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The Solomon Islands National Ocean Planning Spatial Information System: Spatial Decision Support Tools for Seaweed Aquaculture Planning in the Solomon Islands

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The Solomon Islands National Ocean Planning Spatial Information System: Spatial Decision Support Tools for Seaweed Aquaculture Planning in the Solomon Islands SINOPSIS – The Solomon Islands National Ocean Planning Spatial Information System: Spatial decision support tools for seaweed aquaculture planning in the Solomon Islands Dan Breen, Antony Vavia, Clayton Chan, Wesley Garofe, Mahuri Robinson, Sebastian Misiga, Sylvester Diake, James Teri2, Rosalie Masu, Ronnelle Panda, Ruben Sulu, Anne-Maree Schwartz, Richard Walter, Jillian Fenigolo, Armagan Sabetian and Lindsey White About NZIPR The New Zealand Institute for Pacific Research (NZIPR) was launched in March 2016. Its primary role is to promote and support excellence in Pacific research. The NZIPR incorporates a wide network of researchers, research institutions and other sources of expertise in the Pacific Islands. Funding for this project was provided by the New Zealand Ministry of Foreign Affairs and Trade through the New Zealand Institute for Pacific Research. Published by SINOPSIS – The Solomon Islands National Ocean Planning Spatial Information System: Spatial decision support tools for seaweed aquaculture planning in the Solomon Islands 2019 Dan Breen1, Antony Vavia1, Clayton Chan1, Wesley Garofe2, Mahuri Robinson4, Sebastian Misiga2, Sylvester Diake2, James Teri2, Rosalie Masu2, Ronnelle Panda2, Ruben Sulu3, Anne-Maree Schwartz3, Richard Walter5, Jillian Fenigolo1, Armagan Sabetian1 and Lindsey White1. ISBN: 978-0-473-47786-8 1 School of Science, Auckland University of Technology 2 Solomon Islands Ministry of Fisheries and Marine Resources 3 Mekem Strong Solomon Island Fisheries (MISSIF) 4 Triland Trading 5 University of Otago Page | 1 2 Abstract Seaweed cultivation can provide important Island at two times, and photos from income in remote areas and significant unmanned aerial vehicles (UAV drones) to exports for nations like the Solomon Islands. build georeferenced photo-mosaics at five It may also provide a sustainable alternative sites in the Western Province. to more extractive industries like forestry, fishing and terrestrial agriculture. This report Findings from the project include that: 1) outlines the spatial decision support tools potentially suitable environments for developed to help planners and policy makers seaweed farms can be identified from GIS in the Solomon Islands identify sites suitable habitat maps produced for the “Millennium for seaweed cultivation. In collaboration with Coral Reef Mapping Project” (Institute for the Solomon Island Ministry of Fisheries Marine Remote Sensing, University of South and Marine Resources we identified physical, Florida (IMaRS-USF), 2005); 2) suitability biological, economic and social factors influ- was also affected by depth, substrata, encing the suitability of locations for seaweed current, wind and wave action and herbivory farms and mapped 35 existing, previous or by fish, dugong and turtles; 3) the market potential locations for seaweed farms. price of seaweed, buying arrangements and competing opportunities for employment in We integrated data from physical and other industries were important economic biological surveys with population census and social constraints; 4) surprisingly, data and suitability scores rated by seaweed distances to the capital city, towns, villages farming experts familiar with the region. The and land appeared less important in an open source geographic information system archipelago linked by networks of coastal QGIS was used to map over 100 variables waterways; 5) while existing databases approximating suitability for seaweed illustrated broad patterns in factors affecting farming in terms of marine habitat, distance suitability, there were subtleties in human to land, rivers, transport, and protected areas judgement and interpretation that were not and possible social and economic constraints. easily captured using GIS data alone; and Data were georeferenced to point locations therefore 6) an interactive exploratory for known seaweed farming areas and to approach combining data and local expertise habitat polygons for reefs throughout the is likely to be more successful; 7) drone Solomon Islands. Multivariate patterns photography and online satellite imagery can among farm sites were analysed and mapped be used to map farming areas at different and a hierarchical multiple criteria model spatial and temporal resolutions; and 8) GIS was used to estimate site suitability according can spatially integrate diverse data sources to standardised site scores and weighted and interactive modelling tools within criteria. We used high-resolution satellite intuitive visual analyses. imagery to map seaweed farms near Wagina Page | 3 Acknowledgements We acknowledge the Government of the Solomon Islands, the Solomon Islands Ministry of Fisheries and Marine Resources, Mekem Strong Solomon Island Fisheries (MISSIF), Triland Trading and the Solomon Islands Government ICTSU (Information Communications and Technology Support Unit) for their support. We thank the many people of the Solomon Islands including the village of Vavanga for their hospitality and logistic support. Funding for this project was provided by the New Zealand Ministry of Foreign Affairs and Trade through the New Zealand Institute for Pacific Research to the School of Science, Auckland University of Technology. 4 Contents Abstract 3 Acknowledgements 4 1. Introduction 9 2. Methods 14 2.1 Location 14 2.2 Expert Knowledge 14 2.3 Site Selection Criteria for Seaweed Farms 14 2.3.1 Physical Selection Criteria 15 2.3.2 Biological Selection Criteria 16 2.3.3 Economic Selection Criteria 17 2.3.4 Social and Cultural Selection Criteria 18 2.4 Geographic Information System and Spatial Data 19 2.5 Geospatial Analyses 20 2.6 Multivariate Analyses 20 2.7 Multiple Criteria Analyses 20 2.8 Satellite Image Interpretation, Drone Photogrammetry and Ground Surveys 21 3. Results 22 3.1 Geographic information system 22 3.2 Marine habitats 22 3.3 Expert Knowledge 23 3.4 Geospatial Analyses 35 3.5 Multiple Criteria Analysis 35 3.6 Interpretation of satellite, drone imagery and site inspections 46 4. Discussion 53 5. Conclusion 57 6. References 58 Appendix 1. Key institutions and individuals consulted for this project 64 Page | 5 List of Figures Figure 1 Location of the Solomon Islands and the Exclusive Economic Zone. 13 Figure 2 SINOPSIS 3.5 (Solomon Islands National Ocean Planning Spatial Information System). A Quantum GIS 3.2.3 project containing ~100 physical, biological, economic, social and analytical formatted data layers to inform planning for sustainable seaweed aquaculture. 24 Figure 3 Locations identified as previous, current or potential sites for seaweed farming. These include some sites with marginal conditions which in some way make them less suitable. 25 Figure 4 Distribution of “shallow terrace” reef habitats in the Solomon Islands. Data from the Millennium Coral Reef Mapping Project, Remote Sensing Centre, University of Florida. (IMaRS-USF, 2005). 26 Figure 5 Percentage area of the most common reef habitats at identified seaweed farm locations in the Solomon Islands. Data from the Millennium Coral Reef Mapping Project, Remote Sensing Centre, University of Florida. (IMaRS-USF, 2005). 27 Figure 6 Area of “shallow terrace” marine habitat in provinces of the Solomon Islands. Data from the Millennium Coral Reef Mapping Project, Remote Sensing Centre, University of Florida (IMaRS-USF, 2005). 28 Figure 7. Overall suitability of identified sites for seaweed farming rated by Manager 1. 29 Figure 8. Overall suitability of identified sites for seaweed farming rated by Manager 2. 30 Figure 9. Comparison of overall rating of site suitability for seaweed farming for Manager 1 and Manager 2 (r=0.8, P<0.001). 30 Figure 10. Principal components analysis (PCA) for ratings of seaweed farm suitability by Manager 1. Principal component axes 1 and 2 (29 % and 23 % of variance explained). 31 Figure 11. Principal components analysis (PCA) for ratings of seaweed farm suitability by Manager 2. Principal component axes 1 and 2 (32 % and 22 % of variance explained). 31 Figure 12. Cluster analysis of site suitability ratings for Manager 1. 32 Figure 13. Cluster analysis of site suitability ratings for Manager 2. 32 Figure 14. Cluster analysis of Euclidean distances between seaweed farm sites for Manager 1 33 Figure 15. Cluster analysis of Euclidean distances between seaweed farm sites for Manager 2 34 Figure 16. Example of geospatial proximity analysis – distance of sites (km) to Honiara. 36 Figure 17. Principal components analysis (PCA) for GIS point estimates of seaweed farm suitability. Principal component axes 1 and 2 (33 % and 16 % of variance explained). 36 Figure 18. Cluster analysis of seaweed farm-site similarity based on geospatial variables. 37 6 Figure 19. Combined weighted multiple criteria score of site suitability for criteria from Manager 1. 39 Figure 20. Weighted multiple criteria score of site suitability from Manager 2. 39 Figure 21. Section of a Criterion Decision Plus multiple criteria model for just Manager 1 ratings nested within biological, physical, economic and social sub-criteria. 40 Figure 22. Combined multiple criteria scores of the overall suitability of potential seaweed farm sites for just Manager 1 (other data sources excluded). All criteria are weighted by the level of importance assigned to each criterion by the manager. 40 Figure 23. Section of a Criterion Decision Plus multiple criteria model for just Manager 2 ratings nested within biological, physical, economic and social sub-criteria. 41 Figure 24 Combined multiple criteria scores of the overall suitability of potential seaweed
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