Mapping salt-marsh vegetation by

multispectral and hyperspectral remote

sensing

Enrica Belluco a, Monica Camuffo b, Sergio Ferrari a,

Lorenza Modenese b, Sonia Silvestri c, Alessandro Marani b,

Marco Marani a,∗

aDipartimento IMAGE and International Centre for Hydrology ”Dino Tonini”,

University of Padova, via Loredan 20, I-35131 Padova, Italy

bEnvironmental Science Department, University of Venice, Dorsoduro 2137,

I-30123 Venice, Italy

cServizio Informativo - CVN - Magistrato alle Acque di Venezia, San Marco 2803,

I-30124 Venice, Italy

Abstract

Tidal marshes are characterized by complex patterns both in their geomorphic and

ecological features. Such patterns arise through the elaboration of a network struc-

ture driven by the tidal forcing and through the interaction between hydrodynami-

cal, geophysical and ecological components (chiefly vegetation). Intertidal morpho-

logical and ecological structures possess characteristic extent (order of kilometers)

and small-scale features (down to tens of centimeters) which are not simultane-

ously accessible through field observations, thus making remote sensing a necessary

observation tool. This paper describes a set of remote sensing observations from

Preprint submitted to Elsevier Science 14 June 2006

several satellite and airborne platforms, the collection of concurrent ground refer-

ence data and the vegetation distributions that may be inferred from them, with

specific application to the lagoon of Venice (Italy). The data set comprises ROSIS,

CASI, MIVIS, IKONOS and QuickBird acquisitions, which cover a wide range of

spatial and spectral resolutions. We show that spatially-detailed and quantitatively

reliable vegetation maps may be derived from remote sensing in tidal environments

through unsupervised (K-means) and supervised algorithms (Maximum Likelihood

and Spectral Angle Mapper). We find that, for the objective of intertidal vegeta-

tion classification, hyperspectral data contain largely redundant information. This

in particular implies that a reduction of the spectral features is required for the ap-

plication of the Maximum Likelihood classifier. A large number of experiments with

different feature extraction/selection algorithms shows that the use of four bands

derived from Maximum Noise Fraction transforms and four RGBI broad bands

obtained by spectral averaging yield very similar classification performances. The

classifications from hyperspectral data are somewhat superior to those from multi-

spectral data, but the close performance and the results of the features reduction

experiments show that spatial resolution affects classification accuracy much more

importantly than spectral resolution. Monitoring schemes of tidal environment veg-

etation may thus be based on high-resolution satellite acquisitions accompanied by

systematic ancillary field observations at a relatively limited number of reference

sites, with practical consequences of some relevance.

Key words: salt marshes, vegetation, hyperspectal data, multispectral data

∗ Corresponding Author: Tel.: +39 49 8275445; Fax: +39 49 8275446; e-mail:

marani@idra.unipd.it

2

1 Introduction

Coastal intertidal areas are transition zones between marine and terrestrial

systems characterized by high biodiversity and primary production, which

play a central role in mediating sea action on the coast, the effects of floods

on estuarine areas, and in buffering nutrient fluxes from the land.

The state and evolutionary trends of such systems are the result of com-

plex interactions among hydrodynamic, ecological, hydrological and sediment

transport processes, forced by tidal fluctuations (e.g. Marani et al., 2004). A

key element in intertidal system dynamics is halophytic vegetation, i.e. plants

which have evolved to develop and reproduce in highly hypoxic and hyper-

saline soils (e.g. Adam, 1990; Cronk and Fennessy, 2001). Halophytes colonize

salt marshes, areas located above mean sea level but below mean high water

level, and thus flooded according to local tidal periodicities. Salt-marsh vege-

tation is largely responsible for the stability of these areas, through feedbacks

involving hydrodynamic and sediment circulations. Plant roots, in fact, stabi-

lize the soil, while the aboveground biomass importantly reduces water flow

velocity and dampens wind-induced waves, thus effectively impeding sediment

resuspension and erosion (e.g. Pethick, 1984; Leonard and Luther, 1995). Fur-

thermore, the biomass produced by halophytes often constitutes the largest

contribution to the local incoming flux of soil and thus is crucial in allowing

marsh accretion to keep pace with soil compaction, subsidence and sea-level

rise (Cahoon and Reed, 1995; Day et al., 1999; Reed, 2002). On the other

hand, the net effect of deposition and erosion processes determines local to-

pography, which, together with tidal forcing and subsurface water flow, in

turn determines the edaphic conditions constraining vegetation development

3

and selecting halophytic species (e.g. Chapman, 1964; Beeftink, 1977; Bockel-

mann et al., 2002; Ursino et al., 2004; Silvestri and Marani, 2004; Silvestri et

al., 2005). This complex ecogeomorphological feedback cycle induces a spatial

distribution of halophytes which is characteristically organized in patches of

a single species, or of a typical species association: a phenomenon known as

zonation (Chapman, 1964; Pignatti, 1966; Silvestri and Marani, 2004).

Because of its key dynamic role, its intrinsic ecological importance and its

value as an ecogeomorphological indicator, it is not surprising that halophytic

vegetation is of central interest in studies concerning intertidal processes and

in management schemes attempting to counteract coastal squeeze phenomena

so relevant throughout the world (related to the common situation in which

the coastal margin is squeezed between a usually artificial fixed landward

boundary and the rising sea level; e.g. Cahoon et al., 1995; Bernhardt and

Koch, 2003; Cox et al., 2003; Hughes and Paramor, 2004; Wolters et al., 2005).

Quantitative, accurate and repeatable observations of vegetation space-time

distributions are therefore of self-evident importance. Such observations must

cover spatial scales ranging between tens of centimeters and some kilometers,

and temporal scales from a single season to a few years. Remote sensing is thus

ideally suited for the task, and there has recently been a growing interest in

the application of remote sensing methods to halophytic vegetation mapping

(e.g. Dale et al., 1986; Johnston and Barson, 1993; Donoghue et al., 1994;

Eastwood et al., 1997; Smith et al., 1998; Thomson et al., 1998a, b; Munyati,

2000; Silvestri et al., 2002; 2003; Marani et al., 2003; 2004; 2005; Shuman and

Ambrose, 2003; Thomson et al., 2004).

Previous approaches to remote sensing mapping of salt-marsh vegetation of-

ten focus on study sites exhibiting relatively limited species diversity or at-

4

tempt to discriminate just broad vegetation communities, each including sev-

eral species (e.g. ‘pioneer species’, ‘creek margin vegetation’, etc., e.g. see

Smith et al., 1998; Brown, 2004). Much of the current literature furthermore

deals with intertidal environments characterized by relatively large-scale veg-

etation structures (e.g. typical of meso- to macro-tidal environments), with

patch sizes comparable to usual satellite sensor resolution (10 m - 30 m) (e.g.

Ramsey and Laine, 1997). Other studies use ground reference data which are

limited in terms of their total amount or are characterized by too small an

areal extension as compared to sensor resolution (e.g. Schmidt et al., 2004).

Under these conditions reference data for classifier training and validation are

not suitable for a conclusive assessment of the accuracy of the resulting veg-

etation maps. In fact, to our knowledge, no previous work has quantitatively

addressed, on the basis of extensive ground reference data, the accuracy of

classifications of highly spatially heterogeneous intertidal vegetation using a

large set of last-generation airborne and satellite sensors with up to 1 meter

resolution. Moreover, previous remote sensing studies of intertidal vegetation

deal with single ‘snapshot’ acquisitions and thus do not allow the appreciation

of mapping reliability on scenes acquired at different times and seasons, under

different atmospheric conditions, with different tidal levels and in the presence

of different vegetation development stages. In this framework, we explore the

possibility of reliably discriminating different salt-marsh vegetation species by

use of several multispectral and hyperspectral data sets acquired within the

Venice Lagoon (Italy) and of concurrent detailed field observations. The ob-

jective is to set a quantitative context for vegetation mapping applications in

tidal environments and to test the performance of widely used classification

procedures. This is relevant both as an assessment of the separability of the

information classes of interest and as a reference for studying and monitoring

5

schemes relying on widely applied classification tools.

2 Study sites and data description

The Lagoon of Venice (Figure 1), located in north-east Italy, is a water body

with a surface of about 550 km2 and an average depth of approximately 1.1 m,

characterized by a semidiurnal tide with a range of about 1.4 m. The Lagoon is

connected with the Adriatic sea by three inlets and receives freshwater inputs

from a few tributaries, contributing a quite small water flux, but a relatively

large associated input of solutes (e.g. nutrients from agricultural areas) (e.g.

Consorzio Venezia Nuova, 2005).

Because of major river diversions, performed between the 14th and the 17th

centuries, and the construction of concrete jetties at the inlets, completed at

the beginning of the 20th century, the net sediment balance of the Venice la-

goon is currently negative. The Lagoon is thus experiencing a transformation

toward a marine system, with important environmental implications. Even

though the general trend is quite clear, the mechanisms shaping the land-

scape of the Venice Lagoon, as for many other tidal environments (e.g., Bird,

1985; Finkl, 1996; Friedrichs and Perry, 2001; Leatherman, 2003; Zhang et

al., 2004), are not well understood and, while some of its parts experience

intense erosion, some salt marshes are actually accreting. The Venice Lagoon

is thus a representative example of a relevant intertidal environment subject

to complex changes, which requires a better understanding of its ecological

and morphological dynamics and reliable and easy-to-implement monitoring

schemes.

6

2.1 Study sites

Figure 1 shows the location, within the Venice Lagoon, of the study site ana-

lyzed here, the San Felice salt marsh. This site is extracted from a larger set

of study marshes (TIDE, 2005) located in the northern part of the lagoon,

which are characterized by ‘healthy’ halophytic vegetation (i.e. not showing

evidence of dieback), relatively small rates of general erosion and, in some

cases, by local accretion. The San Felice salt marsh is located about 2 km

from the northern inlet of the lagoon (Lido inlet), along the homonymous

channel, and was frequently and densely surveyed during a five-year study

period of reference (2000-2004). Its elevation ranges from about 0.01 m above

mean sea level (a.m.s.l.) to 0.68 m a.m.s.l. (with an average of 0.26 m a.m.s.l.),

and its area is mainly colonized by four halophytic species, Spartina maritima

(hereafter ‘Spartina’), Limonium narbonense (hereafter ‘Limonium’), Sarco-

cornia fruticosa (hereafter ‘Sarcocornia’) and Juncus spp. (hereafter ‘Juncus’)

(nomenclature follows Caniglia et al. (1997)). These same species also domi-

nate, to varying degrees, the remaining study marshes.

Interestingly, Spartina was almost entirely displaced by Salicornia veneta (here-

after ‘Salicornia’) in 2004 at all study sites, showing that halophytic vegetation

distribution may significantly vary over relatively short time scales, and the

interest of reliable quantitative remote sensing monitoring schemes to charac-

terize vegetation temporal changes.

7

2.2 Remote sensing observations

We present and analyze data acquired during the period 2000-2004 in the

Venice Lagoon (mostly within the TIDE project, TIDE, 2005), comprising

observations from several multispectral and hyperspectral sensors, and a set

of field ancillary observations systematically acquired within about one week

from each remote sensing acquisition. The objective is to determine optimal

field procedures and sensor configurations with related quantitative assess-

ments of the resulting accuracy.

To minimize directional reflectance effects all hyperspectral flights were per-

formed along directions in the principal plane and under clear sky conditions

(except one flight line of the ROSIS acquisition, which was cloudy) within

two hours of solar noon. Flight planning also attempted to reconcile these

constraints with the requirement that salt marshes should not be flooded dur-

ing acquisitions. These were thus planned for periods of relatively low tides.

A final, but clearly fundamental, constraint was given by weather conditions

and thus several potential time windows were selected satisfying the above

constraints to suitable degrees.

The remote sensing data set available is comprised of:

• ROSIS (Reflective Optics System Imaging Spectrometer, of DLR, data ac-

quired within the HySens project). ROSIS acquires 115 spectral bands in

the Visible (VIS) and Near Infrared (NIR) part of the spectrum (spectral

range: 415.5 - 875.5 nm with a band width of 4 nm). The acquisition was

performed on 8 July 2000 with a ground resolution of 1 m.

8

• CASI (Compact Airborne Spectrographic Imager). This sensor allows the

selection of a number of bands between 400 nm and 950 nm as a function

of bandwidths and flight altitude. Fifteen bands were selected in the VIS

and NIR (band intervals in nm are: 437.15 - 445.65; 484.4 - 494.8; 542.7 -

554.1; 614.65 - 623.35; 631.65 - 640.35; 648.25 - 655.95; 662.5 - 668.3; 677.15

- 682.05; 688.65 - 697.35; 705.75 - 710.65; 745.85 - 750.75; 757.25 - 762.15;

774 - 781.8; 817.15 - 823.05; 860.9 - 869.7) on the basis of previous literature

(Thomson et al., 1998b, Smith et al., 1998) and of the observed characteris-

tics of vegetation spectra collected during previous field campaigns using a

hand-held spectrometer (e.g. TIDE, 2005). Two separate CASI acquisitions

over the study site were performed (29 September 2002 and 8 February

2003), with a ground resolution of 1.3 m. The data acquired in September

2002 are analyzed here.

• MIVIS (Multispectral Infrared and Visible Imaging Spectrometer). This

sensor covers the ranges: 433 nm - 833 nm (20 bands, 20 nm resolution);

1150 nm - 1550 nm (8 spectral bands, 50 nm resolution); 2000 nm - 2500

nm (64 spectral bands, 8 nm resolution); 8200 nm - 12700 nm (10 bands,

400 to 500 nm resolution). Five MIVIS acquisitions were performed over

the study site (20 July 2002, 29 September 2002, 5 July 2003, 29 March

2004, and 30 June 2004), with a 2.6 m ground resolution (for all bands).

The present paper analyzes the data acquired in July 2003 and June 2004

and considers all the spectral information available in the reflective as well

as in the emissive part of the spectrum. The thermal infrared bands can in

particular convey useful information to discriminate halophytic vegetation

species, possibly due to differences in canopy structure, exposed soil and

soil water content.

• IKONOS. The IKONOS data used (made available by the Venice Water

9

Authority, ‘Magistrato alle Acque’) comprise 4 bands (450 - 520; 520 - 600;

630 - 690; 760 - 900 nm) in the visible and near infrared part of the spec-

trum. The original IKONOS resolution is 4 m, but the data used here had

first been downscaled to a 1 m resolution on the basis of the 1 m-resolution

panchromatic band (pan-sharpening, e.g. Zhang, 2002). The IKONOS data

were acquired on 26 June 2001.

• QuickBird. QuickBird 4-band data (450 - 520; 520 - 600; 630 - 690; 760

- 900 nm) have a ground resolution of 2.88 m. Six QuickBird acquisitions

were available over the selected study site: 16 May 2002, 10 February 2003,

25 July 2003, 10 October 2003, 13 September 2004, and 8 June 2005. The

present paper analyzes the data acquired in July 2003.

The flight or acquisition timing and general characteristics of the data ana-

lyzed in the present paper are summarized in Table 1. The period of the year

was an important factor in the selection of the acquisitions to be analyzed in

detail, due to the fact that halophytic species bloom during the summer. Ini-

tial classification experiments showed, in fact, that intertidal species are most

easily discriminated when at their full development stage. Another important

variable was the tidal level, which is indicated in Table 1 for a reference lo-

cation (Punta della Salute, in the city center): due to propagation effects,

differences of the order of a few centimeters should be expected among differ-

ent sites at any fixed time.

10

2.3 Field observations

Extensive sets of field observations were acquired concurrently with remote

sensing campaigns to provide accurately located and quantitative ground ref-

erence data. For each cover type of interest, we randomly selected ground

reference areas (Regions of Interest, ROIs) with an extent larger than sev-

eral times the pixel size throughout the acquisition scene. This procedure was

rather difficult to implement because of the smallest scale of variability of

species spatial distribution (order of tens of centimeters) and to the often

small size of some vegetation patches. However, in all cases (i.e. for all cam-

paigns and all sensors) the size of the ROIs was at least 3 pixels by 3 pixels,

even for the coarsest multispectral data.

The boundaries of the ROIs were accurately delimited using either differential

GPS (DGPS) or a laser theodolite (minimum accuracy of ± 1 cm), while their

superficial properties were quantitatively characterized in terms of relative

cover. Even though the resolution of sub-pixel scale variability is not explic-

itly attempted here, ROI characterization was designed to provide detailed

sub-pixel scale information. For each ROI species presence and their relative

covers were estimated using a standard Braun-Blanquet visual method (e.g.

Mueller-Dombois and Ellenberg, 1974; Silvestri et al., 2002), which assigns

vegetation presence with reference to a sub-division of the 0% - 100% range

into five intervals. In order to construct a quantitative and less subjective

ground reference dataset, relative ground cover was also estimated by acquir-

ing several photographs within each ROI from a nadir-looking digital camera

mounted on a 2.5 m-high pole (resulting in a resolution of 2 mm). Relative

covers were estimated by overlaying a regular grid (with cell size of 25 cm)

11

onto the digital images and by identifying the type of cover characterizing

the pixels at the intersections of the grid (at least 96 within every image of

about 5.8 m2) (e.g. Thomson et al., 1998a). Interestingly, Braun-Blanquet es-

timates of relative covers made by different trained operators were always very

consistent with the more objective, camera-based values.

For the scope of the present paper, pixel relative cover information was reduced

to the determination of the dominant cover type for each ROI. In this context

each pixel is considered to belong to a single class if its area is occupied for

more than 60% by a single cover type. Otherwise the pixel is excluded from the

reference data set. Majority mapping of vegetation species is thus the subject

of the classification procedures described hereafter.

The ground reference dataset constructed in this manner is constituted by

a set of geocoded ROIs for each of the classes of interest, which include the

five dominant halophytic species (Juncus, Spartina, Limonium, Sarcocornia,

Salicornia) with the addition of the bare soil and water classes. These seven

classes are known to describe the greater part of surface types within the study

site.

Finally, ancillary field observations also include sun-photometric measure-

ments (using a CIMEL CE 318 operating in the following bands: 440 nm,

670 nm, 870 nm, 936 nm, 1020 nm, see TIDE, 2005 for details) performed

during hyperspectral overflights and horizontal visibility observations for all

multispectral/hyperspectral acquisitions (as described in Table 1).

12

3 Methods

3.1 Preliminary data processing

ROSIS and IKONOS acquisitions were atmospherically corrected using MOD-

TRAN (in its ATCOR implementation, Richter and Schlapfer, 2002), while

the remaining datasets were transformed into reflectance values by use of the

6S model (Vermote et al., 1997, using a ‘Maritime’ type of atmospheric pro-

file) on the basis of sun-photometer-derived atmospheric optical thickness or

horizontal visibility observations according to availability. The atmospheric

correction is known not to influence the result of classifications obtained us-

ing within-scene training and validation ROIs (e.g. Hoffbeck and Landgrebe,

1994), but was here performed to produce a homogeneous dataset for future

direct comparisons or analyses, e.g. based on vegetation indexes.

After the atmospheric correction, geometric correction schemes were applied

to data from airborne sensors to minimize distortions induced by perturba-

tions in aircraft attitude and flight direction. While ROSIS and CASI data

were corrected by the data producers using a full set of ancillary information

(onboard GPS and inertial system data), MIVIS data were corrected using the

PARGE model (Schlapfer and Richter, 2002), based just on the onboard GPS

acquisitions. Finally, all multispectral and hyperspectral data were accurately

geocoded. In order to maximize the accuracy of image geocoding, a set of

Ground Control Points (GCP) was selected on the basis of a reference aerial

photograph (0.16 m resolution) acquired in the year 2000 and previously ge-

ometrically corrected in the Gauss-Boaga Italian reference system, by means

of a large number of visible markers laid out throughout the scene. GCPs for

13

remote sensing data geocoding were selected using the few existing buildings

(mainly fisherman huts) and easily distinguishable points along marsh edges

or at tidal creek intersections. More than 150 points per km2 were used with

an approximately homogeneous distribution. Geocoding was performed using

a nearest-neighbor resampling and yielded Root-Mean-Square errors smaller

than 0.5 pixels in all cases.

ROIs for the vegetation and soil classes were constructed by selecting just

the inner pixels within each ground reference area, as border pixels are more

likely to be mixed. The characteristics of the resulting ROIs are summarized

in Table 2. It should be noted that, because of their very characteristic spec-

tral signature, maps of water pixels are very accurate (and all very similar)

using any classifier. We thus adopted the K-means classification of the water

class as a reference. This was then used as a mask excluded from the further

classification experiments performed for the vegetation and soil classes on the

remaining pixels. The number of reference pixels in Table 2 is of course depen-

dent on the resolution of the sensor and on the vegetation species. Different

halophytes, in fact, are characterized by different overall presence and by dif-

ferent patch sizes (e.g. Juncus is relatively less frequent and its patches are

typically smaller than for all other plants), thus limiting the number and size

of ROIs available.

The set of pixels within the reference areas in Table 2 was divided into a train-

ing set, for classifier calibration, and a validation set, to provide an indepen-

dent test of classification performance. Calibration and validation reference

pixels varied according to sensor resolution, but they were always selected

from separate vegetation patches to ensure statistical independence and to

allow assessments of the actual generalization capabilities of the classifiers.

14

Three classifiers are here examined: The unsupervised K-means classification

procedure (e.g. Tou and Gonzalez, 1974), and the supervised Spectral An-

gle Mapper (SAM, e.g. Kruse et al., 1993) and Maximum Likelihood (ML,

e.g. Hoffbeck, 1995) algorithms. These classifiers were selected because they

are most widely and generally applied, while specific applications to inter-

tidal vegetation are still relatively uncommon. Also, their application poses

different constraints on the spectral characteristics of the data, and on the

type and amount of reference information required. Indications on the relative

and absolute performance of these classifiers applied to halophytic vegetation

mapping under different conditions and with different remotely sensed data

are thus highly desirable.

In order to explore and optimally use the information content of the different

hyperspectral data sets considered, we performed analyses using: i) the original

spectral bands; ii) a progressively reduced number of bands, selected using

a feature selection and a feature extraction algorithm, based, respectively,

on the Bhattacharyya distance (e.g. Jimenez and Landgrebe, 1998) and on

the Maximum Noise Fraction (MNF) transform (Green et al., 1988); iii) a

reduced number of broader bands obtained by spectral averaging, mimicking

multispectral RGBI data. The latter strategy is interesting because it allows

the quantification of the accuracy that would be obtained from a multispectral

sensor (typically from a satellite platform) with the same spatial resolution of

the hyperspectral sensor considered.

15

3.2 Unsupervised classifications

The K-means unsupervised classification procedure was applied by tentatively

fixing a number of classes and by evaluating the resulting map by visual ap-

praisal and by comparison with ground reference data. The results obtained

by using about 50 classes, which are then merged together on the basis of

calibration ROIs and of the operator direct knowledge of the site (Figures 2

(c) for CASI and 3 (c) for IKONOS), show that the information classes of

interest are spectrally separable.

3.3 Supervised classifications

The reference spectra necessary for the application of the SAM classifier were

defined by simply averaging the spectra corresponding to the pixels within

the training sets. In the case of the ML algorithm, the training spectra were

used to compute the statistics (mean and covariance matrix for all the spectral

channels) necessary for its application.

The application of the SAM classifier requires the definition of threshold spec-

tral angles above which an unknown spectrum is left unclassified (e.g. Kruse

et al., 1993). These parameters were determined, for each class of interest, by

optimizing the classifier accuracy as characterized by the Confusion Matrix

(e.g. Congalton and Green, 1999), and by verifying that classification results

were consistent with direct field observations. Sample results of the applica-

tion of the SAM algorithm to the San Felice marsh are shown in Figures 2

(b), 3 (b) and 4. The calibration of the SAM algorithm requires a relatively

small amount of training spectra (e.g. as compared to Maximum Likelihood)

16

to provide its maximal performance.

The performance of the ML classifier tends to be appreciably degraded when

the number of spectral bands available is increased, because of the large num-

ber of covariances that need be estimated (Hughes’ effect, Hughes, 1968; Swain

and Davis, 1978). The quantity of ground reference data required by the

ML classifier thus rapidly increases with the number of bands. As discussed

above, in order to reduce the dimensionality of the data and thus minimize

the Hughes’ effect, we applied ML to spectral data obtained using feature

selection and extraction algorithms, whose optimality was evaluated on the

basis of validation areas and direct field knowledge.

3.4 Accuracy assessment

The performance of different classifiers is evaluated both in terms of visual

comparisons with general field information on known vegetation structures

and of the Confusion Matrix. A very important first evaluation of classifier

results, in fact, uses the large amount of direct information on the spatial

distribution of vegetation collected during the field campaigns. The use of

this information was facilitated by the fact that different halophytes colonize

quite distinct portions of the marsh: Spartina and Salicornia colonize lower

areas in the middle of the marsh and, moving to higher grounds towards the

nearest channel, one usually first encounters Limonium-dominated areas, and

then Sarcocornia zones on even higher soils (e.g. Silvestri et al., 2005). The

resulting species patches (zonation), several of which were identified during

field activities, constitute a quite stringent criterion for the evaluation of the

vegetation maps produced. It was thus possible to discard or accept classifi-

17

cations characterized by relatively good validation statistics on the basis of

their consistency with macroscopic features of vegetation distribution.

The direct quantitative comparison of classifier performances is based on the

Confusion Matrix, whose information will in the following be often summa-

rized by the Overall Accuracy, A, defined as the ratio of the number of val-

idation pixels that are classified correctly to the total number of validation

pixels irrespective of the class (e.g. Foody, 2002). A further important Confu-

sion Matrix statistics used here is the Kappa coefficient, K, which describes

the proportion of correctly classified validation sites after random agreements

are removed (Rosenfield and Fitzpartick-Lins, 1986). Comparisons between

classifications were performed by testing differences in Kappa values for sta-

tistical significance at the 95% significance level (Congalton, 1983; Hudson

and Ramm, 1987).

Because the K-means, SAM and ML classifiers involve different procedures and

require a different amount of calibration ROIs, the comparative evaluation of

their performance cannot be based on a single choice of calibration/validation

data sets. Different types of accuracy tests were thus performed. The first

test (TEST1) consists of determining by trial-and-error, for each classifier and

each sensor, the ‘optimal’ selection of training ROIs (with the constraint of

ensuring that the training ROIs were about half of the total available reference

areas) and of classifier parameters, which maximize classification performance.

In TEST1, the minimum size of the training set required to obtain the max-

imal classifier performance was determined. The results of TEST1 constitute

an estimate of the best classification performance that should be expected

when an independent validation set is available. Training and validation ROIs

are non-overlapping, as described above, and allow a statistically significant

18

assessment of the classifier performance and generalization capabilities. The

fact that, in TEST1, ‘optimal’ training and validation ROIs are selected sepa-

rately for each classifier, does not allow the comparison of the performance of

the different classification schemes on a common validation set. A second type

of test was thus performed (TEST2), based on a resubstitution approach (e.g.

Fukunaga, 1990), which uses all available ROIs both for training and valida-

tion. Training and validation spectra for TEST2 are identical, to determine

the ability of each classifier to discriminate the information classes of interest

within the ground reference dataset. However, TEST2 allows a coherent com-

parison of different algorithms on a single calibration-validation set and the

estimation of an upper bound for the performance attainable by a classifier.

TEST3 consists of the evaluation of classifier performances using ‘optimal’

training sets (i.e. the training sets of TEST1) and all ROIs as validation sets

(i.e. validation ROIs used in TEST2). Comparisons between the results of

TEST2 and TEST3 provide indications as to the generalization capabilities

of the classifiers. Similar performances of TEST2 and TEST3 indicate that,

given a limited number of training spectra, a classifier correctly identifies a

proportion of spectra close to the ‘upper-limit’ proportion it can correctly

identify when trained on the whole set of reference ROIs (and thus under

optimal conditions). On the contrary, a decreased performance in TEST3 with

respect to TEST2, indicates partial failure of the classifier to identify a general

classification rule applicable to spectra which it was not trained on.

The calibration of the K-means classifier was performed using all the infor-

mation available to the operator (including direct field information) because

all the ground reference observations are required to group the initial spec-

tral classes produced by the algorithm into meaningful information classes

19

corresponding to different vegetation species. Because the K-means maps are

validated on the entire ROI set, TEST2 and TEST3 are in this case identical.

4 Results

Tables 3 and 4 show the ‘validation’ confusion matrices for CASI and IKONOS

using the validation ROIs of TEST2. The confusion matrices for the remaining

sensors are not shown here for brevity, but the overall classifier performance

for each sensor and each TEST is summarized in Table 5. This Table describes

the results of the classifications obtained using the original spectral dataset for

K-means and SAM (except for the ROSIS data, in which case the blue part of

the spectrum was noisy and had to be excluded to obtain optimal results, as

described in detail later) and the first 4 MNF bands for ML (which resulted

to be optimal in almost all cases, please see below). Comparisons with the

performance obtained with the numerous band configurations explored are

given at appropriate points in the detailed discussions below.

4.1 ROSIS classifications

The tidal level was quite low at the time of the ROSIS acquisition (about - 0.16

m a.m.s.l., compared to an average marsh level of 0.26 m a.m.s.l.), resulting in

wide areas of exposed bare soil and in a small probability of water standing on

the marsh surface. These are likely optimal conditions, in particular because

possible effects of variable soil moisture are minimized. The data used are

composed of two adjacent flight lines. Unfortunately the second flight line (in

the upper-right part of the scene in Figure 4 (a)) was acquired under cloudy

20

conditions, with visible effects in the corresponding vegetation maps.

Visual inspection and experiments with SAM classifications, exhibiting a de-

crease in accuracy when the bands covering the ‘Blue’ part of the spectrum are

used, indicate that the first 27 ROSIS bands are affected by significant noise.

As an example, SAM classifications using all 115 bands yielded A=71.3%

(TEST2), while the use of 88 bands (in the range 523.5 nm - 875.5 nm) re-

sulted in A=93.9% and K=0.92 (see Table 5).

The application of the Maximum Likelihood algorithm requires a reduction

in the number of spectral bands because of the well-known sensitivity of the

estimation of band covariances on the size of the calibration set with respect

to the number of bands (e.g. Hughes, 1968). We first performed classifica-

tion experiments using a progressively increasing number of bands selected by

maximizing the Bhattacharyya distance between the distributions of spectra

defined by reference areas of each class. The classifications show a progres-

sive increase in the accuracy with the number of bands up to 4 to 6 bands.

When the number of bands is increased further, the overall accuracy hardly

shows any improvement and the coherence of the vegetation maps with known

macroscopic features of species spatial arrangement tends to decrease. This cir-

cumstance may be related to the existence of a tradeoff between the number

of bands, the amount of non-redundant spectral information, and the Hughes’

effect. The approximately optimal configuration was found to correspond to

the selection of 4 spectral bands (547.5 - 551.5, 667.5 - 671.5, 703.5 - 707.5,

767.5 - 771.5 nm, note that the feature selection algorithm excludes the noisy

bands in the ‘Blue’ part of the spectrum), yielding A=98.3% and K=0.98

(TEST2). We also reduced the number of bands by spectral averaging into

broader bands and by use of the MNF transform. Band averaging to an RGBI

21

spectral configuration produced satisfactory classifications on the basis of both

the Confusion Matrix and of visual inspection. Table 5 includes ML accuracies

referring to an aggregation into the 4 spectral bands: 451.5 - 523.5; 523.5 -

603.5; 631.5 - 691.5; 759.5 - 875.5 nm (a band configuration similar to Quick-

Bird and IKONOS sensors). For comparison with the Bhattacharyya-selected

bands, the classification using the averaged broad bands yielded A=97.6% and

K=0.97 (TEST2, the difference in K not being statistically significant at the

95% confidence level). Note that the RGBI configuration includes the noisy

bands in the ‘Blue’ part of the spectrum. Averaging to four, equally spaced,

broad bands excluding the ‘Blue’ channels (555.5 - 635.5; 635.5 - 715.5; 715.5 -

795.5; 795.5 - 875.5 nm) improves this performance to A=99.9% and K=1.00

(TEST2, with a good visual correspondence with known vegetation struc-

tures), showing that indeed the classification accuracy on broad bands is high

when noise is eliminated. Classifications on MNF-transformed data also con-

firm that a greater number of bands does not contribute an amount of informa-

tion capable of balancing the greater uncertainty related to the larger number

of statistics needed to apply the ML algorithm (Hughes’ effect). The best ML

classification performance in this case was obtained using the first four bands

produced by the MNF transformation (A=99.0%, K=0.99 for TEST2, see Ta-

ble 5, virtually indistinguishable from the broad-band classification). We also

applied a Principal Component Analysis (PCA), as a reference for MNF re-

sults. A classification based on the first four PCA bands yield: A=96.5% and

K=0.95. The slightly decreased performance may be ascribed to the different

noise component present within each ROSIS band, as PCA and MNF results

should be the same when the noise variance is the same in all bands (e.g.

Green et al., 1988).

22

The SAM algorithm does not require a reduction of the number of bands,

as it does not make use of possibly ill-estimated band covariances. The best

results were obtained when the noisy bands in the ‘Blue’ region of the spec-

trum were excluded from the original band set, as discussed above (A=93.9%,

K=0.92 for TEST2, see Table 5). The results are also in visual good agreement

and consistent with direct field information, except for a minimal number of

pixels which are erroneously assigned to the Juncus class. Experiments in

band number reduction were carried out also with the SAM algorithm, us-

ing MNF transforms and band-averaging. Such experiments indicate that the

SAM classification performance (characterized both visually and statistically)

is appreciably decreased when a limited number of bands is used. For ex-

ample, use of the first four MNF bands yields A=94.8%, K=0.93 (TEST2),

but the corresponding spatial vegetation distribution is not entirely consistent

with the overall observed vegetation distribution. Use of four averaged RGBI

bands (representing QuickBird- and IKONOS-like bands as described above)

results in A=80.9% and K=0.75 (TEST2), likely due to the inclusion of the

noisy Blue-region bands in the first averaged band (the accuracy increases to

A=87.4% and K=0.84, when the four equally-spaced broad bands excluding

the ‘Blue’ region are used).

Application of the K-means unsupervised classifier using 88 bands, i.e. ex-

cluding the first 27 noisy bands, results in A=90.4% and K=0.87. K-means

performances are thus inferior to both SAM (using the same spectral infor-

mation) and ML (using a smaller set of bands).

As may be seen in Table 5, TEST2 and TEST3 results are quite similar for

the ML classifier, indicating that this algorithm is able to effectively classify

spectra not used for its training. The SAM algorithm, on the contrary, does

23

not seem to be able to efficiently extract general information from the limited

training set of TEST3. The overall comparison of classification accuracies ac-

cording to the different tests indicates that ML tends to perform better than

SAM in the ROSIS case.

From a more qualitative viewpoint, all maps indicate an excessive presence

of Juncus, which is in contrast with direct field knowledge. This observation

may be interpreted by considering that Juncus-dominated areas tend to con-

tain non-negligible amounts of other species. Therefore, because Juncus train-

ing pixels are highly mixed, it is likely that the classifiers tend to interpret

as Juncus any mixed pixel, whose spectral reflectance is decisively different

from the remaining vegetation classes. However, the maps retrieved exhibit

a remarkable overall agreement with observed vegetation spatial structures,

particularly for Limonium and Sarcocornia.

4.2 CASI classifications

The tidal level at the time of the CASI acquisition considered (29 September

2002) was quite high (the highest among all the acquisitions considered), at

about 18 cm a.m.s.l., resulting in a greater presence of water particularly in

the smaller channels (see Figure 2). The San Felice site was covered using

two flight lines, acquired under slightly different illumination conditions, with

consequences for classifications results.

The application of the ML classifier again showed that the Hughes’ effect

dominates when a large number of bands is used. We thus experimented

with different band selections according to the Bhattacharyya distance be-

24

tween the classes. An optimal performance, based on the Confusion Matrix

(A=95.1% and K=0.93, TEST2) and comparisons to known vegetation struc-

tures, is obtained when just 4 bands are selected: 437.15 - 445.65; 542.7 -

554.1; 677.15 - 682.05; 860.9 - 869.7 nm. Band averaging experiments yield

an optimal performance (A=96.0% and K=0.94, TEST2) when CASI bands

are averaged down to 4 RGBI (QuickBird- or IKONOS-like) spectral bands:

437.15 - 494.80; 542.70 - 554.10; 614.65 - 697.35; 705.75 - 869.70 nm. Again,

this may be ascribed to a tradeoff between the amount of spectral informa-

tion used and the uncertainty in the estimation of an increasing number of

band covariances. The ML classification on MNF-transformed bands yielded

A=96.3% and K=0.94 (TEST2, see Table 5). The results are identical (also

visually, see Figure 2 (a)) to the broad-band classification. A classification on

4 PCA bands gives A=96.6% and K=0.94 (TEST2). The very similar perfor-

mances obtained using PCA and MNF bands suggest that CASI bands are

homogeneously affected by a relatively small noise component.

It is worth noting that, overall, the use of averaged bands yields performances

which are equivalent to those obtained from feature selection or extraction

algorithms.

The application of the SAM classifier to the full 15-band set gives A=89.4%,

K=0.84 for TEST2, though the associated vegetation map likely overestimates

the presence of Limonium with respect to Sarcocornia (Figure 2 (b)). Tests of

the SAM algorithm on the averaged RGBI spectral bands, yielding satisfac-

tory results for ML, gave significantly poorer results (A=80.2%, K=0.70 for

TEST2) than in the case of the full 15-band data. Classifications based on a

decreasing number of MNF-selected bands are consistently characterized by

poorer performances both statistically (for the 4 most signal-containing MNF

25

bands: A=89.2%, K=0.83) and visually.

TEST3 results for ML are just slightly inferior to those for TEST2, while the

differences for SAM are not statistically significant. This indicates that both

classifiers can reasonably generalize the spectral information contained in the

training set.

K-means accuracies are comparable (A=89.9% and K=0.85, Figure 2 (c)))

to SAM performances (at the 95% confidence level) and the corresponding

vegetation map is in good agreement with the overall species distribution.

In summary, the CASI classifications explored yield vegetation distributions

in satisfactory agreement with observations. As for ROSIS, the ML algorithm

outperforms the SAM and K-means classifiers when the number of bands is

reduced by spectral averaging or feature extraction/selection schemes.

It is worth noting here that ML classifications using all the band configurations

explored (e.g. see Figure 2 (a)) show difficulties in discriminating bare soil and

Spartina (e.g. in some portions from the wide expanses of low-elevation bare

soil areas in the upper part of the marsh). This is probably due to the fact that

Spartina is composed by almost-vertical stems and that a great portion of soil

is thus visible from above. The resulting spectrum is therefore a combination

of Spartina’s own signature and the signature of the soil. On the other hand,

the marsh surface sediment is covered by variable proportions of microalgae

(microphytobenthos, e.g. Aspden et al., 2004), containing significant quantities

of chlorophyll. The spectral reflectance of marsh soil is thus a combination of

a characteristic soil signature and of a vegetation-like spectrum (with the

typical red edge), contributed by the microphytobenthos, and may thus be

very similar to the signature of Spartina (e.g. Thomson et al., 2003).

26

4.3 MIVIS classifications

4.3.1 MIVIS 2003

The tidal level during the acquisition (5 July 2003) was very low (the minimum

level among all acquisitions considered), at -0.28 m a.m.s.l., resulting in wide

areas of exposed bare soil and in relatively little soil moisture variability.

The application of the ML classifier using the bands selected by maximiz-

ing the intra-class Bhattacharyya distance (513 - 533 nm; 593 - 613 nm; 773

- 793 nm; 8.20 - 8.60 µm) gives A=96.6%, K=0.95 (TEST2). The selection

of a thermal band indicates that the emissive part of the spectrum indeed

contains useful information to enhance vegetation species separability. The

classification result is however in evident disagreement with field observations

of vegetation structure, mainly because Sarcocornia is often misclassified as

Juncus and Spartina is mistakenly mapped as soil. The high performance in-

dicated by the Confusion Matrix statistics is therefore in this case misleading.

This can partly be attributed to the relatively small number of reference areas

available for MIVIS acquired in 2003, which, combined with the quite coarse

resolution of the MIVIS data (2.6 m), results in a limited amount of pixels

falling within the ground reference areas for each class (e.g. see Table 2). In

this case direct information on vegetation structures, however subjective, is

invaluable in the accuracy assessment process (e.g. Foody, 2002).

However, the effects of geometric resolution are not likely to entirely explain

the poorer performance of MIVIS classifications if one considers the note-

worthy coherence of QuickBird vegetation maps (which have an even coarser

resolution, see below the detailed discussion) with respect to field observations.

27

Speculatively, one may suggest the poorer performance of MIVIS classifica-

tions to be related to inaccuracies in geometric corrections of MIVIS data (e.g.

due the lack of an onboard inertial system).

The application of the ML classifier to 4 averaged RGBI broad bands (453 -

533; 533 - 613; 633 - 693; 753 - 833 nm) yields A=94.3% and K=0.91 (TEST2)

and vegetation distributions again in disagreement with observed features.

Similarly visually unrealistic results are obtained when the first 4 MNF bands

(A=94.0% and K=0.91, TEST2, see Table 5) or PCA bands (A=88.9% and

K=0.83, TEST2) are used.

SAM results for the original set of bands gives A=82.0% and K=0.72, with

a slight improvement in the estimated abundance of Sarcocornia with respect

to ML vegetation maps (see Figure 4 (b)).

Also the application of K-means to MIVIS 2003 data produces vegetation

maps in disagreement with the known distribution of species (especially due

to an underestimation of Sarcocornia), with statistics also indicating a low

performance (A=81.2% and K=0.69).

The Confusion Matrix statistics suggest a slight superiority of ML, particu-

larly on the basis of TEST2 and TEST3. This superiority is not confirmed

by comparisons to the overall known species distributions, which e.g. indi-

cate diffuse misclassifications of bare soil and Spartina areas in ML maps. In

conclusion, in spite of the statistics results, the direct comparisons to known

vegetation structures indicate that SAM classifications are somewhat more ro-

bust in capturing the overall vegetation and soil patterns in the case of MIVIS

2003.

28

4.3.2 MIVIS 2004

The tidal level was quite high (0.17 m a.m.s.l.) during the acquisition (30 June

2004) and thus a large part of bare soil areas, which were visible in the MIVIS

2003 acquisition, were covered by water in the MIVIS 2004 image.

MIVIS 2004 data are quite interesting because they document a sudden change

in vegetation patterns, which were otherwise relatively stable in the period

2000-2003. The vegetation maps from all classifiers, in fact, show the almost

complete replacement of Spartina by Salicornia (coral color), which was not

previously present in the San Felice salt marsh. This event is confirmed by

field inspections and has occurred on a lagoon-wide scale.

The application of ML to the 4 bands selected according to the Bhattacharyya

distance (653 - 673 nm; 693 - 713 nm; 1.15 - 1.20 µm; 1.45 - 1.5 µm) gives

A=90.9% K=0.87 (TEST2). The selection of two thermal bands confirms that

this part of the spectrum contains useful information for halophytic vegetation

mapping. The vegetation map shows an excessive presence of Juncus. The ML

results from 4 averaged RGBI bands (453 - 533; 533 - 613; 633 - 693; 753 -

833 nm) yield A=88.1% and K=0.82 (TEST2), while the application to the

4 most significant MNF bands gives A=93.5% and K=0.90 (TEST2, also see

Table 5). In all cases, Juncus is clearly overestimated (results not shown here

for brevity).

The K-means (A=77.0%, K=0.67, TEST2) and SAM (A=84.2%, K=0.77)

maps obtained from the original set of MIVIS bands (Figure 4 (c)) indicate

very similar patterns for the newly appeared Salicornia.

Even though the Confusion Matrix statistics indicate a marginally better per-

29

formance of ML (Table 5), comparisons with known vegetation structures are

suggestive of a superior performance of the SAM scheme, in particular because

ML indicates an excessive presence of Juncus. Due to the relatively coarse res-

olution the amount of reference MIVIS pixels is smaller than for the remaining

hyperspectral data. Therefore the field information on the overall species dis-

tribution is in this case important to conclude that SAM results should be

considered superior to those from ML and K-means.

4.4 IKONOS classifications

The tidal level during the acquisition was about -0.20 m a.m.s.l., thus mini-

mizing the spatial variability of water presence over the marsh.

The application of ML, SAM, and K-means to IKONOS data (as well as to

QuickBird data, as seen below) is much more straightforward than in the

case of hyperspectral sensors, as it does not require feature selection or ex-

traction procedures. The maps obtained from the three classifiers (Figure 3)

exhibit vegetation structures which are in good agreement with one another

and are coherent with ground reference observations (Table 4) and direct field

knowledge. Considering that IKONOS data have been pan-sharpened to a 1

m resolution from the original multispectral 4 m resolution, the convincing

classification results are indirect evidence of a good performance of the pan-

sharpening procedures.

All classifiers indicate a more widespread presence of Spartina than seen in any

other acquisition and relatively few bare soil areas. This may in part be due to

the cited difficulty in discriminating bare soil from Spartina areas, but direct

30

field information suggests that this mainly reflects an actual abundance of

Spartina presence resulting from the interannual variability of species presence.

Overall, the ML algorithm outperforms K-means and SAM schemes (as con-

firmed by statistical significance tests performed on K values at the 95% sig-

nificance level). However, ML performance is, as usual, very sensitive to the

number of training pixels used (experiments not shown for brevity). The de-

creased performance measured in TEST3 with respect to TEST2 (Table 5)

reasonably indicates that the SAM classifier has a greater difficulty in gen-

eralizing the representative spectral properties of the information classes of

interest, possibly due to the limited number of bands available.

4.5 QuickBird classifications

The tidal level during the acquisition (25 July 2003) was 0.08 m a.m.s.l. It is

interesting to compare the results of QuickBird classifications to those from

MIVIS 2003, acquired nearly in the same period (Figures 4 (b) and (d)). The

overall patterns are quite similar and consistent with observations (except

for a pronounced fragmentation in the MIVIS 2003 map, possibly due to the

inaccuracies of MIVIS data discussed previously).

The ML map derived using the training set of TEST1 (not shown) tends to

depart from K-means (not shown) and SAM classifications (mainly due to

a misclassification of Sarcocornia and Spartina in different areas) and is the

least accurate on the basis of visual appraisal. The Confusion Matrix statistics,

on the contrary, indicate a superior performance of ML (A=96.6%, K=0.95

for TEST1, see Table 5) with respect to the remaining two algorithms. This

31

again points to the importance of comparisons with direct field information,

particularly when the relatively coarser resolution (2.88 m) causes the pixels

to be more highly mixed, and reduces the number of training pixels, thereby

reducing the statistical significance of the Confusion Matrix. In fact, the use

of all reference areas (TEST2) for ML training reconciles the indications of

the Confusion Matrix and of visual appraisal, yielding vegetation distribu-

tion estimates in agreement with other classifiers and with known vegetation

structures.

K-means and SAM classifications are in satisfactory agreement and capture

well the general known vegetation structures, which are mostly coherent with

those from MIVIS 2003.

The performances of the ML and SAM classifiers are similar for TEST2 and

TEST3, showing that both algorithms can effectively generalize the informa-

tion contained in a limited training set.

Once again, QuickBird classifications exhibit the greatest inaccuracies in map-

ping Juncus. This species is indeed correctly identified by the classifiers where

present, but its extent is usually importantly overestimated.

5 Discussion and Conclusions

In general, it may be concluded that, for the San Felice marsh as for the

other study sites not explicitly discussed here, vegetation maps obtained from

remote sensing observations at the 1 m scale are in qualitative and quantitative

agreement with direct observations.

32

When a large amount of reference information is available (with respect to

the number of bands used) the ML classifier outperforms SAM and K-means.

In the case of hyperspectral sensors the optimal application of ML requires

the reduction of the number of spectral channels used, in order to obtain reli-

able estimates of band covariances and avoid incurring in the Hughes’ effect.

The classification experiments performed using i) a feature selection algorithm

(based on maximizing the Bhattacharyya distance between the classes), ii) fea-

tures extraction procedures (MNF and PCA), and iii) simple band averaging,

show that indeed there exists a tradeoff between the number of bands, the

signal-to-noise ratio (which was a critical factor e.g. for ROSIS and MIVIS)

and the amount of ground reference pixels. The overall balance is obtained

when adopting a higher spatial resolution, a small number of bands (4 in our

case study) and a decreased signal-to-noise ratio, at the cost of a reduced

spectral resolution. Considering the limited amount of ground reference infor-

mation usually available particularly in coastal applications, a higher spatial

resolution has the advantage of resulting in a larger number of reference pixels

to be used in classifier training (even more so in the case of highly heteroge-

neous vegetation distributions, which limit the possible extent of reference

areas). A higher spatial resolution also reduces the within-pixel heterogene-

ity, thus increasing their spectral separability (e.g. the case of Juncus in the

present application). These conclusions are also supported by the observa-

tion that classifications of MIVIS hyperspectral data (characterized by the

relatively coarse resolution of 2.6 m) have comparable, and at times poorer,

performances with respect to multispectral sensors with a high spatial resolu-

tion (particularly IKONOS with a resolution of 1 m).

We found MNF and band averaging to be the most effective methods of fea-

33

tures reduction. MNF was efficient in extracting information-containing bands

and in identifying the noise component in the general case of unequal noise

content over different bands (differently from PCA, e.g. see ROSIS classifica-

tions). Spectral averaging to obtain RGBI data sets allows performances which

are very close to those obtained using MNF transformed bands. The satisfac-

tory performance of classifications of band-averaged data and their similarity

with multispectral data classifications is a further support of the prevalent

importance of spatial with respect to spectral resolution.

When the spatial resolution is relatively low, and significantly reduces the

number of reference pixels (e.g. MIVIS and QuickBird), the validation using

statistics from the Confusion Matrix may be misleading. In this situation,

direct field information, though of a more subjective nature, assumes a crucial

role in providing adequate constraints for classifier calibration and validation.

When reference spectra are relatively less numerous, the use of SAM and K-

means provides more robust and reliable classification results with respect to

ML, because they can exploit the entire spectral information available and

reduce the Hughes’ effect.

The grouping of spectral classes into information classes to produce a K-means

classification requires quite a significant amount of information, which, given

the small typical scale of spatial heterogeneity of halophytic vegetation, is

often not available in the form of geocoded ROIs. The operator must thus

make decisions on the basis of less objective broad knowledge of vegetation

patch position or of species characteristics (e.g., the tendency of some species

to colonize the marsh interior, rather than areas near creeks). K-means may

thus be useful when the marsh area to be classified is known in its broad

spatial vegetation structures and few reference spectra are available.

34

Irrespective of the classifier adopted, the classifications of ROSIS and CASI

hyperspectral data are somewhat superior to those from multispectral obser-

vations, which have however comparable performances. This circumstance and

experiments with features reduction schemes, suggest that much of the infor-

mation contained in hyperspectral data is redundant for halophytic vegetation

classification.

In the light of the greater importance of geometric resolution with respect to

spectral resolution, and considering the small scale spatial variability of halo-

phytes, we conclude (differently from previous studies, e.g. Thomson et al.,

2003) that the use of high-resolution multispectral satellite sensors is highly

suited to effectively map intertidal vegetation. This conclusion is of some rel-

evance for studying and monitoring schemes which require repeated observa-

tions and thus may benefit from the easier acquisition of satellite data and

their relatively lower cost.

Regarding the different halophytic species occurring in the study sites consid-

ered, classification results indicate that Limonium and Sarcocornia are gen-

erally quite correctly and consistently identified using all sensors. The com-

parison of classifier results for all the acquisitions described indicates that a

robust identification of Juncus is quite problematic. This is likely due to the

fact that patches of Juncus are usually quite small (order of a few square me-

ters), compared to the resolutions of the sensors. It is therefore quite difficult

to identify a statistically significant number of reference pixels. Calibration

and validation reference areas are thus inevitably composed by a large pro-

portion of mixed pixels, whose spectra are not truly representative of Juncus.

This circumstance generates both commission and omission errors: i) the ref-

erence spectra used for classification are not ‘truly pure’ and may be similar

35

to spectra from pixels with ‘mixed vegetation’ (which are thus erroneously

be classified as Juncus); ii) pixels containing Juncus rarely display a ‘pure’

Juncus spectrum, due to their ‘mixed’ nature, and may thus be mistakenly

attributed to another class.

Remote sensing mapping of Spartina may also be at times uncertain. This can

be explained by considering that in Spartina areas plant density and structure

are such that a substantial amount of soil is visible. The spectral signature

of a Spartina area is thus a combination of the spectral signature of these

species and that of soil. This mixture of spectra may be confused with the

spectral signature typical of a soil containing microphytobenthos, which carries

significant quantities of chlorophyll, thus causing an overestimation of Spartina

areas. This situation is mostly relevant for the lowest areas of the marsh, where

typically both Spartina and bare soil areas occur.

In conclusion, remote sensing has been shown to be a useful tool for salt-marsh

vegetation mapping and for the quantitative characterization of its spatial dis-

tribution. Remote sensing classifications should be considered the technique

of choice for salt-marsh study and monitoring. In particular, remotely sensed

vegetation maps allow the exploration of the wide range of scales of interest in

intertidal areas and do not involve the inaccuracies associated with extensive

interpolation and extrapolation of (necessarily point) field observations. Fur-

thermore, direct field mapping does not provide ‘instantaneous’ descriptions

of vegetation distribution, requiring very long periods of time to be completed

in large areas, and e.g. cannot quantitatively describe events such as the ob-

served replacement, at all study sites, of Spartina by Salicornia between years

2003 and 2004. On the contrary, the ability of remote sensing classifications

to describe the distribution of halophytes in space and time illustrates the

36

suitability of remote sensing classifications for an accurate monitoring of the

spatial structure and the time evolution of salt-marsh vegetation.

Acknowledgements. This research was funded by TIDE EU RTD Project

(EVK3-CT-2001-00064). The ROSIS data analyzed were acquired through

the Hysens project 2000 DLR-EU. The authors wish to thank Magistrato alle

Acque di Venezia for making the IKONOS data available.

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Table Captions

Table 1 - Characteristics of the sensors considered and of the corresponding

acquisitions. Tidal level, Visibility, Azimuthal Angle and Zenithal Angle values

are given for an intermediate time between beginning and end of acquisition.

Table 2 - Number of ROIs (NR) and total number of pixels (NP) in ROIs

for each vegetation type and sensor. Lim = Limonium; Jun = Juncus; Spa =

Spartina; Sar = Sarcocornia; Sal = Salicornia.

Table 3 - TEST2 Confusion Matrix for CASI classifications of San Felice salt

marsh using K-means (KM), Spectral Angle Mapper (SAM) and Maximum

Likelihood (ML) classifiers. Lim = Limonium; Jun = Juncus; Spa = Spartina;

Sar = Sarcocornia.

Table 4 - TEST2 Confusion Matrix for IKONOS classifications of San Fe-

lice salt marsh using Maximum Likelihood (ML), Spectral Angle Mapper

(SAM) and K-means (KM) classifiers. Lim = Limonium; Jun = Juncus; Spa

= Spartina; Sar = Sarcocornia.

Table 5 - Overall Accuracy (A) and Kappa coefficient (K) summarizing the

Confusion Matrix characteristics for all sensors and validation TESTs consid-

ered. The classifications referred to here are based on the original set of bands

for SAM and K-means (except ROSIS classifications, which were performed

by excluding the noisy bands in the ’blue’ region) and on the first 4 MNF

bands for ML.

45

Figure Captions

Fig. 1. The San Felice salt marsh in the northern part of the Venice Lagoon (Italy).

Fig. 2. Classifications of San Felice CASI September 2002 data using all reference ar-

eas (TEST2). ((a) Maximum Likelihood, (b) Spectral Angle Mapper, (c) K-means).

Fig. 3. Classifications of San Felice IKONOS June 2001 data using all reference areas

(TEST2). ((a) Maximum Likelihood, (b) Spectral Angle Mapper, (c) K-means).

Fig. 4. Spectral Angle Mapper vegetation maps (TEST2) for the ROSIS (a), MIVIS

2003 (b), MIVIS 2004 (c), and QuickBird (d) data considered.

46

Table 1Characteristics of the sensors considered and of the corresponding acquisitions.

Tidal level, Visibility, Azimuthal Angle and Zenithal Angle values are given for anintermediate time between beginning and end of acquisition.

ROSIS CASI MIVIS IKONOS QuickBirdPlatform Airborne Airborne Airborne Satellite SatelliteSpatial 1 m 1.3 m 2.6 m P: 1 m P: 0.72 m

resolution M: 4 m M: 2.88 mSpectral 430-850 nm 432.9-874.1 nm 430-12700 nm P: 450-900 nm P: 450-900 nmrange M: 450-880 nm M: 450-900 nm

Radiometric 12 bit 16 bit 12 bit 11 bit 16 bitresolution

Bands 115 15 102 1 + 4 1 + 4Altitude 1000 m 840 m 1300 m 680 km 450 km

Acquisition date 08/07/2000 29/09/2002 05/07/2003 30/06/2004 26/06/2001 25/07/2003Flight time (GMT) 8:00 8:55 9:30 11:30 10:00 9:45

Tidal level -16 cm +18 cm -28 cm +17 cm -20 cm +8 cmVisibility 6.5 km 48 km 56.5 km 42 km 12 km 47 km

Azimuthal Angle 103◦ 138◦ 128◦ 190◦ 140◦ 136◦

Zenithal Angle 46◦ 56◦ 31◦ 23◦ 27◦ 32◦

47

Table 2Number of ROIs (NR) and total number of pixels (NP) in ROIs for each vegeta-tion type and sensor. Lim = Limonium; Jun = Juncus; Spa = Spartina; Sar =Sarcocornia; Sal = Salicornia.

Sensor Lim Jun Spa Sar Sal SoilROSIS NR=3 NR=2 NR=4 NR=4 — NR=2

NP=317 NP=150 NP=463 NP=451 — NP=188CASI NR=4 NR=1 NR=2 NR=3 — NR=2

NP=470 NP=40 NP=174 NP=207 — NP=32MIVIS 2003 NR=3 NR=2 NR=1 NR=2 — NR=2

NP=197 NP=36 NP=44 NP=58 — NP=16MIVIS 2004 NR=7 NR=4 NR=1 NR=3 NR=2 NR=3

NP=244 NP=45 NP=16 NP=68 NP=48 NP=41IKONOS NR=3 NR=2 NR=4 NR=4 — NR=2

NP=317 NP=150 NP=463 NP=451 — NP=187QuickBird NR=3 NR=2 NR=1 NR=2 — NR=2

NP=165 NP=36 NP=44 NP=53 — NP=16

48

Table 3TEST2 Confusion Matrix for CASI classifications of San Felice salt marsh usingK-means (KM), Spectral Angle Mapper (SAM) and Maximum Likelihood (ML) clas-sifiers. Lim = Limonium; Jun = Juncus; Spa = Spartina; Sar = Sarcocornia.

Test areas (pixel)Classes Lim Jun Spa Sar Soil Total

ML 455 0 6 4 0 465Lim SAM 412 0 4 11 0 427

KM 417 2 1 7 0 427ML 1 39 8 0 0 48

Jun SAM 0 35 10 0 0 45KM 0 28 4 0 0 32ML 2 1 160 0 0 163

Spa SAM 2 5 150 0 0 157KM 30 6 153 0 0 189ML 12 0 0 203 0 215

Sar SAM 56 0 8 196 0 260KM 23 0 0 200 0 223ML 0 0 0 0 32 32

Soil SAM 0 0 2 0 32 34KM 0 4 16 0 32 52ML 470 40 174 207 32 923

Total SAM 470 40 174 207 32 923KM 470 40 174 207 32 923

49

Table 4TEST2 Confusion Matrix for IKONOS classifications of San Felice salt marsh us-ing Maximum Likelihood (ML), Spectral Angle Mapper (SAM) and K-means (KM)classifiers. Lim = Limonium; Jun = Juncus; Spa = Spartina; Sar = Sarcocornia.

Test areas (pixel)Classes Lim Jun Spa Sar Soil Total

ML 285 3 10 3 3 304Lim SAM 276 15 14 39 3 347

KM 269 4 6 116 0 395ML 7 125 3 3 16 154

Jun SAM 12 131 6 19 24 192KM 7 69 1 13 9 99ML 16 3 448 1 2 470

Spa SAM 0 0 423 0 3 426KM 12 3 406 3 0 424ML 9 19 0 443 2 473

Sar SAM 29 0 18 393 0 440KM 27 12 32 318 3 392ML 0 0 0 1 164 165

Soil SAM 0 4 0 0 157 161KM 2 62 16 1 175 256ML 317 150 463 451 187 1568

Total SAM 317 150 463 451 187 1568KM 317 150 463 451 187 1568

50

Table 5Overall Accuracy (A) and Kappa coefficient (K) summarizing the Confusion Matrixcharacteristics for all sensors and validation TESTs considered. The classificationsreferred to here are based on the original set of bands for SAM and K-means (exceptROSIS classifications, which were performed by excluding the noisy bands in the’blue’ region) and on the first 4 MNF bands for ML.

TEST1 TEST2 TEST3A K A K A K

ML 99.2% 0.99 99.0% 0.99 97.8% 0.97ROSIS SAM 92.6% 0.90 93.9% 0.92 85.8% 0.81

KM — — 90.4% 0.87 Same as TEST2ML 92.6% 0.89 96.3% 0.94 93.4% 0.90

CASI SAM 96.1% 0.94 89.4% 0.84 90.9% 0.86KM — — 89.9% 0.85 Same as TEST2ML 88.3% 0.84 94.0% 0.91 93.4% 0.89

MIVIS03 SAM 89.2% 0.83 82.0% 0.72 76.3% 0.65KM — — 81.2% 0.69 Same as TEST2ML 80.8% 0.59 93.5% 0.90 89.0% 0.83

MIVIS04 SAM 85.5% 0.76 84.2% 0.77 80.5% 0.71KM — — 77.0% 0.67 Same as TEST2ML 97.2% 0.96 94.8% 0.93 93.4% 0.91

IKONOS SAM 89.0% 0.85 88.0% 0.84 74.6% 0.67KM — — 78.9% 0.73 Same as TEST2ML 96.6% 0.95 92.3% 0.88 91.1% 0.86

QuickBird SAM 89.5% 0.86 81.5% 0.72 81.2% 0.72KM — — 86.9% 0.80 Same as TEST2

51

VENICE

STUDY

SITE

ITALY

UTM, WGS-84Zone 33 NorthUTM, WGS-84Zone 33 North

0 10Km

Fig. 1. The San Felice salt marsh in the northern part of the Venice Lagoon (Italy).

52

0 100m

LimoniumJuncus

SpartinaSarcocornia

SoilWater

CASI 29/09/2002

a) ML

c) K-means

b) SAM

Fig. 2. Classifications of San Felice CASI September 2002 data using all reference ar-eas (TEST2). ((a) Maximum Likelihood, (b) Spectral Angle Mapper, (c) K-means).

53

IKONOS 26/06/20010 100

m

LimoniumJuncus

SpartinaSarcocornia

SoilWater

a) ML

b) SAM

c) K-means

Fig. 3. Classifications of San Felice IKONOS June 2001 data using all reference ar-eas (TEST2). ((a) Maximum Likelihood, (b) Spectral Angle Mapper, (c) K-means).

54

a) ROSIS 08/07/2000

b) MIVIS 05/07/2003

c) MIVIS 30/06/2004

LimoniumJuncus

SpartinaSarcocornia

SoilWater

m0 100

Salicornia

d) QuickBird 25/07/2003

Fig. 4. Spectral Angle Mapper vegetation maps (TEST2) for the ROSIS (a), MIVIS2003 (b), MIVIS 2004 (c), and QuickBird (d) data considered.

55