Department of Geography and Environmental Studies

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Browsing Department of Geography and Environmental Studies by browse.metadata.advisor "De Klerk, H. M."

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    Wetland ecotones: testing remote sensing techniques to map ecotones in a Fynbos embedded wetland
    (Stellenbosch : Stellenbosch University, 2021-12) Seymour, Danielle Afton; De Klerk, H. M.; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies.
    ENGLISH ABSTRACT: Various researchers starting as early as 1903, have developed many definitions of an ecotone (Clements 1905; Livingston 1903; Odum & Barrett 1971). The definition by Holland (1988) described ecotones as zones of transition between adjacent ecological systems, having a set of unique characteristics defined by space and time scales, and by the strength of interactions between adjacent ecological systems (Holland 1988). This definition paves the way for research that may exemplify various aspects of landscape ecology and spatial heterogeneity. Although a niche of high scientific interest, ecotonal research is very understudied, especially research on using Remote Sensing to identify and map fine-scale wetland ecotones. A bibliometric analysis and literature review showed that limited research has been conducted on wetland ecotones in southern Africa, however with sufficient literature covered on wetland delineation, classification, and mapping. Wetlands which are highly dynamic and considered moving entities in a landscape due to their varying hydroperiods, are especially challenging to map. Two main experiments were carried out both of which used Machine Learning (ML) algorithms namely Random Forest (RF) and the naïve Bayes classifier. The aim of the first experiment was to review and test remote sensing techniques to accurately identify and map distinct vegetation communities within the Du Toits River wetland, Western Cape South Africa. The second experiment was then to use probabilistic classification measures to map and characterize the ecotones prevailing in a fynbos embedded wetland ecosystem. The study used freely available satellite imagery namely Landsat 8 Surface Reflectance Tier 1, and Sentinel-2 MSI: MultiSpectral Instrument, Level-2A, obtained from the United States Geological Survey (USGS) through open-source resources such as Google Earth Engine (GEE). This research suggests that Random Forest (RF) classifier showed great potential in accurately mapping landcover, specifically four distinct and dominant vegetation types within the wetland namely Prionium serratum, Psoralea pinnata (referred to as palmiet wetland vegetation), a condensed group of Pteridium aquilinum, Restio paniculatus and Merxmuellera cincta (referred to as Sclerophyllous Wetland Vegetation), and Temporary Wetland Fynbos. RF results showed little spectral confusion between classes and produced moderate to high overall accuracies for classifications run through both the winter and summer seasons. The efficacy of using the fuzzy logic i.e. supervised probabilistic measures to identify and map ecotones in a spatially heterogenous landscape was showcased. Probabilistic mapping and fuzzy graphs showed complex and diverse ecotones within the wetland. It was evident that clear ecotones in the form of rapid and sharp high probabilities of one vegetation type intersected and replaced another. These ecotones may provide useful information about wetland ecosystem functioning and how vegetation zones may contribute to wetland ecosystem services (e.g. flood attenuation and carbon storage). Using a per-pixel based approach to map ecotones is highly useful as ecotones are more complex in reality and mapping them as single vector lines is not useful nor accurate. Although this study aimed to identify and map fine-scale wetland ecotones, further research using even finer scale data and in-depth field analysis that specifically focuses on the identified and mapped ecotonal areas will be significant.