In July 2001 an aerial survey with MIVIS (Multispectral Infrared and Visible Spectrometer) hyperspectral sensor and
an in situ survey campaign were performed on Venice lagoon to map benthic macro-algae and sea phanerogams distribution.
On MIVIS VIS spectral range images, training areas for benthic macro-algae and sea phanerogams have
been selected by using sea truth data collected by CNR-ISMAR from in situ campaign and periodic area surveys
used in the lagoon by the local authorities. The derived spectral signature has been used to classify the area in order
to produce the maps of the pure and mixture submersed vegetation population. The algorithm applied to the data is
based on the Subpixel Spectral Analytical Process (SSAP) method. The method assumes that the spectrum of a single
pixel is composed of a fraction of the material of interest while the remainder of the observed spectra contains
background materials. In terms of recognition processes the produced maps present a very good agreement with the
sea truth data even though the fraction material expressed in the maps does not represent a quantitative estimation of
the material of interest

Alberotanza, L.

Cavalli, R. M.

Pignatti, S.

Zandonella, A.

Earth-prints Repository

271
ANNALS  OF  GEOPHYSICS, VOL.  49, N.  1, February  2006
Key  words  MIVIS – benthic macro-algae – sea
phanerogams – subpixel classification
1. Introduction
Information on the submersed aquatic vege-
tation distribution in the Venice lagoon is of par-
ticular importance for the following reasons: i)
environmental protection and therefore all the
problems connected to pollution, eutrophication
and water quality; ii) lagoon morphological con-
trol; iii) industrial use of some algae species.
For these reasons submersed vegetation
species recognition and mapping are periodically
performed during the vegetative period by the lo-
cal authorities. These activities are done to control
the ecosystem status, to plan all activities of sea
phanerogam plantation for the lagoon bottom
morphological balancing and, in the past, for a se-
lective harvesting of benthic macro-algae species.
Mapping techniques currently used are based
on in situ observations and aerial photo-interpre-
tations. For many years airborne and satellite-
borne remote sensing images have been used to
monitor the submersed vegetation distribution.
The main airborne remote sensing airborne in-
struments were metric cameras, LIDAR systems,
multispectral scanner systems while satellite-
borne sensors were mainly Landsat TM.
The automatic detection of the vegetation
was limited to vegetation cover and just to some
Classification of submersed aquatic
vegetation of the Venice lagoon
using MIVIS airborne data
Luigi Alberotanza (1), Rosa Maria Cavalli (2), Stefano Pignatti (2)(3) and Angelo Zandonella (4)
(1) Istituto di Scienze Marine (ISMAR), CNR, Venezia, Italy
(2) Laboratorio Aereo Ricerche Ambientali (LARA), IIA-CNR, Tor Vergata (RM), Italy
(3) Istituto di Metodologie per l’Analisi Ambientale (IMAA), CNR, Tito Scalo (PZ), Italy
(4) AdTec, Roma, Italy
Abstract
In July 2001 an aerial survey with MIVIS (Multispectral Infrared and Visible Spectrometer) hyperspectral sensor and
an in situ survey campaign were performed on Venice lagoon to map benthic macro-algae and sea phanerogams dis-
tribution. On MIVIS VIS spectral range images, training areas for benthic macro-algae and sea phanerogams have
been selected by using sea truth data collected by CNR-ISMAR from in situ campaign and periodic area surveys
used in the lagoon by the local authorities. The derived spectral signature has been used to classify the area in order
to produce the maps of the pure and mixture submersed vegetation population. The algorithm applied to the data is
based on the Subpixel Spectral Analytical Process (SSAP) method. The method assumes that the spectrum of a sin-
gle pixel is composed of a fraction of the material of interest while the remainder of the observed spectra contains
background materials. In terms of recognition processes the produced maps present a very good agreement with the
sea truth data even though the fraction material expressed in the maps does not represent a quantitative estimation of
the material of interest.
Mailing address: Dr. Rosa Maria Cavalli, Laboratorio
Aereo Ricerche Ambientali (LARA), IIA-CNR, Via del
Fosso del Cavaliere 100, 00133 Tor Vergata (RM), Italy; e-
mail: cavalli@lara.rm.cnr.it
272
Luigi Alberotanza, Rosa Maria Cavalli, Stefano Pignatti and Angelo Zandonella
species like Ulva rigida widely diffuse in time
periods. The main reason for these difficulties
in detecting vegetation could be ascribed to: i)
the mixture of numerous submersed vegetation
species, detectable in pixels of a few metres of
spatial resolution; ii) the high level of water tur-
bidity in the Northern and Central Basins of the
lagoon; iii) the variability of the water thickness
overhanging the submersed vegetation and mod-
ified by the tide conditions.
The methodological approaches proposed by
researchers (Tonelli, 1987; Alberotanza et al.,
1989, 1991; Baschieri et al., 1990; Tassan, 1990)
to process data vary from band math operations
to artificial intelligent techniques (i.e. neural net-
works).
In this section, a short discussion of the sub-
mersed vegetation spectral characteristics is re-
ported and the procedures used for an automat-
ic recognition of the more diffuse submersed
vegetation species at the MIVIS data acquisi-
tion date is described.
2. Spectral characteristics of the submersed
vegetation
The main spectral characteristics of sub-
mersed vegetation (fig. 1) in lagoon environ-
ments may be summarized as follows:
– Low reflectivity between 400 and 500 nm
due to the absorption of blue light (maximum
absorption at about 440 nm).
– Maximum green reflectivity between 550
and 570 nm.
– Low reflectivity between 660 and 690 nm
due to red light absorption (maximum absorp-
tion between 670 and 680 nm).
– Low reflectivity in the near infrared be-
tween 755 and 765 nm during pigmentation
phase. An increasing of reflectivity occurs dur-
ing blooming periods and in emerged vegeta-
tion conditions.
The substances responsible for the absorp-
tion of light are the chlorophyll pigments, caro-
tenoids and other types of pigments.
Fig. 1. Spectral radiance mean values of some submersed vegetation species.
273
Classification of submersed aquatic vegetation of the Venice lagoon using MIVIS airborne data
Chlorophyll-a is found in all green plants
and is the predominant pigment in benthic
macro-algae (absorption peaks at 440, 675, 695
nm of wavelength).
Other major pigments are phycoerythrin
found in red macro-algae (absorption peak at 565
nm), phycocyanin found in red and blue-green al-
gae (absorption peak at 620 nm), fucoxanthin
(absorption peak at 470 nm) (Wezermak et al.,
1976).
These characteristics are derived from many
investigations performed in the laboratory (Rund-
quist et al., 1996) and in lagoon, in different sites
and growth conditions (Alberotanza et al., 1991),
using portable filed spectrometers. 
According to these observations, hyperspec-
tral instruments seem to be more powerful in the
discrimination of the lagoon submersed vegeta-
tion. In this framework the airborne hyperspectral
MIVIS scanner presents a spectral band coverage
useful for the  study of the submersed vegetation.
The visible part of the spectrum is covered by
MIVIS through eighteen bands ranging between
443 and 783 nm of wavelength with a bandwidth
of 20 nm. 
The main factors affecting the measure-
ments of the submersed vegetation spectral sig-
natures are: atmospheric effects; water turbidi-
ty; water thickness overhanging the submersed
vegetation; presence of more species within a
pixel of a few metres of resolution.
The effects of atmosphere are widely dis-
cussed in the literature.
Water turbidity in the lagoon is mainly due to
upwelling sedimentation for tidal inflow effects,
wind stress, phytoplankton blooms, organic sub-
stances in solution and, in recent years, to poach-
ers. This effect generally misleads spectral re-
flectance measurements that may indicate the
presence of a species different from the real one.
Organic substances instead determine a change
in the reflectance values in bands of the red and
near-infrared (Gitelson, 1992; Rundquist et al.,
1996). Phytoplankton blooming occurs in well
known lagoon areas and periods of the year.
Water thickness overhanging the submersed
vegetation depends on the bottom depth, the
vegetation development phase and the tidal
conditions. The lagoon mean water depth (ex-
cluding lagoon channels) is about 1 m. In tidal
inflow condition the mean water depth increas-
es by about 40 cm.
The mixing of vegetation species within a
pixel determine for each pixel a composite
spectral signature of pure species plus the la-
goon bottom and atmospheric effects spectral
contribution. Therefore from a remote sensing
point of view the analytical approach is basical-
ly a subpixel mixture behaviour.
3. MIVIS aerial survey
The MIVIS aerial survey was performed on
26 July 2001 at 9:36 GMT from an altitude of
4000 m a.s.l. corresponding to a pixel spatial res-
olution of 8 m. The aerial strip starts from Mar-
co Polo Airport and ends over Chioggia city cov-
ering the areas with benthic macro-algae located
in the Northern and Central Basins of the lagoon
and a wide part of the area with sea phanerogams
located in the Southern Basin of the lagoon. 
Tidal conditions were in inflow tide starting
conditions with a sea mean level of 4 cm at Pun-
ta della Salute. Consequently water thickness,
overhanging the submersed vegetation in the
Southern Basin, was estimated at about 50 cm.
On the scene a negligible atmospheric haze
and a high level of water turbidity in the North-
ern and Central Basins of the lagoon was de-
tected by visual analysis of the quasi natural
colour composite of MIVIS images.
4. Image data processing
The first eighteen MIVIS spectral bands were
selected and pre-processed to obtain apparent re-
flectance data. The water body area was extract-
ed using as mask image derived from thresholds
applied to a histogram of MIVIS spectral band of
1500-1550 nm. In order to minimize the scatter-
ing effects, Chaves (1988) method was applied to
the MIVIS VIS spectral bands. 
From sea truth data collected by CNR-IS-
DGM from in situ survey 2001 campaign and
from periodic surveys in the lagoon performed by
the local authorities, training areas for benthic
macro-algae (Ulva rigida, Gracilaria confervoi-
des, Chaetomorpha aerea) and sea phanerogams
274
Luigi Alberotanza, Rosa Maria Cavalli, Stefano Pignatti and Angelo Zandonella
(Zostera marina, Zostera noltii, Cymodocea no-
dosa) were selected from the MIVIS data set.
Samples of Ulva rigida signatures were ex-
tracted in the area of Campalto channel, while
samples of Gracilaria confervoides in the area in
front of Marco Polo Airport. Additional samples
of Cymodocea nodosa signatures were extracted
in the area near Forte San Felice of Chioggia city,
for Zostera noltii along the edges of Rocchetta
channel.
The selected training areas were used to clas-
sify the surveyed area to determine the spatial dis-
tribution of the pure and mixture  submersed veg-
etation population. The algorithm applied to the
data is based on a particular implementation of
the Subpixel Spectral Analytical Process (SSAP)
method (Huguenin, 1994; Karaska et al., 1995).
SSAP method assumes that the spectrum of a
single pixel is composed of a fraction of the mate-
rial of interest (e.g., submersed vegetation specie)
while the remainder of the observed spectra con-
tains background materials (i.e. other minor vege-
tation species, mixture species, lagoon bottom).
SSAP method detects the most abundant material
of interest of the pixel under investigation by sub-
tracting iteratively fractions of candidates back-
ground spectra. The background and fraction re-
mainders that produce the residual spectrum clos-
est to the spectrum of the material of interest are
then identified. The output of this procedure is
presented in the form of fraction planes (fraction
maps) for each material of interest.
From a formal point of view, the method as-
sumes no transmittance through the materials in
at least one of the spectral bands. The radiant
contributions of material of interest and those of
background for the pixel «i» and the band «j»,
for j={1, …, N}, may be written as follows:
where Ri[ j] is the spectral reflectance value of
pixel «i» and band « j»; M[ j] is the spectral re-
flectance value for the material of interest of the
band « j»; M (spectral signature of the specie
object of investigation) is assumed invariant
from pixel to pixel; Bi[ j] is the spectral re-
flectance value for the background materials of
pixel «i» and band « j»;  fi[ j] is the fraction of
f [M B-[ j [f #j[ [ ]R 1i i i i#= + j]j j ]] ]^ ^^h h h# -
M for pixel «i» and band « j». This fraction can
vary from band to band, because the radiant
contrast between the material of interest and the
background can vary from band to band; (1+
−fi[ j]) is the fraction for the background mate-
rials of pixel «i» and band « j». 
Each material of interest is searched for inde-
pendently. The spectrum for the material of inter-
est of a pixel is detected by subtracting, iterative-
ly, fractions of candidate background spectra.
For each candidate background, residuals are
computed that produce the best spectral match to
the spectrum for the material of interest.
The residual spectrum MRi[ j ] is computed
as follows:
where fi is the identified fraction of the materi-
al of interest for pixel «i» and constant for each
spectral band; (1− fi) is the fraction of back-
ground materials Bi[ j] removed from Ri[ j ].
The residual spectrum found using this
equation is considered valid when all of its val-
ues are greater than zero. An invalid residual in-
dicates that a candidate background is not rep-
resentative of the actual background for the pix-
el under investigation. If all the candidate back-
grounds fail to generate a valid residual, then
the pixel does not contain a detectable amount
of the material of interest.
The degree of spectral match between the
residual MRi[j] and the signature spectrum M[j],
is computed by minimizing, for each pixel «i»
These methods were applied separately for the
Northern and Central Basins (from Marco Polo
Airport to Malamocco inlet) and for the Southern
Basin (from Malamocco inlet to the end of aerial
strip, i.e. over Chioggia city) due to the high wa-
ter turbidity level in the Northern and Central
Basins of the lagoon and the water transparency
of the Southern Basin during the MIVIS survey.
In the computations as candidate back-
grounds we considered the composite signatures
( )
[ [
.
N
MR M
1
i
j
N
1
2
-
-
=
j j] ]^d h n/
[BR j[
MR
f
f1
i
i
i i i#
=
- - ]j] ^^ h h# -
275
Classification of submersed aquatic vegetation of the Venice lagoon using MIVIS airborne data
extracted from the training areas using the de-
scribed procedure for all combinations of the
species under investigation plus a spectral signa-
ture of lagoon bottom without submersed vegeta-
tion for a total of 31 combinations. 
5. Discussion and conclusions 
For each species a fraction map was pro-
duced representing the material of interest in
terms of percentage. For benthic macro-algae
Fig. 2a-c. Subpixel classification results for the Chioggia city areas superimposed on the MIVIS spectral band
of 1500-1550 nm. a) Sea truth data map; b) benthic macro-alga species distribution; c) Ulva rigida fraction map.
a
b
c
276
Luigi Alberotanza, Rosa Maria Cavalli, Stefano Pignatti and Angelo Zandonella
and sea pha-nerogams the maps of pure and
mixture species distribution were produced sep-
arately. 
A subset of these maps for Ulva rigida (fig.
2c) and for the synthesis benthic algae map for
the Chioggia city areas (fig. 2b) compared with
the sea truth maps (fig. 2a) is shown in fig. 2a-c.
In terms of recognition processes, the maps pro-
duced present a good agreement with the sea
truth data even though the fraction material ex-
pressed in the maps of fig. 2a does not represent
a quantitative estimation of the material of inter-
est, because it is affected by scattering process
occurring in the water column. 
This particular implementation of the SSAP
method currently represents the only approach
to solve the complex problem of submersed
vegetation species spectra classification for the
Venice lagoon using the spectral information
gathered by hyperspectral remote sensing in-
struments. This method has been used to spec-
trally separate pixel spectra (submersed vegeta-
tion species) that are from a static point of view
highly significantly correlated. 
Further developments of this classification
procedure will include the correction of the wa-
ter column effects to enhance the pixel spectra
dynamic range and to achieve a realistic estima-
tion of the fraction of the material of interest.
This procedure will also be tested on the new
hyperspectral space-borne data (EO-1/Hyperi-
on) that on June 7, 2001 overpass the Venice
Lagoon acquiring images in 225 spectral chan-
nels with a pixel spatial resolution of 30 m.
Acknowledgements
The authors thank Nuova Telespazio SpA
for funding their activities; Magistrato alle
Acque di Venezia and Consorzio Venezia Nuo-
va – Servizio Ambiente for the courtesy used in
providing sea truth maps acquired within the
projects for Venice and lagoon protection (Art.
3, Law 798/1984).
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Classification of submersed aquatic  vegetation of the Venice lagoon using MIVIS airborne data

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Classification of submersed aquatic vegetation of the Venice lagoon using MIVIS airborne data

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