23 August 2005 Relation of phytoplankton species to ecosystem definition in the northwest Atlantic using remote sensing data
Author Affiliations +
Proceedings Volume 5885, Remote Sensing of the Coastal Oceanic Environment; 58850P (2005); doi: 10.1117/12.620086
Event: Optics and Photonics 2005, 2005, San Diego, California, United States
In this work, we present a new method for dynamic assignment of the boundaries of the ecological provinces of the North West Atlantic. The results are compared with the distribution of diatoms in the study area. Both analyses rely on ocean-colour data for the region. Diatoms were identified using remoteely-sensed data on the basis of their species-dependent absorption characteristics, which were embedded in a simple reflectance model(Sathyendranath et al., 2004). Maps of diatom distributions were produced for the area. Satellite-derived chlorophyll biomass and sea surface temperature (MODIS data) for the same period were used to redefine, in a dynamic way, the static borders of the ecological provinces (Sathyendranath et al., 1995; Longhurst 1998). The analyses were carried on two-week composite images, at different times of the year (April-May, July and October), to examine seasonal variability in the boundaries. The boundaries of provinces and the occurrence of diatoms were spatially coherent. Diatoms were favoured in rich waters on the continental shelf and in cold waters at high latitudes. In provinces labelled as oligotrophic (subtropical gyre and Gulf Stream), very negligible fractions of diatoms were found at any time of the year.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Emmanuel Devred, Shubha Sathyendranath, Cesar Fuentes-yaco, Heidi Maass, Trevor Platt, "Relation of phytoplankton species to ecosystem definition in the northwest Atlantic using remote sensing data", Proc. SPIE 5885, Remote Sensing of the Coastal Oceanic Environment, 58850P (23 August 2005); doi: 10.1117/12.620086; https://doi.org/10.1117/12.620086



Remote sensing

Statistical analysis



Algorithm development

Back to Top