UWCScholar :: Browsing by Author "Mutanga, Onisimo"

Browsing by Author "Mutanga, Onisimo"

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Advancements in satellite remote sensing for mapping and monitoring ofalien invasive plant species (AIPs)
(Elsevier, 2019) Royimani, Lwando; Mutanga, Onisimo; Dube, Timothy
Detecting and mapping the occurrence, spatial distribution and abundance of Alien Invasive Plants (AIPs) have recently gained substantial attention, globally. This work, therefore, provides an overview of advancements in satellite remote sensing for mapping and monitoring of AIPs and associated challenges and opportunities. Satellite remote sensing techniques have been successful in detecting and mapping the spatial and temporal distribution of AIPs in rangeland ecosystems. Also, the launch of high spatial resolution and hyperspectral remote sensing sensors marked a major breakthrough to precise characterization of earth surface feature as well as optimal resource monitoring. Although essential, the improvements in spatial and spectral properties of remote sensing sensors presented a number of challenges including the excessive acquisition and limited temporal resolution. Therefore, the use of high spatial and hyperspectral datasets is not a plausible alternative to continued and operational scale earth observation, especially in financially constrained countries. On the other hand, literature shows that image classification algorithms have been instrumental in compensating the poor spatial and spectral resolution of remote sensing sensors. Furthermore, the emergence of robust and advanced non-parametric image classification algorithms have been a major development in image classification algorithms.
Application of drone technologies in surface water resources monitoring and assessment: A systematic review of progress, challenges, and opportunities in the global south
(MPDI, 2021) Sibanda, Mbulisi; Mutanga, Onisimo; Chimonyo, Vimbayi G. P.
Accurate and timely information on surface water quality and quantity is critical for various applications, including irrigation agriculture. In-field water quality and quantity data from unmanned aerial vehicle systems (UAVs) could be useful in closing spatial data gaps through the generation of near-real-time, fine resolution, spatially explicit information required for water resources accounting. This study assessed the progress, opportunities, and challenges in mapping and modelling water quality and quantity using data from UAVs. To achieve this research objective, a systematic review was adopted. The results show modest progress in the utility of UAVs, especially in the global south. This could be attributed, in part, to high costs, a lack of relevant skills, and the regulations associated with drone procurement and operational costs. The progress is further compounded by a general lack of research focusing on UAV application in water resources monitoring and assessment. More importantly, the lack of robust and reliable water quantity and quality data needed to parameterise models remains challenging. However, there are opportunities to advance scientific inquiry for water quality and quantity accounting by integrating UAV data and machine learning.
Assessing edge effect on the spatial distribution of selected forest biochemical properties derived using the worldview data in Dukuduku forests, South Africa
(Wiley, 2019) Mutanga, Onisimo; Dube, Timothy; Omer, Galal
This work explores the potential of the high‐resolution WorldView‐2 sensor in quan‐tifying edge effects on the spatial distribution of selected forest biochemical proper‐ties in fragmented Dukuduku forest in KwaZulu‐Natal, South Africa. Specifically, we sought to map fragmented patches within forested areas in Dukuduku area, using very high spatial resolution WorldView‐2 remotely sensed data and to statistically determine the effect of these fragmented patches on the spatial distribution of se‐lected forest biochemical properties.
Assessing the prospects of remote sensing maize leaf area index using uav-derived multi-spectral data in smallholder farms across the growing season
(MDPI, 2023) Buthelezi, Siphiwokuhle; Mutanga, Onisimo; Sibanda, Mbulisi
Maize (Zea Mays) is one of the most valuable food crops in sub-Saharan Africa and is a critical component of local, national and regional economies. Whereas over 50% of maize production in the region is produced by smallholder farmers, spatially explicit information on smallholder farm maize production, which is necessary for optimizing productivity, remains scarce due to a lack of appropriate technologies. Maize leaf area index (LAI) is closely related to and influences its canopy physiological processes, which closely relate to its productivity. Hence, understanding maize LAI is critical in assessing maize crop productivity. Unmanned Aerial Vehicle (UAV) imagery in concert with vegetation indices (VIs) obtained at high spatial resolution provides appropriate technologies for determining maize LAI at a farm scale. Five DJI Matrice 300 UAV images were acquired during the maize growing season, and 57 vegetation indices (VIs) were generated from the derived images.
Carbon stock prediction in managed forest ecosystems using Bayesian and frequentist geostatistical techniques and new generation remote sensing metrics
(MDPI, 2023) Chinembiri, Tsikai Solomon; Mutanga, Onisimo; Dube, Timothy
The study compares the performance of a hierarchical Bayesian geostatistical methodology with a frequentist geostatistical approach, specifically, Kriging with External Drift (KED), for predicting C stock using prediction aides from the Landsat-8 and Sentinel-2 multispectral remote sensing platforms. The frequentist geostatistical approach’s reliance on the long-run frequency of repeated experiments for constructing confidence intervals is not always practical or feasible, as practitioners typically have access to a single dataset due to cost constraints on surveys and sampling. We evaluated two approaches for C stock prediction using two new generation multispectral remote sensing datasets because of the inherent uncertainty characterizing spatial prediction problems in the unsampled locations, as well as differences in how the Bayesian and frequentist geostatistical paradigms handle uncertainty.
Characterizing the spatio-temporal variations of C3 and C4 dominated grasslands aboveground biomass in the Drakensberg, South Africa
(Elsevier, 2018) Shoko, Cletah; Mutanga, Onisimo; Dube, Timothy; Slotow, Rob
C3 and C4 grass species composition, with different physiological, morphological and most importantly phenological characteristics, influence Aboveground Biomass (AGB) and their ability to provide ecosystem goods and services, over space and time. For decades, the lack of appropriate remote sensing data sources compromised C3 and C4 grasses AGB estimation, over space and time. This resulted in uncertainties in understanding their potential and contribution to the provision of services. This study therefore examined the utility of the new multi-temporal Sentinel 2 to estimate and map C3 and C4 grasses AGB over time, using the advanced Sparse Partial Least Squares Regression (SPLSR) model. Overall results have shown the variability in AGB between C3 and C4 grasses, estimation accuracies and the performance of the SPLSR model, over time. Themeda (C4) produced higher AGB from February to April, whereas from May to September, Festuca produced higher AGB. Both species also showed a decrease in AGB in August and September, although this was most apparent for Themeda than its counterpart. Spectral bands information predicted species AGB with lowest accuracies and an improvement was observed when both spectral bands and vegetation indices were applied. For instance, in the month of May, spectral bands predicted species AGB with lowest accuracies for Festuca (R2=0.57; 31.70% of the mean), Themeda (R2=0.59; 24.02% of the mean) and combined species (R2=0.61; 15.64% of the mean); the use of spectral bands and vegetation indices yielded 0.77; (18.64%), 0.75 (14.27%) and 0.73 (16.47%), for Festuca, Themeda and combined species, respectively. The red edge (at 0.705 and 0.74 μm) and derived indices, NIR and SWIR 2 (2.19 μm) were found to contribute more to grass species AGB estimation, over time. Findings have also revealed the potential of the SPLSR model in estimating C3 and C4 grasses AGB using Sentinel 2 images, over time. The AGB spatial variability maps produced in this study can be used to quantify C3 and C4 forage availability or accumulating fuel, as well as for developing operational management strategies.
Correction: sibanda et al. application of drone technologies in surface water resources monitoring and assessment: a systematic review of progress, challenges, and opportunities in the global south. drones 2021, 5, 84
(Drones, 2022) Dube, Timothy; Mazvimavi, Dominic; Sibanda, Mbulisi; Mutanga, Onisimo
In the original publication [1], “Fahad Alawadi. Detection of surface algal blooms using the newly developed algorithm surface algal bloom index (SABI)”, “Proc. SPIE 7825, Remote Sensing of the Ocean, Sea Ice, and Large Water Regions 2010, 782506 (18 October 2010); https://doi.org/10.1117/12.862096” [2] was not cited. The citation has now been inserted in “3.5. The Role of Drone Data Derived Vegetation Indices and Machine Algorithms in Remote Sensing Water Quality and Quantity” as reference [60] and should read: “Numerous vegetation indices were derived from drone remotely sensed data for characterizing surface water quality and quantity. The most widely used sections of the electromagnetic spectrum in detecting water quality parameters were the visible section (blue and green) and the NIR wavebands. In this regard, vegetation indices such as the red and near-infrared (NIR), Surface Algal Bloom Index (SABI) [60], two-band algorithm (2BDA) [26], NDVI, and Green NDV [33], as well as band combinations and differencing such as (R+NIR/G) were used mostly in characterizing chlorophyll content as well as TSS.
Determining optimal new generation satellite derived metrics for accurate C3 and C4 grass species aboveground biomass estimation in South Africa
(MDPI, 2018) Shoko, Cletah; Mutanga, Onisimo; Dube, Timothy
While satellite data has proved to be a powerful tool in estimating C3 and C4 grass species Aboveground Biomass (AGB), finding an appropriate sensor that can accurately characterize the inherent variations remains a challenge. This limitation has hampered the remote sensing community from continuously and precisely monitoring their productivity. This study assessed the potential of a Sentinel 2 MultiSpectral Instrument, Landsat 8 Operational Land Imager, and WorldView-2 sensors, with improved earth imaging characteristics, in estimating C3 and C4 grasses AGB in the Cathedral Peak, South Africa. Overall, all sensors have shown considerable potential in estimating species AGB; with the use of different combinations of the derived spectral bands and vegetation indices producing better accuracies. However,WorldView-2 derived variables yielded better predictive accuracies (R2 ranging between 0.71 and 0.83; RMSEs between 6.92% and 9.84%), followed by Sentinel 2, with R2 between 0.60 and 0.79; and an RMSE 7.66% and 14.66%. Comparatively, Landsat 8 yielded weaker estimates, with R2 ranging between 0.52 and 0.71 and high RMSEs ranging between 9.07% and 19.88%. In addition, spectral bands located within the red edge (e.g., centered at 0.705 and 0.745 m for Sentinel 2), SWIR, and NIR, as well as the derived indices, were found to be very important in predicting C3 and C4 AGB from the three sensors. The competence of these bands, especially of the free-available Landsat 8 and Sentinel 2 dataset, was also confirmed from the fusion of the datasets. Most importantly, the three sensors managed to capture and show the spatial variations in AGB for the target C3 and C4 grassland area. This work therefore provides a new horizon and a fundamental step towards C3 and C4 grass productivity monitoring for carbon accounting, forage mapping, and modelling the influence of environmental changes on their productivity.
Discrimination of tomato plants (solanum lycopersicum) grown under anaerobic baffled reactor effluent, nitrified urine concentrates and commercial hydroponic fertilizer regimes using simulated sensor spectral settings
(MPDI, 2019) Sibanda, Mbulisi; Mutanga, Onisimo; Dube, Timothy
We assess the discriminative strength of three different satellite spectral settings (HyspIRI, the forthcoming Landsat 9 and Sentinel 2-MSI), in mapping tomato (Solanum lycopersicum Linnaeus) plants grown under hydroponic system, using human-excreta derived materials (HEDM), namely, anaerobic baffled reactor (ABR) effluent and nitrified urine concentrate (NUC) and commercial hydroponic fertilizer mix (CHFM) as main sources of nutrients. Simulated spectral settings of HyspIRI, Landsat 9 and Sentinel 2-MSI were resampled from spectrometric proximally sensed data. Discriminant analysis (DA) was applied in discriminating tomatoes grown under these different nutrient sources. Results showed that the simulated spectral settings of HyspIRI sensor better discriminate tomatoes grown under different fertilizer regimes when compared to Landsat 9 OLI and Sentinel-2 MSI spectral configurations. Using the DA algorithm, HyspIRI exhibited high overall accuracy (OA) of 0.99 and a kappa statistic of 0.99 whereas Landsat OLI and Sentinel-2 MSI exhibited OA of 0.94 and 0.95 and 0.79 and 0.85 kappa statistics, respectively. Simulated HyspIRI wavebands 710, 720, 690, 840, 1370 and 2110 nm, Sentinel 2-MSI bands 7 (783 nm), 6 (740 nm), 5 (705 nm) and 8a (865 nm) as well as Landsat bands 5 (865 nm), 6 (1610 nm), 7 (2200 nm) and 8 (590 nm), in order of importance, were selected as the most suitable bands for discriminating tomatoes grown under different fertilizer regimes. Overall, the performance of simulated HyspIRI, Landsat 9 OLI-2 and Sentinel-2 MSI spectral bands seem to bring new opportunities for crop monitoring.
Effect of landscape pattern and spatial configuration of vegetation patches on urban warming and cooling in Harare metropolitan city, Zimbabwe
(Bellweather Publishing, 2021) Kowe, Pedzisai; Mutanga, Onisimo; Dube, Timothy
The spatial configuration of vegetation patches in the landscape has implications for the provision of ecosystem services, human adaptation to climate change, enhancement, or mitigation of urban heat island. Until recently, the effect of spatial configuration of vegetation to enhance or mitigate urban heat island has received little consideration in urban thermal assessments. This study examines the impact of spatial configuration of vegetation patches on urban thermal warming and cooling in Harare metropolitan city, Zimbabwe. The study used Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Landsat and Sentinel 2 data acquired between 1994 and 2017 to derive detailed information on vegetation patches, landscape metrics, and land surface temperature LST(°C). The spatial configuration of urban vegetation patterns was analyzed using landscape metrics in Fragstats program. Getis Ord Gi* as a Local Indicator of Spatial Association (LISA) was used to characterize the spatial clustering and dispersion of urban vegetation patches. Results of the Getis Ord Gi* showed that clustered vegetation lowers surface temperatures more effectively than dispersed and fragmented patterns of vegetation. The size, density, shape complexity, and cohesion of vegetation patches conferred different levels of cooling but Patch Cohesion Index had the strongest negative relationship with LST(°C) at three spatial resolutions of 10 m (Sentinel 2), 15 m (ASTER) and 30 m (Landsat 8). The Spatial Lag Regression model performed better than the Ordinary Least Squares regression analysis in exploring the relationship between LST(°C) and landscape metrics. Specifically, the Spatial Lag Regression model showed higher R2 values and log likelihood, lower Schwarz criteria, and Akaike information criterion, and reduced spatial autocorrelations. The overall information provides important insights into the provision of larger, connected, and less fragmented urban vegetation patches to derive maximum and higher cooling effects which is critical for urban planning and design approaches for mitigating increasing surface temperatures in cities.
Estimating and monitoring land surface phenology in rangelands: A review of progress and challenges
(MPDI, 2021) Matongera, Trylee Nyasha; Mutanga, Onisimo; Sibanda, Mbulisi
Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment.
Hierarchical Bayesian geostatistics for C stock prediction in disturbed plantation forest in Zimbabwe
(Ecological Informatics, 2022) Chinembiri, Tsikai S.; Mutanga, Onisimo; Dube, Timothy
We develop and present a novel Bayesian hierarchical geostatistical model for the prediction of plantation forest carbon stock (C stock) in the eastern highlands of Zimbabwe using multispectral Landsat-8 and Sentinel-2 remotely sensed data. Specifically, we adopt a Bayesian hierarchical methodology encompassing a model based inferential framework making use of efficient Markov Chain Monte Carlo (MCMC) techniques for assessing model input parameters. Our proposed hierarchical modelling framework evaluates the influence of two but related covariate information sources in C stock prediction in order to build sustainable capacity on carbon reporting and monitoring. The perceived improvements in the spectral and spatial properties of Landsat-8 and Sentinel-2 data and their potential to predict C stock with shorter uncertainty bounds is tested in the developed hierarchical Bayesian models
The impact of land-use/land cover changes on water balance of the heterogeneous Buzi sub-catchment, Zimbabwe
(Elsevier, 2020) Chemura, Abel; Rwasoka, Donald Tendayi; Mutanga, Onisimo
The nature of interactions between ecological, physical and hydrological characteristics that determine the effects of land cover change on surface and sub-surface hydrology is not well understood in both natural and disturbed environments. The spatiotemporal dynamics of water fluxes and their relationship with land cover changes between 2009 and 2017 in the headwater Buzi sub-catchment in Zimbabwe is evaluated. To achieve this, land cover dynamics for the area under study were characterised from the 30 m Landsat data, using the eXtreme Gradient Boosting (XGBoost) algorithm.
Impacts of eco-environmental quality, spatial configuration, and landscape connectivity of urban vegetation patterns on seasonal land surface temperature in Harare metropolitan city, Zimbabwe
(Taylor and Francis Group, 2022) Kowe, Pedzisai; Mutanga, Onisimo; Dube, Timothy
The study examined the impact of eco-environmental quality conditions, spatial configurations and landscape connectivity of urban vegetation on seasonal land surface temperature (LST) in Harare, Zimbabwe between May and October 2018. The results showed that densely built-up areas with sparse vegetation experienced extremely poor eco-environmental conditions. Clustered and highly connected were more beneficial in decreasing LST. These findings have important urban and landscape planning implications regarding how the spatial configuration and land-scape connectivity patterns of urban vegetation can be optimized to mitigate Urban Heat Island (UHI) effects and to improve the thermal comfort conditions in rapidly urbanizing cities.
Inter-Seasonal Estimation of Grass Water Content Indicators Using Multisource Remotely Sensed Data Metrics and the Cloud-Computing Google Earth Engine Platform
(MDPI, 2023) Masenyama, Anita; Mutanga, Onisimo; Dube, Timothy; Sibanda, Mbulisi
Indicators of grass water content (GWC) have a significant impact on eco-hydrological processes such as evapotranspiration and rainfall interception. Several site-specific factors such as seasonal precipitation, temperature, and topographic variations cause soil and ground moisture content variations, which have significant impacts on GWC. Estimating GWC using multisource data may provide robust and accurate predictions, making it a useful tool for plant water quantification and management at various landscape scales. In this study, Sentinel-2 MSI bands, spectral derivatives combined with topographic and climatic variables, were used to estimate leaf area index (LAI), canopy storage capacity (CSC), canopy water content (CWC) and equivalent water thickness (EWT) as indicators of GWC within the communal grasslands in Vulindlela across wet and dry seasons based on single-year data. The results illustrate that the use of combined spectral and topo-climatic variables, coupled with random forest (RF) in the Google Earth Engine (GEE), improved the prediction accuracies of GWC variables across wet and dry seasons. LAI was optimally estimated in the wet season with an RMSE of 0.03 m􀀀2 and R2 of 0.83, comparable to the dry season results, which exhibited an RMSE of 0.04 m􀀀2 and R2 of 0.90. Similarly, CSC was estimated with high accuracy in the wet season (RMSE = 0.01 mm and R2 = 0.86) when compared to the RMSE of 0.03 mm and R2 of 0.93 obtained in the dry season. Meanwhile, for CWC, the wet season results show an RMSE of 19.42 g/m􀀀2 and R2 of 0.76, which were lower than the accuracy of RMSE = 1.35 g/m􀀀2 and R2 = 0.87 obtained in the dry season. Finally, EWT was best estimated in the dry season, yielding a model accuracy of RMSE = 2.01 g/m􀀀2 and R2 = 0.91 as compared to the wet season (RMSE = 10.75 g/m􀀀2 and R2 = 0.65). CSC was best optimally predicted amongst all GWC variables in both seasons. The optimal variables for estimating these GWC variables included the red-edge, near-infrared region (NIR) and short-wave infrared region (SWIR) bands and spectral derivatives, as well as environmental variables such as rainfall and temperature across both seasons. The use of multisource data improved the prediction accuracies for GWC indicators across both seasons. Such information is crucial for rangeland managers in understanding GWC variations across different seasons as well as different ecological gradients.
Landsat-8 and sentinel-2 based prediction of forest plantation c stock using spatially varying coefficient bayesian hierarchical models
(Remote Sensing, 2022) Dube, Timothy; Chinembiri, Tsikai Solomon; Mutanga, Onisimo
This study sought to establish the performance of Spatially Varying Coefficient (SVC) Bayesian Hierarchical models using Landsat-8, and Sentinel-2 derived auxiliary information in predicting plantation forest carbon (C) stock in the eastern highlands of Zimbabwe. The development and implementation of Zimbabwe’s land reform program undertaken in the year 2000 and the subsequent redistribution and resizing of large-scale land holdings are hypothesized to have created heterogeneity in aboveground forest biomass in plantation ecosystems. The Bayesian hierarchical framework, accommodating residual spatial dependence and non-stationarity of model predictors, was evaluated. Firstly, SVC models utilizing Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI), derived from Landsat-8 and Sentinel-2 data and 191 sampled C stock observations were constructed
Landsat-8 and sentinel-2 based prediction of forest plantation c stock using spatially varying coefficient Bayesian hierarchical models
(MDPI, 2022) Chinembiri, Tsikai Solomon; Mutanga, Onisimo; Dube, Timothy
This study sought to establish the performance of Spatially Varying Coefficient (SVC) Bayesian Hierarchical models using Landsat-8, and Sentinel-2 derived auxiliary information in predicting plantation forest carbon (C) stock in the eastern highlands of Zimbabwe. The development and implementation of Zimbabwe’s land reform program undertaken in the year 2000 and the subsequent redistribution and resizing of large-scale land holdings are hypothesized to have created heterogeneity in aboveground forest biomass in plantation ecosystems. The Bayesian hierarchical framework, accommodating residual spatial dependence and non-stationarity of model predictors, was evaluated. Firstly, SVC models utilizing Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI), derived from Landsat-8 and Sentinel-2 data and 191 sampled C stock observations were constructed. The SVC models built for each of the two multispectral remote sensing data sets were assessed based on the goodness of fit criterion as well as the predictive performance using a 10-fold cross-validation technique.
Mapping rangeland ecosystems vulnerability to lantana camara invasion in semi-arid savannahs in South Africa
(African journal of ecology Wiley, 2022) Dube, Timothy; Maluleke, Xivutiso Glenny; Mutanga, Onisimo
We mapped and modelled the potential areas vulnerable to Lantana camara (L. camara) invasion in semi-arid savannah ecosystems in the communal lands of Bushbuckridge and Kruger National Park, South Africa. Specifically, we modelled potentially vulnerable areas based on remotely sensed data and environmental variables. The Maximal Entropy (Maxent) algorithm was used to model the vulnerable area. The reliability of the modelled results was assessed using Skills Statistic (TSS), Area Under Curve (AUC) and Kappa statistics. According to the results, Bushbuckridge communal lands are more susceptible to L. camara invasions than Kruger National Park. The risk of L. camara invasion in the study site was modelled with high accuracy (AUC score of 0.95) using the best model (Model 7), which is a composite of all model variables (remote sensing and environmental variables). The spatial distribution maps derived from Maxent showed that L. camara was more likely to invade communal lands than protected areas. Using remotely sensed spectral indices as standalone model variables (Model 4) showed the lowest accuracy, with an AUC score of 0.85. Overall, model input variables such as elevation had a significant influence on the spatial distribution of L. camara in the study area.
Mapping rangeland ecosystems vulnerability to Lantana camarainvasion in semi-arid savannahs in South Africa
(Journal of Ecology, 2022) Dube, Timothy; Maluleke, Xivutiso Glenn; Mutanga, Onisimo
We mapped and modelled the potential areas vulnerable to Lantana camara (L. camara) invasion in semi-arid savannah ecosystems in the communal lands of Bushbuckridge and Kruger National Park, South Africa. Specifically, we modelled potentially vulner-able areas based on remotely sensed data and environmental variables. The Maximal Entropy (Maxent) algorithm was used to model the vulnerable area. The reliability of the modelled results was assessed using Skills Statistic (TSS), Area Under Curve (AUC) and Kappa statistics. According to the results, Bushbuckridge communal lands are more susceptible to L. camara invasions than Kruger National Park.
Pansharpened landsat 8 thermal-infrared data for improved land surface temperature characterization in a heterogeneous urban landscape
(Elsevier, 2022) Mushore, Terence Darlington; Mutanga, Onisimo; Dube, Timothy
Challenges associated with adolescents are prevalent in South African societies. During the adolescence stage, children may become involved in deviant behaviour. Although a significant number of studies have focused on the factors that contribute to adolescents’ deviant behaviour, including parental factors, there is paucity of research specifically in rural communities. This study explores the contribution of parental factors to adolescents’ deviant behaviour in rural communities in South Africa. Guided by the qualitative approach, the present study makes use of semi-structured interviews to collect data and thematic analysis to analyse data.

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