|Título||Estimation of available seagrass meadow area in Portugal for transplanting purposes|
|Publication Type||Journal Article|
|Authors||Cunha, AH, Assis, JF, Serrão, EA|
|Year of Publication||2009|
|Journal||Journal of Coastal Research|
Seagrasses are marine flowering plants found in shallow coastal habitats around the world. These plants create a habitat of substantial importance from an ecological, economic and biodiversity point of view. Unfortunately, there have been considerable losses of seagrass habitat worldwide, leading to increasing interest on the development of seagrass restoration and rehabilitation projects. These projects, often developed as a mitigation tool, deeply benefit from the spatially explicit information included in Geographic Information Systems (GIS). Thus, to have seagrass area estimates for transplanting purposes and, to be able to monitor transplanting impacts, a large-scale GIS map was build for Sado and Mira River Estuaries, Portinho da Arrábida Bay and Ria Formosa regions using: (1) aerial photography analysis, (2) photo-interpretation, (3) on-site groundtruthing surveys and (4) statistical analysis. Habitat boundaries were evaluated through aerial photography, and a complete set of selected sites were visited for groundtruth validation, using 4 types of transect methods (along the shore-line, free-diving, scuba diving and boat transects). Twelve thousand, six hundred and fifty two hectares (12652.17 ha) were assessed, 3944 groundtruth points were recorded and 3 seagrass species were identified and mapped (Zostera marina, Zostera noltii and Cymodocea nodosa). Ria Formosa had the largest distribution area of seagrass species (241.04 ha), followed by Sado Estuary (32.68 ha). Mira Estuary had only one seagrass meadow and in Portinho da Arrábida Bay no seagrass meadows were registered. Zostera noltii was the most abundant species in both regions, followed by Cymodocea nodosa and Zostera marina. The error assessment for species distribution area and diversity, estimated through kappa statistics based on error matrices, gave a perfect agreement value (K=0.912) to the methodology used.