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Biomass burning on the South African Highveld annually injects substantial amounts of aerosols and trace gases into the atmosphere, impacting the global radiative balance, cloud microphysics, and regional air quality. These aerosols are transported as plumes over long distances, posing challenges to existing in situ and satellite-based monitoring techniques because of their limited spatial and temporal resolution, particularly in environments with low-level sources. This study aims to develop and validate a novel radar-based methodology to detect, track, and characterise aerosol plumes, addressing the limitations of existing in situ and satellite monitoring techniques. Using high-resolution volumetric reflectivity data from an S-band radar in Pretoria, South Africa, a traditional storm tracking algorithm is adapted to improve plume identification. Case studies of plume events in June and August 2013 demonstrate the radar's effectiveness in distinguishing lower vertical profiles and reduced reflectivity of plumes compared with storm echoes. The adapted algorithm successfully tracked the spatial and temporal evolution of the plumes, revealing their short-lived nature. Results indicate that radar-derived geospatial characteristics have the potential to contribute significantly to understanding the impacts of plumes on local air quality. These findings underscore the critical need for high spatio-temporal resolution data to support effective air quality management and inform policy development in regions affected by biomass burning. [ABSTRACT FROM AUTHOR]

Analysis of land use/land cover (LULC) in catchment areas is the first action toward safeguarding freshwater resources. LULC information in the watershed has gained popularity in the natural science field as it helps water resource managers and environmental health specialists develop natural resource conservation strategies based on available quantitative information. Thus, remote sensing is the cornerstone in addressing environmental-related issues at the catchment level. In this study, the performance of four machine learning algorithms (MLAs), namely Random Forests (RFs), Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Naïve Bayes (NB), were investigated to classify the catchment into nine relevant classes of the undulating watershed landscape using Landsat 8 Operational Land Imager (L8-OLI) imagery. The assessment of the MLAs was based on a visual inspection of the analyst and commonly used assessment metrics, such as user's accuracy (UA), producers' accuracy (PA), overall accuracy (OA), and the kappa coefficient. The MLAs produced good results, where RF (OA = 97.02%, Kappa = 0.96), SVM (OA = 89.74%, Kappa = 0.88), ANN (OA = 87%, Kappa = 0.86), and NB (OA = 68.64%, Kappa = 0.58). The results show the outstanding performance of the RF model over SVM and ANN with a significant margin. While NB yielded satisfactory results, its sensitivity to limited training samples could primarily influence these results. In contrast, the robust performance of RF could be due to an ability to classify high-dimensional data with limited training data. [ABSTRACT FROM AUTHOR]

Koh-i-Noor and other world-famous diamonds such as Hope, Orloff, Great Mogul, Nizam, and Pitt, well-known in the industry as 'Golkonda Diamonds' are very well recognised for their rare colours, large carat sizes, and because of the paucity of nitrogen atoms majority of them have been classified as Type IIa diamonds. These renowned Golkonda diamonds were recovered from placers mined on the banks of the Krishna River in southern India; however, their primary source rocks (either kimberlites or lamproites) remain questioned and untraced. Precise identification of the primary sources of such large-sized, dominantly Type IIa diamonds (i.e., CLIPPIRS) is crucial for understanding their deep mantle origin, nature and timing of magmatism carrying them and essential from economic and geological perspectives. We employed a multidisciplinary approach incorporating xenocrystic mineral composition and bulk-rock geochemistry, field geological and remote sensing (GIS) studies to locate the probable primary sources of these renowned diamonds, know the origin of Type IIa Golkonda diamonds in southern India, and to understand the mechanism and timing of diamond transport and dispersal as placers in the Krishna River basin. Our study rules out the possibility of various lamproite occurrences of the Eastern Dharwar Craton and Banganapalle conglomerates as being sources of Koh-i-Noor and other Golkonda diamonds. The absence of Type IIa diamonds in the highly diamondiferous Late Cretaceous kimberlites of Wajrakarur likewise excludes them as source of Golkonda diamonds. Among southern India's two significant kimberlite fields, i.e., Wajrakarur and Narayanpet, compositions of indicator minerals from the Wajrakarur Kimberlite Field (WKF) reveal their ultimate diamond preservation potential, presence of strong diamondiferous mantle roots and deeper source regions, hence recognising them to be the potential sources of Golkonda diamonds. GIS and remote sensing tools were used to calculate moisture content, vegetation indices, and to locate paleo-channel of the Penner River, which was primarily responsible for the transportation of diamonds from their Mesoproterozoic (ca. 1.1 Ga) source rocks at Wajrakarur to their final sites of recovery, i.e., Kolluru and other mines situated on the banks of the Krishna River. The occurrence of alluvial placer deposits in Krishna River drainage system is analogous to the Orange River drainage system in South Africa. Both areas have diamonds sourced from primary kimberlite pipes, transported by rivers, and deposited in specific areas. Similarities in the origin, mechanism of diamond transport, dispersal and deposition have played a crucial role in significant diamond production from alluvial deposits in Krishna and Orange Rivers. [ABSTRACT FROM AUTHOR]

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This study aims to highlight the importance of south-to-south cooperation in remote sensing and present preliminary results from our joint project between China and South Africa, focusing on multi-sensor remote sensing data for food security. Scientometric analysis was used to demonstrate growing research output and cooperation among countries in the Global South. Furthermore, the first results of our crop classification methods were presented on a test site in the Sanjiang Plain, China. The preliminary crop classification results from this test site showed promising accuracy. South-to-south cooperation in remote sensing has the potential to address shared challenges and promote sustainable development through exchange of knowledge and resources. The ongoing joint project between China and South Africa demonstrates the benefits of such collaboration in developing robust remote sensing techniques for improved food security monitoring and decision making in the Global South. [ABSTRACT FROM AUTHOR]

Gradient-Boosted Decision Trees (GBDTs), particularly when tuned with Bayesian optimisation, are powerful machine learning techniques known for their effectiveness in handling complex, non-linear data. However, the performance of these models can be significantly influenced by the characteristics of the terrain being analysed. In this study, we assess the performance of three Bayesian-optimised GBDTs (XGBoost, LightGBM and CatBoost) using digital elevation model (DEM) error correction as a case study. The performance of the models is investigated across five landscapes in Cape Town South Africa: urban/industrial, agricultural, mountain, peninsula and grassland/shrubland. The models were trained using a selection of datasets (elevation, terrain parameters and land cover). The comparison entailed an analysis of the model execution times, regression error metrics, and level of improvement in the corrected DEMs. Generally, the optimised models performed considerably well and demonstrated excellent predictive capability. CatBoost emerged with the best results in the level of improvement recorded in the corrected DEMs, while LightGBM was the fastest of all models in the execution time for Bayesian optimisation and model training. These findings offer valuable insights for applying machine learning and hyperparameter tuning in remote sensing. [ABSTRACT FROM AUTHOR]

Grasslands are key to the Earth's system and provide crucial ecosystem services. The degradation of the grassland ecosystem in South Africa is increasing alarmingly, and fire is regarded as one of the major culprits. Globally, anthropogenic climate changes have altered fire regimes in the grassland biome. Integrated fire-risk assessment systems provide an integral approach to fire prevention and mitigate the negative impacts of fire. However, fire risk-assessment is extremely challenging, owing to the myriad of factors that influence fire ignition and behaviour. Most fire danger systems do not consider fire causes; therefore, they are inadequate in validating the estimation of fire danger. Thus, fire danger assessment models should comprise the potential causes of fire. Understanding the key drivers of fire occurrence is key to the sustainable management of South Africa's grassland ecosystems. Therefore, this study explored six statistical and machine learning models—the frequency ratio (FR), weight of evidence (WoE), logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) in Google Earth Engine (GEE) to assess fire danger in an Afromontane grassland protected area (PA). The area under the receiver operating characteristic curve results (ROC/AUC) revealed that DT showed the highest precision on model fit and success rate, while the WoE was used to record the highest prediction rate (AUC = 0.74). The WoE model showed that 53% of the study area is susceptible to fire. The land surface temperature (LST) and vegetation condition index (VCI) were the most influential factors. Corresponding analysis suggested that the fire regime of the study area is fuel-dominated. Thus, fire danger management strategies within the Golden Gate Highlands National Park (GGHNP) should include fuel management aiming at correctly weighing the effects of fuel in fire ignition and spread. [ABSTRACT FROM AUTHOR]

Due to the need to continuously monitor and understand the thermal environment and its socioeconomic implications, this study used remotely sensed data to analyze thermal comfort variation in LCZs, including along the rural to urban gradient of the eThekwini Municipality in KwaZulu-Natal province of South Africa. LCZs were mapped using multi-temporal and multi-spectral Landsat 8 and Landsat 9 data using the approach by World Urban Database and Access Portal Tools (WUDAPT), while thermal data were used to retrieve land surface temperatures (LSTs). Data for training classification of LCZs and accuracy assessment were digitized from GoogleEarth guided by knowledge gained and data collected during a field survey in March 2022 as well as pre-existing maps. LCZs were mapped using the random forest classifier in SAGA GIS software while a single channel algorithm based on band 10 was used to compute LST for different scenes. The LSTs were adjusted and further used to derive thermal comfort based on the Universal Thermal Comfort Index (UTCI) categories as an indicator for outdoor thermal comfort on the extremely low- and extremely high-temperature periods in the cool and hot seasons, respectively. LCZs were mapped with high accuracy (overall accuracy of 90.1% and kappa of 0.88) while inter-class separability was high (>1.5) for all LCZ pairs. Built-up LCZs dominate the eastern parts of the municipality, signifying the influence of the sea on development within the area. Average LST was coolest in the dense forest, open low-rise and water LCZs in the cool and hot seasons, respectively. The compact high-rise LCZ was the warmest in both the hot (36 °C) and the cool (23 °C) seasons. The sea sands were among coolest regions in both seasons due to their high water content, attributed to their high water table and close proximity to the ocean. There was no thermal stress during the cool season, while most areas recorded moderate to strong heat stress in the hot season. Some areas in the densely built-up LCZs recorded very strong heat stress in the hot season. The findings suggest that policies and strategies should enhance heat mitigation capacities in strong-heat-stress areas during the hot season. Municipal authorities and citizens must work together to build strategies to minimize temperature extremes and associated socioeconomic pressures. Urban development policies, plans and strategies should consider implications on the thermal environment as well as the value of conservation of LCZs with high-heat mitigation value such as dense forests and expansion of built-up LCZs with low-heat absorption levels such as open low-rise. The study was based mainly on remotely sensed temperatures with some ground data used to validate results, which may limit the assessment. Overall, the study provides insights towards achievement of global sustainable and climate-smart development targets. [ABSTRACT FROM AUTHOR]

Geospatial technologies have become an essential component of archaeological research, aiding in the identification, mapping, and analysis of archaeological sites. Several journals have published existing narratives on the development and impact of geospatial technologies in the study of archaeology and cultural heritage. However, this has not been supported by a systematic review of articles and papers, where meticulously collected evidence is methodically analysed. This article systematically reviews the trends in the use of geospatial technologies in archaeology and cultural heritage through the search for keywords or terms associated with geospatial technologies used in the two fields on the Scopus database from 1990 to 2022. Bibliometric analysis using the Scopus Analyze tool and analysis of bibliometric networks using VOSviewer visualisations reveals how modern archaeological studies are now a significant discipline of spatial sciences and how the discipline enjoys the tools of geomatic engineering for establishing temporal and spatial controls on the material being studied and observing patterns in the archaeological records. The key concepts or themes or distinct knowledge domains that shape research in the use of geospatial technologies in archaeology and cultural heritage, according to the Scopus database (1990–2022), are cultural heritage, archaeology, geographic information systems, remote sensing, virtual reality, and spatial analysis. Augmented reality, 3D scanning, 3D modelling, 3D reconstruction, lidar, digital elevation modelling, artificial intelligence, spatiotemporal analysis, ground penetrating radar, optical radar, aerial photography, and unmanned aerial vehicles (UAVs) are some of the geospatial technology tools and research themes that are less explored or less interconnected concepts that have potential gaps in research or underexplored topics that might be worth investigating in archaeology and cultural heritage. [ABSTRACT FROM AUTHOR]

Rapid and accurate agricultural parcel mapping from high-resolution remote sensing imagery is fundamental to precision agriculture for smallholder farming systems. However, due to the narrow and small-size parcels, and the significant spatio-spectral variability, the existing two-stage segmentation methods cannot extract individual parcels automatically. In this article, the end-to-end vectorization of smallholder agricultural parcel boundaries (E2EVAP) framework is proposed for extracting the vertices of each parcel boundary individually in smallholder farming regions, where the semantic-contour interaction and topological loss through hierarchical instance representation (called "channel to instance") are designed for aggregating the foreground features and jointly establishing the topological relationship between instances to alleviate the topological overlap between parcel objects. Vertex shift correction based on the deep attention corner snake module guided by the parcel boundaries is also proposed to adaptively correct the boundary vertex location shift of large-scale irregular parcels. The comprehensive experimental results obtained on the iFLYTEK public agricultural parcel dataset confirm that E2EVAP shows a superior performance (with a mask mAP of 0.335 and a boundary mAP of 0.201), compared with the pixel-based (such as Mask R-CNN, HTC, and SOLOv2) and contour-based (such as DeepSnake and E2EC) benchmark methods. We believe that E2EVAP has the potential to be widely used for accurate vectorization mapping of agricultural parcels in smallholder farming areas such as South Africa and southern China. The code of E2EVAP is at https://github.com/YangPanHZAU/E2EVAP. [ABSTRACT FROM AUTHOR]

Commercial forest plantations in South Africa are homogeneous monocultures of highly bred exotic species grown to deliver timber products of the best potential quality. As such, these stands are susceptible to adverse effects of biotic and abiotic factors, and therefore require intense management to mitigate these risks. A sustainable forest monitoring system that can detect real-time changes in the physiological state of these plantations is needed for timeous management intervention to reduce losses. The use of machine learning algorithms has recently become popular, with acceptable levels of success. This study explores the application of deep learning neural networks for early detection of damage caused by baboons in evergreen plantations of Pinus species. Using PlanetScope imagery (spectral band 590–860 nm), which is captured by a constellation of Dove nanosatellites, with a high temporal resolution available daily at 3 m spatial resolution, the study achieved an overall accuracy of 81.54%, with a kappa value of 0.69, using a deep neural network. In comparison, using a random-forest classifier produced 74.04% accuracy and a kappa value of 0.62. The study successfully mapped different levels of baboon damage within commercial pine forests. We provide a repeatable method for daily monitoring initiatives, and attest to the utility of higher-resolution imagery such as PlanetScope for mapping health and damage severity at the tree level. [ABSTRACT FROM AUTHOR]

Megaherbivores play a critical role in the ecology of African savannas and grasslands. In addition, these systems are forecast to experience more frequent and severe droughts as a product of changes in the global climate. Thus, the continued conservation of megaherbivores and their associated ecosystems will require a better understanding of how megaherbivores respond to drought by shifting their movement, diet and social behaviour. We address this need by investigating the factors affecting changes in the abundance of common hippopotamus (Hippopotamus amphibius; hereafter: 'hippos') throughout the six major rivers of Kruger National Park, South Africa, during and following the severe drought of 2015/2016. Specifically, we aimed to understand the role of two environmental characteristics that have relevance to hippos and that changed in response to drought: vegetation condition and the extent of pooled surface water. In addition, we investigated the extent to which pre‐drought density affected changes in hippo abundance. Although vegetation and daytime refugia both appeared to influence pre‐drought hippo abundance, these factors were less important to the change in hippo abundance related to the drought. Instead, the response to drought was most strongly related to the pre‐drought abundance of hippos, where river segments supporting more than 50 individuals prior to the drought in 2015 decreased by more than half on average. Furthermore, we show that the degree of aggregation decreased from 2015 to 2016 because of the drought, but then began to increase again as the rains returned in 2017. Our results suggest that in addition to the large pools that support large aggregations of hippos in typical years, additional smaller pools are likely important for accommodating this drought‐induced dispersion. However, maintaining this distribution of pools will likely become more challenging as southern Africa's population and water demands increase. [ABSTRACT FROM AUTHOR]

Introduction: Object detection in remotely sensed satellite images is critical to socio-economic, bio-physical, and environmental monitoring, necessary for the prevention of natural disasters such as flooding and fires, socio-economic service delivery, and general urban and rural planning and management. Whereas deep learning approaches have recently gained popularity in remotely sensed image analysis, they have been unable to efficiently detect image objects due to complex landscape heterogeneity, high inter-class similarity and intra-class diversity, and difficulty in acquiring suitable training data that represents the complexities, among others. Methods: To address these challenges, this study employed multi-object detection deep learning algorithms with a transfer learning approach on remotely sensed satellite imagery captured on a heterogeneous landscape. In the study, a new dataset of diverse features with five object classes collected from Google Earth Engine in various locations in southern KwaZulu-Natal province in South Africa was used to evaluate the models. The dataset images were characterized with objects that have varying sizes and resolutions. Five (5) object detection methods based on R-CNN and YOLO architectures were investigated via experiments on our newly created dataset. Conclusions: This paper provides a comprehensive performance evaluation and analysis of the recent deep learning-based object detection methods for detecting objects in high-resolution remote sensing satellite images. The models were also evaluated on two publicly available datasets: Visdron and PASCAL VOC2007. Results showed that the highest detection accuracy of the vegetation and swimming pool instances was more than 90%, and the fastest detection speed 0.2 ms was observed in YOLOv8. [ABSTRACT FROM AUTHOR]

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 × 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 vegetation metrics from acquisitions with different flight patterns and sensor parameters, we found that both flight altitude and pattern had significant impacts on derived structure metrics, with variation in altitude causing the largest impacts. Flying higher resulted in lower point cloud heights, leading to a consistent downward trend in percentile height metrics and fractional cover. The magnitude and direction of these trends also varied depending on the vegetation type sampled (trees, shrubs or grasses), showing that the structure and composition of savanna vegetation can interact with the lidar signal and alter derived metrics. While there were statistically significant differences in metrics among acquisitions, the average differences were often on the order of a few centimetres or less, which shows great promise for future comparison studies.We discuss how these results apply in practice, explaining the potential trade‐offs of flying at higher altitudes and with alternate patterns. We highlight how flight and sensor parameters can be geared toward specific ecological applications and vegetation types, and we explore future opportunities for optimizing UAV‐lidar sampling designs in savannas. [ABSTRACT FROM AUTHOR]

South Africa is an important partner of China's cooperation with Africa in the "Belt and Road" Initiative. Analyzing the sustainable development of South African coastal areas will provide a reference for high-quality cooperation between China and South Africa. Combining remote sensing data and statistical data, a sustainable development assessment index system was constructed based on the pressure-state-response (PSR) model to analysis the level of sustainable development and differences in the coastal areas of South Africa from 2002 to 2018. The research results show that: (1) The pressure, status, and response factors of various regions increased from 2002 to 2018, the pressure index values of KwaZulu-Natal and Western Cape provinces were higher than those of other regions; the state index values of each region remained relatively stable and increase, and the Namakwa District was obviously different from other regions, less than half of other provinces; among the response index values, the Namakwa District and Western Cape province had improved rapidly, and the regional gap has gradually narrowed. (2) The level of sustainable development in South Africa coastal areas increased from 0. 460 to 0. 529 from 2002 to 2018. The regional difference in the level of sustainable development continued to shrink, Eastern Cape and KwaZulu-Natal province ranked high by the comprehensive sustainable development index, and the sustainable development of coastal regions were constantly improving. The coastal areas of South Africa had enormous potential for sustainable development, and Eastern Cape and KwaZulu-Natal province may be the ideal areas for China-South Africa cooperation in the "Belt and Road". [ABSTRACT FROM AUTHOR]

Remote sensing data play a crucial role in precision agriculture and natural resource monitoring. The use of unmanned aerial vehicles (UAVs) can provide solutions to challenges faced by farmers and natural resource managers due to its high spatial resolution and flexibility compared to satellite remote sensing. This paper presents UAV and spectral datasets collected from different provinces in South Africa, covering different crops at the farm level as well as natural resources. UAV datasets consist of five multispectral bands corrected for atmospheric effects using the PIX4D mapper software to produce surface reflectance images. The spectral datasets are filtered using a Savitzky–Golay filter, corrected for Multiplicative Scatter Correction (MSC). The first and second derivatives and the Continuous Wavelet Transform (CWT) spectra are also calculated. These datasets can provide baseline information for developing solutions for precision agriculture and natural resource challenges. For example, UAV and spectral data of different crop fields captured at spatial and temporal resolutions can contribute towards calibrating satellite images, thus improving the accuracy of the derived satellite products. Dataset: Available on request to munghemezuluc@arc.agric.za. The ARC (Agricultural Research Council) is in the process of developing an interface that will allow users to have access to the data directly from our server in line with our policies on data sharing. Therefore, in the meantime, users can request the data. Dataset License: CC BY-NC 4.0 [ABSTRACT FROM AUTHOR]

Understanding long-term land use/land cover (LULC) change patterns is vital to implementing policies for effective environmental management practices and sustainable land use. This study assessed patterns of change in LULC in the Vaal Dam Catchment area, one of the most critically important areas in South Africa, since it contributes a vast portion of water to the Vaal Dam Reservoir. The reservoir has been used to supply water to about 13 million inhabitants in Gauteng province and its surrounding areas. Multi-temporal Landsat imagery series were used to map LULC changes between 1986 and 2021. The LULC classification was performed by applying the random forest (RF) algorithm to the Landsat data. The change-detection analysis showed grassland being the dominant land cover type (ranging from 52% to 57% of the study area) during the entire period. The second most dominant land cover type was agricultural land, which included cleared fields, while cultivated land covered around 41% of the study area. Other land use types covering small portions of the study area included settlements, mining activities, water bodies and woody vegetation. Time series analysis showed patterns of increasing and decreasing changes for all land cover types, except in the settlement class, which showed continuous increase owing to population growth. From the study results, the settlement class increased considerably for 1986–1993, 1993–2000, 2000–2007, 2007–2014 and 2014–2021 by 712.64 ha (0.02%), 10245.94 ha (0.26%), 3736.62 ha (0.1%), 1872.09 ha (0.05%) and 3801.06 ha (0.1%), respectively. This study highlights the importance of using remote sensing techniques in detecting LULC changes in this vitally important catchment. [ABSTRACT FROM AUTHOR]

Multifunctional green infrastructure, a key component of compact sustainable cities, is challenged by the pressures associated with rapid urbanization. In this paper, we present a method that uses remote sensing, GIS modeling and stakeholder engagement to produce a decision support tool that communicates the availability and need for green infrastructure benefits. The case study presented is the City of Tshwane, South Africa, a Global South city facing rapid urbanization. We found that this method of mapping green infrastructure benefits can provide simultaneous oversight on multiple objectives for green infrastructure, including climate change adaptation, biodiversity, and equitable distribution of urban green space. We found that low-scoring benefit areas occur in dense urban areas where small-scale nature-based solutions or rehabilitation activities are required. Moderate benefit scores occurred in parts of the city that are vulnerable to urban expansion and densification activities, warranting the careful planning of green infrastructure provision, and that moderate-to-high-scoring areas can be protected as conservation areas. The results are discussed in terms of the role of decision support tools for urban planning practice. Composite indexes can provide important guidance to decision-makers involved in spatial planning and urban upgrading and expansion activities. [ABSTRACT FROM AUTHOR]

Shoreline analysis in response to the rapid erosion of sandy beaches has evolved along with geospatial and computer technology; it remains an essential task for sustainable coastal management. This severe and rapid erosion has been reported at several sandy beaches worldwide, including Yzerfontein beaches, on the southwest coast of South Africa. We determined this vulnerability from 1937 to 2020 and predicted its change by 2040 by manually delineating shoreline positions from 1937, 1960, and 1977 from aerial photographs and Landsat products between 1985 and 2020 in an automated fashion using the CoastSat toolkit and Google Earth Engine. We then integrated these datasets to calculate the extent of shoreline dynamics over the past eight decades using the Digital Shoreline Analysis System (DSAS). Our results show that the coastline changed dynamically between 1937 and 2020, culminating in an average net erosion of 38 m, with the most extensive erosion occurring between 2015 and 2020. However, coastal projections indicate a slight change in shoreline position over the next two decades. Further studies should integrate additional high resolution remote sensing data and non-remote sensing data (e.g., field surveys) to improve our results and provide a more thorough understanding of the coastal environment and overcome some of remotely-sensed data underlying uncertainties. [ABSTRACT FROM AUTHOR]

This paper examines a feature-level fusion framework for detecting and mapping land degradation (LD) and enabling sustainable land management (SLM) in semi-arid areas using optical sensors and Synthetic Aperture Radar (SAR) satellite data. The objectives of this review were to (i) determine the trends and geographical location of land degradation mapping publications, (ii) to identify and report current challenges pertaining to mapping LD using multiscale remote sensing data, (iii) to recommend a way forward for monitoring LD using multiscale remote sensing data. The study reviewed 78 peer-reviewed research articles published over the past 24 years (1998–2022). Image fusion has the potential to be more useful in various remote sensing applications than individual sensor image data, making it more informative and valuable in the interpretation process. In addition, this review discusses the importance of SAR and optical image fusion, pixel-level techniques, applications, and major classes of quality metrics for objectively assessing fusion performance. The literature review alluded that the SAR and optical image fusion in the detection and mapping of land degradation and enabling sustainable land management has not been fully explored. Advanced techniques such as the fusion of SAR and optical satellite imageries need to be incorporated for the detection and mapping of LD, as well as the promotion of SLM in halting LD in South African drylands and around the world. We conclude that there is scope for further research on the fusion of SAR and optical images, as new micro-wave and optical sensors with higher resolution are introduced on a regular basis. The results of this review contribute to a better understanding of the applications of SAR and optical image fusion in future research in the severely degraded drylands of southern Africa. The fusion of SAR and optical data still remains an open challenge. The future of different remote sensing applications lies in this kind of fusion. Land degradation is one of the greatest challenges amongst the environmental problems in South Africa, causing a reduction in the capacity of the land to perform ecosystem functions and services that support society and development. Yet, in South Africa, there are no studies that have widely investigated the potential for a fusion of SAR and optical data to detect and map land degradation and SLM practices. This paper established a baseline for understanding the application of a fusion of SAR and optical data as rapid tools for mapping, monitoring, and evaluating LD, as well as the impacts of SLM practices in South Africa's degraded drylands. [ABSTRACT FROM AUTHOR]

Gully erosion is an environmental problem recognized as one of the worst land degradation processes worldwide. Insight into regional gully perturbations is required to combat the serious on‐ and off‐site impacts of gullying on a catchment management scale. In response, we intersect different perspectives on gully erosion‐specific views in South Africa (SA), a country that exhibits various physiographic properties and spans 1.22 million km2. While the debate surrounding gully origin continues, there is consensus that anthropogenic activities are a major contemporary driver. The anthropogenic impact caused gullying to transcend climatic, geomorphic, and land‐use boundaries, although it becomes more prominent in central to eastern SA. Soil erodibility plays a crucial role in what extent of gully erosion severity is attained from human impact, contributing to the east–west imbalance of erosion in SA. Soil erosion rates from gullying and badlands are limited but suggest that it ranges between 30 and 123 t ha−1 yr−1 in the more prominent areas. These soil loss rates are comparable to global rates where gullying is concerned; moreover, they are up to four orders of magnitude higher than the estimated baseline erosion rate. On a national scale, the complexity of gullying is evident from the different temporal timings of (re)activation or stabilizing and different evolution rates. Continued efforts are required to understand the intricate interplay of human activities, climate, and preconditions determining soil erodibility. In SA, more medium‐ to long‐term studies are required to understand better how changing control factors affect gully evolution. More research is needed to implement and appraise mitigation measures, especially using indigenous knowledge. Establishing (semi)‐automated mapping procedures would aid in gully monitoring and assessing the effectiveness of implemented mitigation measures. More urgently, the expected changes in climate and land‐use necessitate further research on how environmental change affects short‐term gully erosion dynamics. [ABSTRACT FROM AUTHOR]

This study explored the use of multi-source remotely sensed data in monitoring the spatial distribution of pools and pool dynamics in two distinct semi-arid sites in South Africa. The factors that control the pool dynamics were also examined. Three water extraction indices were used, these included Normalised Difference Water Index (NDWI), Modified NDWI and Normalised Difference Vegetation Index. In addition, random forest classifier and Sentinel-1 SAR data were used in mapping pools and pools dynamics for both sites. Overall, the remotely-sensed methods detected and mapped pools with acceptable accuracy, except for small pools (<400 m²). The results suggest that flow occurrences and rainfall are key in controlling temporal changes in pools sizes, and there was no interaction between pools and groundwater. The study showed that remote sensing methods are essential for filling ground monitoring gaps in non-perennial rivers and determining hydrological processes and water availability from pools in semi-arid environments. [ABSTRACT FROM AUTHOR]

South Africa is home to a native Tamarix species, while two were introduced in the early 1900s to mitigate the effects of mining on soil. The introduced species have spread to other ecosystems resulting in ecological deteriorations. The problem is compounded by hybridization of the species making identification between the native and exotic species difficult. This study investigated the potential of remote sensing in identifying native, nonnative and hybrid Tamarix species recorded in South Africa. Leafand canopy-level classifications of the species were conducted using field spectroradiometer data that provided two inputs: original hyperspectral data and bands simulated according to Landsat-8, Sentinel-2, SPOT-6 and WorldView-3. The original hyperspectral data yielded high accuracies for leaf- and plot-level discriminations (>90%), while promising accuracies were also obtained using Landsat-8, Sentinel-2 and Worldview-3 simulations (>75%). These findings encourage for investigating the performance of actual space-borne multispectral data in classifying the species. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing already is important in geoscientific research in the fields of geology, soil, exploration and mining. New hyperspectral satellite systems are already in operation (e.g. PRISMA and DESIS; Caporusso et al. 2020; Alonso et al. (Sensors 19(20):4471–4515, 2019)) and more systems are planned e.g. the European Copernicus Next Generation Hyperspectral Satellite CHIME (Nieke and Rast 2018). The German system EnMAP was successfully launched into space on 1st of April 2022 (DLR 2022). The potential of hyperspectral airborne and satellite borne data for mining-related applications is discussed. Investigated are the information contents of hyperspectral data for exploration target recognition and their dependency on spatial resolutions of different sensor platforms. Airborne data offer high spatial resolution of 2.5 m with limited areal data acquisition, whereas hyperspectral spaceborne sensors guaranty nearly worldwide data availability with the same spectral characteristics but medium spatial resolution (30 m). The aspects of high spectral resolution and high versus medium spatial resolution targeted mineral mapping is investigated. The methodological concept includes processing aspects, standardized data availability for mineral mapping and mineralization targeting for operational application, to maintain/allow application of hyperspectral data even for non-remote sensing experts. Based on hyperspectral airborne data acquired in the Aggeneys region in South Africa, spaceborne hyperspectral data are simulated following the EnMAP specifications, and the concept for targeted mapping of surface alterations of a lead zinc deposit is discussed. [ABSTRACT FROM AUTHOR]

Forest cover change analyses have an essential role in forest management. Thus, this study adopted Landsat satellite imagery to assess the decadal spatiotemporal forest cover changes that occurred between 1989 and 2019 and predicted the 2029 land cover distribution of the Nkandla forest reserve, facing encroachment threats. The support vector machine algorithm and Land Change Modeling were utilized to classify and detect changes that occurred between 1989–1999, 1999–2009, 2009–2019. The Markov Chain Model and Multi-Layer Perceptron were adopted for the future land cover prediction. Consistent changes through inter-transitioning between the land cover types (closed canopy forest, open canopy forest, grassland, and bare sites) were detected. The closed canopy forest increased from 883.46 ha to 1059.23 ha, whereas the open canopy forest declined from 1091.89 ha to 910.60 ha between 1989 and 2019. Generally, the observed changes were caused by ecological processes and human disturbances. The future cover prediction indicated that the closed canopy forest will decline between 2019 and 2029, whereas the open canopy forest, grassland, and bare sites will increase. The information provided through this study will support the management of the Nkandla forest to ensure its continual supply of ecosystem services of national and global importance. [ABSTRACT FROM AUTHOR]

(1) We monitored post-fire shrubland recovery responses to changes in rainfall seasonality using a multi-year field experiment in the Cape Floristic Region (CFR) of South Africa. A primary objective was to test the utility of UAVs for monitoring ultra-fine-scale vegetation changes in the early post-fire context. (2) By comparison with detailed ground-based measurements, we showed that UAVs improved detection of integrated community growth responses, given that the appropriate relative radiometric normalisation techniques were applied to repeated imagery data. UAVs supported ground-based findings and, moreover, helped to identify previously undetected growth form responses. However, due to the limitations in detecting species-specific demographic changes, UAVs could not completely replace ground-based measurements. (3) Our combined UAV-based and ground-based monitoring approaches indicated strong coupling between post-fire shrubland recovery and seasonal rainfall patterns in the CFR but also demonstrated that sensitivity to rainfall seasonality could differ between neighbouring shrubland communities occurring on different soil types. (4) The careful integration of UAV-based and ground-based monitoring approaches provided the fullest understanding of early post-fire shrubland recovery patterns. [ABSTRACT FROM AUTHOR]

South Africa is home to thousands of architectural remnants such as stone-walled structures and ash middens from the Late Iron Age (AD 1300–1800). Ash middens reflect the political and economic lifeways of Iron Age communities. However, the process of identifying and mapping ash middens with traditional survey techniques can be time-consuming and difficult due to dense vegetation. This report aims to assess the performance of two supervised classification techniques, Maximum Likelihood Classification (MLC) and Support Vector Machine (SVM), in detecting ash middens on two multispectral satellite images – GeoEye1 and SPOT5 – in Gauteng, South Africa. The objective is to also assess the ability of the sensors in capturing the spectral signatures of the ash middens. The high reflectance of ash middens relative to other land-cover classes indicates that they have distinct spectral signatures. GeoEye1 is better than SPOT5 in the detection of ash middens because its high spectral and spatial resolution allows for more detailed and accurate mapping. SVM, although advanced, is not a significantly better classification technique for detecting ash middens compared to MLC. This report presents a promising avenue for detecting archaeological ash middens in this part of the world using remote sensing techniques. [ABSTRACT FROM AUTHOR]

• The coast of South Africa requires close monitoring and up to date information for proper management and effective decision-making. • A remote sensing-based ecosystem classification of the South African coastline is presented. • A total of 45,912 km2 was classified using the model which obtained the highest overall accuracy of 86.70%. • The number of input variables used in the random forest model had the most significant impact on classification accuracy. Coastlines worldwide are home to an increasing number of people and are subject to many pressures. This, combined with natural dynamics and hazards, often results in the degradation of coastal and marine ecosystems and infrastructure. Therefore, it is necessary to adopt effective management strategies to ensure sustainable use of coastal ecosystems, which requires up-to-date data on the extent of coastal ecosystems. This research aimed to create a coastal ecosystem land cover map for South Africa using the random forest algorithm to classify Landsat 8 imagery. Processing was done using the Google Earth Engine platform. A total of 522 Landsat 8 images were called to create a median image for classification. The impact of the number of trees, the number of variables per split, and variable selection on overall classification accuracy and Kappa values were evaluated. This was done by increasing the number of trees from 100 to 500 with increments of 100, setting the number of variables per split to three, four or five, and reducing the number of input variables from the original 18 variables, to the 10 most important variables, to the 5 most important variables, based on variable importance scores. Results suggest that the number of input variables used in the model had a greater impact on accuracy than the number of trees used, or the number of variables used per split. The average overall accuracy was 82.28%, with values ranging between 75.33% and 86.70%, while the average Kappa was 0.8068 and values ranged between 0.7310 and 0.8550. The model with the highest overall accuracy was the model using all input variables, 500 trees, and three variables per split. A major challenge was the misclassification of certain vegetation classes due to the complex successional mosaic they form, causing mixed signals and generally lower classification accuracy. Despite model limitations, results were satisfactory and have shown that coastal land cover classification and monitoring could be aided by the rapid classification of Landsat 8 imagery in Google Earth Engine using the random forest algorithm. [ABSTRACT FROM AUTHOR]

Land degradation is a complex environmental problem that requires robust decision-making processes that embrace diversity of local indigenous knowledge. Participatory approaches are essential in capturing the complexity in land degradation processes while overcoming limitations presented by scientific methods that disregard socio-economic factors. Hence, the triangulation approach can be used to combine participatory expert maps with satellite imagery by overlaying both maps to enhance understanding of land degradation. This study incorporated social and physical factors that cause land degradation through an expert assessment. This was achieved through a participatory expert mapping exercise using the World Overview Conservation Approaches and Technology (WOCAT) mapping questionnaire and remote sensing techniques to map land degradation severity that was integrated by overlaying, i.e. triangulation approach. The WOCAT results were validated using field observations and discussions on land covers. Climatic variability, improper soil management and unsustainable land-use practices were identified as leading causes of land degradation while soil erosion and loss of vegetation cover were the perceived prominent types of land degradation. The land degradation severity modelling assessment achieved an accuracy of R2 0.86, RMSE 7.72 and relRMSE 12.94% in estimating bare soil cover, which was used as an indicator of potential land degradation severity. Variables such as leaf area index (LAI) and soil adjusted vegetation index (SAVI) were essential in determining degraded landscapes. The severity maps indicated that low-lying areas are moderately degraded due to overgrazing. The study concluded that a triangulation approach provides a better understanding of land degradation. [ABSTRACT FROM AUTHOR]

Member states of the United Nations Convention to Combat Desertification are required to report on the proportion of land that is degraded in their countries, a requirement that is also tied into the UN Sustainable Development Goals (SDGs). National land degradation assessments are often conducted with the use of remote sensing data which are not always ground truthed. Google Street View (GSV) provides high resolution, panoramic imagery across large parts of the world that has the potential to be used to ground truth land degradation assessments. We apply three different methodologies (visual interpretation of GSV images, GSV image classification and vegetation index extraction) to derive vegetation cover estimates from Google Street View imagery for the Hardeveld bioregion of the Succulent Karoo biome in South Africa. Visual estimates of cover best predict known habitat condition values (adjusted R2 = 0.86), whilst estimates derived from an unsupervised classification of GSV images also predict habitat condition relatively well (adjusted R2 = 0.52). These results show the potential for using GSV imagery, and other large collections of ground-level landscape photographs, as a rough ground-truthing tool, especially in instances where more traditional ground-truthing approaches are not possible. [ABSTRACT FROM AUTHOR]

The Hartbeespoort Dam is a discharge point of a catchment that is characterized by urbanization, mining, industrial, and agricultural activities. These activities coupled with fluxes of heavily polluted wastewater from informal settlements, wastewater treatment works, as well as runoff from golf courses have led to the development of recurring harmful algal blooms (HABs). The predominant cyanobacteria scum that is largely covering the Dam water is toxic to fish and poses serious public health risks. Phosphorus is the limiting nutrient in terrestrial aquatic systems and excess concentration in the waters usually results in eutrophication. The productivity level in Hartbeespoort Dam is also a function of total phosphorous (TP) level, showing a positive correlation with chlorophyll-a, an index for phytoplankton which are predominantly HABs in this Dam. Analysis of long-term in situ water quality data (1980–2020) show that TP is not the only driver, changes in surface water temperatures also affect the productivity level, especially, when TP levels are below a threshold of approximately 0.4 mg/L. Chlorophyll-a was retrieved from current generation high resolution satellite (Landsat and Sentinel) at 5-year interval. Standard band ratio-based ocean color model applied to satellite data produced an accuracy of R2 = 0.86 and RMSE of 5.56 µg/L. Time series analysis of in situ and satellite data show similar trends including capturing the effect of biocontrol on productivity levels between the late 1980s and the early 1990s, after which productivity increased with an increased flux of TP. Since 2015, the average annual surface temperature in the Dam has decreased leading to the decline in productivity level despite increasing levels of TP. The spatial dynamics of the HABs is a function of the discharges levels of the various rivers draining into the Dam as well as its geometry. Relatively higher concentrations are observed near river discharges and in areas of restricted water circulation. [ABSTRACT FROM AUTHOR]

We mapped and modelled the potential areas vulnerable to Lantana camara (L. camara) invasion in semi‐arid savannah ecosystems in the communal lands of Bushbuckridge and Kruger National Park, South Africa. Specifically, we modelled potentially vulnerable areas based on remotely sensed data and environmental variables. The Maximal Entropy (Maxent) algorithm was used to model the vulnerable area. The reliability of the modelled results was assessed using Skills Statistic (TSS), Area Under Curve (AUC) and Kappa statistics. According to the results, Bushbuckridge communal lands are more susceptible to L. camara invasions than Kruger National Park. The risk of L. camara invasion in the study site was modelled with high accuracy (AUC score of 0.95) using the best model (Model 7), which is a composite of all model variables (remote sensing and environmental variables). The spatial distribution maps derived from Maxent showed that L. camara was more likely to invade communal lands than protected areas. Using remotely sensed spectral indices as standalone model variables (Model 4) showed the lowest accuracy, with an AUC score of 0.85. Overall, model input variables such as elevation had a significant influence on the spatial distribution of L. camara in the study area. [ABSTRACT FROM AUTHOR]

This paper investigates the invasion of a reach of the Touws River by the deciduous alien tree Tamarix ramosissima Ledeb. (Pink Tamarisk, Saltcedar). The objectives of the paper were to investigate the utility of a delta-normalised difference vegetation index (dNDVI) in evaluating the status, pattern and process of invasion, and to evaluate the implications of the invasion for riverscape morphodynamics and physical habitat structure and function. The spectral classification developed had an overall accuracy of 59.89%, omission error of 28.25%, and commission error of 11.86%. The classification of vegetation was used to quantify the aerial extent and pattern of invasion (currently at least 65.50% of the valley floor vegetation cover). Field observations and existing knowledge of seed dispersal mechanisms and germination constraints of the invasive tree were compared with those of the indigenous sweet thorn Vachellia karroo, to provide insight into invasion patterns and processes within the reach. The linear channel-flanking distribution of the invasion has the potential to alter reach hydrology and morphodynamics, which are key building blocks of physical habitat structure and function. The spectral classification approach demonstrated is relatively simple, has a strong biophysical basis in that it exploits a known, predictable and observable seasonal vegetation response that produces a clear spectral signature, and can be applied to long reaches of rivers impacted by alien vegetation invasions. [ABSTRACT FROM AUTHOR]

Remote sensing of coastal sediments for the purpose of automated mapping of their physical properties (grain size, mineralogy and carbonate content) across space has not been widely applied globally or in South Africa. This paper describes a baseline study towards achieving this aim by examining the spectral reflectance signatures of field sediment samples from a beach–dune system at Oyster Bay, Eastern Cape, South Africa. Laboratory measurements of grain size and carbonate content of field samples (n = 134) were compared to laboratory measurements of the spectral signature of these samples using an analytical spectral device (ASD), and the results interrogated using different statistical methods. These results show that the proportion of fine sand, CaCO3 content and the distributional range of sediment grain sizes within a sample (here termed span) are the parameters with greatest statistical significance—and thus greatest potential interpretive value—with respect to their spectral signatures measured by the ASD. These parameters are also statistically associated with specific wavebands in the visible and near infrared, and the shortwave infrared parts of the spectrum. These results show the potential of spectral reflectance data for discriminating elements of grain size properties of coastal sediments, and thus can provide the baseline towards achieving automated spatial mapping of sediment properties across coastal beach–dune environments using hyperspectral remote sensing techniques. [ABSTRACT FROM AUTHOR]

Accurate measurement of surface visibility is vital for transportation. Satellite remote sensing technology offers the advantage of monitoring vast areas over extended time periods, providing a novel approach to visibility monitoring. However, it currently lacks the capability to directly measure visibility, necessitating further theoretical exploration of visibility remote sensing methods. In this study, we introduced an innovative model for remote retrieval of global surface visibility. It leverages the Extreme Random Forest (ERF) model to optimize the residual components of the physical method. The proposed model comprehensively explores the intricate relationship among surface visibility, aerosol optical depth (AOD), atmospheric extinction coefficient, and aerosol heights. Utilizing surface visibility data collected from global ground-based meteorological stations in 2018, we assessed the performance of the proposed model through 10-fold cross-validation, yielding correlation coefficient (R) of 0.85, root mean square error (RMSE) of 2.7 km, and mean absolute error (MAE) of 1.6 km. Results from temporal and site cross-validation further confirm the model's strong performance in both time and spatial domains. Additionally, this study has developed a global visibility dataset at a daily resolution of 0.1° × 0.1°. Temporal analysis of this dataset reveals that visibility values are higher from June to October, while values are lower in other months. Regarding the spatial distribution of visibility, oceanic areas exhibit markedly superior visibility compared to land areas. Additionally, Oceania, South Africa, and South America boast notably better visibility than other regions. [Display omitted] • A novel model was proposed for remotely retrieving global surface visibility. • The model was derived by physical mechanism and Extreme Random Forest (ERF) method. • The proposed model has performed well in both time and spatial domains. [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]

• Differenced Normalised Burn Ratio field-verified as an accurate index of fire severity in Afrotemperate Forest • Used a simple field survey method of assessing fire severity in terms of fire damage to main stems of forest trees • Spatially characterised fire severity in Southern Cape Afrotemperate forest in the November 2018 George fire Temperate evergreen broad-leaved forest seldom experiences fire, and little is known about the severity at which they burn. During October-November 2018, 70 000 ha of fynbos, thicket, Afrotemperate forest and other lesser vegetation types burnt in the southern Cape of South Africa. This study set out to verify through field observations the accuracy of the differenced Normalised Burn Ratio (dNBR) as an index of fire severity, derived from Sentinel 2 images, in Afrotemperate forest. Stem fire severity and char height were observed for 1 648 trees in 87 plots and linear regression used to test whether these observed measures were related to dNBR. Stem fire severity and char height were collinear, and the latter dropped from further analysis. The strong linear relationship between dNBR and stem fire severity (r2 = 0.69, p < 0.001) confirmed the accuracy of dNBR as a measure of fire severity in Afrotemperate forest. The verification of dNBR enables the mapping and characterization of fire severity in Afrotemperate forest. In the fire investigated, a total of 4 628 ha of Afrotemperate forest burnt of which 67 % burnt at low severity, 21 % at medium severity and 12 % at high severity. Such information on the distribution of fire severity facilitates investigation of further questions regarding past and future fire regimes and the fire ecology of Afrotemperate forest. [ABSTRACT FROM AUTHOR]

Monitoring vegetation dynamics is essential for improving our understanding of how natural and managed agricultural landscapes respond to climate variability and change in the long term. Amathole District Municipality (ADM) in Eastern Cape Province of South Africa has been majorly threatened by climate variability and change during the last decades. This study explored long-term dynamics of vegetation and its response to climate variations using the satellite-derived normalized difference vegetation index from the third-generation Global Inventory Modeling and Mapping Studies (GIMMS NDVI3g) and the ERA5-Land global reanalysis product. A non-parametric trend and partial correlation analyses were used to evaluate the long-term vegetation changes and the role of climatic variables (temperature, precipitation, solar radiation and wind speed) during the period 1981–2015. The results of the ADM's seasonal NDVI3g characteristics suggested that negative vegetation changes (browning trends) dominated most of the landscape from winter to summer while positive (greening) trends dominated in autumn during the study period. Much of these changes were reflected in forest landscapes with a higher coefficient of variation (CV ≈ 15) than other vegetation types (CV ≈ 10). In addition, the pixel-wise correlation analyses indicated a positive (negative) relationship between the NDVI3g and the ERA5-Land precipitation in spring–autumn (winter) seasons, while the reverse was the case with other climatic variables across vegetation types. However, the relationships between the NDVI3g and the climatic variables were relatively low (R < 0.5) across vegetation types and seasons, the results somewhat suggest the potential role of atmospheric variations in vegetation changes in ADM. The findings of this study provide invaluable insights into potential consequences of climate change and the need for well-informed decisions that underpin the evaluation and management of regional vegetation and forest resources. [ABSTRACT FROM AUTHOR]

The study sought to assess the impacts of land use and land cover (LULC) changes on two wetland systems (Makuleke and Nylsvley Nature Reserve) in the Limpopo Transfrontier River Basin (LTRB) in South Africa between 2014 and 2018. To fulfil this objective, multi-date Landsat images were used. Furthermore, the maximum likelihood classification algorithm was used to identify various LULC classes within delineated wetlands. The LULC changes were mapped from the two wetlands, with high overall accuracies, ranging from 80% to 89% for both study areas. The spatial extent of the Makuleke wetland declined by 2% between 2014 and 2018, whereas the Nylsvley wetland decreased by 3%. Built-up areas have increased slightly over the 2014 and 2018 period because of population growth and infrastructure development, which occupy a portion of the wetland. In Nylsvley wetland, it was evident that during the 5-year monitoring period, croplands increased steadily in the Nylsvley catchment. Overall, the results demonstrated a steady decline in natural vegetation cover in both wetlands. This information can aid in enforcing wetland legislations and LULC management practices that can help protect them from further encroachment and degradation. [ABSTRACT FROM AUTHOR]

The Zaaiplaats tin field is host to two of the historically most significant tin mines in South Africa. The geological maps of Zaaiplaats and Groenfontein have not been updated since the 1980s and 1950s respectively and warrant a renewed investigation. Cassiterite is hosted in the Bobbejaankop and Lease granites, of the Zaaiplaats and Groenfontein tin mines, which are part of the Lebowa Granite Suite of the Bushveld Complex. Tin mineralisation is primarily hosted as low-grade disseminations and within networks of high-grade hydrothermal pipes and lenticular ore-bodies. One difficulty in mapping such formations arises from the limited lithological variability between mineralised and unmineralised granitic facies. In order to map the granitic lithologies and discriminate alteration zones, an integrated approach is applied by combining remote sensing and in situ portable X-ray fluorescence (pXRF) mapping. The pXRF large ion lithophile (LIL) element distribution mapping, specifically correlating Rb, Sr and Ba with Sn, points to the concentration of late-stage magmatic-hydrothermal fluids, which are associated with endogranitic mineralisation. The use of the Rb/Ba ratio highlights regions of late-stage magmatic-hydrothermal alteration, effectively delineates granitic facies and identifies zones of potential tin mineralisation. Spectral image processing techniques were used as tools to support the mapping of these granites, their variable degrees of magmatic-hydrothermal alteration and regions of endogranitic disseminated mineralisation. We thus exemplify that an expert-based and synergic approach, combining inexpensive in situ pXRF and freely available satellite remote sensing data, supports the mapping and identification of endogranitic mineralisation in early exploration stages. Furthermore, due to its versatility, this approach can easily be applied to other styles of plutonic mineralisation. [ABSTRACT FROM AUTHOR]

Three weed biological control (biocontrol) programmes are described, all of which are considered to be 'transfer projects' that were initiated elsewhere, and on which South Africa has piggybacked its biocontrol efforts. Using knowledge and expertise from international collaborators, South African weed researchers are following a long tradition of transfer projects, which has been a largely successful and practical approach to biocontrol. Two Brazilian weeds, the Brazilian pepper tree Schinus terebinthifolia and the spiderwort Tradescantia fluminensis are being targeted, along with the Old-World trees Tamarix ramosissima and T. chinensis. The potential biocontrol agents are described and ranked for the two trees according to what has been discovered elsewhere, while the agent already released against T. fluminensis is rated (as poor), and other potential agents are considered. The addition of molecular techniques, climate matching and remote sensing in transfer projects can increase the chance of successful biocontrol and the inclusion of these techniques in the three new programmes is discussed. Transfer projects are a cost-effective and pragmatic way to pick winning biocontrol programmes. [ABSTRACT FROM AUTHOR]

We retrieved aerosol effective height (AEH) values from TROPOspheric monitoring instrument (TROPOMI) measurements based on the O4 absorption at 477 nm. As the first step in the AEH retrieval algorithm, TROPOMI carbon monoxide and aerosol index products were used for the first time to determine aerosol type. An online radiative transfer calculation method was introduced to the AEH retrieval process. The AEHs were retrieved over northeast Asia (various aerosol types), South Africa (biomass-burning smoke), and the Sahara Desert (dust). The retrieved AEHs were compared with AEHs derived using aerosol extinction profiles from ground and satellite-based lidar measurements. A preliminary evaluation based on comparison with lidar suggests reasonable performance, but shows that cloud masking is the main source of uncertainty. The errors associated with the online-based AEH retrieval algorithm were calculated using synthetic radiance. The total AEH retrieval errors were 0.91 km (0.4 ≤ AOD < 1) and 0.30 km (2 ≤ AOD < 3), highlighting the increased sensitivity of the AEH retrieval algorithm under high AOD conditions. This study demonstrates that the O4 absorption band at 477 nm is applicable to aerosol height retrieval from TROPOMI with a high spatial resolution. [ABSTRACT FROM AUTHOR]

Accurate information on the amount of aboveground grass biomass (AGBg) stored in savannah rangelands remains unknown. Therefore, in this study we assessed the capability of a new multispectral sensor (Landsat‐8 OLI) in estimating aboveground grass biomass stored within two different land use management units in savannah rangelands using the stepwise multiple linear regression methods. Specifically, we evaluated the performance of different Landsat‐8 OLI derivatives (i.e. (a) spectral bands, (b) derived vegetation indices and (c) a combination of spectral bands and vegetation indices) to quantify aboveground grass biomass. The results highlighted that the use of spectral bands as stand‐alone model variables yielded considerably high accuracies in terms of the coefficient of determination (r2) and the root mean square error (RMSE). An r2 of 0.73 (72.81%) and RMSE of 37.88% for the protected area and an r2 of 0.75 (75.18%) and with a RMSE of 33.16% for the nonprotected area were attained. The use of vegetation indices, however, demonstrated a very weak model performance yielding an r2 of 0.31 (30.68%) and a RMSE of 51.51%, for the protected area and an r2 of 0.50 (50.43%) and RMSE of 40% for the nonprotected areas. Comparatively, combining Landsat‐8 OLI derived spectral bands and vegetation indices demonstrated improved model performance, yielding an r2 of 0.92 (91.90%), and RMSE of 41.3% for the protected area and an r2 of 0.94 (93.82%) with a RMSE of 33.14% for the nonprotected area. Therefore based on the observed results, AGBg in savannah rangelands can be satisfactorily estimated using broadband multispectral derivatives. [ABSTRACT FROM AUTHOR]

A revised lithostratigraphic framework for Mozaan Group-equivalent strata within the Nkandla sub-basin is presented based on new field data, remote sensing and genetic sequence stratigraphic interpretations. Although previous literature has suggested that no Mozaan Group lithologies were deposited within the sub-basin, reinterpretations presented here indicate that 90% of the lithostratigraphy developed within the main basin occurs within the Nkandla and Mhlatuze inliers. Mozaan Group units previously defined as the Vutshini and Ekombe formations are correlated with stratigraphy from the lowermost Sinqeni Formation to the Gabela Formation. Although thinner than units within the type area in the main basin, thicknesses of the Sinqeni Formation are comparable to those observed within the White Mfolozi Inlier. A ~1 000 m composite reference profile is measured within the Mdlelanga Syncline of the Nkandla Inlier. Further profiles were measured for sequences in the Gem-Vuleka Syncline of the Nkandla Inlier, as well as within the Mhlatuze Inlier. These latter profiles, however, host only lower Mozaan Group strata. In all sections the basal portion of the sequence comprises two quartz arenite units, separated by a ferruginous shale, which hosts minor iron formation interbeds. This predominantly coarse-grained lower sequence is overlain by a shale-dominated succession with multiple sandstone interbeds. A prominent coarse-grained quartz arenite unit forms a distinct marker in the middle portion of the sequence. This is overlain by a sequence of shales and sandstones with two prominent igneous units present. Genetic sequence stratigraphic interpretations indicate cyclical deposition of dominantly shallow marine sediments with condensed sections, marked by iron formations or ferruginous shales, denoting periods of marine highstand along the southeastern margin of the Kaapvaal Craton. The evidence of Mozaan Group stratigraphy within the Nkandla sub-basin supports a passive margin tectonic model whereby deposition occurred in an arcuate shallow continental margin which opened to the southeast. The extension of Mozaan Group strata into the Nkandla sub-basin suggests that the Mozaan Basin likely formed a single depository rather than separate sub-basins as previously proposed. [ABSTRACT FROM AUTHOR]

Satellites allow large‐scale surveys to be conducted in short time periods with repeat surveys possible at intervals of <24 h. Very‐high‐resolution satellite imagery has been successfully used to detect and count a number of wildlife species in open, homogeneous landscapes and seascapes where target animals have a strong contrast with their environment. However, no research to date has detected animals in complex heterogeneous environments or detected elephants from space using very‐high‐resolution satellite imagery and deep learning. In this study, we apply a Convolution Neural Network (CNN) model to automatically detect and count African elephants in a woodland savanna ecosystem in South Africa. We use WorldView‐3 and 4 satellite data –the highest resolution satellite imagery commercially available. We train and test the model on 11 images from 2014 to 2019. We compare the performance accuracy of the CNN against human accuracy. Additionally, we apply the model on a coarser resolution satellite image (GeoEye‐1) captured in Kenya, without any additional training data, to test if the algorithm can generalize to an elephant population outside of the training area. Our results show that the CNN performs with high accuracy, comparable to human detection capabilities. The detection accuracy (i.e., F2 score) of the CNN models was 0.78 in heterogeneous areas and 0.73 in homogenous areas. This compares with the detection accuracy of the human labels with an averaged F2 score 0.77 in heterogeneous areas and 0.80 in homogenous areas. The CNN model can generalize to detect elephants in a different geographical location and from a lower resolution satellite. Our study demonstrates the feasibility of applying state‐of‐the‐art satellite remote sensing and deep learning technologies for detecting and counting African elephants in heterogeneous landscapes. The study showcases the feasibility of using high resolution satellite imagery as a promising new wildlife surveying technique. Through creation of a customized training dataset and application of a Convolutional Neural Network, we have automated the detection of elephants in satellite imagery with accuracy as high as human detection capabilities. The success of the model to detect elephants outside of the training data site demonstrates the generalizability of the technique. [ABSTRACT FROM AUTHOR]

Aim: With plant biodiversity under global threat, there is an urgent need to monitor the spatial distribution of multiple axes of biodiversity. Remote sensing is a critical tool in this endeavour. One remote sensing approach for detecting biodiversity is based on the hypothesis that the spectral diversity of plant communities is a surrogate of multiple dimensions of biodiversity. We investigated the generality of this 'surrogacy' for spectral, species, functional and phylogenetic diversity across 1,267 plots in the Greater Cape Floristic Region (GCFR), a hyper‐diverse region comprising several biomes and two adjacent global biodiversity hotspots. Location: The GCFR centred in south‐western and western South Africa. Time period: All data were collected between 1978–2014. Major taxa studied: Vascular plants within the GCFR. Methods: Spectral diversity was calculated using leaf reflectance spectra (450–950 nm) and was related to other dimensions of biodiversity via linear models. The accuracy of different spectral diversity metrics was compared using 10‐fold cross‐validation. Results: We found that a distance‐based spectral diversity metric was a robust predictor of species, functional and phylogenetic biodiversity. This result serves as a proof‐of‐concept that spectral diversity is a potential surrogate of biodiversity across a hyper‐diverse biogeographic region. While our results support the generality of spectral diversity as a biodiversity surrogate, we also find that relationships vary between different geographic subregions and biomes, suggesting that differences in broad‐scale community composition can affect these relationships. Main conclusions: Spectral diversity was shown to be a robust surrogate of multiple dimensions of biodiversity across biomes and a widely varying biogeographic region. We also extend these surrogacy relationships to ecological redundancy to demonstrate the potential for additional insights into community structure based on spectral reflectance. [ABSTRACT FROM AUTHOR]

The world including South Africa is faced with unprecedented environmental changes, which can be linked to climate-related disasters such as drought and extreme heat. Several studies have indicated that these changes are likely to accelerate in the future and cause an adverse impact on the environment. The Eastern Cape Province of South Africa, especially Amathole District Municipality (ADM), has recorded a high number of climate change–related disasters including prolonged drought conditions witnessed during the winter season of 2008, 2009, 2014 and 2015 among others. Consequently, this study aimed at exploring remote sensing information to assess and document drought occurrences in the ADM from 2007 to 2017. To accomplish the aim, the Normalized Difference Vegetation Index, Land Surface Temperature and Precipitation were utilised to access drought spatiotemporal variations. For the analysis, a total of 396 satellite imageries (MODIS and TRMM) were used. The results revealed that different correlations exist between the three variables. The significance of correlations differed from one season to another. Furthermore, it was revealed that the drought conditions in the district differed in the spatial distribution. The study accurately identified the drought episodes that occurred in the ADM in the years 2008, 2009, 2014, 2015 and 2016. The chosen methodology and variables proved to be suitable for analysing drought conditions offering space and temporal variation dimension, which is vital in monitoring drought events. [ABSTRACT FROM AUTHOR]