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Modelling Coastal Vulnerability: An integrated approach to coastal management using Earth Observation techniques in Belize / HARRY COOK

Swansea University Author: HARRY COOK

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Abstract

This thesis presents an adapted method to derive coastal vulnerability through the application of Earth Observation (EO) data in the quantification of forcing variables. A modelled assessment for vulnerability has been produced using the Coastal Vulnerability Index (CVI) approach developed by Gornit...

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Published: Swansea, Wales, UK 2023
Institution: Swansea University
Degree level: Master of Research
Degree name: MSc by Research
Supervisor: North, Peter. and Los, Sietse
URI: https://cronfa.swan.ac.uk/Record/cronfa63553
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first_indexed 2023-05-31T10:50:36Z
last_indexed 2023-05-31T10:50:36Z
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spelling v2 63553 2023-05-31 Modelling Coastal Vulnerability: An integrated approach to coastal management using Earth Observation techniques in Belize f07c1b067453e1b621b5906fd4ec07dd HARRY COOK HARRY COOK true false 2023-05-31 This thesis presents an adapted method to derive coastal vulnerability through the application of Earth Observation (EO) data in the quantification of forcing variables. A modelled assessment for vulnerability has been produced using the Coastal Vulnerability Index (CVI) approach developed by Gornitz (1991) and enhanced using Machine learning (ML) clustering. ML has been employed to divide the coastline based on the geotechnical conditions observed to establish relative vulnerability. This has been demonstrated to alleviate bias and enhanced the scalability of the approach – especially in areas with poor data coverage – a known hinderance to the CVI approach (Koroglu et al., 2019).Belize provides a demonstrator for this novel methodology due to limited existing data coverage and the recent removal of the Mesoamerican Reef from the International Union for Conservation of Nature (IUCN) List of World Heritage In Danger. A strong characterization of the coastal zone and associated pressures is paramount to support effective management and enhance resilience to ensure this status is retained.Areas of consistent vulnerability have been identified using the KMeans classifier; predominantly Caye Caulker and San Pedro. The ability to automatically scale to conditions in Belize has demonstrated disparities to vulnerability along the coastline and has provided more realistic estimates than the traditional CVI groups. Resulting vulnerability assessments have indicated that 19% of the coastline at the highest risk with a seaward distribution to high risk observed. Using data derived using Sentinel-2, this study has also increased the accuracy of existing habitat maps and enhanced survey coverage of uncharted areas.Results from this investigation have been situated within the ability to enhance community resilience through supporting regional policies. Further research should be completed to test the robust nature of this model through an application in regions with different geographic conditions and with higher resolution input datasets. E-Thesis Swansea, Wales, UK Remote sensing, earth observation, coastal vulnerability, satellite derived bathymetry, machine learning, KMeans, Belize 5 5 2023 2023-05-05 A selection of third party content is redacted or is partially redacted from this thesis due to copyright restrictions. COLLEGE NANME COLLEGE CODE Swansea University North, Peter. and Los, Sietse Master of Research MSc by Research 2023-10-27T15:54:24.8773582 2023-05-31T11:46:49.0010632 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Geography HARRY COOK 1 63553__27654__9cd18e5b9f224b21a3f2ef672508f333.pdf 2023_Cook_H.final.63553.pdf 2023-05-31T12:05:05.9606290 Output 8849592 application/pdf Redacted version - open access true Copyright: The Author, Harry Cook, 2023. true eng
title Modelling Coastal Vulnerability: An integrated approach to coastal management using Earth Observation techniques in Belize
spellingShingle Modelling Coastal Vulnerability: An integrated approach to coastal management using Earth Observation techniques in Belize
HARRY COOK
title_short Modelling Coastal Vulnerability: An integrated approach to coastal management using Earth Observation techniques in Belize
title_full Modelling Coastal Vulnerability: An integrated approach to coastal management using Earth Observation techniques in Belize
title_fullStr Modelling Coastal Vulnerability: An integrated approach to coastal management using Earth Observation techniques in Belize
title_full_unstemmed Modelling Coastal Vulnerability: An integrated approach to coastal management using Earth Observation techniques in Belize
title_sort Modelling Coastal Vulnerability: An integrated approach to coastal management using Earth Observation techniques in Belize
author_id_str_mv f07c1b067453e1b621b5906fd4ec07dd
author_id_fullname_str_mv f07c1b067453e1b621b5906fd4ec07dd_***_HARRY COOK
author HARRY COOK
author2 HARRY COOK
format E-Thesis
publishDate 2023
institution Swansea University
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Geography{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Geography
document_store_str 1
active_str 0
description This thesis presents an adapted method to derive coastal vulnerability through the application of Earth Observation (EO) data in the quantification of forcing variables. A modelled assessment for vulnerability has been produced using the Coastal Vulnerability Index (CVI) approach developed by Gornitz (1991) and enhanced using Machine learning (ML) clustering. ML has been employed to divide the coastline based on the geotechnical conditions observed to establish relative vulnerability. This has been demonstrated to alleviate bias and enhanced the scalability of the approach – especially in areas with poor data coverage – a known hinderance to the CVI approach (Koroglu et al., 2019).Belize provides a demonstrator for this novel methodology due to limited existing data coverage and the recent removal of the Mesoamerican Reef from the International Union for Conservation of Nature (IUCN) List of World Heritage In Danger. A strong characterization of the coastal zone and associated pressures is paramount to support effective management and enhance resilience to ensure this status is retained.Areas of consistent vulnerability have been identified using the KMeans classifier; predominantly Caye Caulker and San Pedro. The ability to automatically scale to conditions in Belize has demonstrated disparities to vulnerability along the coastline and has provided more realistic estimates than the traditional CVI groups. Resulting vulnerability assessments have indicated that 19% of the coastline at the highest risk with a seaward distribution to high risk observed. Using data derived using Sentinel-2, this study has also increased the accuracy of existing habitat maps and enhanced survey coverage of uncharted areas.Results from this investigation have been situated within the ability to enhance community resilience through supporting regional policies. Further research should be completed to test the robust nature of this model through an application in regions with different geographic conditions and with higher resolution input datasets.
published_date 2023-05-05T15:54:23Z
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score 11.01353

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