The classification of submerged vegetation using hyperspectral MIVIS data
Main Article Content
Abstract
The aim of this research is to use hyperspectral MIVIS data to map the Posidonia oceanica prairies in a coastal
lagoon (Stagnone di Marsala). It is approximately 12 km long and 2 km wide and is linked to the open sea by
two shallow openings. This environment is characterised by prairies of phanerogams, the most common of which
is Posidonia oceanica, an ideal habitat for numerous species of fish, molluscs and crustaceans. A knowledge of
the distribution of submerged vegetation is useful to monitor the health of the lagoon. In order to classify the
MIVIS imagery, the attenuation effects of the water column have been removed from the signal using Lyzengas
technique. A comparison between classifications using indices obtained using band pairs from only the first
spectrometer, and using band pairs of the first and second spectrometers, shows that the best classification is obtained
from some indices derived from the first spectrometer. Field controls carried out in July 2002 were used
to determine the training sites for the supervised classification. Twelve classes of bottom coverage were obtained
from the classification, of which four are homogeneous and eight are mixed coverage. The methodology applied
demonstrates that hyperspectral sensors can be used to effectively map submerged vegetation in shallow waters.
lagoon (Stagnone di Marsala). It is approximately 12 km long and 2 km wide and is linked to the open sea by
two shallow openings. This environment is characterised by prairies of phanerogams, the most common of which
is Posidonia oceanica, an ideal habitat for numerous species of fish, molluscs and crustaceans. A knowledge of
the distribution of submerged vegetation is useful to monitor the health of the lagoon. In order to classify the
MIVIS imagery, the attenuation effects of the water column have been removed from the signal using Lyzengas
technique. A comparison between classifications using indices obtained using band pairs from only the first
spectrometer, and using band pairs of the first and second spectrometers, shows that the best classification is obtained
from some indices derived from the first spectrometer. Field controls carried out in July 2002 were used
to determine the training sites for the supervised classification. Twelve classes of bottom coverage were obtained
from the classification, of which four are homogeneous and eight are mixed coverage. The methodology applied
demonstrates that hyperspectral sensors can be used to effectively map submerged vegetation in shallow waters.
Article Details
How to Cite
Ciraolo, G., Cox, E., La Loggia, G. and Maltese, A. (2006) “The classification of submerged vegetation using hyperspectral MIVIS data”, Annals of Geophysics, 49(1). doi: 10.4401/ag-3152.
Issue
Section
OLD
Open-Access License
No Permission Required
Istituto Nazionale di Geofisica e Vulcanologia applies the Creative Commons Attribution License (CCAL) to all works we publish.
Under the CCAL, authors retain ownership of the copyright for their article, but authors allow anyone to download, reuse, reprint, modify, distribute, so long as the original authors and source are cited. No permission is required from the authors or the publishers.
In most cases, appropriate attribution can be provided by simply citing the original article.
If the item you plan to reuse is not part of a published article (e.g., a featured issue image), then please indicate the originator of the work, and the volume, issue, and date of the journal in which the item appeared. For any reuse or redistribution of a work, you must also make clear the license terms under which the work was published.
This broad license was developed to facilitate open access to, and free use of, original works of all types. Applying this standard license to your own work will ensure your right to make your work freely and openly available. For queries about the license, please contact ann.geophys@ingv.it.