Unlocking the potential of remote sensing for kelp biomass estimation in South African Kelp Concession Areas

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dc.contributor.advisorBolton, John
dc.contributor.advisorRothman, Mark
dc.contributor.advisorBray, Kate
dc.contributor.authorSearle, Lauren Jane
dc.date.accessioned2025-04-01T07:53:26Z
dc.date.available2025-04-01T07:53:26Z
dc.date.issued2024
dc.date.updated2025-04-01T07:17:13Z
dc.description.abstractThe 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.
dc.identifier.apacitationSearle, L. J. (2024). <i>Unlocking the potential of remote sensing for kelp biomass estimation in South African Kelp Concession Areas</i>. (). University of Cape Town ,Faculty of Science ,Department of Biological Sciences. Retrieved from http://hdl.handle.net/11427/41310en_ZA
dc.identifier.chicagocitationSearle, Lauren Jane. <i>"Unlocking the potential of remote sensing for kelp biomass estimation in South African Kelp Concession Areas."</i> ., University of Cape Town ,Faculty of Science ,Department of Biological Sciences, 2024. http://hdl.handle.net/11427/41310en_ZA
dc.identifier.citationSearle, L.J. 2024. Unlocking the potential of remote sensing for kelp biomass estimation in South African Kelp Concession Areas. . University of Cape Town ,Faculty of Science ,Department of Biological Sciences. http://hdl.handle.net/11427/41310en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Searle, Lauren Jane AB - 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. DA - 2024 DB - OpenUCT DP - University of Cape Town KW - High-resolution imagery KW - Ecklonia maxima KW - GIS KW - kelp mapping LK - https://open.uct.ac.za PB - University of Cape Town PY - 2024 T1 - Unlocking the potential of remote sensing for kelp biomass estimation in South African Kelp Concession Areas TI - Unlocking the potential of remote sensing for kelp biomass estimation in South African Kelp Concession Areas UR - http://hdl.handle.net/11427/41310 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/41310
dc.identifier.vancouvercitationSearle LJ. Unlocking the potential of remote sensing for kelp biomass estimation in South African Kelp Concession Areas. []. University of Cape Town ,Faculty of Science ,Department of Biological Sciences, 2024 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/41310en_ZA
dc.language.rfc3066eng
dc.publisher.departmentDepartment of Biological Sciences
dc.publisher.facultyFaculty of Science
dc.publisher.institutionUniversity of Cape Town
dc.subjectHigh-resolution imagery
dc.subjectEcklonia maxima
dc.subjectGIS
dc.subjectkelp mapping
dc.titleUnlocking the potential of remote sensing for kelp biomass estimation in South African Kelp Concession Areas
dc.typeThesis / Dissertation
dc.type.qualificationlevelMasters
dc.type.qualificationlevelMSc
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