IMIS | Flanders Marine Institute

Flanders Marine Institute

Platform for marine research


Publications | Institutes | Persons | Datasets | Projects | Maps
[ report an error in this record ]basket (0): add | show Printer-friendly version

Assessment of AHS (Airborne Hyperspectral Scanner) sensor to map macroalgal communities on the Ría de vigo and Ría de Aldán coast (NW Spain)
Casal, G.; Sánchez-Carnero, N.; Domínguez-Gómez, J.A.; Kutser, T.; Freire, J. (2012). Assessment of AHS (Airborne Hyperspectral Scanner) sensor to map macroalgal communities on the Ría de vigo and Ría de Aldán coast (NW Spain). Mar. Biol. (Berl.) 159(9): 1997-2013.
In: Marine Biology. Springer: Heidelberg; Berlin. ISSN 0025-3162, more
Peer reviewed article  

Available in  Authors 


Authors  Top 
  • Casal, G.
  • Sánchez-Carnero, N.
  • Domínguez-Gómez, J.A.
  • Kutser, T.
  • Freire, J.

    Ría de Vigo and Ría de Aldán have high biological richness that is reflected in the number of environmental protection areas like the Atlantic Islands National Park and five places of community interest. Benthic algal communities play an important role in these ecosystems due to their ecological functions and support a great part of this biological richness. We tested by means of bio-optical modelling and Airborne Hyperspectral Scanner (AHS) images to what extent remote sensing could be used to map these communities in Ría de Vigo and Ría de Aldán (NW Spain). Reflectance spectra of dominating macroalgae groups were modelled for different water depths in order to estimate the separability of different bottom types based on their spectral signatures and the spectral characteristics of the AHS. Our results indicate that separation between three macroalgae groups (green, brown and red) as well as sand is possible when the bottoms are emerged during low tide. The spectra differences decrease rapidly with increasing water depth. Two types of classifications were carried out with the three AHS images: maximum likelihood and spectral angle mapper (SAM). Maximum likelihood showed positive results reaching overall accuracy percentages higher than 95 % and kappa coefficients higher than 0.90 for the bottom classes: shallow sand, deep sand, emerged rock, emerged macroalgae and submerged macroalgae. Sand and algae substrates were then separately analysed with SAM. These classifications showed positive results for differentiation between green and brown macroalgae until 5 m depth and high differences between all macroalgae and sandy substrate. However, differences between red and brown macroalgae are only detectable when the algae are emerged.

All data in IMIS is subject to the VLIZ privacy policy Top | Authors