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Unfettered agricultural activities have severely degraded vast areas of grasslands over the last decade. To rehabilitate and restore the productivity in affected grasslands, rangeland management practices still institute vast nitrogen-based fertilization regimes. However, excessive fertilization can often have damaging environmental effects. Over-fertilization can lead to nitrogen saturation. Although early indicators of nitrogen saturation have been documented, research detailing the near-real-time nitrogen saturation status of grasslands is required to better facilitate management protocols and optimize biomass production within degraded grasslands. Hence, the aim of this study was to discriminate nitrogen-saturated tropical grasses grown under a diverse fertilization treatment trial, using Worldview-3 satellite imagery and decision tree techniques. To accomplish this, nitrogen-saturated plots were first identified through specific physiological-based criteria. Thereafter, Worldview-3 satellite imagery (400–1040 nm) and decision tree techniques were applied to discriminate between nitrogen-saturated and -unsaturated grassland plots. The results showed net nitrate (NO3−-N) concentrations and net pH levels to be significantly different (α = 0.05) between saturated and non-saturated plots. Moreover, the random forest model (overall accuracy of 91%) demonstrated a greater ability to classify saturated plots as opposed to the classification and regression tree method (overall accuracy of 79%). The most important variables for classifying saturated plots were identified as: the Red-Edge (705–745 nm), Coastal (400–450 nm), Near-Infrared 3 (838–950 nm), Soil-Adjusted Vegetation Index (SAVI) and the Normalized Difference Vegetation Index 3 (NDVI3). These results provide a framework to assist rangeland managers in identifying grasslands within the initial stages of nitrogen saturation. This will enable fertilization treatments to be adjusted in near-real-time according to ecosystem demand and thereby maintain the health and longevity of Southern African grasslands. [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]

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]