Research Articles (Geography, Geoinformatics and Meteorology)

Permanent URI for this collectionhttp://hdl.handle.net/2263/1936

A collection containing some of the full text peer-reviewed/ refereed articles published by researchers from the Department of Geography

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Multiple fuel use in low-income communities: socio-economic determinants and impacts on household air pollution and respiratory health in South Africa
Wernecke, Bianca; Wright, Caradee Yael; Langerman, Kristy; Mathee, Angela; Abdelatif, Nada; Howard, Marcus A.; Jafta, Nkosana; Pauw, Christiaan; Phaswana, Shumani; Asharam, Kareshma; Seocharan, Ishen; Smith, Hendrik; Naidoo, Rajen N. (Elsevier, 2025)
Domestic fuel use contributes significantly to household air pollution levels and to the disease burden in low-income households in South Africa. The link between residential fuel stacking and switching, and respiratory health, mediated by household air pollution, remains underexplored, posing challenges to transition to cleaner fuels. This study identified socio-economic determinants of fuel use patterns in two low-income communities of KwaZamokuhle and eMzinoni in South Africa. It also examined the impacts of these patterns on household air pollution levels and respiratory health outcomes. Over half of households relied on dirty fuels across all needs. Average household PM2.5 levels exceeded national daily standards (40 μg/m3). Education level and employment status were significant factors in determining fuel choice, with employed participants less likely to rely on dirty fuels. Town-specific characteristics also influenced household fuel use patterns. In terms of health, 9.5 % of participants had obstructive airways disease and 26.9 % tested positive for inhalant allergens. Heating fuels were strongest predictor of obstructive airways disease (>75 %) whereas cooking fuels were the main predictor of allergen sensitivity (∼75 %). The stepwise introduction of cleaner fuels predicted better respiratory health outcomes. The findings of this study suggest that even the partial adoption of cleaner fuels has health benefits and supports the formulation of context-specific mitigation efforts aiming to address negative health effects associated with household air pollution.
Impact of solid fuel use on household air pollution and respiratory health in two-low-income communities in Mpumalanga, South Africa
Wernecke, Bianca; Langerman, Kristy; Mathee, Angela; Abdelatif, Nada; Howard, Marcus A.; Jafta, Nkosana; Pauw, Christiaan; Phaswana, Shumani; Asharam, Kareshma; Seocharan, Ishen; Smith, Hendrik; Naidoo, Rajen N.; Naidoo , Natasha; Wright, Caradee Yael (Ubiquity Press, 2025-10-08)
INTRODUCTION : Household air pollution from domestic solid fuel use remains a global public health concern, particularly in low‑income communities. This study assessed associations between household fuel use, indoor air pollution, and respiratory health outcomes in two Mpumalanga communities in South Africa. METHODS : A cross‑sectional study was conducted in KwaZamokuhle and eMzinoni between July 2019 and February 2020. Indoor PM2‧5 concentrations were measured using Airmetrics MiniVol samplers and TSI DustTrak II monitors. We carried out household surveys, lung function tests and allergen sensitivity testing and performed multivariable logistic regression to assess associations between indoor pollutant exposure and respiratory health outcomes. RESULTS : Indoor and ambient PM2‧5 concentrations in KwaZamokuhle were more than twice as high as those in eMzinoni, exceeding both national standards and WHO Air Quality Guidelines. Coal use for heating was more prevalent in KwaZamokuhle and appeared directly related to elevated PM2‧5 levels. Approximately 9% of participants exhibited signs of obstructive airway disease, and 25% had positive results for allergen sensitisation. Although the associations between PM2‧5 levels, solid fuel use and measured respiratory outcomes did not reach statistical significance, consistent trends in the expected direction were observed, suggesting a potential relationship that warrants longitudinal studies with larger sample sizes. CONCLUSION : These findings suggest complex, possibly nonlinear relationships between indoor air pollution and respiratory health effects. The study underscores the urgent need for a greater use of clean energy alternatives and increased public awareness about the risks of household air pollution in low‑income South African communities.
Bush encroachment and invasive alien plant species' linkage to outmigration
Newete, Solomon W.; Chirima, Johannes George; Tswai, Richard (Springer, 2025-06)
The most prominent drivers of international migration across borders and internal migration-rural to urban areas are explained by the ‘push and pull’ migration model. However, this model falls short in addressing migrations driven by the impacts of climate change, such as the movement from rural to urban area as a coping strategy for environmental degradation. Factors like deforestation, desertification, droughts, and floods are key drivers of such migration. Additionally, bush encroachment and the spread of invasive alien plant species disrupt landscapes and negatively affect ecosystem goods and services in many arid and semi-arid regions around the world. This phenomenon directly affects the livelihoods of rural communities by depriving them of their croplands, rangelands, and ranches. Despite this, there is a lack of sufficient information on how these factors are linked to migratory movements, whether from rural to urban areas or between rural regions. To explore this connection, this study reviewed scientific publications, including journal articles and books using key phrases such as, ‘push and pull migration factors’, ‘impact of bush encroachment on migration’ and ‘impact of invasive alien plants on migration factors’, among others. A total of 155 documents were downloaded via Google Scholar, of which 99 were thoroughly reviewed and included in the study. The remaining 53 documents were skimmed and excluded due to their irrelevance, or limited contribution to the research. The study found that the bush encroachment and invasive plant species in rangelands are significant push factors for driving migration, both between rural areas and rural to urban areas. It is, therefore, recommended that these two factors be given a greater attention when addressing outmigration from rural regions.
Perspectives on aquatic emerging pollutant monitoring in sub-Saharan Africa with a focus on disinfection byproducts
Van der Merwe, Petra; Booysen, Amogelang; Forbes, Patricia B.C. (Oxford University Press, 2025)
The provision of clean water is of key importance in sub-Saharan Africa (SSA) where there is a rapid rate of urbanization. Aside from socio-economic factors, aspects such as a changing climate and polluted water sources create additional challenges towards achieving Sustainable Development Goal (SDG) 6: to "Ensure availability and sustainable management of water and sanitation for all". In order to allow for sustainable development and improved quality of life, water safety must be prioritized, which requires the effective monitoring of a range of potential water contaminants, including emerging chemical pollutants (ECPs), which pose risks to both human health and the environment. Here we provide perspectives on the current monitoring status of ECPs in the SSA region, with a focus on disinfection byproducts. Regulatory frameworks and reported monitoring practices are discussed in the context of suitability and accessibility for SSA. It was found that in recent years efforts to increase monitoring of ECPs has grown in some countries, although the majority of the countries in the region do not demonstrate these efforts.
Extreme event attribution using km-scale simulations reveals the pronounced role of climate change in the Durban floods
Engelbrecht , Francois A.; Steinkopf, Jessica; Chang, Nicolette; Biskop, Sophie; Malherbe, Johan; Engelbrecht, Christina Johanna; Grab, Stefan; Le Roux, Alize; Vogel, Coleen; Padavatan, Jonathan; Thatcher, Marcus; McGregor, John L. (Nature Research, 2025-07-01)
The Durban floods of 11–12 April 2022 is the worst flood disaster in South Africa’s history and raised questions about the role of climate change in the event. Meso-scale dynamics, involving processes that cannot be resolved at the spatial resolutions of current global climate models, played an important role in the heavy falls of rain. Here we report on the development of a convection-permitting conditional extreme event attribution modelling system, well-suited to explore the role of climate change in meso- and convective-scale extreme weather events. The African-based attribution system makes use of a computationally-efficient variable-resolution atmospheric model and runs on a local high-performance computer in South Africa. Similar systems can potentially be rolled out across the Global South (and North). The system relies on a km-scale perturbed-physics ensemble to describe the simulation/structural uncertainty associated with an extreme weather event in an anthropogenically-warmed world, compared to counterfactual cooler worlds where the effects of anthropogenic forcing are (partially) removed. Simulations reveal a pronounced role of climate change in the Durban floods. Average rainfall in the Durban region is simulated to have been at least 40% higher during the two days of the flood, relative to rainfall in a counterfactual cooler world.
Participatory governance for people and nature in multifunctional landscapes — insights from Biosphere Reserves
Sinare, Hanna; Coetzer, Kaera L.; Schultz, Lisen (Elsevier, 2025-12)
Participatory approaches are put forward to ensure that governance for the well-being of humans and nature is legitimate and effective, particularly responding to global challenges of ecosystem degradation and climate change. As model areas for sustainable development with explicit goals of participation, the UNESCO World Network of Biosphere Reserves can provide insights on participatory governance arrangements, outcomes of participation, and obstacles for participation. Through a literature review, we found that transparent communication and fair distribution of benefits and trade-offs enhance participation. Early involvement, skilled facilitation, and the capacity to develop shared values among diverse interests improve outcomes. Project-driven participation, deficient capacity to handle conflicting interests, and mechanisms of exclusion hinder participation. Biosphere Reserves (BRs) can leverage already existing actor initiatives, local knowledge, and practices. We identified a need for studies of causal links between participation and BR outcomes, including unpacking the meaning of different modes of participation. HIGHLIGHTS • Experts and BR actors consider participation to be key to success in BRs. • Awareness of the BR and its purpose is a necessary first step, but not sufficient. • Fair distribution of benefits and trade-offs enhances participation. • Early involvement of actors and capacity to develop shared values are key. • Studies of causal links between participation and BR outcomes are needed.
Integrating air quality and climate change : a policy imperative for human well-being and equity for the G20
Feig, Gregor Timothy, Tumelo; Garland, Rebecca M.; Langerman, Kristy E.; Perumal, Sarisha (National Association for Clean Air, 2025-06)
South Africa has assumed the Presidency of the Group of 20 (G20) of the world’s most significant economies in 2025 with a theme of “Fostering Solidarity, Equity and Sustainable Development.” During this period, the Academy of Science of South Africa (ASSAf) has led discussions among the academies of the G20 countries including the African Union in a process known as the Science 20 (S20). The theme for the S20 is “Climate Change and Well-being.” In February 2025, ASSAf hosted a series of discussions with the aim of producing a Communiqué for submission to the G20 Summit in September 2025.
Identification of maize leaf diseases using red, green, blue-based images with convolutional neural network (CNN) and residual network (ResNet50) models
Nkuna, Basani Lammy; Abutaleb, Khaled; Chirima, Johannes George; Newete, Solomon W.; Van der Walt, Adriaan Johannes; Nyamugama, Adolph (Elsevier, 2025-12)
Maize (Zea mays) is a crucial global staple crop that serves as a primary source of food and income, especially for smallholder farmers. However, it is susceptible to diseases that drastically reduce yields if not controlled. Traditional methods of disease detection of visual inspections are often inaccurate and uncertain. Recent advances in computer vision and deep learning techniques have shown promise in improving image recognition for crop disease detection. This study aims to develop models for detecting maize leaf diseases at the subfieldlevel using red, green, and blue (RGB)-based images using convolutional neural network (CNN) and residual network (ResNet50) models. A dataset of 1500 maize leaf images representing seven categories of maize disease symptoms was collected from the maize fields in Mopani District, Limpopo, South Africa. The data were processed to train and compare two deep learning models, CNNs and ResNet50. Both models demonstrated good classification accuracy with ResNet50 outperforming CNN, achieving an accuracy of 78.76% compared to 71.01% for CNN. The findings underscore ResNet50 enhanced capability to classify maize leaf diseases more accurately than CNN, attributed to its deeper architecture. This study illustrates the potential for deploying deep learning model in detecting maize leaf diseases. This study supports the transformative potential of deep learning in advancing agricultural practices, serving as a vital tool for early disease detection and contributing to food security in maize-producing regions, particularly smallholder farming systems. Therefore, this study trains the models that can be included in the mobile applications to be used to detected diseases in a sub-field level of the smallholder farms. HIGHLIGHTS • Maize disease detection with CNN and ResNet50 models using RGB images. • Dataset included both nutrient deficiencies and disease symptoms. • Image preprocessing and data augmentation to increase training data and reduce overfitting. • Demonstrated the potential of deep learning for multi-disease detection.
Assessing the performance of the WRF model in simulating squall line processes over the South African highveld
Mbokodo , Innocent L.; Burger , Roelof P.; Fridlind, Ann; Ndarana, Thando; Maisha , Robert; Chikoore, Hector; Bopape, Mary-Jane M. (MDPI, 2025-09-06)
Squall lines are some of the most common types of mesoscale cloud systems in tropical and subtropical regions. Thunderstorms associated with these systems are among the major causes of weather-related disasters and socio-economic losses in many regions across the world. This study investigates the capability of the Weather Research and Forecasting (WRF) model in simulating squall line features over the South African Highveld region. Two squall line cases were selected based on the availability of South African Weather Service (SAWS) weather radar data: 21 October 2017 (early austral summer) and 31 January–1 February 2018 (late austral summer). The European Centre for Medium-Range Weather Forecasts ERA5 datasets were used as observational proxies to analyze squall line features and compare them with WRF simulations. Mid-tropospheric perturbations were observed along westerly waves in both cases. These perturbations were coupled with surface troughs over central interior together with the high-pressure systems to the south and southeast of the country creating strong pressure gradients over the plateau, which also transports relative humidity onshore and extending to the Highveld region. The 2018 case also had a zonal structured ridging High, which was responsible for driving moisture from the southwest Indian Ocean towards the eastern parts of South Africa. Both ERA5 and WRF captured onshore near surface (800 hPa) winds and high-moisture contents over the eastern parts of the Highveld. A well-defined dryline was observed and well simulated for the 2017 event, while both ERA5 and WRF did not show any dryline for the 2018 case that was triggered by orography. While WRF successfully reproduced the synoptic-scale processes of these extreme weather events, the simulated rainfall over the area of interest exhibited a broader spatial distribution, with large-scale precipitation overestimated and convective rainfall underestimated. Our study shows that models are able to capture these systems but with some shortcomings, highlighting the need for further improvement in forecasts.
Land use and environmental drivers of methane and nitrous oxide emissions in Eswatini peatlands
Ndlela, Thandeka; Beckedahl, Heinz; Glatzel, Stephan; Grundling, Piet-Louis (International Mire Conservation Group and International Peatland Society, 2025-08)
Please read abstract in the article.
Transforming air pollution and health research into action in low- and middle-income countries
Samet, Jonathan; Shairsingh, Kerolyn; Ye, Wenlu; Gumy, Sophie; Mudu, Pierpaolo; Andersen, Zorana; Huang, Wei; Krzyzanowski, Michal; Mehta, Sumi; Petach, Helen; Peters, Annette; Pillarisetti, Ajay; West, Jason; Wright, Caradee Yael; Clasen, Thomas (Lippincott, Williams and Wilkins, 2025-12)
This commentary highlights the need for actionable and context-appropriate research on air pollution and health that will continue to drive policies to reduce exposures and disease burden. Research on air pollution and health has been substantial in high-income countries (HIC), leading to causal conclusions on the adverse effects of air pollution. Despite bearing the greatest disease burden from air pollution, low- and middle-income countries (LMICs) have had scant research funding, a trend that may well be aggravated due to changing political priorities in some HICs. High-quality data from LMICs is urgently needed to help motivate local, subnational, and national policies to raise awareness and identify priority actions to improve health. The new evidence will also provide a more complete understanding of air pollution and health globally. We highlight a framework for moving from research to action and address how this framework differs in HIC and LMIC contexts. We propose a hierarchy of research needs that begins with having the necessary air pollution monitoring and health data, and the capacity to use the data for informative analytics, risk assessment, valuation, and policy formulation. Building technical capacity may be needed for this purpose, as will development of a functioning regulatory system in parallel. We call for greater emphasis on surveillance studies to demonstrate the benefits of action and address barriers to action. The global community would benefit from a broad research agenda with priorities and adequate funding dedicated to building evidence that leads to positive policy change. We urge priority for advancing actionable research and improving research capacity in LMICs, including investments in routine collection of relevant data, emphasizing the foundation of risk monitoring and health data systems, and building a cadre of researchers and informed policy-makers.
Detecting and mapping invasive Populus alba species in mountainous ecosystems using Sentinel-2 imagery and random forest classification
Mapuru, Morena; Xulu, Sifiso; Gebreslasie, Michael; Sadiki, Maleho Mpho (Wiley, 2025-08-21)
Mapping invasive alien plants (IAPs) has become essential for land and biodiversity conservation authorities, as these species can transform the areas they invade. Fortunately, advances in remote sensing using publicly available products such as Sentinel-2 have improved this process, especially in hard-to-access mountainous regions. In South Africa, poplar (Populus alba) is among the IAPs of concern and is found in the eastern Free State and elsewhere in the country, but remote sensing has not yet been used to map this species. Using Sentinel-2 imagery and the random forest (RF) algorithm, this study allowed us to: (a) map and distinguish poplar trees from other land covers throughout the year in the eastern Free State’s mountainous region, (b) evaluate influential bands and their combinations in classification, and (c) assess the accuracy of the classification for the first and second halves of the year. The results showed that images from the first half of the year (January–June) had higher classification accuracy (overall accuracy [OA] = 91% and kappa = 0.89) than those from the second half (Jul–Dec) (OA = 87% and kappa = 0.84). Poplar and other classes were separable, with poplar mostly found in riparian areas. The study identified variables such as short-wave infrared-1 (SWIR-1), normalized difference vegetation index (NDVI), blue, poplar detection index-1 (PI-1), modified normalized difference water index (MNDWI), near-infrared (NIR), and PI-3 as key parameters for classifying poplar trees in mountainous regions. Overall, our findings demonstrate that Sentinel-2 bands and indices combined with an RF classifier provide an effective method for mapping poplar invasive trees in mountainous ecosystems.
Leaf area index-based phenotypic assessment of sweet potato varieties using UAV multispectral imagery and a hybrid retrieval approach
Tsele, Philemon; Ramoelo, Abel; Moleleki, Lucy Novungayo; Laurie, Sunette; Mphela, Whelma; Tshuma, Natasha (Elsevier, 2025-08)
Phenotyping based on the estimation of plant traits such as the leaf area index (LAI) could aid the identification and monitoring of the sweet potato health, growth status and gross primary productivity. Integrating radiative transfer models (RTMs), active learning algorithms and non-parametric regression methods using unmanned aerial vehicle (UAV) multispectral imagery have the potential for accurately estimating LAI across multiple crop varieties at varying growth stages. This study tested the boosted regression trees (BRT) and kernel ridge regression (KRR) for inversion of the PROSAIL RTM to retrieve LAI across 20 sweet potato varieties during peak growth stage. Furthermore, the study attempted to constrain the inversion process by using active learning (AL) techniques which ensured the selection of informative samples from a pool of RTM simulations. Results show that the most accurate LAI retrieval over the heterogeneous sweet potato canopy was achieved by integrating smaller PROSAIL simulations with the random sampling AL and KRR methods. The LAI retrieval accuracy had a coefficient of determination (R2) of 0.52, root mean squared error (RMSE) of 0.88 m2.m-2 and relative RMSE of 12.23 %. However, the BRT performance in-comparison to KRR, captured more spatial variability of observed LAI with a better prediction accuracy across the 20 sweet potato varieties. The hybrid approach developed in this study, show potential for accurate phenotyping of LAI dynamics across multiple sweet potato varieties during a matured growth stage. These findings have significant implications for sweet potato breeding programmes that are critical for developing new cultivars in South Africa.
A contaminated regression model for count health data
Otto, Arnoldus F.; Ferreira, Johannes Theodorus; Tomarchio, Salvatore Daniele; Bekker, Andriette, 1958-; Punzo, Antonio (Sage, 2025-02)
In medical and health research, investigators are often interested in countable quantities such as hospital length of stay (e.g., in days) or the number of doctor visits. Poisson regression is commonly used to model such count data, but this approach can’t accommodate overdispersion—when the variance exceeds the mean. To address this issue, the negative binomial (NB) distribution (NB-D) and, by extension, NB regression provide a well-documented alternative. However, real-data applications present additional challenges that must be considered. Two such challenges are (i) the presence of (mild) outliers that can influence the performance of the NB-D and (ii) the availability of covariates that can enhance inference about the mean of the count variable of interest. To jointly address these issues, we propose the contaminated NB (cNB) distribution that exhibits the necessary flexibility to accommodate mild outliers. This model is shown to be simple and intuitive in interpretation. In addition to the parameters of the NB-D, our proposed model has a parameter describing the proportion of mild outliers and one specifying the degree of contamination. To allow available covariates to improve the estimation of the mean of the cNB distribution, we propose the cNB regression model. An expectation-maximization algorithm is outlined for parameter estimation, and its performance is evaluated through a parameter recovery study. The effectiveness of our model is demonstrated via a sensitivity analysis and on two health datasets, where it outperforms well-known count models. The methodology proposed is implemented in an R package which is available at https://github.com/arnootto/cNB.
Human health risk assessment of PM2.5, NO2, and SO2 and its impact on mortality in Nkangala and Gert Sibande, South Africa
Millar, Danielle Ann; Kapwata, Thandi; Howard, Marcus A.; Oosthuizen, Rietha; Naidoo, Natasha; Wright, Caradee Yael (Ubiquity Press, 2025-08)
Please read abstract in the article.
Monitoring coastal estuarine habitats for biodiversity along the temperate bioregion of South Africa
Campbell, Anthony; Adam, Elhadi; Adams, Janine B.; Barrenblitt, Abigail; Fatoyinbo, Temilola; Jensen, Daniel; Naidoo, Laven; Riddin, Taryn; Simard, Marc; Smith, Kyle; Thakali, Pati; Van Deventer, Heidi; Van Niekerk, Lara; Stovall, Atticus (Wiley, 2025-10)
Coastal wetlands provide critical ecosystem services, including the enhancement of biodiversity, carbon sequestration, and flood protection. Although these ecosystems have been mapped for country-level biodiversity typing, improved extent mapping is necessary to account for estuarine dynamics and improved reporting to the Kunming-Montreal Global Biodiversity Framework (GBF) by 2030. We achieved an overall coastal wetland accuracy of 90.7% (95% confidence interval: 90.2%–91.4%) utilizing a dense time series of very high spatial resolution (3 m) PlanetScope satellite imagery to map coastal wetlands with a combination of Random Forest to develop training data, U-Net convolutional neural networks, and a final decision tree to determine discrete ecosystem extents. Across the 84 mapped estuaries totaling 67,452 ha and 2,135 images, we mapped 9,131.1 ± 1,596.9 ha (13.5% of total estuarine functional zone extent) of salt marsh & reed beds and 1,718.6 ± 234.3 ha (2.5%) of Submerged Aquatic Vegetation (SAV). In addition to our earth observation analysis, we calculated tidal amplitudes and water level trends for 20 water level gauges across the region. We found tidal amplitude was a significant driver of salt marsh extent, explaining 33.6% of the variation (F (1,19) = 9.62, p = 0.005). We demonstrate a repeatable methodology for improved mapping of ecosystem zonation and utilize water level data to explore potential drivers of ecosystem distribution. Our method could be incorporated into a robust earth observation approach for reporting progress toward the goals of the/reporting to the GBF and Sustainable Development Goals (SDGs). PLAIN LANGUAGE SUMMARY Coastal wetlands provide many benefits to humans, including as habitat for a variety of species, accumulation of carbon in their soils, and protection from flooding and storm events. Global and regional maps of these ecosystems exist, but they lack precision in their identification of ecosystem zones. Improved maps could be used for improved reporting to international agreements and inform coastal management. We mapped three coastal wetland habitats to a high degree of accuracy (90.7%) utilizing a time series of commercial satellite data and machine learning algorithms. Across the 84 mapped estuaries totaling 67,452 ha, we mapped 9131.1 ± 1596.9 ha (13.5% of total estuarine functional zone extent) of salt marsh & reed beds and 1718.6 ± 234.3 ha (2.5%) of Submerged Aquatic Vegetation (SAV). We conducted additional analysis on how tidal amplitude, water level, and impervious surface influence the distribution of habitats in the region, finding that higher tidal amplitudes correlated with more salt marsh extent. Our methodology is repeatable and could improve the monitoring of these ecosystems in South Africa. KEY POINTS • We mapped coastal wetland habitats (salt marsh, reeds and sedges, and submerged aquatic vegetation) with a U-Net approach at >90% accuracy • We identified tidal amplitude as a major driver of salt marsh habitat, explaining 33.6% of the variation (F (1,19) = 9.62, p = 0.005). • Salt marsh was only 19.4% of coastal wetland extent, 40% of this extent was found within Knysna Estuary and Langebaan Lagoon.
Globale biodiversiteitsraamwerk vir varswatervleilande van Suid-Afrika : voorlopige berekening van die vordering om die restourasiemikpunt van doelwit 2 te bereik
Van Deventer, Heidi (Suid-Afrikaanse Akademie vir Wetenskap en Kuns, 2025-07)
AFRIKAANS : Die Nasionale Biodiversiteitsanalise van 2018 het bevind dat vleilande (riviermondings en varswatervleilande) die mees bedreigde van al die ekostelsels in Suid-Afrika is. Teen 2030 moet Suid-Afrika aan die Verenigde Nasies se Globale Biodiversiteitsraamwerk (GBR) rapporteer of Doelwit 2 bereik is, naamlik om 30% van gedegradeerde stelsels in die proses van herstel te hê. Hierdie studie het beoog om die voorlopige omvang as persentasie van varswatervleilande wat aan ekologiese ingrypings onderworpe was in verhouding tot die totale omvang van gedegradeerde varswatervleilande in Suid-Afrika te bereken. Ons het ook die persentasieomvang in verhouding tot eienaarskap van die gedegradeerde varswatervleilande en dié wat onder ekologiese restourasie is, bepaal. Die Werk vir Vleilandeprogram en die Werk vir Waterprogram se beskikbare data is ingesamel en met die Nasionale Vleilandkaart weergawe 6, gekombineer om die persentasies te bereken. Die meerderheid van Suid-Afrika se varswatervleilande (51%) is gemodelleer om degradasie te bepaal, met >2.0 miljoen hektaar van die 4 miljoen hektaar Suid-Afrikaanse vleilande wat impakte toon ten opsigte van veranderinge aan die hidrologiese siklus, waterkwaliteitsimpakte, fragmentasie en verlies van habitatte, voorkoms van indringerspesies en klimaatverandering, of ‘n kombinasie van hierdie impakte. Die 30% van Doelwit 2 beteken dus dat amper 613 136 ha van varswatervleilande teen 2030 onder ekologiese herstel moet wees. Ekologiese herstelprogramme het tot dusver slegs ongeveer 203 283 ha (10%) van Doelwit 2 bereik. Die meerderheid (82,8%) van vleilande is op privaat grond geleë, waarvan meer as die helfte gedegradeer is. Baie van die impakte op vleilande, asook die restourasieinisiatiewe wat deur die privaat sektor of individue uitgevoer word, word nie in hierdie berekening weerspieël nie. Monitering en kwantifisering van alle varswaterhabitatte is dus noodsaaklik om Doelwit 2 van die GBR teen 2030 te bereik. ENGLISH : The National Biodiversity Assessment of 2018 listed wetlands (estuaries and freshwater ecosystems) as the most threatened ecosystem of South Africa. By 2030, South Africa must report to the United Nations’ Global Biodiversity Framework (GBF) to which degree we have reached Target 2 that aims to have 30% of the extent of degraded ecosystems under restoration. This study aimed to calculate the preliminary extent as a percentage of wetlands that have been under ecological restoration interventions, relative to the total extent of degraded freshwater wetlands of South Africa. We also assessed the percentage of extent relative to ownership of the degraded wetlands and those that are under ecological restoration. Data released by the Working for Wetlands and Working for Water programmes were combined with the National Wetland Map version 6 as well as information on land ownership and protection level status of the country. The majority of the freshwater wetlands (51%) were modelled as degraded, with > 2 million ha of the 4 million ha of wetlands showed impacts resulting from various pressures, including changes to the hydrological cycle, water quality, fragmentation and degradation of habitats, climate change, or a combination of these pressures. The 30% GBF Target 2 requires that almost 613 136 ha of freshwater wetlands should be under restoration by 2030. The government’s two restoration programmes have reached only 203 283 ha (10%) of the desired target. The majority (82,8%) of freshwater wetlands is located on private land, of which the majority is degraded. Many of the impacts and none of the restoration interventions undertaken by the private sector or individuals are reflected. Monitoring and quantification of all freshwater habitats are therefore needed to attain the 30% extent target of the GBF.
Remote sensing monitoring of soil moisture for South African wetlands
Van Deventer, Heidi; Naidoo, Laven; Le Roux, Jason; Blaauw, Ciara; Tema, Hebert (Water Research Commission, 2025-04)
Surface soil moisture is an essential climate variable (ECV; https://gcos.wmo.int/en/essential-climate-variables/soilmoisture) which is monitored to inform our understanding of changes in the atmosphere and earth. Soil moisture is also an important indicator, in addition to vegetation and soil types, of the presence of a wetland.
African political ecologies
Ramutsindela, Maano F.; Mba, Chika C.; Mushonga, Tafadzwa; Aremu, Adeyemi Oladapo; Mutune, Jane Mutheu; Matose, Frank; Dzingirai, Vupenyu; Muthama Muasya, A.; Dorvlo, Selorm Yaotse; Odhiambo, E.G. (Annual Reviews, 2025-10)
This review locates African political ecologies at the intersection of the broader fields of political ecology and African studies. It focuses on African ecological thought and practices in relation to environmental challenges in Africa. Methodologically, it eschews the sectoral approach to the study of African environments by drawing together three interrelated themes of African epistemologies, African agency, and socioecological debt. It treats these themes as the bedrock of African political ecologies that are crucial for engaging and resolving socioecological challenges on the continent and for governing the African commons. These foundational themes offer avenues for appreciating African epistemologies, experiences, and actions as well as the possibilities for context-specific environmental justice within the broader frame of reparation. The review concludes by delineating the scope and agenda for the African political ecologies crucial for broadening political ecological research and African studies.
Soft computing for the posterior of a matrix t graphical network
Pillay, Jason; Bekker, Andriette, 1958-; Ferreira, Johannes Theodorus; Arashi, Mohammad (Elsevier, 2025-05)
Modeling noisy data in a network context remains an unavoidable obstacle; fortunately, random matrix theory may comprehensively describe network environments. Noisy data necessitates the probabilistic characterization of these networks using matrix variate models. Denoising network data using a Bayesian approach is not common in surveyed literature. Therefore, this paper adopts the Bayesian viewpoint and introduces a new version of the matrix variate t graphical network. This model's prior beliefs rely on the matrix variate gamma distribution to handle the noise process flexibly; from a statistical learning viewpoint, such a theoretical consideration benefits the comprehension of structures and processes that cause network-based noise in data as part of machine learning and offers real-world interpretation. A proposed Gibbs algorithm is provided for computing and approximating the resulting posterior probability distribution of interest to assess the considered model's network centrality measures. Experiments with synthetic and real-world stock price data are performed to validate the proposed algorithm's capabilities and show that this model has wider flexibility than the model proposed by [13]. HIGHLIGHTS • Expanding the framework for denoising financial data inside the realm of graphical network theory, where the assumption of normality in the model is inadequate to account for the variation. • Introduction of the matrix variate gamma and inverse matrix variate gamma as priors for the covariance matrices; the univariate scale parameter β may be fixed or subject to a prior. • Following Bayesian inference with more flexible priors, there is an improvement based on relevant accuracy measures. • Experimental results indicate that our proposed framework and results outperform those of [13].

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