GIS and remote sensing-based models for development of aquaculture and fisheries in the coastal zone : a case study in Baia de Sepetiba, Brazil

摘要

GIS AND REMOTE SENSING - BASED MODELS FOR DEVELOPMENT OF AQUACULTURE AND FISHERIES IN THE COASTAL ZONE: A case study in Baía de Sepetiba, BraziL. by Philip Conrad Scott The use of Geographical Information Systems (GIS) in regional development is now becoming recognized as an important research tool in identifying potential aquaculture development and promoting better use of fishery resources on a regional basis. Modelling tools of GIS were investigated within a database specifically built for the region of Sepetiba Bay (W44°50', S23°00') Rio de Janeiro - Brazil, where, water based aquaculture development potential for two native species 0 f molluscs: P ema p ema (brown mussel) and Crassostrea rhizophorae (mangrove oyster) was identified, and additionally potential for development of land-based aquaculture of the white shrimp, Litopenaeus vannamei. Taking into consideration a mix of production functions including environmental factors such as water temperature, salinity, dissolved oxygen content, natural food availability as well as shelter from exposed conditions, several aquaculture development potential areas were found. The integration of sub-models comprised of thematic layers in the GIS including human resources available, general infrastructure present, regional markets as well as constraints to aquaculture development was developed. Multi-criteria evaluation within sub-models and between sub-models resulted in identification of several distinct potential areas for mollusc aquaculture development, indicating significant production potential and job creation. Basic field environmental data were collected in field trips in 1996, 1997 and 1998. Fresh market data were collected in 2001-2002 and were used to analyse market potentiaL. The map analyses undertaken with GIS based models support the hypothesis that promising locations for aquaculture development, their extent and potential production capacity can be predicted, making GIS use a useful tool for natural resource management and decision support.