Mapping and Quantification of the Dwarf Eelgrass Zostera noltei Using a Random Forest Algorithm on a SPOT 7 Satellite Image
Résumé
TThe dwarf eelgrass Zostera noltei Hornemann (Z. noltei) is the most dominant seagrass
in semi-enclosed coastal systems of the Atlantic coast of Morocco. The species is experiencing a
worldwide decline and monitoring the extent of its meadows would be a useful approach to estimate
the impacts of natural and anthropogenic stressors. Here, we aimed to map the Z. noltei meadows
in the Merja Zerga coastal lagoon (Atlantic coast of Morocco) using remote sensing. We used a
random forest algorithm combined with field data to classify a SPOT 7 satellite image. Despite the
difficulties related to the non-synchronization of the satellite images with the high tide coefficient, our
results revealed, with an accuracy of 95%, that dwarf eelgrass beds can be discriminated successfully
from other habitats in the lagoon. The estimated area was 160.76 ha when considering mixed beds
(Z. noltei-associated macroalgae). The use of SPOT 7 satellite images seems to be satisfactory for
long-term monitoring of Z. noltei meadows in the Merja Zerga lagoon and for biomass estimation
using an NDVI–biomass quantitative relationship. Nevertheless, using this method of biomass
estimation for dwarf eelgrass meadows could be unsuccessful when it comes to areas where the
NDVI is saturated due to the stacking of many layers.
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