Browsing by Author "Rothman, Mark"

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    Mapping and assessing ecosystem threat status of South African kelp forests
    (2020) Dunga, Victor Loyiso; Bolton, John; Sink, Kerry; Blamey, Laura; Rothman, Mark; Lück-Vogel, Melanie
    At a global scale, kelp forests play a significant ecological, social and economic role through their provision of ecosystem services. South African kelp forests are no exception and recent studies have established their value. To maintain these benefits, informed management is needed. An understanding of kelp forest distribution, ecosystem functioning, pressures, and ecosystem state are key requirements for effective management. The South African National Biodiversity Assessment (NBA) synthesises research to report on the status of ecosystems to guide policy, planning and decision making. Kelp forests were excluded from two previous national assessments, as they were not represented on the National Map of Marine Ecosystem types. This thesis aimed to address this omission by producing a map of kelp forest ecosystem types and conducting the first assessment of their threat status. This study sought to develop a modern method for mapping South African kelp forests to update previous maps developed in the mid-2000s. The novel approach extracts the Vegetation Index from kelp forests using advanced multi-resolution Sentinel-2 (A and B) satellite imagery. Using Geographic Information Systems, spectral bands 4 (RED) and 8 (NIR) (10 m resolution) were utilized to calculate Normalised Difference Vegetation Index (NDVI). An expert-guided trial and error approach was adopted to set the NDVI threshold (-0.2) at a level suitable for detecting both subtidal and surface buoyant kelp to the limits of the Sentinel-2 platform. The results showed that the high resolution and deeper water column penetration of this platform enabled the filling of previous gaps and detect both subtidal and surface protruding kelps at low cost. Additionally, the map includes for the first time, the kelp recently reported to have shifted eastwards along the south coast. A total of 1300 km of kelp forest was mapped and three biogeographical subtypes distinguished. Combining the NDVI threshold method and Supervised Classification yielded satisfactory results and an accuracy of 76%. Sentinel-2 imagery was validated using observational classification from Google Earth, field surveys expert knowledge and previous maps. However, the Sentinel's depth penetration was affected by environmental heterogeneity along this coast. Results confirmed the complexities of retrieving spectral indices from environments with varying turbidity, depths, wave climates and the challenges associated with ground-truthing the expansive marine environment. This study advises comprehensive ground-truthing for the three kelp forest ecosystem types as a fundamental step towards long-term monitoring of South African kelp forests. The method developed advanced application of the NDVI in submerged aquatic vegetation mapping and could be modified to support mapping of other ecosystem types such as seagrasses, other seaweed habitats and inland aquatic vegetation. South Africa's new kelp forest map was then used to facilitate the first ecosystem threat status assessment for South African kelp forests using three criteria from the emerging International Union for Conservation of Nature Red List of Ecosystems (IUCN RLE). To assess threat status, ecosystem extent and condition were considered. Three kelp forest ecosystem types were assessed; namely Namaqua, Cape and Agulhas Kelp Forests and an additional combined single South African Kelp Forest to explore the effect of scale in assessing threat status. Literature was reviewed to develop a conceptual model to support the assessment and define ecosystem collapse. Thirteen relevant pressures were mapped to determine ecosystem degradation across the extent of each kelp forest type using a cumulative pressure mapping approach. Four categories of ecosystem condition were recognized in alignment with the IUCN thresholds for ecosystem degradation. The results of the ecosystem threat status assessment show sensitivity to the different assessment criteria, the scale of ecosystem delineation and assessment approaches. There is no reported reduction in the distribution for any of the South African kelp forest ecosystem types, therefore, the decline in extent under criterion (A) was assessed as Least Concern for all types. For criterion (B) which is related to geographic extent and threat, results were most sensitive to ecosystem delineation with results ranging between Least Concern and Critically Endangered under different sub-criteria and for different ecosystem types. Also, for the criterion (C) which is related to the extent of abiotic degradation, the results ranged from Vulnerable to Endangered under different sub-criteria and for different ecosystem types. Further work is needed to validate kelp forest ecosystem types; consider the implications of multiple scales of classification, mapping and assessment; improve pressure data, groundtruth ecosystem condition, and assess the disruption of biotic processes. In line with the protocols of the IUCN RLE, South African kelp forest ecosystem types appear threatened with plausible results ranging between Vulnerable and Endangered. The accuracy of these assessments can be strengthened by more research to refine conceptual models, calibrate assessments of degradation and better define thresholds of collapsed ecosystems.
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    Unlocking the potential of remote sensing for kelp biomass estimation in South African Kelp Concession Areas
    (2024) Searle, Lauren Jane; Bolton, John; Rothman, Mark; Bray, Kate
    The use of high-resolution imagery (HRI) has the potential to improve the accuracy of kelp biomass estimates, ensuring the implementation of sustainable harvesting. However, definitive research on the potential of HRI in this application is lacking in the current literature. An accurate estimation of kelp biomass is crucial to calculate maximum sustainable yield (MSY) in South African kelp Concessions. This study seeks to fill the knowledge gap by exploring the effectiveness of HRI for estimating the biomass of kelp along a specific stretch of coastline. The study aim is achieved by analysing HRI of Concession Area 6 taken from an aircraft. Maps quantifying kelp extent are derived from image classification methods applied to the HRI. A total biomass figure is then determined using the product of the calculated kelp extent and an average biomass figure of 14.5 kg/m-2 , taken from the literature. A total biomass of 40527.9 tonnes wet weight was calculated for Concession Area 6. The classification of HRI provided an overall accuracy of 95%, which is relatively high when compared to Sentinel-2 satellite imagery which resulted in an overall accuracy of 75%. When compared to the kelp extent measured in previous studies, HRI-derived maps had consistently less kelp coverage than maps from other imagery, suggesting that other imagery overestimates kelp extent (likely due to resolution). However, this was confounded given different imagery used at different times and so it was not possible to rule out change in kelp coverage over time. The results demonstrate the value of HRI in the mapping of kelp extent, which can ultimately be used to produce more accurate MSY assessments and support sustainable harvesting practices. However, before HRI can be integrated into MSY assessments, it is imperative to calculate more accurate biomass figures that are specific to the Concession Area, rather than relying on region wide estimates. Additionally, it's important to acknowledge that while HRI excels in precision, other imagery may be more suitable for large-scale estimates where accuracy is not a primary concern and due to its cost-effectiveness.