Doctoral Degrees (Environmental Science)

Permanent URI for this collectionhttps://hdl.handle.net/10413/7407

Browse

Now showing 1 - 20 of 82

Spatiotemporal analysis of vegetation fires using satellite data.
(2024) Mupfiga, Upenyu Naume.; Mutanga, Onisimo.
Although vegetation fires are key in maintaining the savanna ecosystem, their uncontrolled occurrence profoundly threatens ecosystem stability, economies, and human safety. The increased risk of climate change requires robust spatiotemporal analysis methods to understand the impact of fire on ecosystems. Additionally, the accurate prediction of vegetation fire and the associated key drivers are critical in understanding fire regimes and the implementation of effective fire management strategies. This research utilised Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data to analyse the spatiotemporal dynamics of vegetation fires. The first objective focused on systematically reviewing literature on the effects of burning on various ecosystem services. The reviewed articles were extracted from Elsevier’s Scopus, Web of Science and PubMed databases and analysed based on the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) method. The findings from the review highlighted that there has been an increase in publications since 2010 and most studies were carried out in Asia and the United States of America. The most common satellite data used for analysing the effects of burning on ecosystem services was Landsat, whilst information on fire occurrence was extracted from the MODIS satellite data. Very few studies utilised AVIRIS, PlanetScope, and ASTER satellite data. Moreso, findings from the review revealed fire as a threat to grassland, forest, soil and wetland ecosystems with the forest landscapes being widely studied. The atmosphere is also affected by vegetation fires through particulate matter and carbon emissions. The second objective focused on detecting fire intensity hotspots and cold spots in Zimbabwe by utilising spatial statistics and MODIS-derived fire radiative power (FRP), a proxy for fire intensity. The variability of fire intensity clusters within various topographic and vegetation conditions was also analysed. The results indicated that most (44%) of the vegetation fires remotely sensed in Zimbabwe by the MODIS satellite sensor were of low intensity, mostly occurring in the shrublands. On the other hand, high intensity fires (22%) were generally distributed within Zimbabwe’s eastern and western regions. The third objective focused on detecting long-term spatiotemporal fire patterns in Zimbabwe using MODIS fire location data and a spatially explicit method (Emerging Hot Spot Analysis). The study also statistically analysed how the spatiotemporal distribution of vegetation fires is related to environmental factors. The research findings show that the occurrence of vegetation fire varies with seasons with the highest number of fires occurring in September. New information unveiled from the third objective indicated that fire activity tends to be high in June, July, and November despite these months being excluded from the official fire season in Zimbabwe, generally observed from August to October. Persistent, diminishing, oscillating, and historical spatiotemporal fire hotspots were observed in the northern regions of Zimbabwe. The final objective assessed the various topographic, bioclimatic, topographic, vegetation and anthropogenic factors that influence the occurrence of fires in Zimbabwe. The fire hazard levels were also predicted using the Maxent model based on the analysis of MODIS fire location data combined with topographic, bioclimatic, topographic, vegetation and anthropogenic factors. The jack-knife test evaluated the contribution of each variable towards the performance of the model, while the AUC (receiver operating characteristic curve) was used to estimate the model's accuracy. The research findings identified temperature annual range, precipitation seasonality, human influence and elevation as contributing highly to the occurrence of vegetation fires across Zimbabwe’s landscapes. The average AUC of 0.77 demonstrated good model accuracy. Conclusively, results from this thesis reveal the utility of spatial statistics and machine learning methods based on satellite fire data to understand spatiotemporal patterns of fire in Zimbabwe. Specifically, the detection of spatiotemporal patterns of vegetation fires, fire intensity clusters and their predicted hazard levels were successfully mapped. The information derived from this study is valuable in improving fire management in Zimbabwe and other regions. The detection of spatiotemporal patterns of fire, fire intensity and fire hazard levels result in new valuable information important for the mplementation of key fire management policies and strategies in Savanna ecosystems.
Forest suitability mapping and land use change analysis in eThekwini Municipality: leveraging remote sensing and machine learning for forest restoration and rehabilitation efforts.
(2024) Buthelezi, Mthokozisi Ndumiso Mzuzuwentokozo.; Lottering, Romano Trent.; Peerbhay, Kabir Yunus.; Mutanga, Onisimo.
Ecosystem services are vital in environmental policy, emphasising the crucial role of forests in providing these services. However, urbanisation, deforestation, forest degradation, and climate change continue to threaten forests globally. Moreover, despite international efforts like deforestation and forest degradation in developing countries (REDD+) and growing recognition of their importance, forests continue to face significant threats from deforestation, degradation, and climate change. While regulatory frameworks and incentives are important, they may fall short without advancements in forest restoration processes. As such, this study aimed to streamline forest restoration and rehabilitation by employing remote sensing and machine learning techniques to enhance natural forest monitoring and regional forest suitability modelling. Objectives included reviewing and analysing global publications on forest rehabilitation and restoration efforts to investigate trends, explore various practices, and identify opportunities for enhancing the success of these initiatives. This was achieved through the usage of a systematic review methodology. Findings for this objective revealed an increasing research activity in recent years, indicating growing interest in forest rehabilitation and restoration. Geographic analysis highlighted regional disparities, with Asian countries leading in research frequency. Policy recommendations underscored the importance of community participation, efficient fire control, and government support in forest rehabilitation efforts. The second objective reviewed and analysed publications on the utilisation of forest suitability models and remote sensing techniques for identifying areas suitable for forest vegetation, also using a systematic review methodology. The findings also indicate a notable increase in research output. Furthermore, the analysis of reviewed articles revealed a preference for medium to high-resolution remote sensing data, with Landsat being the predominant sensor used for forest suitability assessments. Maximum entropy (MaxEnt) was identified as the most utilised model, followed by the increasingly popular random forests (RF). However, the research revealed a significant geographical disparity, with a heavy concentration of publications in the Americas and Asia. The third objective explored mapping land use and land cover (LULC) changes within the eThekwini Municipality from 2002 to 2022 using remote sensing data from the three most recent Landsat sensors and machine learning algorithms. It utilised RF, support vector machine (SVM), and extreme gradient boosting (XGBoost) to conduct LULC classifications. The generated maps revealed a significant decrease in cropland and an increase in impervious surfaces. As such, this research established a framework for continuous LULC mapping and highlighted Landsat 9's potential in LULC classifications. The fourth objective assessed land degradation within the eThekwini Municipality by focusing on land cover change and soil organic carbon (SOC) stock using medium-resolution remote sensing data and machine learning algorithms. Variables for land cover change and SOC stock prediction were extracted and analysed using XGBoost, light gradient boosting (LightGBM), RF, and SVM models. LightGBM outperformed other models, revealing a notable land cover shift, with forests and shrubland being converted to cropland and urban areas within the municipality. The fifth objective sought to provide a framework for monitoring natural forests at a municipal scale using the last three Landsat Missions, focusing on the eThekwini Municipality, to facilitate forest rehabilitation and restoration. Classifications based on Landsat 7 significantly underestimated the extent of natural forests within the study area, whereas Landsat 8 and Landsat 9 data revealed an increase in natural forests from 2015 to 2023. The final objective aimed to model the suitability of areas for forest species within the eThekwini Municipality using species distribution modelling (SDM)/environmental niche modelling (ENM) methodology. The study modelled current forest suitability (2023) using bioclimatic variables from the WorldClim dataset, and elevation and slope data from the Shuttle Radar Topography Mission (SRTM). Remote sensing data was obtained from Landsat 9 and Sentinel-2A. For future forest suitability (2021–2040), bioclimatic variables from two Global Climate Models (GCMs) under four WorldClim shared socioeconomic pathway (SSP)-based representative concentration pathway (RCP) scenarios were used. The models employed were RF, LightGBM, and artificial neural networks (ANN), with data processing conducted via Google Earth Engine (GEE), QGIS, and Python. Currently, 30% of the municipality's land is deemed suitable, primarily concentrated in the central region. Future projections highlight the mountainous north-western region as most suitable, notably under the SSP370 scenario with a projected suitable area of 63%. Overall, findings from this study highlight the potential of remote sensing and machine learning in supporting forest restoration and rehabilitation efforts, with significant implications for informing policy and prioritising areas for future interventions. Ultimately, this research provides a comprehensive framework for leveraging modern technological advancements to streamline forest restoration initiatives, ensuring sustainable management and conservation of forest ecosystems amidst escalating environmental challenges.
The implementation of UAV multispectral imagery for gully mapping, Okhombe Valley, KwaZulu-Natal Drakensberg, South Africa.
(2023) Riddle, Lyndon Paul.; Hill, Trevor Raymond.; Clulow, Alistair David.
The availability, cost, and applicability of Unmanned Aerial Vehicles (UAVs) are rapidly altering the way we perceive the landscape, can access sites, and model the landscape as it unfolds through remote sensing technologies. It falls to present and future researchers to take advantage of this and use the technology to improve our understanding of our surroundings. One way that geomorphologists would be able to take advantage of the improvements in technology is to use the UAV to map soil processes, in particular erosion. The current methods and technology for mapping gully erosion do not look at the smaller features but rather the landscape as a whole. The purpose of this research was to assess the prospects of UAV technology mapping gully erosion. Specifically, the study investigates the application of the UAV imagery to other technologies or methods in mapping gully erosion activities. This was achieved by testing the multiple available modelling software, by mapping objects of known shapes and volumes to determine which software produced results comparable to that of the known shapes. Tests were conducted to determine if this software and UAV combination would have the ability to map and 3D render soil erosion, thus allowing researchers to verify if this technique could be implemented into mapping gully systems. From these tests, it was found the UAV was able to improve the resolution to that of the available methods. Obtaining a resolution of 0.03m would allow geomorphologists to map and situate the soil erosional landscapes and map the test objects. The software package AgiSoft MetaShape had the highest accuracy and reliability in mapping various objects making it ideal in the mapping of smaller erosional features. Multiple erosional landscapes were mapped using a UAV and the attached RGB sensor and a second UAV with a multi-spectral sensor and AgiSoft MetaShape software. Both sensors mapped plant health, capturing plant health with band widths related to each sensor. Ideally the multi-spectral sensors were seen to be more versatile, however the RGB sensor can be useful. The combination of both UAVs created a 3D-rendered model, and the resulting data was useful in determining the potential future areas of erosion and existing areas of high erosion risk. The multi-sensor camera allowed the user to determine micro-features that could potentially become erosional areas in the future as it identified areas with a concentration in water flow. In conclusion, the UAV is a useful spatial tool to monitor, measure and manage soil erosion and gully mapping.
A Bayesian geo-statistical approach for plantation forest productivity assessment after the fast-track land reform in Zimbabwe = Inqubo eyaziwa nge- Bayesian Geo-statistical Approach ukuhlola ukukhiqiza kwamahlathi atshaliwe emva kohlelo lokuphucwa kwabamhlophe imihlaba olwaziwa nge-Fast-Track Land Reform eZimbabwe
(2023) Chinembiri, Tsikai Solomon.; Mutanga, Onisimo.; Dube, Timothy.
The principal objective of the current study was to investigate how the new generation multispectral remote sensing, along with variants of the Bayesian hierarchical geostatistical methodology, could handle prediction uncertainty of carbon (C) stock. The assessment of C stock prediction uncertainty was conducted in a managed and disturbed plantation forest ecosystem located in Manicaland province of Zimbabwe. To achieve this, the study made use of ancillary data from the multispectral (Landsat-8 and Sentinel-2) remote sensing platforms, which informed the application of different inferential and methodological variants within the Bayesian hierarchical geostatistical framework. Allometric equations suited for the target plantation tree species in the sampled region were used to derive C stock from Above ground Biomass (AGB) sampled on 500 m2 circular supports. These Bayesian geostatistical models utilized a combination of Landsat-8 and Sentinel-2 derived vegetation indices, along with climatic and topographic variables. The study found that the Normalized Difference Vegetation Index (𝑁𝐷𝑉𝐼), Distance to settlements (𝐷𝐼𝑆𝑇), and Soil Adjusted Vegetation Index (𝑆𝐴𝑉𝐼) played crucial roles in influencing the spatial distribution of C stock in the studied region. Enhanced Vegetation Index (𝐸𝑉𝐼) is an insignificant predictor for both Landsat-8 and Sentinel-2 driven C stock predictions. Among the tested Bayesian approaches, the spatially varying coefficient (SVC) model, the multi-source data-driven Bayesian geostatistical approach, and the frequentist geostatistical framework were examined. Regardless of the various specifications for independent variables in the predictive C stock modelling within the Bayesian framework, 𝑁𝐷𝑉𝐼 and 𝐷𝐼𝑆𝑇 emerged as significant predictors of the modelled response variable. The non-stationary and Sentinel-2 driven Bayesian hierarchical model, with 𝑁𝐷𝑉𝐼 and DIST covariables, proved to be the most effective prediction model in the studied plantation forest ecosystem in Zimbabwe. This best-performing C stock predictive model was subsequently used to predict C stock under both current (1970-2000) and future (SSP5-8.5) 2075 climate scenarios. The results of the Bayesian constructed hierarchical model indicate a significant shrinkage of forest C stock density and distribution under the future SSP5-8 (2075) business-as-usual climate projection. Basically, the findings of this study highlight the critical role of new generation multispectral remote sensing and Bayesian geostatistical approaches in assessing and predicting carbon stock uncertainty in forest ecosystems. These insights have significant implications for informed land management strategies, aligning with the goals and recommendations of the Intergovernmental Panel on Climate Change (IPCC) to effectively address climate challenges and enhance sustainable land management practices. Iqoqa. Inhloso ephambili yalolu cwaningo ukuhlola ukuthi uhlobo olusha lwezindlela ezikwazi ukubona/ukuqopha imifanekiso kude, luhambisana nezinhlobo ezahlukene zendlelakwenza eyaziwa nge- Bayesian hierarchical geostatistical methodology, zingakwazi yini ukubhekana nokungahlongeki kahle kobungako bekhabhoni (C). Ukuhlolwa kokungahlongeki kahle kobungako be-C kwenziwa endaweni yohlelonhlaliswano lwamahlathi atshaliwe elawulekile kanye nethikameziwe esifundazweni saseManicaland, eZimbabwe. Imininingo elekelelayo kuleyo etholakala ngezindlela ezikwazi ukubona imifanekiso kude iyona eyasetshenziselwa izinhlobo ezahlukene zokususelwa kukho kanye nezindlelakwenza ezitholakala ohlakeni lwe-Bayesian hierarchical geostatistical framework. Izibalo zokukhula kwezingxenye zezihlahla zohlobo oluqokiwe esifundeni esicwaningwayo zasetshenziselwa ukuthola i-C stock ngaphezulu komhlabathi okuyi-Above Ground Biomass (AGB) ezisampulwe endaweni eyisiyingi engama-500 m2. Lawa mamodeli ayi-Bayesian geostatistic model asebenzisa inhlanganisela ye-Landsat-8 kanye ne-Sentinel-2 eyizinkomba ezitholakala ezimileni, kanye namavariyebuli esimonkathinde sezulu kanye nawokuma komhlaba/kwendawo. Ucwaningo lwathola ukuthi inkomba eyaziwa nge-Normalized Difference Vegetation Index (NDVI), ibanga lokuqhelelana nezindawo okuhlalwa kuzo okuyi-Distance to settlements (DIST), kanye nenkomba eyaziwa nge-Soil Adjusted Vegetation Index (SAVI), konke lokhu kwadlala indima ekubeni nomthelela ekusatshalalisweni kwe-C stock esifundeni esasicwaningwa. I-Enhanced Vegetation Index (EVI) iyisibikezeli esingabalulekile ekuhlongozweni kwe-C stock nge-Landsat-8 kanye ne-Sentinel-2. Kulolu cwaningo kwahlolwa imodeli eyaziwa nge-spatially varying coefficient (SVC), inqubo emthombongxube yokuthola imininingo eyaziwa nge-Bayesian geostatistical approach, kanye nohlakakusebenza olwaziwa nge-frequentist geostatistical framework. Nakuba kwakunemininingwane yamavariyebuli azimele kwimodelingi ye-C stock esiyisibikezeli kuhlakakusebenza lwe-Bayesian, i-NDVI kanye ne-DIST zavela njengezibikezeli ezibalulekile zevariyebuli elindelekile. Imodeli engami ndawonye kanye nehlela ngokwezigaba ze-Bayesian esuselwa ku-Sentinel-2, enamavariyebuli abambisene e-NDVI kanye ne-DIST yakhombisa ukuba yimodeli ekwazi ukubikezela ngendlela efanele kuleyo ndawo yamahlathi atshaliwe ayecwaningwa. Imiphumela yemodeli eyakhiwe ngokwezigaba ze-Bayesian esebenzisa imodeli ephambili yokubikezela i-C stock ikhombisa ukuncipha kokuminyana kwe-C stock yamahlathi. Okuchaza ukuthi imiphumela yocwaningo igqamisa indima ebalulekile edlalwa uhlobo olusha lwezindlela ezikwazi ukubona/ukuqopha imifanekiso kude, kanye nendlelakwenza eyi-Bayesian geostatistical methodology ekuholeni kanye nokubikezela ukungaqondakali kwe-C stock kuhlelonhlaliswano lokuphilayo nokungaphili emahlathini. Lolu lwazi lunemithelela ebalulekile ekwakhiweni kwamaqhinga asebenzayo ekunganyelweni kwezinhlelonhlaliswano zamahlathi, okungamaqhinga azosebenza ngokufanele ekubhekaneni nezinselelo zesimonkathinde sezulu kwikhuluminyaka lama-21.
Integrating texture analysis and innovative modelling approaches for capturing morphological diversities and dynamics of informal settlements in Durban metropolitan area, South Africa.
(2022) Matarira, Dadirai.; Mutanga, Onisimo.; Naidu, Maheshvari.
Abstract available in PDF.
The application of deep learning for remote sensing of soil organic carbon stocks distribution in South Africa = Ukusetshenziswa kokufunda okujulile kokuzwa kude kokusatshalaliswa kwesitokwe sekhabhoni ephilayo enhlabathini eNingizimu Afrikha.
(2022) Odebiri, Omosalewa Olamide.; Mutanga, Onisimo.; Odindi, John Odhiambo.
Soil organic carbon (SOC) is a vital measure for ecosystem health and offers opportunities to understand carbon fluxes and associated implications. However, unprecedented anthropogenic disturbances have significantly altered SOC distribution across the globe, leading to considerable carbon losses. In addition, reliable SOC estimates, particularly over large spatial extents remain a major challenge due to among others limited sample points, quality of simulation data and suitable algorithms. Remote sensing (RS) approaches have emerged as a suitable alternative to field and laboratory SOC determination, especially at large spatial extent. Nevertheless, reliable determination of SOC distribution using RS data requires robust analytical approaches. Compared to linear and classical machine learning (ML) models, deep learning (DL) models offer a considerable improvement in data analysis due to their ability to extract more representative features and identify complex spatial patterns associated with big data. Hence, advancements in remote sensing, proliferation of big data, and deep learning architecture offer great potential for large-scale SOC mapping. However, there is paucity in literature on the application of DL-based remote sensing approaches for SOC prediction. To this end, this study is aimed at exploring DL-based approaches for the remote sensing of SOC stocks distribution across South Africa. The first objective sought to provide a synopsis of the use of traditional neural network (TNN) and DL-based remote sensing of SOC with emphasis on basic concepts, differences, similarities and limitations, while the second objective provided an in-depth review of the history, utility, challenges, and prospects of DL-based remote sensing approaches for mapping SOC. A quantitative evaluation between the use of TNN and DL frameworks was also conducted. Findings show that majority of published literature were conducted in the Northern Hemisphere while Africa have only four publications. Results also reveal that most studies adopted hyperspectral data, particularly spectrometers as compared to multispectral data. In comparison to DL (10%), TNN (90%) models were more commonly utilized in the literature; yet, DL models produced higher median accuracy (93%) than TNN (85%) models. The review concludes by highlighting future opportunities for retrieving SOC from remotely sensed data using DL frameworks. The third objective compared the accuracy of DL—deep neural network (DNN) model and a TNN—artificial neural network (ANN), as well as other popular classical ML models that include random forest (RF) and support vector machine (SVM), for national scale SOC mapping using Sentinel-3 data. With a root mean square error (RMSE) of 10.35 t/ha, the DNN model produced the best results, followed by RF (11.2 t/ha), ANN (11.6 t/ha), and SVM (13.6 t/ha). The DNN's analytical abilities, combined with its capacity to handle large amounts of data is a key advantage over other classical ML models. Having established the superiority of DL models over TNN and other classical models, the fourth objective focused on investigating SOC stocks distribution across South Africa’s major land uses, using Deep Neural Networks (DNN) and Sentinel-3 satellite data. Findings show that grasslands contributed the most to overall SOC stocks (31.36 %), while urban vegetation contributed the least (0.04%). Results also show that commercial (46.06 t/h) and natural (44.34 t/h) forests had better carbon sequestration capacity than other classes. These findings provide an important guideline for managing SOC stocks in South Africa, useful in climate change mitigation by promoting sustainable land-use practices. The fifth objective sought to determine the distribution of SOC within South Africa’s major biomes using remotely sensed-topo-climatic data and Concrete Autoencoder-Deep Neural Networks (CAE-DNN). Findings show that the CAE-DNN model (built from 26 selected variables) had the best accuracy of the DNNs examined, with an RMSE of 7.91 t/h. Soil organic carbon stock was also shown to be related to biome coverage, with the grassland (32.38%) and savanna (31.28%) biomes contributing the most to the overall SOC pool in South Africa. forests (44.12 t/h) and the Indian ocean coastal belt (43.05 t/h) biomes, despite having smaller footprints, have the highest SOC sequestration capacity. To increase SOC storage, it is recommended that degraded biomes be restored; however, a balance must be maintained between carbon sequestration capability, biodiversity health, and adequate provision of ecosystem services. The sixth objective sought to project the present SOC stocks in South Africa into the future (i.e. 2050). Soil organic carbon variations generated by projected climate change and land cover were mapped and analysed using a digital soil mapping (DSM) technique combined with space-for-time substitution (SFTS) procedures over South Africa through 2050. The potential SOC stocks variations across South Africa's major land uses were also assessed from current (2021) to future (2050). The first part of the study uses a Deep Neural Network (DNN) to estimate current SOC content (2021), while the second phase uses an average of five WorldClim General Circulation Models to project SOC to the future (2050) under four Shared Socio-economic Pathways (SSPs). Results show a general decline in projected future SOC stocks by 2050, ranging from 4.97 to 5.38 Pg, compared to estimated current stocks of 5.64 Pg. The findings are critical for government and policymakers in assessing the efficacy of current management systems in South Africa. Overall, this study provides a cost-effective framework for national scale mapping of SOC stocks, which is the largest terrestrial carbon pool using advanced DL-based remote sensing approach. These findings are valuable for designing appropriate management strategies to promote carbon uptake, soil quality, and measuring terrestrial ecosystem responses and feedbacks to climate change. This study is also the first DL-based remote sensing of SOC stocks distribution in South Africa. Iqoqa Ikhabhoni yomhlabathi engaguquliwe, phecelezi i-soil organic carbon (SOC) ibaluleke kakhulu ebudlelwaneni bezinto eziphilayo nendawo eziphila kuyona futhi isiza ukuqonda kabanzi ubudlelwane bamakhabhoni nezinto eziyidingayo, nokuthi lokho kunayiphi imiphumela. Nokho, ukungcola okungajwayelekile okudalwa ngabantu kubonakala kuza noshintsho olukhulu ekuhanjisweni kwe-SOC kuwona wonke umhlaba, okuholela ekutheni kube nokulahleka kwekhabhoni eningi. Ukwengeza, ukuhlawumbisela okuthembekile nge-SOC, ikakhulukazi okuthinta umthamo omkhulu kubonakala kuqhubeka nokuba yinkinga ngenxa yesizathu sokuthi kukhona izibonelo zayo ezimbalwa, kanti nezinga elihle liyagqoza, nendlela eyiyo okumele ilandelwe ukukala. Indlela yokuhlola buqamama, phecelezi i-Remote sensing (RS) iyona ebonakala njengendlela engalandelwa ukuhlola iSOC ensimini noma egunjini lokuhlolela, ikakhulukazi uma kuthinta indawo enkulu. Nokho, ukuhlonza indlela ethembekile yokusatshalaliswa kwe-SOC kusetshenziswa imininingwane ye-RS kudinga izindlela eziseqophelweni eliphezulu zokuhlaziya. Ukuqhathanisa nendleya ye-linear ne-classical machine learning (ML), Indlela ye-deep learning (DL) yona ibonakala iletha ubungcono obukhulu ekuhlaziyeni imininingo ngenxa yokukwazi ukuhlonza izinto ezidingekalayo nokuveza izinto eziyinkinga kuleyo ndawo enemininingo eminingi. Ukuthuthuka kwendlela yokuhlola buqamama, ukuhlaziywa kwemininingo eminingi, nokufunda ngobuciko bokwakha indlela yokwenza, konke kuza namathuba amahle okuphaka uhlelo ngobuningi balo be-SOC. Nakuba kunjalo, kubonakala kunolwazi oluyingcosana emibhalweni yocwaningo mayelana nokusebenza kwezindlela zokuhlola buqamama ngesihlawumbiselo se-SOC. Kuze kube manje, lolu cwaningo beluhlose ukucubungula izindlela zokuhlaziya buqamama ze-DL ngokusatshalaliswa kwe-SOC eNingizimu-Afrikha yonkana. Inhloso yokuqala bekuwukunikeza ulwazi olufushane mayelana nendlela ejwayelekile yokusabalalisa, phecelezi i- traditional neural network (TNN) nendlela yokuhlaziya buqamama ye-DL ye-SOC ngokugcizelela ukubaluleka kwezinto ezijwayelekile, nomahluko. Okufanayo nezingqinamba, kanti inhloso yesibili inikeze ukucubungula okujulile komlando, ukusetshenziswa kwento, izinselelo, kanye namathuba okusebenza kwendlela yokuhlaziya buqamama ye-DL ekusabalaliseni i-SOC. Ukuhlaziya ngokwekhwalithethivu ekusebenziseni i-TNN ne-DL nakho kwenziwa. Imiphumela iveza ukuthi imibhalo eminingi yocwaningo yashicilelwa eNyakatho nomhlaba kanti lapha e-Afrika khona kwashicilelwa emine nje kuphela. Imiphumela iphinde iveze ukuthi ucwaningo oluningi lulandela indlela ebheka imininingo ehlanngene, ikakhulukazi ukubheka imibala eminingi yokukhanya, uma kuqhathaniswa nengxubevange yemininingo. Ukuqhathanisa nezindlela ze-DL (10%) ne-TNN (90%) yizona ezithandwa kakhulu emibhalweni yocwaningo, kodwa, indlela ye-DL ikhiqize okuneqinso (93%) kunendlela ye-TNN (85%). Ukucubungula kuphetha ngokugqamisa amathuba azayo okuthola ulwazi lwe-SOC kusetshenziswa indlela yokuhlola buqamama nge-DL. Inhloso yesithathu yona yayiqhathanisa ukuthembeka kwe-DL-deep neural network (DNN) kanye ne-TNN—artificial neural network (ANN), kanye nenye indlela endala edumile ye-ML efaka phakathi i-random forest (RF) kanye ne-support vector machine (SVM), ukhubheka isikalo sikazwelonke se-SOC kusetshenziswa imininingo ye-Sentinel-3. Nge-root mean square error (RMSE) ye-10.35 t/ha, indlela ye-DNN yakhiqiza imiphumela emihle kakhulu, ilandelwa yi-RF (11.2 t/ha), i-ANN (11.6 t/ha), ne-SVM (13.6 t/ha). Amandla okuhlaziya e-DNN, ehlanganiswe namandla okukwazi ukubhekana nemininingo eminingi kakhulu yikhona okwenza ibaluleke ukwedlula indlela ye-ML. Emva kokuhlonza amandla e-DL phezu kwawe-TNN kanye nezinye izindlela ezaziwayo, inhloso yesine yona ibigxile ekuhloleni ukusabalaliswa kwe-SOC ekusetshenzisweni komhlaba ngobuningi bawo lapha eNingizimu-Afrika, kusetshenziswa i-Deep Neural Networks (DNN) ne-Sentinel-3 satellite data. Imiphumela iveza ukuthi indawo enotshani iyona eletha izibalo eziphezulu ze-SOC sekuhlangene yonke into (31.36 %), kanti izimilo zasedolobheni zona zaletha izibalo ezincane (0.04%). Imiphumela iphinde ikhombise ukuthi amahlathi atshalelwe ukudayisa (46.06 t/h) kanye nawemvelo (44.34 t/h) ayekhombisa ukuba nekhabhoni enhle uma kuqhathaniswa namanye. Le miphumela ingumhlahlandlela obalulekile mayelana nokugcinwa kwe-SOC lapha eNingizimu-Afrikha, okuyinto ebaluleke kakhulu ukubhekana nokuguquguquka kwesimo sezulu ngokugqugquzela izindlela eziyizo zokusebenzisa umhlaba. Inhloso yesihlanu yona ibihlose ukuthola ulwazi ngokusatshalaliswa kwe-SOC ezinhlakeni ezahlukene zomphakathi lapha eNingizimu-Afrikha kusetshenziswa indlela yokuhlaziya buqama kanye ne-Concrete Autoencoder-Deep Neural Networks (CAE-DNN). Imiphumela iveza ukuthi indlela ye-CAE-DNN (eyakhiwe ngezinto ezikhethiweyo ezingama-26) yakhombisa ubuqiniso obukhulu be-DNNs, eyayihloliwe, nge-RMSE of 7.91 t/h. I-SOC yaphinde yakhombisa ukuba nobudlelwane nokusabalala kohlaza olumilile, kukhona indawo enotshani (32.38%) nendawo engenazihlahla yohlaza lotshani (31.28%) okuyiyona ephethe umhlaba omningi uma usuhlanganisiwe we-SOC lapha eNingizimu-Afrikha, amahlathi (44.12 t/h) kanye nomhlaba onohlaza lotshani ukugudla ulwandle i Indian Ocean (43.05 t/h), ngale okubonakala kancane, kodwa inokugcwala okuningi kwe-SOC. Ukukhulisa indawo yokugcina i-SOC, kuphakanyiswa ukuba indawo yohlaza lotshani ebisagciniwe iphinde isetshenziswe; nokho-ke, kumele kube khona ukulinganisa okuyikhona phakathi kokuthathwa kwekhabhoni, impilo yokubhekwayo kanye nokunakekelwa kwezinto eziphilayo nendawo eziphila kuyona. Inhloso yesithupha yayihlose ukuvikela ukukhiqizwa kwe-SOC lapha eNingizimu-Afrikha ngisho eminyakeni eminingi ezayo (okuwunyaka wezi-2050). Izinhlobo ezahlukene ze-SOC ezikhiqizwe ngokuguquguquka okwahlukene kwezimo zezulu nomhlaba kwakahlwa futhi kwahlaziywa kusetshenziswa isu le-digital soil mapping (DSM) lihlanganiswe nezindlela ze-space-for-time substitution (SFTS) eNingizimu-Afrikha kuze kube unyaka wezi-2050. Abasebenzisi bakusasa bomhlaba okhiqiza izinhlobo ezahlukene ze-SOC nabo bahlolwa kusukela kwabamanje (2021) kuye kwabangomuso (2050). Ingxenye yokuqala yalolu cwaningo isebenzisa i-Deep Neural Network (DNN) ukugagula isimo ngqo sengqikithi ye-SOC (2021), kanti ingxenye yesibili yona isebenzisa okungenani izindlela ezinhlanu zokusabalalisa nge-WorldClim General Circulation kumaphrojekthi e-SOC nangeminyaka ezayo (2050) ngaphansi kwe-Shared Socio-economic Pathways (SSPs) emine. Imiphumela iveza ukwehla kwe-SOC ngokwezibalo zangomuso ngeminyaka yezi-2050, kusukela ku-4.97 kuya ku-5.38 Pg, uma kuqhathaniswa ne-SOC ekhona manje engu-5.64 Pg. Le miphumela ibaluleke kakhulu kuhulumeni nakulaba abakhipha izinqubomgomo ukubheka ukusebenza kahle kwezinhlaka zokuphatha eNingizimu-Afrikha. Sekukonke, lolu cwaningo lunikeza indlela yokucabanga nokwenza engabizi neyimpumelelo yokusabalalisa i-SOC, okuyiyona enkulu kakhulu ekukhiqizweni kwekhaboni kusetshenziswa indlela yokuhlola buqamama ye-DL. Lolu cwaningo lubalulekile ukuqhamuka nezindlela ezintsha zokuphatha ukuze kugqugquzeleke ukukhiqizwa kwekhabhoni, umhlaba ovundile, nokukala izinto eziphilayo nendawo eziphila kuyona kanye nokunikeza impendulo mayelana nokuguquguquka kwesimo sezulu. Lolu cwaningo lungolokuqala ngqa lokubheka indlela yokuhlaziya buqamama nge-DL lapha eNingizimu Afrikha.
Integrating local, indigenous knowledge and geographical information system in mapping flood vulnerability at Quarry Road West informal settlement in Durban, KwaZulu-Natal = Ukuhlanganiswa kolwazi lwendabuko nohlelo lolwazi ngezezwe ukwakha ibalazwe labasengozini ngezikhukhula emgwaqweni owumgubane endaweni yemikhukhu eThekwini, KwaZulu-Natali.
(2022) Membele, Garikai Martin.; Naidu, Maheshvari.; Mutanga, Onisimo.
Reducing flood vulnerability is crucial in reducing flood impacts, and mapping flood vulnerability is one of the most useful options for reducing flood vulnerability. This is because it helps locate where the vulnerable households or areas are, which in turn, supports policy and strategic interventions. However, the complex nature of flood vulnerability, especially in Mutanga, Onisimo.Mutanga, Onisimo. requires holistic consideration of the dweller’s experiential, contextual, and situational knowledge in mapping flood vulnerability. This study sought to establish a methodological approach for integrating Local, Indigenous Knowledge and Geographical Information System to map flood vulnerability in Quarry Road West informal settlement in Durban, South Africa. A convergent parallel mixed methods approach which involved a digital household survey (n=359), interviews with key informants (n=10), focus group discussions (n=2) and a global positioning system was used in the study. Descriptive and inferential statistics were used to analyse the quantitative data while thematic analysis was used to analyse the qualitative data. The findings reveal that using Local and Indigenous Knowledge that community members possess, generated context-specific indicators for mapping flood vulnerability in Quarry Road West informal settlement. The findings also reveal that the proximity of houses to the Palmiet River and the main roads, the nature of the soil and the type of materials people were using to build their houses hugely contributed to the vulnerability of people to flooding in the study area. The study further showed that flood vulnerability in the study area was a result of socio-economic, physical and institutional challenges. Using the Analytical Network Process helped to foster community participation and comprehensively integrated Local and Indigenous Knowledge with Geographical Information System in mapping flood vulnerability in Quarry Road West informal settlement. Flood vulnerability in the informal settlement exhibited spatial differentiations. Households along the Palmiet River were highly vulnerable to flooding and a section of the settlement called Mcondo 1 was highly vulnerable to flooding while maMsuthu had low flood vulnerability. The study concludes that using community members’ Local and Indigenous Knowledge to select indicators was crucial for mapping flood vulnerability in an informal settlement, as it provided a more nuanced understanding of flood vulnerability. The methodological approach presented in this study can help decision-makers and other stakeholders to have sight of sustainable solutions and context-specific strategies that could be employed to increase the resilience of people at local levels to flooding. IQOQA Ukunciphisa ubungozi nesisindo sezikhukhula kubalulekile futhi nokwenza ibalazwe mayelana nobungozi bezikhukhula yinto ngempela esizayo ukunciphisa ubungozi bezikhukhula. Lokhu kungenxa yokuthi kuyasiza ukuthola izindawo ezisengozini, okuwukuthi kusekela inqubomgomo kanye neqhinga lokungenelela. Yize kunjalo, inkinga enkulu mayelana nobungozi bezikhukhula, ikakhulukazi endaweni yemijondolo kufuneka ukucabanga nxazonke mayelana nabahlali asebesazi isimo kanye nolwazi ekudwebeni ibalazwe mayelana nobungozi bezikhukhula. Lesi sifundo sasiphokophelele ukusebenzisa ulwazi lwamalungu omphakathi kanye nolwendabuko ukukhetha izinkomba kwabaluleka kakhulu ekwakhiweni kwebalazwe mayelana nobungozi bezikhukhula endaweni yemijondolo, njengoba inikeza okuningi ukuqonda ngobungozi bezikhukhula. Izindlela zohlelo lokwenza ezethulwe kulesi sifundo zingasiza kulabo abathatha izinqumo kanye nabanye ababamba iqhaza ukuba babe nehlo ekutheni kube nezisombululo eziqinile kanye namaqhinga aqonde ngqo ngokwendawosimo angasetshenziswa ukwandisa ukuzithemba nofuqufuqu kubantu ngokwamazinga endawo mayelana nezikhukhula. Okutholakele futhi kuveza ukuthi ukusondelana kwezindlu ngasemfuleni uPalmiet kanye nomgwaqo omkhulu, isimo senhlabathi kanye nezinto abantu abazisebenzisayo ukwakha izindlu zabo kwaba nomthelela ekubekeni izimpilo zabantu ebungozini bezikhukhula endaweni. Okufundwayo kwaqhubeka kwaveza ukuthi ubungozi bezikhukhula endaweni efundwayo kwaba imiphumela yesimo sezomnotho kanye nezinselelo zesimo sezakhiwo. Ukusebenzisa uhlaziyo lokuxhumana kusizile ukukhuthaza umphakathi ukuthi ubambe iqhaza kanye nokuhlanganisa okuphelele okwendawo kanye nolwazi lwendabuko nohlelo lolwazi ngezezwe ekwakheni ibalazwe ngobungozi bezikhukhula emgwaqweni uQuarry otholakala eNtshonalanga yendawo yemijondolo. Ubungozi bezikhukhula endaweni yemijondolo kuveza umehluko mayelana nokuhlelwa kwezindawo zokuhlala. Izakhamuzi eduzana nomfula iPalmeit zabasengozini kakhulu ngezikhathi zezikhukhula nanokuthi enye ingxenye yendawo ebizwa ngoMcondo 1 nayo yaba sengozini kakhulu ngezikhathi zezikhukhula, kanti kwamaMsuthu izinga lobungozi lezikhukhula laliphansi.
Understanding the spatial relations of wetland plant functional traits and environmental gradients incorporating remote sensing.
(2021) Nondlazi, Basanda Xhantilomzi.; Cho, Moses Azong.; Van Deventer, Heidi.; Sieben, Erwin Jacobus Joannes.
Abstract available in PDF.
Mapping natural forest cover, tree species diversity and carbon stocks of a subtropical Afromontane forest using remote sensing.
(2021) Gyamfi-Ampadu, Enoch.; Gebreslasie, Michael Teweldemedhin.
Natural forests cover about a third of terrestrial landmass and provides benefits such as carbon sequestration, and regulation of biogeochemical cycles. It is essential that adequate information is available to support forest management. Remote Sensing imageries provide data for mapping natural forests. Hence, our study aimed at mapping the Nkandla Forest Reserve attributes with Remote Sensing imageries. Quantitative information on the forest attributes is non-existent for many of these forests, including the sub-tropical Afromontane Nkandla Forest Reserve. This does not support scientific and evidence based natural forest management. A review of literature revealed that progress has been made in Remote Sensing monitoring of natural forest attributes. The Random Forest (RF) and Support Vector Machine (SVM) were applied to Landsat 8 in classifying the land use land cover (LULC) classes of the forest. Each of the algorithms produced higher accuracy of above 95% with the SVM performing slightly better than the RF. The SVM, Markov Chain and Multi-Layer Perceptron Neural Network (MLPNN) were adopted for a spatiotemporal change detection over the last 30 years at decadal interval for the forest. There were consistent changes in each of the four LULC classes. The study further conducted a forecasting of LULC distribution for 2029. Aboveground carbon (AGC) estimation was carried out using Sentinel 2 imagery and RF modelling. Four models made up smade of Sentinel 2 products could successfully map the AGC with high accuracies. The last two studies focused on tree species diversity with the first evaluating the influence of spatial and spectral resolution on prediction accuracies by comparing the PlanetScope, RapidEye, Sentinel 2 and Landsat 8. Both the spatial and spectral resolution were found to influence accuracies with the Sentinel 2 emerging as the best imagery. The second aspect focused on identifying the best season for the prediction of tree species diversity. Summer imagery emerged as the best season and the winter being the least performer. Overall, our study indicates that Remote Sensing imageries could be used for successful mapping of natural forest attributes. The outputs of our studies could also be of interest to forest managers and Remote Sensing experts.
Urban sustainability and social ecological systems: linking civic ecology, nature and ecosystems services for the achievement of the SDGs.
(2021) Davids, Rashieda.; Slotow, Robert Hugh.; Rouget, Mathieu Jean Francois.
To address global environmental change and ensure well-being, an improved understanding of complex human-environment relationships is needed. It further requires that the role of natural systems and ecosystem services are recognised for their contributions to the Sustainable Development Goals (SDGs), are included in a broad range of development sectors, and are managed and protected appropriately to safeguard those contributions. This PhD contributed to the evolution of the application of sustainability frameworks, from global to local level, by providing local-level evidence from two sources of change, civic / community action and local government actions (eThekwini Municipality). Through the five papers produced in this PhD research, I developed and assessed contributions of civic ecology, research organisation processes, and government planning and management, to global sustainability, using socialecological systems and ecosystem services theory as a foundation. In Chapters 2, 3 and 4, a mixed methodological approach was used (household surveys, interviews, field observations and impact assessment) to identify the systemic linkages between civic ecology interventions of the Wise Wayz Water Care programme (case study), ecosystem services, SDGs, and human well-being. Chapter 5 analysed virtual vs face-to-face international conferences of the Sustainable and Healthy Food Systems programme (case study) and identified impacts on inclusivity, organisational learning, carbon footprints, barriers and enabling conditions for improved efficiency, and environmental sustainability, of international research collaborations. Chapter 6 used the Durban Research Action Partnership (D’RAP) transdisciplinary scienceaction collaboration as a case study, to explore the links between social outcomes and ecosystem services from multiple viewpoints, through expert collaboration and engagement for urban planning and sustainability. The main contributions made by this work are: (1) Identification, quantification, and assessment of civic ecology interventions as a tool to improve human well-being, using a social-ecological systems approach; (2) Linking local interventions to global policy outcomes through quantified systems mapping of civic ecology, natural capital, and ecosystem services enhancement, related to the SDGs; (3) Linking ecosystem services to human well-being improvements and policy implementation through transdisciplinary approaches. This thesis provided insights, tools, methods and evidence for local-level actions, yielding national and international sustainability wins.
UAV and field spectrometer based remote sensing for maize phenotyping, varietal discrimination and yield forecasting.
(2020) Chivasa, Walter.; Mutanga, Onisimo.; Biradar, Chandrashekhar.
Maize is the major staple food crop in the majority of Sub-Saharan African (SSA) countries. However, production statistics (croplands and yields) are rarely measured, and where they are recorded, accuracy is poor because the statistics are updated through the farm survey method, which is error-prone and is time-consuming, and expensive. There is an urgent need to use affordable, accurate, timely, and readily accessible data collection and spatial analysis tools, including robust data extraction and processing techniques for precise yield forecasting for decision support and early warning systems. Meeting Africa’s rising food demand, which is driven by population growth and low productivity requires doubling the current production of major grain crops like maize by 2050. This requires innovative approaches and mechanisms that support accurate yield forecasting for early warning systems coupled with accelerated crop genetic improvement. Recent advances in remote sensing and geographical information system (GIS) have enabled detailed cropland mapping, spatial analysis of land suitability, crop type, and varietal discrimination, and ultimately grain yield forecasting in the developed world. However, although remote sensing and spatial analysis afforded us unprecedented opportunities for detailed data collection, their application in maize in Africa is still limited. In Africa, the challenge of crop yield forecasting using remote sensing is a daunting task because agriculture is highly fragmented, cropland is spatially heterogeneous, and cropping systems are highly diverse and mosaic. The dearth of data on the application of remote sensing and GIS in crop yield forecasting and land suitability analysis is not only worrying but catastrophic to food security monitoring and early warning systems in a continent burdened with chronic food shortages. Furthermore, accelerated crop genetic improvement to increase yield and achieve better adaptation to climate change is an issue of increasing urgency in order to satisfy the ever-increasing food demand. Recently, crop improvement programs are exploring the use of remotely sensed data that can be used cost-effectively for varietal evaluation and analysis in crop phenotyping, which currently remains a major bottleneck in crop genetic improvement. Yet studies on evaluation of maize varietal response to abiotic and biotic stresses found in the target production environments are limited. Therefore, the aim of this study was to model spatial land suitability for maize production using GIS and explore the potential use of field spectrometer and unmanned aerial vehicles (UAV) based remotely sensed data in maize varietal discrimination, high-throughput phenotyping, and yield prediction. Firstly, an overview of major remote-sensing platforms and their applicability to estimating maize grain yield in the African agricultural context, including research challenges was provided. Secondly, maize land suitability analysis using GIS and analytical hierarchical process (AHP) was performed in Zimbabwe. Finally, the utility of proximal and UAV-based remotely sensed data for maize phenotyping, varietal discrimination, and yield forecasting were explored. The results showed that the use of remote sensing data in estimating maize yield in the African agricultural systems is still limited and obtaining accurate and reliable maize yield estimates using remotely sensed data remains a challenge due to the highly fragmented and spatially heterogeneous nature of the cropping systems. Our results underscored the urgent need to use sensors with high spatial, temporal and spectral resolution, coupled with appropriate classification techniques and accurate ground truth data in estimating maize yield and its spatiotemporal dynamics in heterogeneous African agricultural landscapes for designing appropriate food security interventions. In addition, using modern spatial analysis tools is effective in assessing land suitability for targeting location-specific interventions and can serve as a decision support tool for policymakers and land-use planners regarding maize production and varietal placement. Discriminating maize varieties using remotely sensed data is crucial for crop monitoring, high throughput phenotyping, and yield forecasting. Using proximal sensing, our study showed that maize varietal discrimination is possible at certain phenological growth stages at the field level, which is crucial for yield forecasting and varietal phenotyping in crop improvement. In addition, the use of proximal remote sensing data with appropriate pre-processing algorithms such as auto scaling and generalized least squares weighting significantly improved the discrimination ability of partial least square discriminant analysis, and identify optimal spectral bands for maize varietal discrimination. Using proximal sensing was not only able to discriminate maize varieties but also identified the ideal phenological stage for varietal discrimination. Flowering and onset of senescence appeared to be the most ideal stages for accurate varietal discrimination using our data. In this study, we also demonstrated the potential use of UAV-based remotely sensed data in maize varietal phenotyping in crop improvement. Using multi-temporal UAV-derived multispectral data and Random Forest (RF) algorithm, our study identified not only the optimal bands and indices but also the ideal growth stage for accurate varietal phenotyping under maize streak virus (MSV) infection. The RF classifier selected green normalized difference vegetation index (GNDVI), green Chlorophyll Index (CIgreen), Red-edge Chlorophyll Index (CIred-edge), and the Red band as the most important variables for classification. The results demonstrated that spectral bands and vegetation indices measured at the vegetative stage are the most important for the classification of maize varietal response to MSV. Further analysis to predict MSV disease and grain yield using UAV-derived multispectral imaging data using multiple models showed that Red and NIR bands were frequently selected in most of the models that gave the highest prediction precision for grain yield. Combining the NIR band with Red band improved the explanatory power of the prediction models. This was also true with the selected indices. Thus, not all indices or bands measure the same aspect of biophysical parameters or crop productivity, and combining them increased the joint predictive power, consequently increased complementarity. Overall, the study has demonstrated the potential use of spatial analysis tools in land suitability analysis for maize production and the utility of remotely sensed data in maize varietal discrimination, phenotyping, and yield prediction. These results are useful for targeting location-specific interventions for varietal placement and integrating UAV-based high-throughput phenotyping systems in crop genetic improvement to address continental food security, especially as climate change accelerates.
A spatially explicit approach for analysing the landscape pattern of urban vegetation using remotely sensed data and its impacts on urban surface temperature.
(2020) Kowe, Pedzisai.; Mutanga, Onisimo.
The landscape pattern of urban green spaces and vegetation plays a significant role in supplying essential benefits and ecological services including sequestering and storing carbon, purification of air and water, regulating climate and providing recreational opportunities. However, due to the negative impacts of land cover change and rapid rates of urbanization, vegetation in an urban landscape typically becomes isolated and highly heterogeneous in space and time, relative to non-urban landscapes or natural areas. This research aimed to develop a spatially explicit approach based on remotely sensed data to quantify and monitor vegetation fragmentation and landscape structure of urban vegetation over time and its related impacts on the urban thermal environment using Harare metropolitan city in Zimbabwe as a case study. Specifically, multi-temporal Sentinel 2, Landsat 8 and Aster data were used in achieving the above objectives. Results based on the forest fragmentation model showed that the patch vegetation conditions, which represents the highest and severe vegetation fragmentation level, were dominant across the landscape, followed by edge, transition and perforated, whilst the core vegetation covered a small portion of the city. The decrease of large, connected and contiguous vegetation to a more scattered and fragmented vegetated patches was common across the city but more dominant in the heavily built-up areas of western, eastern and the southern parts of the city, indicating the significant impact of urban development. The small, isolated and scattered vegetation patches were associated with low positive and negative spatial autocorrelation of Local Indicators of Spatial Association (LISA) indices. On the other hand, the more homogeneous (clustered) vegetation was associated with high positive spatial autocorrelation in the northern part of Harare metropolitan city. Furthermore, the study showed that clustered, highly connected vegetation produces stronger cooling effects than dispersed, isolated and smaller patches of vegetation. Overall, spatial explicit approach and tools including the forest fragmentation model and LISA indices could play a significant role in landscape ecology with significant implications for conservation and restoration efforts based on the delineation of spatially explicit clusters of high or low vegetation cover, core or patch or edge vegetation conditions.
Cities as hotspots for invasions: the case of the eThekwini Municipality.
(2019) Padayachee, Ashlyn Levadia.; Proches, Serban Mihai.
Increased anthropogenic activities (trade and travel) have caused an increase in the introduction of biological organisms outside of their native range. Biological invasions result in serious negative ecological, economic and social impacts in their invaded range and are responsible for a decline in native biodiversity. These negative impacts become more prominent in highly transformed environments, such as those found in cities which are often the first points of introduction for alien species. Durban (eThekwini) is situated on the east coast of South Africa and is one of the largest port cities on the African continent, making it an important economic centre for the country. It is the third most populated city in South Africa and is a major contributor towards tourism. Additionally, Durban is located in the Maputaland-Pondoland Albany, one of thirty-four global hotspots of biodiversity. This study focuses on the patterns, processes and drivers of biological invasions in Durban. I investigated three important aspects of alien species responses in urban environments: 1) precaution through the prevention of alien species introduction; 2) prioritisation through using a combination of early warning systems and techniques to identify potentially high-risk alien species; and 3) preparedness and response for a potential incursion event of Solenopsis invicta in Durban. I investigated the importance of preventing alien species introductions by identifying the pathways which facilitate the highest number of introductions for prioritisation for prevention efforts. Furthermore, I identified vectors responsible for secondary spread of alien species in cities. The majority of alien species were either released into nature or escaped from captivity and spread within cities through unaided dispersal. It is difficult to control the natural spread of species, therefore preventing alien species introductions is paramount. However, preventing the introduction of all alien species to a new area is difficult to achieve. Therefore, prioritising alien species for prevention efforts is an essential component of responding to biological invasions which will allow decision makers to more carefully allocate limited resources and time to species with the potential to result in severely negative impacts. Incorporating a holistic prioritisation approach based not only on alien species with a high-risk of invading new areas, but also the pathways which facilitate their introduction and the areas which are most at risk of being invaded is beneficial for decision makers in targeting priority species for prevention efforts. I developed a methodology, integrating these three aspects (species, pathways and sites), to select priority species to target for prevention efforts and identified areas most at risk of being invaded by these species using climatic suitability modelling to select priority targets for prevention efforts. Additionally, I used climatic models and pathway information to identify potential points of first introduction and sites of first naturalisation to target for active and passive surveillance endeavours. Solenopsis invicta Buren (the red imported fire ant) was identified as a potentially high-risk species posing serious ecological and socio-economic threats for Durban. I then explored opportunities for strategic response planning for Solenopsis invicta for Durban, South Africa. In doing so, I identified key priorities to help decision makers initiate strategic response planning for a potential incursion of this species to Durban. The research presented in this study outlines approaches that can assist with the prevention, prioritisation, and preparedness in responding to alien species in urban environments.
The application of geographic information systems (GIS) to armed violent conflicts resolution in the Great Lakes region (GLR) of Central and East Africa.
(2020) Rwandarugali, Stanislas.; Njoya, Ngetar Silas.
Armed violent conflict is a persistent global problem, and its severity is more prominent in developing countries, including Africa. In the past decades and more recently, the GLR in east Africa has experienced various armed violent conflicts, notably the 1994 Rwandan genocide, a protracted civil war in Uganda, the Burundi ethnic conflicts, sporadic persistent cross-border ethnic conflicts in Tanzania and an unending guerrilla and civil war in the Democratic Republic of Congo (DRC). Many efforts have been made through conventional approaches, notably negotiations, peace talks, peacekeeping operations (PKO), and peace stabilization, to address these conflicts but sustainable peace remains a challenge and elusive. Most of these conventional approaches emphasize on economic and political aspects and tend to ignore the spatial component in peace talks and decisions making. GIS has been recognized as an invaluable tool in the resolution of armed violent conflicts in other parts of the world. GIS has the capability of integrating, synthesizing, and modelling spatial data, which can assist in policy and decision-making. However, GIS by itself cannot resolve any conflict, but it is a decision support system that can assist different stakeholders in sustainable peace negotiations. This study aims to explore the application of GIS to armed violent conflicts resolution in the GLR. It is built upon an array of qualitative and quantitative approaches aimed at identifying the origin and evolution of armed violent conflicts; patterns and dynamics of present conflict zones and areas that are currently not experiencing conflicts but may be prone to future armed violent conflicts in GLR in east Africa. In an attempt to trace the origin and evolution of persistent armed violent conflicts in the GLR, and the application of GIS in conflict resolution and peacebuilding, an extensive literature review was conducted. To detect past arm conflict clusters, hotspots, and areas at risk to future outbreaks of armed violent conflicts, GIS spatial analytical techniques were employed, including geocoding, autocorrelation analysis (Moran's I), Hotspot (Getis-Ord Gi*) analysis, and predictive modelling. While geocoding, cluster, and hot spot analyses were performed in ArcMap GIS software to assess the spatial distribution and patterns of armed violent conflicts in the GLR from 1998 – 2017, Microsoft Excel was used to develop a predictive Conflict Risk Model (CRM) for the probability of armed conflicts occurring from 2018 -2038. Thereafter, a conflict risk equation was developed from the CRM to predict areas at risk of future armed conflict outbreak. In response to the absence of a combined spatial data hub in the GLR, a new regional file geodatabase was created in ArcMap, ArcCatalog 10.4 using data from various referenced, survey and institutional sources. As part of a comprehensive plan to bring sustainable peace in the GLR, this study has identified the Hima –Tutsi empire ideology and the presence of mineral resources in the region as significant factors explaining the origin and evolution of persistent armed violence in the GLR. The study also highlights the application of GIS to identify and assess the spatial distribution, clusters, hot and risk spots of armed conflicts in the GLR and as a decision support tool for armed conflict resolution. From 1998-2017, armed violent conflicts were prevalent in the whole country of Burundi, eastern DRC and northern Uganda. During the same period, there was a significant clustering of armed violent conflict in the GLR at 99% confidence (p < 0.01), however eastern DRC emerged as the area with the highest armed conflicts hot spots at 99% confidence. In general, the predictive CRM analysis revealed a 66% probability of armed conflict occurring in the GLR between 2018 and 2038, with DRC predicted to be the most at risk (81%) and Tanzania the least at risk (50%). Together with the newly created regional file geodatabase, these results provide a framework for armed conflict resolution and roadmap for the possibility of sustainable peacebuilding in the GLR. Areas of future research in the GLR include the development of a geodatabase at country level, the socio-economic and environmental impact of armed conflicts in the GLR, and the development of a robust conflict risk model in the GLR and Africa as a continent. Such a robust conflict risk model including local, regional, and international stakeholders, should assist in proactively, rather than reactively identifying and managing armed violent conflicts in region.
Waste sector small and medium-sized enterprises and their role in the extended producer responsibility; a case study of environmental responsibility in SMEs in eThekwini, KwaZulu-Natal (South Africa).
(2014) Haines, Carla Jane.; Hill, Trevor Raymond.; Whyte, Christopher.
Both the waste sector and corporate enterprises, under the banner of corporate social and environmental responsibility (CSER), have a role to play in sustainable development, particularly in the South African context where legislation supports the waste hierarchy in its approach to waste management, and the promotion of employment and small and mediumsized enterprises (SMEs). SMEs, due to their vast number and the significance of their aggregate contribution to the global economy, have been identified as key contributors to sustainable development. Global supply chains rely heavily on SME suppliers and service providers, yet the combined environmental impact of SMEs remains un-quantified and their engagement in CSER is underexplored. This research explores the role that SMEs play in extended producer and environmental responsibility from a waste management perspective in the eThekwini Municipal area, describes the barriers that SMEs face when implementing environmental measures and provides a critical assessment of environmental responsibility in waste management supply chains. Case studies, where interviews and documentations were used as data collection methods, on waste management supply chains are provided. It is evident that there is a culture of outsourcing of the waste management function in the eThekwini municipal area and SMEs are an important component of the waste management value chain. However, environmental responsibility amongst the SMEs is poor as the SMEs response to supply chain or legislative pressure is weak. The bureaucracy of legal requirements of the waste sector, an ill-informed public and business sector regarding environmental issues, and the highly competitive nature of the waste sector are common obstacles experienced. In the face of difficulties such as limited resources, some SMEs are responding to legislative pressure and adopting the ISO 14001 certification. Many SMEs are responding to supply chain pressure in terms of the Broad Based Black Economic Empowerment Act and participating in social responsibility activities. Findings from this research support the government’s vision of the creation of employment, the promotion of small business within the waste sector and the role that SMEs play in sustainable development in South Africa however; there is a need for strategies to address the environmental problems of small business.
Enhancing integrated coastal management decision making in KwaZulu-Natal, South Africa through knowledge transfer and information sharing.
(2019) Goble, Bronwyn Jane.; Hill, Trevor Raymond.; Phillips, M. R.
Coastal environments are complex systems being sought-after for a myriad of environmental, socioeconomic and cultural activities, supporting an estimated 44% of the world’s population. The demand for coastal space and resources has created extreme pressure in coastal areas, leading to reduced coastal functionality and amplified risk of natural hazards. These stresses and changes require proactive management, in particular through policies and legislation that ensure protection and longterm sustainability, thus the emergence of Integrated Coastal Management (ICM) as a ‘holistic’ approach. South Africa, being a country of high marine and coastal biodiversity, recognised the need for better coastal management in the 1970s; however, it was only in 2009 that an Integrated Coastal Management Act (ICM Act) was promulgated. The Act attempts to tackle the interlinked problems of coastal development and conservation; however to date implementation has been frustratingly slow, with capacity constraints and knowledge gaps being the primary limitations. If ICM is to be effective, coastal managers require a broad range of scientific and social information, modelled data and environmental indicators, meaning that the scope and complexity of coastal management is strongly dependent upon capacity. However, in South Africa, these functions do not rest with such experts, but are assigned to various government departments at the local municipality level. Thus ICM initiatives, that integrate natural and social sciences and empower managers with best available knowledge, are required. This research focused on the KwaZulu-Natal (KZN) Province, one of four coastal provinces in South Africa grappling with ICM implementation. Consequently, the KZN provincial government committed financial resources to improving knowledge transfer, information sharing and capacity building. KZN-specific barriers to ICM implementation were identified through a series of interviews and surveys, from which requirements for an information support tool were determined. The tool, devised from a coastal management perspective, enables continued knowledge acquisition and retention, thereby acting as an ‘institutional knowledge bank’. Development followed a participatory approach that ensured the needs of target users were met, however while such tools can improve understanding and lead to improved decision-making, their effectiveness depends on continued use by managers. Additionally, this research shows the value-add of such a tool in conjunction with traditional capacity building sessions and how these complementary approaches assisted ICM implementation. Lessons learned from KZN can be up-scaled to inform Government on the value of the information support tool by incorporating national data and information sharing for ICM capacity building.
Climatic, environmental and socio-economic factors for malaria transmission modelling in KwaZulu-Natal, South Africa.
(2018) Ebhuoma, Osadolor Obiahon.; Gebreslasie, Michael Teweldemedhin.
Sub-Saharan Africa (SSA) largely bears the burden of the global malaria disease, with the transmission and intensity influenced by the interaction of a variety of climatic, environmental, socio-economic, and human factors. Other factors include parasitic and vectoral factors. In South Africa (SA) in general and KwaZulu-Natal (KZN) in particular, the change of the malaria control intervention policy in 2000, may be responsible for the significant progress over the past two decades in reducing malaria case report to near zero. Currently, malaria incidence in KZN is less than 1 case per 1000 persons at risk placing the province in the malaria elimination stage. To meeting the elimination target, it is necessary to study the dynamics of malaria transmission in KZN employing various analytical/statistical models. Thus, the aim of this study was to explore the factors that influence malaria transmission by employing different analytical models and approaches in a setting with low malaria endemicity and transmission. This involves a sound appraisal of the existing literature on the contribution of remote sensing technology in understanding malaria transmission, evaluation of existing malaria control intervention; delineation of empirical map of malaria risk; provide information on the climatic, environmental and socio-economic factors that influences malaria risk and transmission; and formulation of a relevant malaria forecast and surveillance models. The investigator started with a systemic review of studies in chapter two. The studies were aimed at identifying significant remotely-sensed climatic and environmental determinants of malaria transmission for modelling malaria transmission and risk in SSA via a variety of statistical approaches. Normalised difference vegetation index (NDVI) was identified as the most significant remotely-sensed climatic/environmental determinants of malaria transmission in SSA. Majority of the studies employed the generalised linear modelling approach compared to the Bayesian modelling approach. In the third chapter, malaria cases from the endemic areas of KZN with remotely-sensed climatic and environmental data were used to model the climatic and environmental determinants of malaria transmission and develop a malaria risk map in KZN. The spatiotemporal zero inflated Poisson model formulated indicates that at 95% Bayesian credible interval (BCI) NDVI (0.91; 95% BCI = 0.71, -1.12), precipitation (0.11; 95% BCI = 0.08, 0.14), elevation (0.05; 95% BCI = 0.032, 0.07) and night temperature (0.04; 95% BCI = 0.03, 0.04) are significantly related to malaria transmission in KZN, SA. The area with the highest risk of malaria morbidity in KZN was identified as the north-eastern part of the province. The fourth chapter was to establish the socio-economic status (SES) that influence malaria transmission in the endemic areas of KZN, by employing a Bayesian inference approach. The obtained posterior samples revealed that, significant association existed between malaria disease and low SES such as illiteracy, unemployment, no toilet facilities and no electricity at 95% BCI Lack of toilet facilities (odds ration (OR) =12.54; 95% BCI = 0.63, 24.38) exhibited the strongest association with malaria and highest risk of malaria disease. This was followed by no education (OR =11.83; 95% BCI = 0.54, 24.27) and lack of electricity supply (OR =10.56; 95% BCI = 0.43, 23.92) respectively. In the fifth chapter, the seasonal autoregressive integrated moving average (SARIMA) intervention time series analysis (ITSA) was employed to model the effect of the malaria control intervention, dichlorodiphenyltrichloroethane (DDT) on confirmed monthly malaria cases. The result is an abrupt and permanent decline of monthly malaria cases (w0= −1174.781, p-value = 0.003) following the implementation of the intervention policy. Finally, the sixth chapter employed a SARIMA modelling approach to predict malaria cases in the endemic areas of KZN. Three plausible models were identified, and based on the goodness of fit statistics and parameter estimation, the SARIMA (0,1,1) (0,1,1)12 model was identified as the best fit model. The SARIMA (0,1,1)(0,1,1)12 model was used to forecast malaria cases during 2014, and it was observed to fit closely with the reported malaria cases during January to December 2014. The models generated in this study demonstrated the need for the KZN malaria program, relevant policy makers and stakeholders to further strengthen the KZN malaria elimination efforts. The required malaria elimination fortification are not limited to the implementation of additional sustainable developmental approach that combines both improved malaria intervention resources and socio-economic conditions, strengthening of existing community health workers, and strengthening of the already existing cross-border collaborations. However, more studies in the area of statistical modelling as well as practical applications of the generated models are encouraged. These can be accomplished by exploring new avenues via cross-sectional survey to understand the impact of community and social related structures in malaria burden; strengthening of existing community health workers; knowledge, attitude and practices in malaria control and intervention; and the likely effects of temporal/seasonal and spatial variations of malaria incidence in neighbouring endemic countries should be explored.
Exploring the relationship between spectral reflectance and tree species diversity in the savannah woodlands.
(2018) Madonsela, Sabelo.; Mutanga, Onisimo.; Ramoelo, Abel.; Cho, Moses Azong.
Abstract available in PDF file.
Discrimination and biomass estimation of co-existing C3 and C4 grass functional types over time : a view from space.
(2018) Shoko, Cletah.; Mutanga, Onisimo.
The co-existence of C3 and C4 grass species significantly influence their spatio-temporal variations of biochemical cycling, productivity (i.e. biomass) and role in provision of ecosystem goods and services. Consequently, the discrimination of the two species is critical in understanding their spatial distribution and productivity. Such discrimination is particularly valuable for accounting for their socio-economic and environmental contributions, as well as decisions related to climate change mitigation. Due to the growing popularity of remotely sensed approaches, this study sought to discriminate the two grass species and determine their AGB using new generation sensors. Specifically, the potential of Landsat 8, Sentinel 2 and Worldview 2, with improved sensing characteristics were tested in achieving the above objectives. Generally, the results demonstrate the suitability of the adopted sensors in the discrimination and determination of C3 and C4 AGB using Discriminant Analysis and Sparse Partial Least Squares Regression models. Using multi-date Sentinel 2 data, the study established that winter period (May) was the most suitable for discriminating the two grass species. On the other hand, the winter fall (August) was found to be the least optimal period for the two grass species discrimination. The study also established that the amount of AGB for C3 and C4 were higher in winter and summer, respectively; a variability attributed to elevation and rainfall. The study concludes that Sentinel 2 dataset, although had weaker performance than Worldview 2; it offers a valuable opportunity in understanding the C3 and C4 spatial distribution within a landscape; hence useful in understanding both temporal and multi-temporal distribution of the two grass species. Successful seasonal characterization of C3 and C4 AGB allows for inferences on their contribution to forage availability and fire regimes; therefore, this contributes to the development of well-informed conservation strategies, which can lead to sustainable utilization of rangelands, especially in relation to the changing climate.
The ecology of ungulates in the Waterberg Plateau National Park, Namibia.
(2019) Kasiringua, Evert A.; Proches, Serban Mihai.; Kopij, Gregory.
Although ungulate species form an important component of Namibia’s economy through tourism, their population sizes vary substantially in relation to irregular rainfall, poaching, predation and competition, amongst other reasons. Understanding the ecology of ungulates is the key to adaptive ecosystem management and wildlife conservation in semi-arid savanna ecosystems. The study was conducted at Waterberg National Park, to determine habitat preferences, seasonal variation in population structure, daily drinking activities of twelve ungulate species and population dynamics of ungulates over a period of 33 years (1980-2013). The data used included road counts in all four vegetation types in the park (Terminalia sericea- Melhania acuminata vegetation, Terminalia sericea-Thesium megalocarpum, Terminalia sericea-Blepharis integrifolia, and the rock-inhabiting Peltophorum africanum community), waterhole counts, and pre-existing aerial counts. The probability of occurrence of large and medium ungulates was influenced by distance from the waterholes and from the roads. The population structure of seven herbivores varied in intricate ways between species and seasons. Smaller herds of ungulates were recorded most during the dry season as compared to larger herds observed during the wet season. Overall, the most frequent drinking times were between 15:00-22:00 with 18:00-19:00 being the conspicuous peak of the drinking activity, with 15% of animals in attendance. Four groups of ungulates were identified as per their drinking activity patterns: 1) day drinkers (warthog, giraffe, roan, and sable), 2) day/night drinkers (dik-dik, steenbok and common duiker), 3) evening/night drinkers (white rhino, black rhino and buffalo) and 4) night/morning drinkers (eland, gemsbok and kudu). The buffalo and eland population densities comprised together more than half of all ungulates recorded. Roan and sable antelope, kudu and warthog were also fairly common (with 5-12% of all ungulates recorded). White rhino, black rhino, giraffe, and gemsbok were classified as uncommon (together 11.9%), whilst the remaining seven species were rare (together 1.9%). Population size in eland showed a weak positive relationship with the annual average rainfall between the years 1981 - 2013, whereas population sizes in kudu, sable, gemsbok and roan showed a weak negative relationship with the amount of rain. No relationship was detected in giraffe, buffalo and hartebeest populations. The efficient management of wildlife resources that are economically and socially important necessitates regular surveys to monitor population trends in order to develop applicable management options. Thus, monitoring methods which are practical and efficient and provide accurate data are required for sound wildlife management. The results generated from this study provide novel contributions to strengthening management and conservation efforts of ungulates in Waterberg National Park and other wildlife parks in Namibia. More studies in the area of diet analysis of grazers and browsers as well as their preferences for particular plant species, with emphasis on inter- and intra- species competition is recommended.

Browse

Recent Submissions