Using remote sensing as a support to the implementation of the European Marine Strategy Framework Directive in SW Portugal

• Published in: Continental Shelf Research
• Main Authors: Cristina, Sónia, Icely, John, Goela, Priscila Costa, DelValls, Tomás Angel, Newton, Alice
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Zenodo 2015
• Subjects: Marine Strategy Framework Directive; Ocean colour remote sensing; North-east Atlantic; Iberian coast; Indicator; Descriptor; Eutrophication; North East Atlantic
• Online Access: https://doi.org/10.1016/j.csr.2015.03.011

Abstract The exclusive economic zones (EEZ) of coastal countries are coming under increasing pressure from various economic sectors such as fishing, aquaculture, shipping and energy production. In Europe, there is a policy to expand the maritime economic sector without damaging the environment by ensuring that these activities comply with legally binding Directives, such as the Marine Strategy Framework Directive (MSFD). However, monitoring an extensive maritime area is a logistical and economic challenge. Remote sensing is considered one of the most cost effective methods for providing the spatial and temporal environmental data that will be necessary for the effective implementation of the MSFD. However, there is still a concern about the uncertainties associated with remote sensed products. This study has tested how a specific satellite product can contribute to the monitoring of a MSFD Descriptor for “good environmental status” (GES). The results show that the quality of the remote sensing product Algal Pigment Index 1 (API 1) from the MEdium Resolution Imaging Spectrometer (MERIS) sensor of the European Space Agency for ocean colour products can be effectively validated with in situ data from three stations off the SW Iberian Peninsula. The validation results show good agreement between the MERIS API 1 and the in situ data for the two more offshore stations, with a higher coefficient of determination ( R 2 ) of 0.79, and with lower uncertainties for the average relative percentage difference (RPD) of 24.6% and 27.9% and a root mean square error (RMSE) of 0.40 and 0.38 for Stations B and C, respectively. Near to the coast, Station A has the lowest R 2 of 0.63 and the highest uncertainties with an RPD of 112.9% and a RMSE of 1.00. It is also the station most affected by adjacency effects from the land: when the Improved Contrast between Ocean and Land processor (ICOL) is applied the R 2 increases to 0.77 and there is a 30% reduction in the uncertainties estimated by RPD. The MERIS API 1 product decreases from ...