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Background: The KwaZulu-Natal Sandstone Sourveld (KZN SS) is a grassland ecosystem categorised as endangered by the terms of the National Environmental Management: Biodiversity Act (Act 10 of 2004). Pressure from urbanisation has led to the remaining areas of the KZN SS being physically fragmented, causing low connectivity levels which have diminished the biological persistence of this ecosystem. Objectives: This study aimed to quantify the overall level of habitat fragmentation of the KZN SS and determine the level of connectivity within the ecosystem, and patches of the KZN SS occurring in the eThekwini Municipal area. Using graph theory, we compared the effectiveness of broad-scale and fine-scale data sets in quantifying habitat fragmentation. Methods: The Conefor Sensinode software, which employs the bases of graph theory, was chosen to aid in assessing fragmentation levels. The integral index of connectivity (IIC) (values range from 0 to 1, with 1 highlighting optimal connectivity) was chosen as the best index to determine landscape connectivity. Results: The KZN SS was shown to be highly fragmented, especially at dispersal distances less than 500 m. This resulted in very low connectivity levels, with the highest IIC value recorded at 0.0063. Moreover, the use of either fine-scale or broad-scale data to measure connectivity differed substantially at 1000 m. Conclusion: Broad-scale data were shown to overestimate habitat fragmentation and underestimate landscape connectivity. A more apt description of KZN SS connectivity levels was realised with fine-scale data. This study recommends that crucial KZN SS patches be prioritised to safeguard this endangered ecosystem. [ABSTRACT FROM AUTHOR]

• The majority of KwaZulu-Natal's SOC stocks were found to be distributed between 60–200 cm. • Forested areas in KwaZulu-Natal had the greatest SOC concentration per unit Area. • Rainfall, clay content, temperature, elevation, and RVI were found to be key determinants of SOC prediction at different depths. • DNN model performed well in topsoil (0–30 cm), but less accurate with deeper depth. The preservation and augmentation of soil organic carbon (SOC) stocks is critical to designing climate change mitigation strategies and alleviating global warming. However, due to the susceptibility of SOC stocks to environmental and topo-climatic variability and changes, it is essential to obtain a comprehensive understanding of the state of current SOC stocks both spatially and vertically. Consequently, to effectively assess SOC storage and sequestration capacity, precise evaluations at multiple soil depths are required. Hence, this study implemented an advanced Deep Neural Network (DNN) model incorporating Sentinel-1 Synthetic Aperture Radar (SAR) data, topo-climatic features, and soil physical properties to predict SOC stocks at multiple depths (0–30 cm, 30–60 cm, 60–100 cm, and 100–200 cm) across diverse land-use categories in the KwaZulu-Natal province, South Africa. There was a general decline in the accuracy of the DNN model's prediction with increasing soil depth, with the root mean square error (RMSE) ranging from 8.34 t/h to 11.97 t/h for the four depths. These findings imply that the link between environmental covariates and SOC stocks weakens with soil depth. Additionally, distinct factors driving SOC stocks were discovered in both topsoil and deep-soil, with vegetation having the strongest effect in topsoil, and topo-climate factors and soil physical properties becoming more important as depth increases. This underscores the importance of incorporating depth-related soil properties in SOC modelling. Grasslands had the largest SOC stocks, while commercial forests have the highest SOC sequestration rates per unit area. This study offers valuable insights to policymakers and provides a basis for devising regional management strategies that can be used to effectively mitigate climate change. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is a critical measure for ecosystem health and offers opportunities to understand carbon fluxes and associated implications. However, SOC can be significantly influenced by anthropogenic land use change, with intensive and extensive disturbances resulting in considerable SOC loss. Consequently, understanding the spatial distribution of SOC across different land uses, particularly at national level characterised by different biomes, is vital for integrated land-use planning and climate change mitigation. Remote sensing and deep learning (DL) offer a reliable largescale mapping of SOC by leveraging on their big data provision and powerful analytical prowess, respectively. This study modelled SOC stocks across South Africa's major land uses using Deep Neural Networks (DNN) and Sentinel-3 satellite data. Based on 1936 soil samples and 31 spectral predictors, results show a relatively high accuracy with an R2 and RMSE value of 0.685 and 10.15 t/h (26% of the mean), respectively. From the seven land uses evaluated, grasslands (31.36%) contributed the most to the overall SOC stocks while urban vegetation (0.04%) contributed the least. Moreover, although SOC stock was found to be relatively proportional to land coverage, commercial (46.06 t/h) and natural (44.34 t/h) forests showed a higher carbon sequestration capacity. These findings provide an important guideline to managing SOC stocks in South Africa, useful in climate change mitigation through sustainable land-use practices. Whereas landscape restoration, and other relevant interventions are encouraged to improve SOC storage, care must be taken within land-use decision making to maintain an appropriate balance between carbon sequestration, biodiversity, and general ecosystem functions. [ABSTRACT FROM AUTHOR]

The detection and monitoring of lethal pathogenic funguses are important for effectively deploying suppressive measures to sudden and severe outbreaks in plantation forestry. This study successfully investigated the utility of Landsat 8 multispectral satellite imagery to map Uromycladium acacia (wattle rust) induced canopy defoliation across black wattle plantations. The framework developed for the provincial assessment of rust damage proved to be effective by using data collected from field monitoring plots over the year 2015 and 2016. Using a powerful Gradient Boosting Machine (GBM) approach, rust occurrences were mapped at accuracies of 69% for March and 72% for November when using the 2015 dataset and 77% for March and 81% for November using the 2016 dataset. Individual class accuracies for varying levels of defoliation were also evaluated. When aggregating the field and image datasets, a two-year probability map revealed the likelihood of rust defoliation across the black wattle plantation region. Overall, the study showcased the robustness and cost-effectiveness of using multispectral remote sensing methodologies for repeatable forest health monitoring in key commercial forest plantations. [ABSTRACT FROM AUTHOR]

Environmental degradation and carbon emissions have become a major global concern. This has forced policymakers to consider strategic and long-term contingencies to increase carbon sequestration capacity and mitigate the effects of climate change. Soil organic carbon (SOC) provides a reliable long-lasting mechanism to ameliorate climate change and regulate carbon fluxes. However, unanticipated rates of climate change coupled with the dynamic nature of land-use transformation threatens current mitigation approaches and can jeopardise carbon stock assimilation. To effectively manage and protect SOC stocks, large-scale projections that accurately model both current and future SOC pools are necessary. Hence, this study modelled the effects of simulated climate and land-cover change on SOC inventories across South Africa up to the year 2050. A digital soil mapping strategy in concert with a deep neural network (DNN) was used to model current SOC stocks distribution. Subsequently, WorldClim general circulation models and a space-for-time substitution (SFTS) method were used to derive future SOC stocks under four shared socio-economic emission pathways. Results show a relatively high accuracy with RMSE of 7.44 t/h for current stocks, while future stocks ranged from 11.37 to 13.56 t/h. Depending on emission rates, results showed a reduction in SOC inventories, with overall SOC stocks declining from 5.64 Pg to between 4.97 and 5.38 Pg by 2050. Meanwhile, forests, which account for approximately 1.2 Pg of total SOC in South Africa, were found to have lost more than 1% of their total coverage by 2050. These findings provide a glimpse into the state of South Africa's current and future SOC stock inventories and the influence of climate and land-use change. These findings are valuable to among others policymakers, land use managers and climate change experts in assessing the long-term feasibility of South Africa's existing SOC management protocols and land-use planning agenda. However, to adequately protect future SOC stocks, current land-use planning frameworks need to be re-adjusted to prioritize pressing environmental concerns. • First national scale projection (2050) of SOC stocks in South Africa. • South Africa SOC stocks inventory declined from 5.64 Pg to between 4.97 and 5.38 Pg by 2050. • The extent of SOC decrease varies across different shared socio-economic emission pathways. • SOC stocks are proportional to land cover size, but forests have the highest sequestration rate. [ABSTRACT FROM AUTHOR]