Vol. 5, No. 2, 208-223, 2006
Eric De Oliveira, Jacques Populus and Brigitte Guillaumont
Abstract Seaweed presence is directly dependent on the nature of the substratum. In
the intertidal domain we used an alternative, because seaweed beds can be
observed directly. We detected seaweed presence with Spot satellite imagery.
The second parameter is immersion time. For each elevation value (surveyed by
Lidar), we converted water tidal levels into annual percentages of immersion.
The third environmental variable used was exposure to waves. During the
fixation phase, seaweeds cannot withstand high levels of exposure. We used a
model of wave propagation to delineate areas with different exposure levels. The presence of seaweed species for each parameter was estimated from field
sampling, along with 3D measurements (dGPS). Higher and lower limits of
dominant seaweed belts were contoured. With reference to the three
environmental variables selected, the distribution laws for each seaweed
species were estimated. A classification by fuzzy
logic was applied using eCognition software. Two phases were used in this
method: the first phase involved segmentation to obtain polygons, each polygon
being homogenous in terms of the environmental parameters selected: vegetation
cover, immersion time and exposure level. During the second phase, the
distribution laws estimated from field sampling were implemented and finally a
membership value was calculated for each targeted species and the results were
discussed.
Citation EARSeL European Association of Remote Sensing Laboratories, Strasbourg, France BIS-Verlag
The aim of this study is to model the seaweed species distribution with
respect to environmental parameters. We focused on fixed species of algae, such
as fucoids. Firstly, we identified environmental parameters, such as substratum
nature, immersion time, exposure, etc., which determine the seaweed
distribution. Secondly, we used field sampling to compute the distribution laws
for seaweed according to the environmental parameters selected. Thirdly, we
used the distribution laws and the environmental parameters to perform
predictive mapping of seaweed belts with a fuzzy logic method.
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History
Submitted: 27 Oct 2005
Revised: 11 Dec 2006
Accepted: 11 Dec 2006
Published: 30 Dec 2006
De Oliveira E, J Populus & B Guillaumont, 2006.
Predictive modelling of coastal habitats using remote sensing data and fuzzy logic: A case for seaweed in Brittany (France).
EARSeL eProceedings, 5(2): 208-223
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