180 m, and 300 m) and sampling pattern (slowing the flight speed, increasing the overlap between flightlines, and flying a crosshatch pattern), and compared a variety of vertical vegetation metrics related to height and fractional cover. Comparing vegetati...

Data files (CSV and TXT) supporting the analyses in the paper: Megafaunal effects on vegetation structure throughout a densely wooded African landscape

Supplementary methods for estimation of classification model accuracy, community analysis, and field data comparison.