Identification of Suitable Areas for Offshore Macroalgae Cultivation
Research in sustainable macroalgae aquaculture has been gaining hype (e.g. Seafarm) to provide advancements in new significant sources of food, feed, materials and bio-energy. Despite the fact that in Asia the offshore production of macroalgae is established for many decades, in Europe is still in i...
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KTH, Hållbar utveckling, miljövetenskap och teknik
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ndltd-UPSALLA1-oai-DiVA.org-kth-2439112019-02-12T06:16:59ZIdentification of Suitable Areas for Offshore Macroalgae CultivationengNunes da Silva Ramos, Filipe JoséKTH, Hållbar utveckling, miljövetenskap och teknik2016macroalgaeGISMCAGIS-MCDABooleanWLCsustainabilitysuitabilityEnvironmental SciencesMiljövetenskapResearch in sustainable macroalgae aquaculture has been gaining hype (e.g. Seafarm) to provide advancements in new significant sources of food, feed, materials and bio-energy. Despite the fact that in Asia the offshore production of macroalgae is established for many decades, in Europe is still in its infancy. The issue on where to find suitable cultivation areas without conflicting with current uses to respect the environment and the socio-economic activities is a great challenge. This Master of Science thesis aimed to find suitable offshore areas in order to facilitate implementations of macroalgae cultivations in the Swedish West Coast. Thirteen criteria in environmental, economic and social sustainability aspects were identified and employed (e.g. Depth, Distance to Ports, and Natural and Preserved areas (NPAs)), and the tools Geographic Information Systems (GIS) and Multi-criteria Analysis (MCA) were used in the form of GIS-MCDA, an integrated method available in Idrisi. To aggregate the criteria, Boolean and Weighted Linear Combination (WLC) techniques were applied. The results showed that Boolean areas cover 537 km2 (6,98% of the study area). The best suitable areas, possessing the maximum suitability index 10, that resulted from two WLC models comprise 5 km2 (0,07% of the study area) and 26 km2 (0,34% of the study area) including and excluding the criterion NPAs as constraint, respectively. The results further indicated that GIS-MCDA models excelled in providing an overview for effective spatial decision-making. Both techniques play a role in suitability analysis and complement each other in finding an optimal site which could be carefully selected out of the identified areas. It is recommended that areas with a suitability index 10 be chosen inside Boolean suitable areas. Moreover, this study could act as a driving force to build a resilient planning framework that would boost sustainable placement and development of offshore macroalgae cultivations. Student thesisinfo:eu-repo/semantics/bachelorThesistexthttp://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-243911TRITA-IM-EX ; 2016:28application/pdfinfo:eu-repo/semantics/openAccess |
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macroalgae GIS MCA GIS-MCDA Boolean WLC sustainability suitability Environmental Sciences Miljövetenskap |
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macroalgae GIS MCA GIS-MCDA Boolean WLC sustainability suitability Environmental Sciences Miljövetenskap Nunes da Silva Ramos, Filipe José Identification of Suitable Areas for Offshore Macroalgae Cultivation |
description |
Research in sustainable macroalgae aquaculture has been gaining hype (e.g. Seafarm) to provide advancements in new significant sources of food, feed, materials and bio-energy. Despite the fact that in Asia the offshore production of macroalgae is established for many decades, in Europe is still in its infancy. The issue on where to find suitable cultivation areas without conflicting with current uses to respect the environment and the socio-economic activities is a great challenge. This Master of Science thesis aimed to find suitable offshore areas in order to facilitate implementations of macroalgae cultivations in the Swedish West Coast. Thirteen criteria in environmental, economic and social sustainability aspects were identified and employed (e.g. Depth, Distance to Ports, and Natural and Preserved areas (NPAs)), and the tools Geographic Information Systems (GIS) and Multi-criteria Analysis (MCA) were used in the form of GIS-MCDA, an integrated method available in Idrisi. To aggregate the criteria, Boolean and Weighted Linear Combination (WLC) techniques were applied. The results showed that Boolean areas cover 537 km2 (6,98% of the study area). The best suitable areas, possessing the maximum suitability index 10, that resulted from two WLC models comprise 5 km2 (0,07% of the study area) and 26 km2 (0,34% of the study area) including and excluding the criterion NPAs as constraint, respectively. The results further indicated that GIS-MCDA models excelled in providing an overview for effective spatial decision-making. Both techniques play a role in suitability analysis and complement each other in finding an optimal site which could be carefully selected out of the identified areas. It is recommended that areas with a suitability index 10 be chosen inside Boolean suitable areas. Moreover, this study could act as a driving force to build a resilient planning framework that would boost sustainable placement and development of offshore macroalgae cultivations. |
author |
Nunes da Silva Ramos, Filipe José |
author_facet |
Nunes da Silva Ramos, Filipe José |
author_sort |
Nunes da Silva Ramos, Filipe José |
title |
Identification of Suitable Areas for Offshore Macroalgae Cultivation |
title_short |
Identification of Suitable Areas for Offshore Macroalgae Cultivation |
title_full |
Identification of Suitable Areas for Offshore Macroalgae Cultivation |
title_fullStr |
Identification of Suitable Areas for Offshore Macroalgae Cultivation |
title_full_unstemmed |
Identification of Suitable Areas for Offshore Macroalgae Cultivation |
title_sort |
identification of suitable areas for offshore macroalgae cultivation |
publisher |
KTH, Hållbar utveckling, miljövetenskap och teknik |
publishDate |
2016 |
url |
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-243911 |
work_keys_str_mv |
AT nunesdasilvaramosfilipejose identificationofsuitableareasforoffshoremacroalgaecultivation |
_version_ |
1718975883257053184 |