This study assesses the use of Sentinel-2 imagery and cloud-based remote sensing on Google Earth Engine to map the extent of Cape Lowland Freshwater Wetlands in Cape Town, a critically endangered ecosystem. By testing NDWI and MNDWI thresholds, it found −0.3 and −0.4 to be optimal for dry and wet seasons respectively, with the best accuracy at 85%. Limited seasonal variation in wetland extent suggests potential ecosystem degradation. The research supports scalable wetland monitoring and contributes to conservation efforts aligned with South Africa’s national inventory goals and the SDGs.
The Steenbras Nature Reserve near Cape Town, part of the Cape Floral Kingdom, contains seep fynbos wetlands that are both ecologically vital and vulnerable. To support their monitoring and conservation, this study developed a UAV-based workflow for mapping wetland species using multispectral imagery and machine learning. The research addressed four key questions: (1) how to optimize UAV multispectral data for species-level mapping, (2) which classifiers perform best for delineating fynbos species, (3) what the optimal season for species discrimination is, and (4) whether different UAV sensors can produce consistent results. Key vegetation indices included the Normalized Green-Red Difference Index (NGRDI), Normalized Difference Red Edge Index (NDRE), and Chlorophyll Index Red Edge (CIRE), as well as RG, Log Red, and Log Red Edge. Six classifiers consisting of Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), Naive Bayes (NB), Artificial Neural Networks (ANN), XGBoost, and Treebag were tested to classify twelve dominant wetland plant species. RF achieved the highest accuracy at 97%. The findings highlight that late winter is the best time of year for mapping these species, offering the clearest spectral separability among them. The study also showed that sensor characteristics significantly affect classification accuracy, even within the same season. These results contribute a robust and transferable workflow for high-resolution species-level monitoring in complex wetland ecosystems.
Floods remain one of nature's most destructive forces, causing extensive infrastructure damage and loss of human lives globally. The lack of advanced flood risk modelling and real-time monitoring systems hampers the development of effective early warning mechanisms, leaving resource-constrained regions, particularly in Southern Africa, ill-prepared for flood mitigation and community protection. This study addresses the critical need for improved flood risk assessment by leveraging Synthetic Aperture Radar (SAR) data from Sentinel-1 integrated within the Google Earth Engine (GEE) cloud computing platform. The research methodology involved comprehensive mapping of flood-prone zones through integration of multiple data sources including satellite imagery, digital elevation models, land cover maps, and historical flood records to delineate areas affected by flooding during February 2023 in Mpumalanga province, South Africa. Results revealed that flood inundation extent was highest in Ehlanzeni district, followed by Gert Sibande district, while Nkangala district demonstrated comparatively lower flooding likelihood. The increased vulnerability of Ehlanzeni and Gert Sibande districts was primarily attributed to their low relief topography (0-500 m.a.s.l.), contrasting with less flood-prone districts characterized by higher elevation (>500 m.a.s.l.). Rainfall analysis confirmed that areas identified as flood-prone through SAR-derived inundation mapping received relatively high precipitation, establishing precipitation as the primary flooding driver. Land use/land cover analysis revealed significant shifts between grasslands, bare land, forest, and built-up areas, particularly in flood-prone districts. The findings underscore the effectiveness of GEE cloud computing platforms coupled with multi-source Earth Observation data for accurate provincial-level flood risk determination. This research provides a robust, cost-effective methodology particularly beneficial for resource-constrained regions where flood monitoring capabilities are limited. The results are critical for developing targeted flood mitigation interventions and establishing improved early warning systems.
Floods are a major problem affecting many parts of the world. Generally, the Cuvelai Etosha Basin (CEB) of north-central Namibia and specifically Oshakati and Ongwediva towns are no exceptions to this problem. Flood analysis in Oshakati and Ongwediva was carried out to map and determine drainage patterns, inundated zones and flood susceptibility. Results of data analysis has indicated that Oshakati town is prone to flooding than Ongwediva town. This is evident from very high and high flood susceptibility classes that occupies a large area in Oshakati town than in Ongwediva town. Moreover, Oshakati town has more of its land falling within shallow depressions, streams and inundation in comparison to Ongwediva town.
The interaction between statutory and customary land administration in South Africa is examined through two case studies in KwaZulu-Natal and Limpopo provinces, highlighting the inconsistencies and lack of regulation where these systems coexist. Drawing on fieldwork and secondary data, the study reveals both commonalities and differences in land administration practices. Using fit-for-purpose land administration principles, we recommend short-term and long-term strategies for consolidating land information. These include mapping existing settlements and reforming legal frameworks. We conclude that integration is feasible with political will. Further research in required in other former homelands to better understand local dynamics.
