Research Articles (Earth Sciences)

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Using multisource remotely sensed data and cloudcomputing approaches to map non-native species in thesemi-arid savannah rangelands of Mpumalanga, South Africa
(Taylor & Francis, 2025) Maphanga, Thabang; Dube, Timothy; Sibanda, Mbulisi; Gxokwe, Siyamthanda
Semi-arid savannah rangelands are diverse environments (in terms of species) that play an important role in sustaining biodiversity and providing ecosystem services. However, the emergence of non-native species, as well as bush encroachment, are currently threatening these (semi-arid rangeland and grassland) ecosystems. The purpose of this study was therefore to map and quantify the spatial extents of non-native woody vegetation in the Kruger National Park and surrounding communal areas in Mpumalanga, South Africa. To achieve the study’s objectives, Sentinel-1 and Sentinel-2 remotely sensed data were combined and analysed using the random forest (RF) machine-learning algorithm in the Google Earth Engine (GEE) cloud computing platform. Specifically, spectral bands and selected spectral derivatives, e.g. enhanced vegetation index (EVI2), normalized difference moisture index (NDMI) and normalized difference phenology index (NDPI) were computed and used to map non-native woody vegetation. After optimizing the model combination, the classification outputs had an overall accuracy of 70%, with class accuracies such as producer’s accuracy (PA) and user’s accuracy (UA) ranging from 67% to 95%.It was shown in this study that using Sentinel-2 and Sentinel-1 data together led to better overall accuracy than using single sensor models when mapping semi-arid savannah rangelands. It was also found in this study that the overall classification accuracy of non-native (invasive) species using optical sensors was higher than in previous studies. On a free platform like GEE, it was possible to utilize advanced classification processes to fully exploit the informative content of Sentinel-1 and Sentinel-2 data.
Zambezi river basin aquifer systems: opportunities and challenges in using freely available data sources and groundwater flow modelling for spatial exploratory analysis
(Elsevier B.V., 2025) Mengistu, Haile; Banda, Kawawa; Crestaz, Ezio
A groundwater flow model was implemented over the Zambezi River Basin using the state-of-the-art DHI-WASY finite element code Feflow. The analysis was based upon different freely available datasets that include a hydrologically consistent digital elevation model from HYDROSHEDS, the BGS (British Geological Survey) quantitative hydrogeological maps, and the regional hydrogeological SADC-GMI database. The model implementation was aimed at: (i) to identify and analyse challenges and limitations (data scarcity, accuracy of regional datasets, impact of geological, tectonic and hydrogeological complexity on model reliability) in applying groundwater flow modelling at basin scale; (ii) to perform an exploratory spatial analysis with reference to the magnitude and spatial distribution of effective recharge, aquifers’ properties and interlinks between surface water and aquifer systems (surface water – groundwater interactions). High uncertainty is generally associated with the estimation of hydrological and hydrogeological parameters, whose high spatial variability is not necessarily captured by the regional data products. This study evaluates how integrating freely available datasets (such as the DEM, BGS maps) influences model accuracy and uncertainty, particularly in terms of parameter estimation. The findings illustrates that, despite the limitations, freely available datasets can still effectively be used to develop a groundwater model that captures regional piezometric trends and provides insights into spatial variability. This demonstrates the potential for using such models in similar data-scarce regions. The modeling approach is expected to provide valuable quantitative insights into groundwater trends and variability, helping to identify key areas of uncertainty and guiding future data collection and model refinement efforts.
Assessing the impacts of water diversion project on water resource system sustainability
(John Wiley and Sons Inc, 2025) Liu, Dedi; Chen, Wen; Zhang, Ruikang
Interbasin water diversion project has been considered as an effective way to assure water resource system sustainability. In order to assess the impacts of water diversion on sustainability, we propose a framework in terms of reliability, resilience, and vulnerability. The estimated water availability from hydrological models and the projected water demand are input to a water resource allocation model. The water resource allocation model allocates the two available water sources (i.e., the local and the diverted water) in the water-receiving areas. The differences of the allocated water resources between these two water sources are figured out to quantify the impacts of water diversion on water resource system sustainability. The water-receiving area of Bailong River Water Diversion Project, located in Gansu, China, was selected as a case study. The results show that compared to the reference planning years, the runoff in future planning years will be reduced, while their water demands will almost increase under all scenarios. Although the current designed water diversion scheme is effective in increasing resilience, there is still potential for increasing resilience through optimizing the designed scheme. Further, the more unfavorable the water supply and demand conditions are, the larger the space for optimizing the system sustainability. This study can help understand the impacts of water diversion on water resource system sustainability in a changing environment.
Machine learning and spatio temporal analysis for assessing ecological impacts of the billion tree afforestation project
(John Wiley and Sons Ltd, 2025) Dube, Timothy; Mehmood, Kaleem; Anees, Shoaib Ahmad
This study evaluates the Billion Tree Afforestation Project (BTAP) in Pakistan's Khyber Pakhtunkhwa (KPK) province using remote sensing and machine learning. Applying Random Forest (RF) classification to Sentinel-2 imagery, we observed an increase in tree cover from 25.02% in 2015 to 29.99% in 2023 and a decrease in barren land from 20.64% to 16.81%, with an accuracy above 85%. Hotspot and spatial clustering analyses revealed significant vegetation recovery, with high-confidence hotspots rising from 36.76% to 42.56%. A predictive model for the Normalized Difference Vegetation Index (NDVI), supported by SHAP analysis, identified soil moisture and precipitation as primary drivers of vegetation growth, with the ANN model achieving an R2 of 0.8556 and an RMSE of 0.0607 on the testing dataset. These results demonstrate the effectiveness of integrating machine learning with remote sensing as a framework to support data-driven afforestation efforts and inform sustainable environmental management practices.
A validation of fruitlook data using eddy covariance in a fully mature and high-density japanese plum orchard in the western cape, south africa
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Mashabatu, Munashe; Motsei, Nonofo; Jovanovic, Nebojsa
The cultivation of Japanese plums (Prunus salicina Lindl.) in South Africa has increased over the years, yet their water use is unknown. Their cultivation in the Western Cape Province of South Africa is highly dependent on supplementary irrigation, indicating their high water use demand. This study used remote sensing techniques to estimate the actual evapotranspiration (ETc act) of the Japanese plums to assess their water use on a large scale. The accuracy of the procedure had to be validated before getting to tangible conclusions. The eddy covariance was used to measure ETc act in an African Delight plum orchard to validate the FruitLook remote sensing data for the 2023–2024 hydrological year and irrigation season. The seasonal and annual plum crop water requirements measured using the eddy covariance system were 751 and 996 mm, while those estimated by FruitLook were 744 and 948 mm, respectively. Although FruitLook slightly underestimated plum ETc act by a Pbias of −6.15%, it performed well with a Nash–Sutcliffe efficiency (NSE) of 0.91. FruitLook underestimated evapotranspiration mainly during the peak summer season with full vegetation cover when the model may inaccurately represent irrigation impacts, soil moisture availability, and localized advection effects, better captured by the eddy covariance system. Based on the results, FruitLook proved to be sufficiently accurate for large-scale applications to estimate evapotranspiration in Japanese plum orchards in the Western Cape.
Ccu2pdsnse4 and cu2pdsn(s,se)4 palladium-substituted kesterite nanomaterials for thin-film solar cells
(American Chemical Society, 2025) Nwambaekwe, Kelechi; Yussuf, Sodiq; Tshobeni, Ziyanda; Ikpo, Chinwe; January, Jaymi; Cox, Meleskow; Ekwere, Precious; Iwuoha, Emmanuel
Kesterites are being studied intensively as sustainable absorber materials for solar cell development. However, elements such as Zn and Cu exhibit antisite defects that function as charge traps and recombination centers that affect the light absorption and carrier transport efficiencies of kesterite solar cells. The substitution of Zn or Cu with other metals is one of the strategies used to improve the photovoltaic performance of kesterites. This study focuses on the preparation and photovoltaics of Cu2PdSnSe4 (CPTSe) and Cu2PdSn(S,Se)4 (CPTSSe) kesterite nanoparticles (containing Pd instead of Zn) by a modified solvothermal (polyol) microwave synthesis method. The nanomaterials exhibited a tetragonal kesterite crystal structure with polydispersed morphology and average crystallite sizes of 22 and 17 nm for CPTSe and CPTSSe, respectively. DAMMIF ab initio analysis of the small-angle X-ray scattering data determined the shape of CPTSe and CPTSSe nanomaterials to be ellipsoidal. Ultraviolet-visible (UV-vis) spectroscopy revealed red-shift absorption properties, with bandgap energy values of 1.13 eV (CPTSe) and 1.20 eV (CPTSSe), thereby making them suitable light absorber materials for photovoltaic applications. Photoluminescence spectroscopy characterization confirmed the attenuation of defect concentrations in CPTSe and CPTSSe compared to the Zn analogue, which positively impacts the charge-carrier transport and recombination properties. A preliminary test of the materials in superstrate photovoltaic cell devices yielded power conversion efficiency values of 1.32% (CPTSe) and 3.5% (CPTSSe). The CPTSe- and CPTSSe-based photovoltaic devices maintained ∼70% mean open-circuit voltage (Voc), which is a significant improvement over the ∼20% Voc retained by Zn-based kesterites after 24 days.
Spatiotemporal analysis of surface urban heat Island intensity and the role of vegetation in six major Pakistani cities
(Elsevier B.V., 2025) Dube, Timothy; Anees, Shoaib Ahmad; Mehmood, Kaleem
The Urban Heat Island (UHI) phenomenon exacerbates thermal discomfort in urban areas and significantly contributes to urban overheating when combined with climate change. This study investigates the spatiotemporal patterns of Surface Urban Heat Island Intensity (SUHII) in six major cities of Pakistan, focusing on the interplay between urban expansion, vegetation cover, and SUHII. To quantify SUHII dynamics, the impact of urban sprawl and vegetation changes was analyzed. The study offers critical insights into the implications for urban planning and policymaking in Pakistan. Using remote sensing data from Landsat satellites, analyzed with Geographic Information Systems (GIS) techniques, estimates of SUHII, urban expansion, and vegetation cover were derived. Specifically, imagery from Landsat-5 (2010−2013) and Landsat-8 (2014–2022), obtained from the US Geological Survey (USGS), was employed. Statistical analyses, including Pearson's correlation and linear regression, were conducted to assess relationships between these variables from 2010 to 2022. SUHII was found to increase annually by 0.18 °C in Islamabad and 0.19 °C in Peshawar, with corresponding urban expansion rates of 8.07 km2 (8967.75 pixels) and 1.67 km2 (1860.42 pixels) per year, respectively. Vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover (FVC) were inversely correlated with SUHII, explaining up to 50 % of the variance in Peshawar. However, weaker correlations in Lahore suggest the presence of additional factors influencing SUHII. A distinct spatial relationship between increased vegetation and cooler areas was observed. For instance, Islamabad has greater vegetation cover and cool zones over 41.5 km2. In contrast, Lahore's hot spots spanned 127.1 km2, compared to Abbottabad's 10.4 km2, underscoring the thermal impact of reduced vegetation. The findings emphasize the effectiveness of urban greening, particularly in Islamabad's neutral thermal regions, in mitigating SUHII. This study offers important insights for urban planners in developing sustainable, climate-resilient cities within similar urban contexts. While the results are specific to Pakistani cities, the role of vegetation in mitigating SUHII may hold broader relevance for urban planning strategies in comparable settings.
Derivation of allometric equations and carbon content estimation in mangrove forests of Malaysia
(Elsevier B.V., 2025) Dube, Timothy; Khan, Waseem Razzaq; Giani, Michele
Mangrove forests play a vital role in carbon sequestration and climate change mitigation, yet comprehensive data on their carbon storage capacity in Malaysia remain limited. This study investigated allometric relationships and carbon content in Malaysian mangrove forests, aiming to develop site species-specific allometric equations, determine carbon content in tree components, and assess total carbon stock. Research was conducted in four compartments of the Sg. Pulai Permanent Reserved Forest, representing a mixed-species mangrove stand. We measured 1403 trees across ten species, with Rhizophora apiculata identified as the dominant species. Using diameter at breast height (DBH) and tree height, we developed site species-specific allometric equations to estimate aboveground biomass. The total aboveground biomass ranged from 183.30 t ha⁻1 to 187.06 t ha⁻1 across the study area. We calculated the total carbon stock at 91.01 t C ha⁻1, incorporating measurements from trees below 5 cm in diameter, dead and downed wood, and litter. An economic valuation of carbon storage was conducted using two approaches: the social cost of carbon method estimated a value of USD 4054.76 per hectare. In contrast, the market price approach yielded USD 1064.34 per hectare. This study provides essential data for improving biomass and carbon stock estimation methods in Malaysian mangrove ecosystems. Our findings highlight these forests' economic and ecological importance, supporting their integration into climate change mitigation strategies and informing sustainable management and conservation policies for mangrove forests in Malaysia and similar regions.
Understanding the roles of climate change, land use and land cover change and water diversion project in modulating water- and carbon-use efficiency in Han River Basin
(Elsevier Ltd, 2024) Liu, Dedi; Yue, Feng; Xiong, Lihua
Water-use efficiency (WUE) and carbon-use efficiency (CUE) are critical indicators of ecosystem function and hydrologic processes, reflecting the water-carbon flux exchange rate. Climatic variables, land use and land cover change (LUCC) and water diversion project (WDP) have altered water-carbon cycle; however, their roles in modulating WUE and CUE remain uncertain. To explore these effects, a framework is proposed and Han River basin (HRB) in China is selected as a case study including the data sets from both remote sensing and in situ observations during 2000–2020. The process-based Regional Hydro-Ecological Simulation System model and a supervised machine learning model are applied to simulate the impacts of climatic variables, LUCC and WDP on WUE and CUE, which are conducted by designing four experiments. We find that no significant WUE and CUE trends attributed to contrasting trends in the dry (October to March) and wet (April to September) seasons. Temperature variations greatly affect WUE and CUE, with WUE decreasing in the wet season and increasing in the dry season due to minimum temperature changes. LUCC has litter impacts on WUE and CUE changes. From 2014 to 2020, the middle route of the South-to-North WDP decreased WUE by 0.22 gCkg−1H2O in the middle-low HRB's wet season, slightly affecting CUE. Seasonal CUE was stable, with the largest decrease of 0.04 in the upper HRB during the wet season. The WDP also increased WUE sensitivities to minimum and maximum temperatures, while CUE sensitivities remained constant. Our case study has proven that the proposed framework is an effective way to understand the roles of climate change and WDP in modulating WUE and CUE.
Understanding dominant hydrological processes and mechanisms of water flow in a semi-arid mountainous catchment of the Cape Fold Belt
(Elsevier Ltd, 2025) Jumbi, Faith; Mazvimavi, Dominic; Glenday, Julia
Improving our understanding of streamflow characteristics, water storage, and dominant flowpaths in mountainous regions is important as mountains play a vital role in delivering water to lowlands, particularly in semi-arid areas. This work characterized water sources, flowpaths, and streamflow characteristics in the semi-arid, mountainous Kromme catchment in Eastern Cape Province of South Africa. Precipitation, shallow and deep groundwater levels, and streamflow data were analysed to identify patterns that indicate the occurrence and/or dominance of certain processes, responses, and flowpaths. Results of the study demonstrated how the catchment responds to rainfall events across seasons and rainfall intensities. Steep and rocky areas that make up much of the catchment contributed to significant flood peaks after high-intensity storms. Quick and slow responses in flow after rainfall events indicated the dominance of both surface and subsurface flowpaths respectively. Furthermore, surface and subsurface flows were significant in recharging the floodplain alluvial aquifer as well as maintaining streamflow during dry periods. Average annual runoff coefficients were low (0.09), which implied large evapotranspiration (ET) withdrawals from dominant flowpaths and/or storage in inactive groundwater. The Kromme catchment has a sizeable floodplain with large alluvial aquifers, which make significant contributions to catchment storage and outflows. Overall, the catchment streamflow was sustained by baseflow (for ∼50% of the time).
Understanding the spatio-temporal distribution of bush encroachment in savannah rangelands, South Africa
(Taylor and Francis Ltd., 2024) Maphanga, Thabang; Shoko, Cletah; Sibanda, Mbulisi
Bush encroachment threatens rangelands’ biodiversity and productivity, impacting savannah ecosystems based on location, management practices, and factors like erratic rainfall, climate change, and environmental variability. Considering these challenges, this study therefore seeks to evaluate bush encroachment changes over-time (1992–2022) in the Southern part of Kruger National Park and surrounding communities of South Africa. The study estimated the proportion and extent of encroacher plants in relation to native bush species. To achieve this objective, bioclimatic variables, and a digital elevation model in conjunction with the Random Forest model were utilized. Classified satellite imageries achieved an overall accuracy of 72 and 93%, respectively. A gradual increase in bush encroachment was observed from 41,947 hectares (ha) (3.4%) in 1992 to 61,225 ha (10%) in 2022. Additionally, this study observed a decline in the spatial extent of native plant species by 178,163.4 ha, while invasive species have expanded by 44,022.17 ha from 1992 to 2022 wet season.
Advancements in remote sensing technologies for accurate monitoring and management of surface water resources in Africa: an overview, limitations, and future directions
(Taylor and Francis Ltd., 2024) Sigopi, Maria; Dube, Timothy; Shoko, Cletah
This review presents a comprehensive examination of recent advancements in utilizing multi-date satellite data to analyze spatial and temporal variations in seasonal and inter-annual surface water dynamics within arid environments of Africa. Remote sensing offers continuous, precise, and long-term datasets for surface water research. Various sensors with differing spatial resolutions are discussed, with high-resolution multispectral sensors providing superior spatial resolution but at higher costs. Conversely, dual-sensor approaches, incuding optical sensors (Sentinel-2 and Landsat), radar satellites (Sentinel-1 and RADARSAT) and UAVs were investigated. The review further examines the efficiency and applicability of traditional algorithms such as the modified normalized difference water index (MNDWI), normalized difference water index (NDWI), and automated water extraction index (AWEI) in detecting and delineating surface water resources. Additionally, machine learning (ML) algorithms, including support vector machines (SVM), Random Forest (RF), deep learning and emerging methodologies like recurrent tranformer networks, have been explored. Therefore, we recommend that future research endeavours focus on leveraging high-resolution satellite imagery and integrating physical models with deep learning techniques, artificial intelligence, and online big data processing platforms to improve surface water mapping capabilities.
An assessment of long-term and large-scale wetlands change dynamics in the Limpopo transboundary river basin using cloud-based earth observation data
(Springer Science and Business Media B.V., 2024) Gxokwe, Siyamthanda; Dube, Timothy; Mazvimavi, Dominic
Significant progress has been made in monitoring and assessing the effects of land use and land cover (LULC) changes on wetland extent. However, our understanding of wetland within the transboundary basins has been limited by the scarcity of available data on their dynamic changes over time. This study aimed to address this gap by analyzing the long-term and large-scale spatio-temporal extent of wetland in the Limpopo transboundary river basin (LTRB) over a 20-year period (2000–2020). To achieve this, we utilized the Google Earth Engine (GEE) cloud-computing platform and various remotely sensed data. The study had two primary objectives; (1) to examine LULC changes over time using machine learning algorithms applied to multisource remotely sensed data in GEE, and (2) to assess the relationship between LULC changes and the extent of wetlands in the basin. A total of nine land cover classes were identified, including shrublands, croplands, bare-surface, wetlands, sparse vegetation, tree cover, built-up areas, and grasslands. Shrublands covered 76–82% of the LTRB. On the other hand, wetlands and sparse vegetation were the least dominant, with proportions ranging from 0.3 to 2%. The overall accuracy of the classification results was within acceptable ranges, ranging from 77 to 78%. The study further revealed a continuing decline in wetlands extent and sparse vegetation, with average rates of 19% and 44%, respectively. Conversely, shrublands, croplands, and tree cover showed an increase, with average rates of 0.4% and 12.4% respectively. A significant finding was the replacement of a substantial portion (40%) of wetland areas with built-up areas, indicating that urban expansion is a major driver of wetland shrinkage in the study area. These results provide valuable insights into the declining extent of wetlands in the LTRB. Such findings are crucial for environmental management efforts, as they provide information on which wetlands should be prioritized when implementing strategies to prevent the negative impacts of LULC changes on wetlands in the area. Therefore, contributing towards achieving sustainable development goals relating to freshwater ecosystems protection and management.
Using UAV multispectral photography to discriminate plant species in a seep wetland of the fynbos biome
(Springer Science and Business Media B.V., 2024) Musungu, Kevin; Dube, Timothy; Smit, Julian
Wetlands harbour a wide range of vital ecosystems. Hence, mapping wetlands is essential to conserving the ecosystems that depend on them. However, the physical nature of wetlands makes fieldwork difficult and potentially erroneous. This study used multispectral UAV aerial photography to map ten wetland plant species in the Fynbos Biome in the Steenbras Nature Reserve. We developed a methodology that used K-Nearest Neighbour (KNN), Support Vector Machine (SVM), and Random Forest (RF) machine learning algorithms to classify ten wetland plant species using the preselected bands and spectral indices. The study identified Normalized green red difference index (NGRDI), Red Green (RG) index, Green, Log Red Edge (LogRE), Normalized Difference Red-Edge (NDRE), Chlorophyll Index Red-Edge (CIRE), Green Ratio Vegetation Index (GRVI), Normalized Difference Water Index (NDWI), Green Normalized Difference Vegetation Index (GNDVI) and Red as pertinent bands and indices for classifying wetland plant species in the Proteaceae, Iridaceae, Restionaceae, Ericaceae, Asteraceae and Cyperaceae families.
Variation in soil water content and groundwater levels across three land cover types in a floodplain of the kromme catchment, South Africa
(Springer Science and Business Media B.V., 2024) Jumbi, Faith ; Glenday, Julia ; Mazvimavi, Dominic
Invasions of floodplains and riparian areas by alien woody species replacing predominantly herbaceous indigenous vegetation have altered the hydrological and ecosystem functioning in catchments. Although existing studies have examined changes in river flows following the establishment or clearing of alien woody vegetation, our understanding of impacts on soil water content and groundwater remains poor. Limited process knowledge restricts our capacity to reliably model and predict the impacts of land cover changes. As such, this work compared temporal variations in soil water content (SWC) and groundwater levels at three locations with different vegetation types: black wattle (Acacia mearnsii) trees, palmiet (Prionium serratum), and grass (dominated by Pennisetum clandestinum spp), within a floodplain site in the Kromme Catchment in the Eastern Cape Province of South Africa. Soil water content and shallow groundwater levels (< 4 m below ground) were monitored from August 2017 to December 2019 using soil moisture probes and piezometers. Rainfall, vegetation type and antecedent conditions were identified as the major factors controlling observed responses. On average, soil water content and water retention were significantly higher (p < 0.05) at the palmiet site, whilst the wattle site had the lowest SWC among the three sites. Shallow groundwater levels were also higher at the palmiet and grass sites and lowest at the wattle site. Results showed the negative impacts of black wattle trees on SWC and groundwater levels. These results are crucial for improved quantitative predictive capacity which would allow for better catchment management, for example, informing water supply planning and guiding restoration programs focusing on alien plant clearing.
Personal factors influencing emergency evacuation decisions under different flash flood characteristics
(Springer Science and Business Media B.V., 2024) Zhang, Ruikang; Liu, Dedi; Xu, Yongxin
Emergency evacuation has received more attention as an effective tool of flash flood disaster prevention that calls for systematic thinking rooted in natural and social sciences. Although personal factors influencing emergency evacuation decisions (EED) after receiving a flood warning have been widely discussed, few studies have referred this issue to the flash flood characteristics. This study explored the personal factors influencing EED under different flash flood characteristics (i.e., the frequency, occurrence time, and severity of flash floods) through field survey data. Three typical flash flood characteristics in three towns were selected as case studies. An ordinary logistical model and path analysis were used to analyze the independent influence and influence process of the personal factors on evacuation intention under the three flash flood characteristics. The results showed that personalized risk perception and warning type consistently influenced evacuation intention regardless of the flash flood characteristics, while the independent influence of flood experience and reliance on hazard information on evacuation intention was varied with the flash flood characteristics. Perceived exposure influenced evacuation intention through the mediations of flood experience when there were high-frequency, recent, and loss-causing flash floods, and of risk perception when there were low-frequency, distant, and few-loss-causing flash floods.
The effect of diagenetic minerals on the petrophysical properties of sandstone reservoir: a case study of the upper shallow marine sandstones in the central Bredasdorp basin, offshore South Africa
(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Magoba, Moses; Opuwari, Mimonitu; Liu, Kuiwu
The upper shallow marine sandstone reservoirs of the Barremian-to-Valanginian formation are the most porous and permeable sandstone reservoirs in the Bredasdorp basin and an important target for oil and gas exploration. There is a paucity of information on the reservoir characterization and effect of diagenetic mineral studies focusing on the upper shallow marine sandstone reservoirs in the central Bredasdorp basin; thus, there is a need to investigate the effect of diagenetic minerals and to characterize these reservoirs due to their high porosity and permeability. Datasets, including a suite of geophysical wireline logs, routine core analysis, geological well completion reports, description reports, and core samples, were utilized. A total of 642 core porosity measures, core water saturation, and core permeability data were used for calibration with the log-derived parameters, ranging in depth from 3615 m to 4259 m. Rock samples were prepared for diagenetic mineral analyses, such as thin sections and Scanning electron microscopy, for each well to investigate the presence of diagenetic minerals in the selected reservoir units. The petrophysical analyses showed the results of porosity, volume of clay, water saturation, and permeability, ranging from 9% to 27%, 8.6% to 19.8%, 18.9% to 30.4%, and 0.096 mD to 151.8 mD, respectively, indicating a poor-to-good reservoir quality. Mineralogical analyses revealed that micrite calcite, quartz cement, quartz overgrowth, and authigenic pore-filling and grain-coating clay minerals (illite–smectite and illite) negatively affected intergranular porosity.
Comprehensive analysis of land use and cover dynamics in djibouti using machine learning technique: A multi-temporal assessment from 1990 to 2023
(Elsevier B.V., 2024) Pandit, Santa; Dube, Timothy; Shimada, Sawahiko
Understanding land use and land cover (LULC) dynamics in semi-arid regions is vital for unraveling complex environmental processes and resource management. This study delves into the intricate interplay of land patterns and resource dynamics, offering indispensable insights into the environmental repercussions of these changes. The study aims to quantify land use categories in Djibouti's semi-desert region using remote sensing. It analyzes temporal changes and evaluates Random forest (RF) algorithms for land use classification. Through meticulous quantification and comprehensive temporal analysis, the research contributes significantly to remote sensing and environmental science by enhancing understanding of land use dynamics and informing sustainable land management practices. Leveraging machine learning supervised classification on the google earth engine (GEE) platform using lands at data spanning four time periods (1990, 2002, 2012, and 2023), alongside spectral indices and digital elevation model (DEM) data, our study achieves unprecedented insights. Our findings reveal a significant landscape transformation, delineating seven major land cover classes: mangroves, bushes, farmland, built-up areas, water bodies, barren land, and salt plains. With overall accuracy ranging from 89 % to 95 %, our assessments demonstrate significant changes in land use types over the studied period. Notably, mangroves, bushes, farmland, and salt areas witnessed declines, while built-up areas, water bodies, and barren lands expanded.
Assessing the seasonal water requirement of fully mature Japanese plum orchards: A systematic review
(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Mashabatu, Munashe; Motsei, Nonofo; Jovanović, Nebojša; Dube, Timothy; Mathews, Ubaidullah; Nqumkana, Yolanda
Japanese plums have relatively high water requirements, which depend on supplementing rainfall volumes with accurately quantified irrigation water. There is a lack of knowledge on the seasonal water requirements of plum orchards. This gap in the literature poses an imminent threat to the long-term sustainability of the South African plum industry, which is particularly plagued by climate change and diminishing water resources. The systematic literature review conducted in this study aimed to provide a foundation for supporting water management in irrigated Japanese plum [Prunus salicina Lindl.] orchards. Seventeen peer-reviewed articles obtained from the literature were analyzed. Approximately 66% of the cultivars were cultivated under different regulated deficit irrigation regimes for water-saving purposes and to increase fruit quality. This review of our knowledge provided benchmark figures on the annual water requirements of Japanese plums. The full-year plum crop water requirements obtained from the literature ranged between 921 and 1211 mm a−1. Canopy growth, pruning and growing season length were the most common causes of differences in the water requirement estimates. Further research is required to measure the water requirement of plums from planting to full-bearing age and the response of plum trees to water stress, especially in the South African context.
Chlorophyll-a unveiled: unlocking reservoir insights through remote sensing in a subtropical reservoir
(Springer Science and Business Media Deutschland GmbH, 2024) Mpakairi, Kudzai Shaun; Muthivhi, Faith; Dondofema, Farai
Effective water resources management and monitoring are essential amid increasing challenges posed by population growth, industrialization, urbanization, and climate change. Earth observation techniques offer promising opportunities to enhance water resources management and support informed decision-making. This study utilizes Landsat-8 OLI and Sentinel-2 MSI satellite data to estimate chlorophyl-a (chl-a) concentrations in the Nandoni reservoir, Thohoyandou, South Africa. The study estimated chl-a concentrations using random forest models with spectral bands only, spectral indices only (blue difference absorption (BDA), fluorescence line height in the violet region (FLH_violet), and normalized difference chlorophyll index (NDCI)), and combined spectral bands and spectral indices. The results showed that the models using spectral bands from both Landsat-8 OLI and Sentinel-2 MSI performed comparably. The model using Sentinel-2 MSI had a higher accuracy of estimating chl-a when spectral bands alone were used. Sentinel-2 MSI’s additional red-edge spectral bands provided a notable advantage in capturing subtle variations in chl-a concentrations. Lastly, the –chl-a concentration was higher at the edges of the Nandoni reservoir and closer to the reservoir wall

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