RESUMO
Assessment, planning and management for coral reef ecosystems are particularly challenging tasks, especially in developing countries. In this study, a methodological approach which integrates Geographic Information Systems (GIS) and Multicriteria Evaluation (MCE) for the development of suitability assessment models for a variety of uses of coral reef resources is discussed. Such an approach is sustained by an extensive use of local expert knowledge coded in the form of automated decision trees (DTs). The usefulness of the approach and the models developed is demonstrated by their application to the participatory assessment and resources planning at "Alacranes Reef National Park" (ARNP), Yucatán, México. Overlaying of resulting suitability maps was also applied for identifying potential conflicting areas.
Assuntos
Antozoários/crescimento & desenvolvimento , Conservação dos Recursos Naturais/métodos , Monitoramento Ambiental/métodos , Sistemas de Informação Geográfica , Modelos Teóricos , Animais , México , Nephropidae/crescimento & desenvolvimento , Tartarugas/crescimento & desenvolvimentoRESUMO
Management of coral reef resources is a challenging task, in many cases, because of the scarcity or inexistence of accurate sources of information and maps. Remote sensing is a not intrusive, but powerful tool, which has been successfully used for the assessment and mapping of natural resources in coral reef areas. In this study we utilized GIS to combine Landsat TM imagery, aerial photography, aerial video and a digital bathymetric model, to assess and to map submerged habitats for Alacranes reef, Yucatán, México. Our main goal was testing the potential of aerial video as the source of data to produce training areas for the supervised classification of Landsat TM imagery. Submerged habitats were ecologically characterized by using a hierarchical classification of field data. Habitats were identified on an overlaid image, consisting of the three types of remote sensing products and the bathymetric model. Pixels representing those habitats were selected as training areas by using GIS tools. Training areas were used to classify the Landsat TM bands 1, 2 and 3 and the bathymetric model by using a maximum likelihood algorithm. The resulting thematic map was compared against field data classification to improve habitats definition. Contextual editing and reclassification were used to obtain the final thematic map with an overall accuracy of 77%. Analysis of aerial video by a specialist in coral reef ecology was found to be a suitable source of information to produce training areas for the supervised classification of Landsat TM imagery in coral reefs at a coarse scale.