Browsing by browse.metadata.cluster "Advanced Agriculture & Food"

Now showing 1 - 20 of 60
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    A review of feedstock diversification for methanol production: From fossil fuels to renewable resources
    (2026-03) Reddy, Trishen; Seodigeng, T
    Methanol is a critical platform chemical and an increasingly important energy carrier. While global production is currently dominated by fossil-based pathways primarily natural gas (average 65%) and coal (average 35%), there is however an urgent industrial mandate to decarbonize the supply chain. This review provides a rigorous quantitative evaluation of conventional and emerging carbonaceous feedstocks, including biomass, agricultural residues, municipal solid waste, and captured carbon dioxide (CO2). Quantitative analysis reveals that while traditional biogas offers methane concentrations averaging 50–80%, emerging substrates can also provide superior methane yields. A significant contribution of this work is the integration of the latest 2025 findings on semolina processing waste, which demonstrates a high-hydrogen (H2) potential (average 23.0% H2) for biomethanol synthesis. Furthermore, the paper delves into the relevance of process intensification, identifying membrane reactor technology as a primary solution to thermodynamic equilibrium constraints. By addressing critical technical hurdles such as membrane fouling often cited as a major barrier to the 0.2% renewable share in global supply. This review serves as a vital roadmap for industries aiming to transition toward carbon-negative methanol production and enhanced energy resilience.
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    A systematic literature review: The influence of technical, operational and structural factors on the adoption of digital agriculture among small-scale farmers in Sub-Saharan Africa
    (2026-07) Chesi, ALC; Cho, Moses A; Cho, MNA; Ramoelo, A
    This systematic review paper examines how technical, operational, and structural factors influence the adoption of digital agriculture among small-scale farmers in Sub-Saharan Africa. Guided by PRISMA protocols, the study applies a hybrid thematic synthesis across six dimensions: technical, operational, policy and regulatory, governance, social and cultural, and environmental. The findings indicate that digital tools can generate substantial benefits, including yield increases of 10–30% (documented primarily for mobile-based advisory services and precision input management in East African horticulture and West African cocoa value chains) and price gains of 15–25%, with adoption rates of 70–80% in settings characterised by robust infrastructure, strong institutional support, and effective value chain integration. However, these benefits are unevenly distributed and tend to concentrate in “islands of adoption” characterized by robust infrastructure, strong institutional support, and effective value chain integration. While technical (94.9%) and operational (91.5%) factors dominate the literature, their impact is constrained by persistent structural barriers, including weak policy implementation (79.7%), fragmented governance systems (76.3%), and socio-cultural exclusion—such as gender disparities, age-related digital divides, and language misalignment (71.2%). The review identifies five minimum conditions for meaningful adoption: (i) affordable connectivity and access to digital devices; (ii) context-specific digital literacy; (iii) culturally relevant, user-centred design; (iv) robust institutional ecosystems; and (v) enabling policy and financial frameworks. Overall, the findings underscore that digital agriculture adoption is a socio-technical process shaped not only by technological innovation but also by institutional arrangements and user capabilities. Comparative cases, such as Kenya’s Farm.ink and the less successful EZ Farm initiative, further highlight the importance of integrated, context-responsive approaches to ensure that digital agriculture enhances, rather than marginalizes, small-scale farmers.
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    Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission
    (2022-03) Duncanson, L; Kellner, JR; Armston, J; Dubayah, R; Minora, DM; Hancock, S; Healey, SP; Patterson, PL; Main, Russell S; Naidoo, Laven
    NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI’s footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI’s waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.
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    An analysis of Africa’s engagement in EU-led renewable energy initiatives: A case study of LEAP-RE programme
    (Palgrave MacMillan, 2026-05) Mashigo, R; Managa, Lavhelesani R; Dubey, A; Solomon, H
    In line with the climate change response, Africa is on a mission to reduce its reliance on fossil fuels and other sources of energy that are detrimental to the environment. With the increasing population and rising energy demands, Africa-led sustainable renewable energy interventions in Africa are more vital than ever before. Several initiatives have been introduced to support Africa’s response to this challenge. However, ‘externally driven initiatives often surpass the African Union’s (AU) strategies and initiatives in this area. The Agenda 2063 Framework Document (2015) states that as a result of non-African led initiatives (e.g. through the Structural Adjustment Programs of 1980 to early 1990s), it led to “to slow growth, de-industrialisation and increased dependence on raw materials exports” in Africa. In today’s context, these initiatives are usually led and funded by non-African institutions and implemented in Africa with limited consideration of Africa’s capabilities needs. To support the objectives of the European Union’s Green Deal as well as other preceding strategies of the European Union (EU), the EU launched several initiatives, including the Long-term Europe-Africa Partnership on Renewable Energy (LEAP-RE) programme launched in 2021, which seeks to establish a long-term partnership of African and European stakeholders in the field of renewable energy, as a response to the climate change crisis. These projects focus on areas of research, innovation, and technology development that have been jointly devised by both African and European stakeholders. However, these areas are often not in sync with Africa’s strengths and renewable energy potential. This paper contributes to the growing literature that analyses Africa’s investments, priorities, and engagements in responding to climate change. It references the development of the LEAP-RE programme and its relevance to the African context. It concludes with recommendations on the approaches for Africa to leverage external resources to meet the increasing energy demands in the continent while capitalising on its resource potential.
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    Appraising waste from fed aquaculture animals as a food source for sea cucumbers
    (2025-09) Onomu, Abigail J; Suleman, Essa
    Nutrient-rich solid waste and effluent water from aquaculture remain a major problem for aquaculture in terms of environmental impact. Integrated multitrophic aquaculture (IMTA), where lower trophic level species consume the waste of fed animals, has been proposed as an alternative for sustainable aquaculture. The use of deposit-feeding sea cucumbers as extractive species in IMTA has attracted research and commercial interest in recent times, due to their low trophic level, high commercial value as food for humans, and ability to ingest sediment containing organic matter, bacteria, protozoa, diatoms, and detritus. Still, the suitability of using faecal waste from fed aquaculture animals as a potential feed requires further studies to ensure not only palatability but also nutritional value, health, and immune responses of the cultured organism. This review discusses various performance indices, such as palatability, ingestion rate, assimilation rate, faecal production rate, feed conversion ratio, growth, and survival of sea cucumber species fed various faecal wastes from different aquaculture animal sources. It further discusses various IMTA applications of sea cucumbers with selected animals. The compatibility, viability and efficacy of sea cucumbers and some aquatic animals in IMTA are summarised.
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    Aquaculture and biodiversity in global food systems
    (2025) Bremner, J; Stentiford, G; Suleman, Essa; Warham, E
    A One Health approach will be essential to ensure future food systems can address the trade-offs between interventions needed to produce more food of higher nutritional value, with a smaller ecosystem footprint. This will involve consideration of hazards that link or spread between different supply chains, taking a whole-systems approach. Here, we consider how biodiversity maps to aquaculture across the One Health space. Aquaculture poses ecosystem health risks through the biodiversity impacts of disease spread, non-native introductions, farm-level pollution and habitat damage. Less well recognised, biodiversity also links strongly to animal/plant health, through increased risk of hazards (pathogen and pest diversity can create significant stock health challenges) but also by providing opportunities – genetic diversity underpins stock fitness and mixed-species farming improves resilience. Application of One Health principles will allow aquaculture to grow sustainably, but this needs the buy-in of policymakers, farmers and the scientific community.
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    Can Sentinel-2-derived spectral indices improve the accuracy of retrieving optically active water quality parameters using machine learning algorithms?
    (2026-06) Rathupetsane, EM; Kganyago, M; Madonsela, Sabelo; Mvandaba, Vuyelwa
    Study region: This study was conducted in the Cradle of Humankind World Heritage Site (COHWHS), South Africa, an area characterised by interconnected surface waters and sensitive dolomitic aquifers. The region is subject to increasing pressure from land use change, tourism, and nutrient enrichment, making reliable and spatially explicit water quality monitoring essential for protecting its ecological, cultural, and hydrological integrity. Study focus: The study aimed to assess whether Sentinel-2-derived spectral indices improve the retrieval accuracy of optically active water quality parameters, namely Chlorophyll-a (Chl-a) and Total Suspended Solids (TSS). Three input configurations were tested: traditional Landsat-like bands, Sentinel-2 bands, and Sentinel-2 bands combined with spectral indices. These inputs were used within Random Forest and Gaussian Process Regression models to evaluate model performance across wet (summer) and dry (winter) seasons. New hydrological insights for the region: The results show that integrating Sentinel-2 spectral indices substantially improves Chl-a estimation during wet conditions, while TSS retrieval benefits mainly from Sentinel-2 red, red-edge, and SWIR bands. Model performance was strongly seasonal, with reduced accuracy during dry periods due to lower optical variability. The findings provide new insight into how seasonal hydrological conditions and spectral sensitivity influence water quality retrievals in optically complex inland waters of the COHWHS. This approach supports improved regional water quality monitoring and contributes to the protection of connected surface water-groundwater systems in this vulnerable heritage landscape.
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    Cannabis sativa L. biomass valorization as a strategic bioresource for South Africa’s circular bioeconomy: Integrating biorefinery pathways and green extraction
    (2026-07) Motsa, Vuiswa L; Seedat, N; Mekuto, L; Sekoa, P
    South Africa’s legalized Cannabis Sativa L. industry generates substantial biomass waste, including post-harvest materials such as stalks, leaves, roots, and seeds, as well as post-extraction residues rich in lignocellulosic components (cellulose, lignin, hemicellulose) and functional compounds (fibers, phenolic compounds, and proteins) which remain largely unvalorized. This review critically and systematically examines valorization pathways for Cannabis sativa L. biomass within the context of a circular bioeconomy, focusing on its potential as a bioresource amid South Africa’s evolving regulatory, agricultural, and industrial landscape. A systematic scoping review of 65 peer-reviewed studies (2018–2025) was conducted using international databases to evaluate biochemical and thermochemical conversion, integrated biorefinery strategies, and green extraction methods such as supercritical CO2 extraction, microwave-assisted extraction (MAE), and ultrasound-assisted extraction (UAE). Compositional analysis indicates that lignocellulosic fractions from Cannabis sativa L., particularly bast fibers and hurds, are well-suited to cascaded biorefinery applications, enabling the recovery of cannabinoids, carbon-based materials, and bioenergy. However, several challenges persist, including biomass recalcitrance to enzymatic hydrolysis, inconsistent feedstock availability, and the lack of a standardized protocol within South Africa’s regulated environment. Techno-economic assessments (TEA) highlight the need for financial incentives, decentralized infrastructure, and cohesive policies among government entities, including DALRRD, SAHPRA, and DTIC. The review proposes a South African collaboration framework linking policy with research-based process design. Key gaps include the need for life cycle assessment (LCA) and TEA validation at the pilot scale, characterization of landrace cultivars, and development of accessible pretreatment technologies for smallholders.
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    Canned complementary porridges for infants and young children (6–23 months) based on African indigenous crops; nutritional content, consistency, sensory, and affordability compared to traditional porridges based on maize and finger millet
    (2024-11) Løvdal, T; Skaret, J; Drobac, G; Okole, Blessed N; Sone, I; Rosa-Sibakov, N; Varela, P
    Child malnutrition is a major health problem in Sub-Saharan Africa. Complementary foods made from African indigenous and locally available raw materials are often low in protein and nutrients. It is, therefore, important to supply complementary foods that are nutritious and affordable, and with an acceptable consistency and taste. The objective of this study was to develop, on a pilot scale, food-to-food fortified, convenient, canned complementary porridges based on blends of African indigenous crops, i.e., orange fleshed sweet potato (OFSP) flour, and leguminous (i.e., cowpea, and Bambara groundnut) and cereal flours (i.e., teff, finger millet, maize, and amaranth), and milk powder. Plant-based, African complementary foods are often lacking in vitamin A, zinc, iron, and energy. Porridge with OFSP on a 32% dry weight (dw) basis achieved recommended levels of vitamin A (530 µg per 100 g dw). Satisfactory energy (431 Kcal per 100 g dw) was obtained by supplementation of vegetable oil. A nutritious, low-cost porridge (costing 0.15 € per 100 g can) that fulfills consistency constraints was obtained by including supplements of zinc and iron salts as ingredients. The solids content and thus protein/energy could be significantly increased using protein fractionated or germinated cowpea flours without compromising on viscosity. The sensory profile was characterised by more intense vegetable, leguminous, and malty flavours as compared to traditional reference porridges.
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    Catalyzing global reach: Innovative strategies for Baobab and Marula Expansion
    (Into Publishing, 2025) Togarepi, C; Raheem, D; Egbadzor, KF; Horlu, GSA; Dlamini, Nomusa C; Adefila, A; Pratiwi, A
    Baobab and marula have been used for centuries by African communities to provide economic benefits. In this chapter, we explore the commercialization strategies of baobab and marula tree byproducts intended for local, regional, and international markets by delving into what sets them apart from other tree-based products. We examine the perception of baobab and marula across different market segments and highlight successful marketing strategies used by businesses of varying sizes. As consumers increasingly seek natural and organic products, the opportunities for these commodities in the international market are immense. We hope that this chapter will provide an insight into the potential and already realised success stories of these versatile superfoods, shedding light on the value chain and marketing tactics that have been and could be employed to bring them to the market.
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    Characterising the spatio-temporal patterns of water quality parameters in the cradle of humankind world heritage site using Sentinel-2 and random forest regressor
    (2025-07) Ngamile, S; Kganyago, M; Madonsela, Sabelo; Mvandaba, Vuyelwa
    Introduction: Water quality assessment is essential for monitoring and managing freshwater resources, particularly in ecologically and culturally significant areas like the Cradle of Humankind World Heritage Site (COHWHS). This study aimed to predict and map the spatio-temporal patterns of both optically and non-optically active water quality parameters within small inland water bodies located in the COHWHS. Methods: High-resolution Sentinel-2 Multispectral Instrument (MSI) satellite data and two random forest models (Model 1 [consisting of sensitive spectral bands] and Model 2 [consisting of spectral bands + indices]) were used alongside In-situ measurements of chlorophyll-a, suspended solids, dissolved oxygen (DO), pH, Temperature, and electrical conductivity (EC) were integrated to establish empirical relationships and assess spatial variability across high-flow and low-flow conditions. Results: The results indicated that DO could be predicted with the highest accuracy under low-flow conditions, followed by EC. Specifically, Model 2 achieved an R2 of 0.88 and an RMSE of 1.37 for DO, while Model 1 achieved an R2 of 0.63 and an RMSE of 291.48 for EC. For optically active parameters, suspended solids showed the highest prediction accuracy under high-flow conditions using Model 2 (R2p = 0.55; RMSE = 118.19). Due to the over-pixelation of other smaller water bodies within the COHWHS in Sentinel-2 imagery, Cradlemoon Lake was selected to show distinct seasonal (high- and low-flow) and spatial variations in optically and non-optically active water quality parameters. Discussion: Variations in the results were influenced by runoff dynamics and upstream pollution: lower Temperatures and suspended solids under low-flow conditions increased DO concentrations, whereas higher suspended solid concentrations under high-flow conditions likely reduced light penetration, resulting in lower spectral reflectance and chlorophyll-a levels. These findings highlight the potential of Sentinel-2 MSI data and machine learning models for monitoring dynamic water quality variations in freshwater ecosystems.
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    The Circular Economy as Development Opportunity: Exploring Circular Economy Opportunities across South Africa’s Economic Sectors
    (CSIR, 2021-12) Godfrey, Linda K; Nahman, Anton; Oelofse, Suzanna HH; Trotter, Douglas; Khan, Sumaya; Nontso, Zintle; Magweregwede, Fleckson; Sereme, Busisiwe V; Okole, Blessed N; Gordon, Gregory ER; Brown, Bernadette; Pillay, Boyse; Schoeman, Chanel; Fazluddin, Shahed; Ojijo, Vincent O; Cooper, Antony K; Kruger, Daniel M; Napier, Mark; Mokoena, Refiloe; Steenkamp, Anton J; Msimanga, Xolile P; North, Brian C; Seetal, Ashwin R; Mathye, Salamina M; Godfrey, Linda K
    The intention of this book is to present the CSIR’s position and interpretation of the circular economy, and to use it to drive discussions on where immediate circular economy opportunities are achievable in South Africa. Opportunities that can be harnessed by business, government and civil society. These circular economy opportunities are framed in this book within the context of the current challenges facing various economic sectors. The CSIR has selected seven, resource intensive sectors – mining, agriculture, manufacturing, human settlements, mobility, energy and water – for further assessment. Many of these economic sectors have seen significant declines over the past years, with agriculture, manufacturing, transport and construction all showing negative growth pre-COVID. These are all sectors under economic stress and in need of regeneration. South Africa stands on the threshold of profound choices regarding its future development path. Transitioning to a more circular economy provides the country with the opportunity to address many national priorities including manufacturing competitiveness, food security; sustainable, resilient and liveable cities; efficient transport and logistics systems; and energy and water security, while at the same time decarbonising the economy. The transition to a circular economy provides the country with an opportunity for green and inclusive development to be the cornerstone of a post-COVID economic recovery. The titles of this book chapters are the following: Chapter 1: Driving economic growth in South Africa through a low carbon, sustainable and inclusive circular economy. Chapter 2: Placing the South African mining sector in the context of a circular economy transition. Chapter 3: Supporting food security and economic development through circular agriculture. Chapter 4: Supporting the development of a globally competitive manufacturing sector through a more circular economy. Chapter 5: Creating resilient, inclusive, thriving human settlements through a more circular economy. Chapter 6: Facilitating sustainable economic development through circular mobility. Chapter 7: Decoupling South Africa’s development from energy demand through a more circular economy. Chapter 8: Decoupling South Africa’s development from water demand through a circular economy.
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    Coupling radiative transfer models and machine learning for crop trait retrieval in dryland ecosystems
    (2025-07) Masemola, Cecilia R; Bonnet, W; Cho, Moses A
    Radiative Transfer Models (RTMs) such as PROSAIL, which integrates leaf-level (PROSPECT) and canopy-level (SAIL) reflectance simulations, are increasingly employed to support biophysical trait retrieval in crop monitoring applications. In this study, we assess and compare the performance of three PROSAIL configurations—PROSPECT-5 + SAIL, PROSPECT-D + SAIL, and PROSPECT-PRO + SAIL—for estimating Leaf Area Index (LAI) and Canopy Chlorophyll Content (CCC) in dryland maize systems using synthetic Sentinel-2 reflectance data. Results from synthetic test datasets indicate that the PROSPECT-PRO + SAIL configuration achieved superior performance, with LAI retrieved at an R² of 0.88 and RMSE of 0.35 m²/m², and CCC estimated at an R² of 0.83 and RMSE of 4.1 µg/cm². These outcomes highlight the advantage of using the enhanced biochemical and structural parameterizations in PROSPECT-PRO, especially under semi-arid cropping conditions. Comparative analysis confirms that this configuration consistently yielded the lowest normalized RMSE (nRMSE) for both LAI (9.5%) and CCC (10.7%) across the variants tested. The findings substantiate the added value of improved leaf optical modeling for accurate trait estimation and suggest that PROSPECT-PRO + SAIL provides a robust forward modeling basis for data-driven crop monitoring frameworks.
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    Dammarane-type triterpenoids with anti-cancer activity from the leaves of Cleome gynandra
    (2021-06) Mzondo, Buntubonke; Dlamini, Nomusa; Malan, FP; Labuschagne, Philip W; Bovilla, VR; Madhunapantula, SV; Maharaj, V
    Three dammarane-type triterpernoids including two new ones, cleogynones A and B (1 and 2), were isolated from the leaves of Cleome gynandra. The structures of the new triterpenoids were elucidated by spectroscopic data analysis and confirmed by single crystal X-ray crystallography. All three compounds showed moderate cytotoxicity against breast cancer (MDA-MB-468), cleogynone B (2) and compound (3) further showed cytotoxicity against colorectal cancer (HCT-116 & HCT-15). Cleogynone B was also moderately active against lung cancer (A549).
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    Defining brightness-shape-moisture soil parameters for Southern Africa from Hyperion Hyperspectral Imagery
    (2024-08) Bonnet, Wessel J; Cho, Moses A; Masemola, Cecilia R
    An effective methodology is needed to simulate soil spectra on a large scale. The brightness-shape-moisture (BSM) radiative transfer model (RTM) is used to simulate soil spectra for different semiarid and arid biomes within Southern Africa based on hyperspectral imagery obtained from the Hyperion satellite. Such simulation based on hyperspectral data is especially relevant in light of newer hyperspectral missions, such as Prisma providing ongoing data streams. In this particular study, Hyperion’s data are cleaned using the SUREHYP procedure, segmented using the simple linear iterative clustering (SLIC) algorithm, filtered to exclude photosynthetic and senescent vegetation, and parameterized via a Hyperion band calibrated BSM model lookup table to obtain simulation parameter distributions for different biomes. This provides a means to better simulate soil spectra using each biome’s obtained parameter distributions in the BSM forward model.
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    Determining the sensitivity of Sentinel-2 bands to optically and non-optically active water quality parameters under high- and low-flow conditions in the cradle of Humankind World Heritage Site, South Africa
    (2026-04) Ngamile, S; Kganyago, M; Madonsela, Sabelo; Mvandaba, Vuyelwa
    Access to clean water is a global challenge, particularly in the Global South, where scarcity and pollution threaten human and ecosystem health. Traditional water quality monitoring is limited in spatial and temporal coverage, while satellite remote sensing provides an alternative for assessing optically and non-optically active water quality parameters (WQPs). This study evaluated the sensitivity of Sentinel-2 MSI bands to WQPs in the Cradle of the Humankind World Heritage Site, South Africa, an area impacted by acid mine drainage. In situ measurements of DO, EC, pH, temperature, chlorophyll-a, and suspended solids were collected from 40 sites under high- and low-flow conditions. The Sentinel-2 images were atmospherically corrected, resampled to 10 m, and the values were extracted to the sampling points. Using multiple linear regression, random forest, and partial dependence plots methods, results showed that DO strongly influenced most bands, while suspended solids dominated the optically active band responses.
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    Determining the value of different wavelength ranges of non-imaging hyperspectral reflectance to estimate carotenoid content using the PROSPECT-5 model
    (2024-12) Sibiya, Bongokuhle; Cho, Moses A; Mutanga, O; Ondidi, J; Masemola, Cecilia R; Bonnet, Wessel J
    Carotenoids are important plant attributes offering valuable insights into the physiological condition of vegetation and serve as essential indicators for early identification of plant stress. Generally, carotenoids can be extracted using radiative transfer model (RTM) remote sensing techniques like PROSPECT that utilize the entire spectral domain (400–2500 nm) to retrieve carotenoid information. However, such inversions suffer from ill-posed due to model uncertainties. Literature suggests that selecting appropriate bands improves the RTM inversion. Hence, this study proposed a wavelength selection approach using various regions in the visible portion of the electromagnetic spectrum and bands selected by the random forest algorithm to estimate carotenoids using the PROSPECT-5 model. This study utilized three distinct datasets – savanna, tropical forest, and a combination of two. The green spectral region demonstrated the strongest performance in the tropical forest dataset (R2 = 0.90, RMSE = 0.71) than the savanna (R2 = 0.70, RMSE = 1.19) and combined (R2 = 0.72, RMSE = 1.11) datasets, respectively. The bands (green, yellow, and red-edge) selected by the random forest model produced the highest accuracy in the savanna dataset (R2 = 0.84, RMSE = 0.99), followed by combined (R2 = 0.80, RMSE = 1.20) and tropical forest (R2 = 0.78, RMSE = 1.33), respectively. Lastly, the visible region demonstrated strong performance in the tropical forest (R2 = 0.84, RMSE = 0.85), followed by combined datasets (R2 = 0.72, RMSE = 1.15) and savanna (R2 = 0.68, RMSE = 1.27), respectively. The findings suggest that carotenoid retrieval should be limited to the visible portions of the spectrum as it exhibited strong performance in estimating carotenoid content across the savanna, tropical forest, and combined datasets.
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    Determining the wetland-dryland boundary of depressions using littoral gradient analysis of soil edaphic factors
    (2021-08) Nondlazi, Basanda X; Cho, Moses A; Van Deventer, Heidi; Sieben, EJ
    Depressional wetlands are highly vulnerable to changes in land surface temperature and rainfall but little is known about their responses to future climate change. This study assessed the variation in edaphic factors between wetlands and along their littoral gradients to detect the boundary between the endorheic wetlands and upland zones. A sample of 202 paired measurements of three edaphic factors were collected (Soil Moisture Content – SMC-g/g, Bulk Density – BD-g/cm 3 and Salinity as Electrical Conductivity – EC-dS/m) in 10 m plots along 14 belt transects in eight representative wetlands in the Mpumalanga Lake District, South Africa. In general, there were significant differences between the eight wetlands for SMC and BD but not for EC.SMC and BD generally showed negative trends along the littoral gradients. The trends occurred over short distances, ranging from 30 to 70 m, reflecting the extent of the wetlands. Understanding of the spatial variation of edaphic factors helps in the management and monitoring of depressional wetlands under a changing climate. In addition, the study showed that the current wetland buffer zone stipulated in local legislation was too narrow and recommended that this be extended to 100 m.
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    Distinguishing tree species from in situ hyperspectral and temporal measurements through ensemble statistical learning
    (2023-08) Dudeni-Tlhone, Nontembeko; Mutanga, O; Debba, Pravesh; Cho, Moses A
    Hyperspectral sensors capture and compute spectral reflectance of objects over many wavelength bands, resulting in a high-dimensional space with enough information to differentiate between spectrally similar objects. Due to the curse of dimensionality, high spectral dimensionality can also be difficult to handle and analyse, demanding complex processing and the use of advanced analytical techniques. Moreover, when hyperspectral measurements are taken at different temporal frequencies, separation is likely to improve; however, additional complexities in modelling time variability concurrently with this high spectral dimensionality may be created. As a result, the applicability of ensemble-based techniques suitable for high-dimensional data is examined in this research, together with the statistical evaluation of time-induced variability, since spectral measurements of tree species were taken at different time periods. Classification errors for the stochastic gradient boosting (SGB) and random forest (RF) methods ranged between 5.6% and 13.5%, respectively. Differences in classification accuracy or errors were also accounted for in the assessment of the models, with up to 46% of variation in classification error due to the effect of time in the RF model, indicating that measurement time is important in improving discrimination between tree species. This is because optical leaf characteristics can vary during the course of the year due to seasonal effects, health status, or the developmental stage of a tree. Different spectral properties (assumed from relevant wavelength bands) were found to be key factors impacting the models’ discrimination performance at various measurement times.
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    Estimating South African maize biomass using integrated high-resolution UAV and sentinel 1 and 2 datasets
    (2021-07) Naidoo, Laven; Main, Russell S; Cho, Moses A; Madonsela, Sabelo; Majozi, Nobuhle, P
    Sentinel-1 and Sentinel-2 have provided consistent hyper-temporal information (5–7 days or earlier) at high spatial resolutions (10m) on biophysical composition, structural and physiological conditions of crops in a variety of environments. Unmanned aerial vehicles (UAVs) can provide sufficient calibration and validation data for model upscaling and regional extrapolation. Of the numerous maize crop parameters which require regular and accurate modelling, maize above ground biomass (AGB) is important for yield estimates. The aim of this study was to evaluate the Random Forest modelling performance of Sentinel 1 SAR C-band and Sentinel 2 multispectral imagery for maize AGB estimation whilst utilising UAV-derived maize AGB for model upscaling. Results illustrated that Sentinel 2 reflectance bands predicted more accurate estimates of maize AGB than the VV and VH polarisation bands of Sentinel 1 (R2 = 0.91; RMSE = 355.11g/m 2 ; rRMSE = 21.28% versus R2 = 0.31; RMSE = 974.72g/m 2 ; rRMSE = 59.04%).