Modelling the potential distribution of bramble (Rubus Cuneifolius) in the KwaZulu-Natal, Drakensberg, South Africa.

Master of Science in Environmental Science. University of KwaZulu-Natal, Pietermaritzburg, 2018.Invasive Alien Plant (IAP) invasions have been attracting increasing attention as a result of their substantial effects on native ecosystems. Hence, tools for explaining and predicting IAP distributions have been increasingly promoted for proactive ecological management, and Spatial Distribution Models (SDMs) are one such tool. The main aim of this study was to explore the application of SDMs in modelling the potential distribution of invasive American bramble (Rubus cuneifolius) in the Ukhahlamba Drakensberg Park, South Africa. The rapid proliferation of this alien plant has had significant adverse impacts on native plants and the stability of grassland ecosystems. However, there is lack of adequate data on its distribution and factors potentially influencing its present-day habitat range expansions. In that regard, the first objective provides a review of the application of SDMs in modelling the distribution of IAPs and associated challenges and opportunities. As a result of the limitations in traditional methods such as ground surveys, SDMs have demonstrated potential in providing relatively quick and feasible means of predicting IAP distributions, ecological niches and suitability of areas not yet invaded. Literature has shown growth in the use of SDMs for predicting biological invasions with presence-only methods gaining popularity than traditional analyses requiring both presence and absence data. Comparative analyses of model performance found contemporary methods such as Maximum Entropy (Maxent) to have better statistical performance compared to well established modelling approaches. Recent studies also demonstrated that remotely sensed data offers opportunities to explore underlying ecological relationships of species beyond climatic factors and improve the performance of SDMs. The second objective was to model the potential distribution of American bramble using topographic, bioclimatic and remotely sensed data using the Maxent modelling approach. Specifically, this study tested whether variable selection affected model accuracy and the spatial distribution of the species. Model performance was evaluated using the Area Under the curve (AUC), True Skill Statistic (TSS) and Kappa statistic. A quantitative comparison of all models showed that the model built with a composite of all variables yielded the highest AUC score of 0.957. The inclusion of spectral reflectance values improved model accuracy from 0.896 to 0.949. Elevation and rainfall of driest quarter were the most influential variables in modelling bramble distribution. Results of this study showed that bramble are species characteristic of warmer areas with sufficient rainfall and low elevation ranges. In addition, this study demonstrated that the Maxent approach based on topographic, bioclimatic and spectral reflectance values effectively predicted areas susceptible to bramble invasion. Overall, identification of these areas would assist to guide appropriate management measures and control further incursions

An evaluation of hyperspectral and multispectral data for mapping invasive species in an African Savanna.

Master of Science in Geography and Environmental sciences.Invasive alien plant (IAP) species affects a range of ecosystem types in various regions of the world. Therefore are now considered one of the main phenomena causing global change. Invasive alien plants (IAP’s) cause considerable impacts on ecosystem processes and functions, biodiversity, agriculture and human well-being. Parthenium hysterophorus is an IAP which is widely spread across the globe. It is difficult to control and eradicate, and has detrimental impacts on the natural environment and human health. However, there is no record of accurate and up-to-date information on the distributions and extent of P. hysterophorus. This study evaluated the capability of hyperspectral and multispectral data for mapping P. hysterophorus in northern KwaZulu-Natal province, South Africa. First, the study sought to determine an optimal subset of bands from canopy hyperspectral data for discrimination of P. hysterophorus from its co-existing species. A novel hierarchical approach that integrates statistical filters and a wrapper technique has been proposed to select optimal bands to solve the problem of high spectral dimensionality and improve classification accuracy. A non-parametric algorithm, Support Vector Machines (SVM) showed inferior classification accuracy, i.e. 76.19% and 78.57% when using 20 best spectral bands from SVM – Recursive Feature Elimination (SVM-RFE) and entire dataset (n = 1633), respectively. On the other hand, superior overall accuracy of 83.33% was achieved when using ten spectral bands identified by the hierarchical approach. Next, SVM classifier was adopted to evaluate the capability of multispectral data (i.e. Operational Land Imager, OLI and SPOT 6) for determining the distribution and patch sizes of P. hysterophorus. The results showed that SPOT 6 had a higher overall accuracy of 83.33% than OLI, i.e.76.39%. While SPOT 6’s the higher spatial resolution was useful for better characterisation of the distribution and patch sizes, the study found that the spectral configuration of OLI was more important in identifying possible locations infested by P. hysterophorus. Overall, the study demonstrated that fewer spectral bands selected by the proposed hierarchical approach have the greatest potential for reliably discriminating IAP species using airborne and satellite hyperspectral sensors. The study also demonstrated that the current information needs on IAP’s can be addressed using accessible multispectral data, valuable for effective land management, site specific weed management, and site prioritisation