Namibia is a very arid country, which has experienced significant bush encroachment and associated decreased livestock productivity. Therefore, it is essential to monitor bush encroachment and widespread debushing activities, including selective bush thinning and complete bush clearing. The aim of study was to develop a system to map and monitor fractional woody cover (FWC) at national scales (50 m and 75 m resolution) using Synthetic Aperture Radar (SAR) satellite data (Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) global mosaics, 2009, 2010, 2015, 2016) and ancillary variables (mean annual precipitation—MAP, elevation), with machine learning models that were trained with diverse airborne Light Detection and Ranging (LiDAR) data sets (244,032 ha, 2008–2014). When only the SAR variables were used, an average R² of 0.65 (RSME = 0.16) was attained. Adding either elevation or MAP, or both ancillary variables, increased the mean R² to 0.75 (RSME = 0.13), and 0.79 (RSME = 0.12). The inclusion of MAP addressed the overestimation of FWC in very arid areas, but resulted in anomalies in the form of sharp gradients in FWC along a MAP contour which were most likely caused by to the geographic distribution of the LiDAR training data. Additional targeted LiDAR acquisitions could address this issue. This was the first attempt to produce SAR-derived FWC maps for Namibia and the maps contain substantially more detailed spatial information on woody vegetation structure than existing national maps. During the seven-year study period the Shrubland–Woodland Mosaic was the only vegetation structural class that exhibited a regional net gain in FWC of more than 0.2 across 9% (11,906 km²) of its area that may potentially be attributed to bush encroachment. FWC change maps provided regional insights and detailed local patterns related to debushing and regrowth that can inform national rangeland policies and debushing programs.

Forest structural data are essential for assessing biophysical processes and changes, and promoting sustainable forest management. For 18+ years, the Multi-Angle Imaging SpectroRadiometer (MISR) instrument has been observing the land surface reflectance anisotropy, which is known to be related to vegetation structure. This study sought to determine the performance of a new MISR-High Resolution (HR) dataset, recently produced at a full 275 m spatial resolution, and consisting of 36 Bidirectional Reflectance Factors (BRF) and 12 Rahman–Pinty–Verstraete (RPV) parameters, to estimate the mean tree height (H_mean) and canopy cover (CC) across structurally diverse, heterogeneous, and fragmented forest types in South Africa. Airborne LiDAR data were used to train and validate Random Forest models which were tested across various MISR-HR scenarios. The combination of MISR multi-angular and multispectral data was consistently effective in improving the estimation of structural parameters, and produced the lowest relative root mean square error (rRMSE) (33.14% and 38.58%), for H_mean and CC respectively. The combined RPV parameters for all four bands yielded the best results in comparison to the models of the RPV parameters separately: H_mean (R² = 0.71, rRMSE = 34.84%) and CC (R² = 0.60, rRMSE = 40.96%). However, the combined RPV parameters for all four bands in comparison to the MISR-HR BRF 36 band model it performed poorer (rRMSE of 5.1% and 6.2% higher for H_mean and CC, respectively). When considered separately, savanna forest type had greater improvement when adding multi-angular data, with the highest accuracies obtained for the H_mean parameter (R² of 0.67, rRMSE of 31.28%). The findings demonstrate the potential of the optical multi-spectral and multi-directional newly processed data (MISR-HR) for estimating forest structure across Southern African forest types.

The woody component in African Savannahs provides essential ecosystem services such as fuel wood and construction timber to large populations of rural communities. Woody canopy cover (i.e. the percentage area occupied by woody canopy or CC) is a key parameter of the woody component. Synthetic Aperture Radar (SAR) is effective at assessing the woody component, because of its capacity to image within-canopy properties of the vegetation while offering an all-weather capacity to map relatively large extents of the woody component. This study compared the modelling accuracies of woody canopy cover (CC), in South African Savannahs, through the assessment of a set of modelling approaches (Linear Regression, Support Vector Machines, REPTree decision tree, Artificial Neural Network and Random Forest) with the use of X-band (TerraSAR-X), C-band (RADARSAT-2) and L-band (ALOS PALSAR) datasets. This study illustrated that the ANN, REPTree and RF non-parametric modelling algorithms were the most ideal with high CC prediction accuracies throughout the different scenarios. Results also illustrated that the acquisition of L-band data be prioritized due to the high accuracies achieved by the L-band dataset alone in comparison to the individual shorter wavelengths. The study provides promising results for developing regional savannah woody cover maps using limited LiDAR training data and SAR images.

With the significantincrease in the availability of 2-3 daily Harmonized Landsat and Sentinel-2 imagery (HLS), this satellite image time series (SITS) can capture the temporal and spatial features for accurate cropland mapping. This study evaluated the performance of four SITS deep learning (DL) classification approaches, including, convolutional LSTM, convolutional GRU, and 3D UNet, alongside a novel DPC-UNet architecture, for regional cropland mapping inSenegal, using 30 m HLS data. The study focused on the Eastern Casamance (ECAS) and Eastern Transition Zone (ETZ) regions. The study created extensive training data from multidate, 2 m WorldView imagery (WV) covering 15 locations (1500 km$^{2}$) and independently generated ${>}$2000 validation points from the WV images. All the SITS DL models generated cropland maps with high overall accuracy ${>}$ 92% in ECAS and ${>}$ 80% in ETZ. All models displayed relatively low precision of 76 to 80% that was caused by the well-documented overestimation of croplands due to confusion with fallow or abandoned fields and bare sand along drainage lines. The relative performance of the models differed between regions, however, the ConvLSTM marginally had the highest F1 score and overall accuracy in both regions. DPC-UNet displayed the most overestimation in ECAS, but the least in the ETZ. The cropland maps produced by the DL models otherwise displayed strong agreement with the benchmark Global Land Analysis and Discovery (GLAD) cropland map and may also be detecting new expanding fields along the edges of the core cropland clusters. Given their performance and rate of progress, it is likely that SITS DL models will become the standard for operational cropland mapping in the near future, especially with the eminent, dense frequency, 10m HLS data.

This study used remotely-sensed phenology data derived from Advanced Very High Resolution Radiometer (AVHRR), in a fully supervised decision-tree classification based on the new biome map of South Africa. The objectives were: (i) to investigate the long-term spatial patterns and inter-annual variability in satellite-derived vegetation phenology in relation to the recently revised biome map and (ii) to identify the phenological attributes that distinguishes between the different biomes. The long term phenometrics gave ecologically-meaningful results which reflect our current understanding of the spatial patterns of production and seasonality of vegetation growth in southern Africa. Regression tree analysis based on remotely-sensed phenometrics performed as good as, or better than, previous climate-based predictors of biome distribution.