The classification of submerged vegetation using hyperspectral MIVIS data

G. Ciraolo, E. Cox, G. La Loggia, A. Maltese

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.

Keywords


water column correction;shallow water;hyperspectral imagery;submerged vegetation

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References


DOI: https://doi.org/10.4401/ag-3152

Published by INGV, Istituto Nazionale di Geofisica e Vulcanologia - ISSN: 2037-416X