Hyperspectral remote sensing of salt marsh vegetation, morphology and soil topography

Hyperspectral remote sensing of salt marsh vegetation, morphology and soil topography

Physics and Chemistry of the Earth 28 (2003) 15–25 www.elsevier.com/locate/pce Hyperspectral remote sensing of salt marsh vegetation, morphology and ...

580KB Sizes 0 Downloads 54 Views

Physics and Chemistry of the Earth 28 (2003) 15–25 www.elsevier.com/locate/pce

Hyperspectral remote sensing of salt marsh vegetation, morphology and soil topography Sonia Silvestri a

a,*

, Marco Marani a, Alessandro Marani

b,c

Dipartimento IMAGE, Universit a degli studi di Padova, via Loredan 20, Padova 35100, Italy b Dipartimento di Scienze Ambientali, Universit a CaÕ Foscari di Venezia 30100, Italy c Istituto Veneto di Scienze, Lettere ed Arti, Venezia 30100, Italy

Abstract The present paper deals with the relationship between vegetation patterns and salt marsh morphology in the Venice lagoon and with the use of remote sensing to infer salt marsh morphologic characteristics from vegetation mapping. Field measurements indicate that salt marsh vegetation species (halophytes) are reliable indicators of ground elevation and live within typical elevation ranges characterised by standard deviations of less than 5 cm. A model is then developed which uses vegetation as a morphological indicator of soil topography to estimate ground elevation from fractional cover values of each vegetation type. The use of data from an airborne remote hyperspectral sensor is presented as a means of discriminating between different salt marsh vegetation communities. Vegetation maps obtained from unmixing techniques have then been used to produce digital elevation maps (DEM) of salt marsh areas. The DEM based on halophytes cover estimates and extracted from high spatial and spectral resolution data allows a high estimation accuracy, with an error standard deviation of a few centimetres in the considered study area within the Venice lagoon. The accuracy and resolution attainable through this method are comparable and often superior to those obtained through state of the art laser altimetry.  2003 Elsevier Science Ltd. All rights reserved. Keywords: Hyperspectral remote sensing; Unmixing; Salt marsh vegetation; Soil elevation; Morphology

1. Introduction Remote sensing is one of the most efficient methods for environmental monitoring in coastal areas. Salt marshes and tidal flats, in particular, are difficult to access, highly dynamical and extensive environments. A method for extensive 2D sampling of environmental parameters is needed. Remote sensing (airborne or satellite) data can be collected simultaneously over large areas and, given the repeatability of data collection, dynamical processes can be sampled using a sequence of data in time. In the past, colour aerial photographs, satellite multispectral images and airborne digital multispectral camera images have been successfully used to characterise coastal zones and salt marshes (Dale et al., 1986; Donoghue and Shennan, 1987; Silvestri, 1997; Phinn et al., 1999). Improvements have been shown, in this field, by recent applications of classification tech-

*

Corresponding author. Tel.: +39-49-8275449. E-mail address: [email protected] (S. Silvestri).

niques to hyperspectral airborne data (Bajjouk et al., 1996; Eastwood et al., 1997; Smith et al., 1998; Silvestri et al., 2002). The present paper analyses hyperspectral airborne data for salt marsh vegetation mapping, explores the possibility of discriminating among different species and shows that useful information regarding soil topography may be inferred. The development of vegetation over salt marshes (halophytes) is mainly determined by the frequency and duration of floods (Pethick, 1984; Olff et al., 1988), which, in turn, depends on elevation, position and local topography of the marshes. Chapman (1964) hypothesised a plant succession scheme based on the assumption that after the colonisation phase, the substrate would be more stable and sediments would be trapped by the vegetation, allowing other species to invade the marsh. In this view the relationship between soil elevation and vegetation exhibits an important feedback: the overall and local topography of a salt marsh determines the development of vegetation and the latter, in turn, increases the stability of the soil through the action of its roots, promoting the accretion of the marsh

1474-7065/03/$ - see front matter  2003 Elsevier Science Ltd. All rights reserved. doi:10.1016/S1474-7065(03)00004-4

16

S. Silvestri et al. / Physics and Chemistry of the Earth 28 (2003) 15–25

(Pethick, 1984; Adam, 1990). It is thus seen that the topographical features of salt marshes and their stability are intimately linked to the presence and distribution of vegetation which therefore becomes a key study element, both for its intrinsic ecological importance and as a morphological indicator. Vegetation type may be used as a way to study salt marsh soil elevation and its dynamics. The spatial distribution of halophytic vegetation over salt marshes is characterised by the existence of vegetation ‘‘patches’’ (Fig. 1). This characteristic spatial distribution of plants is known as zonation (Chapman, 1964; Pignatti, 1966; Silvestri et al., 2000): most of the salt marsh is occupied only by a single species or by a characteristic association of a few species. The properties of the spatial distribution of vegetation allow their study using remotely sensed data, in fact: (i) the species grow in ‘‘patches’’, areas which maintain homogeneity also from a radiometric point of view; (ii) if environmental conditions do not change, the associations of species within each ‘‘patch’’ remain constant in a given year; (iii) sharp and visible borders separate one patch from another, forming a mosaic of contiguous patches. Previous studies (Silvestri, 1997) have been carried out with the purpose of recognising Venetian halophytes using remotely sensed data. In these studies colour photographs were analysed using classical supervised classification procedures. The analyses allowed only a partial discrimination between vegetation growing along tidal channels/creeks (which is dense and reddish) and vegetation growing on the inner marsh areas. Despite the limited results, the research has contributed to characterising the difficulties in halophyte mapping, which may be summarised as follows: (i) within each patch one species usually prevails occupying the greater part of the area, but other species are usually present with smaller cover percentages; (ii) the patch extension can vary from a few square meters to tens of square meters; (iii) vege-

tation density varies greatly in space; (iv) soil humidity varies greatly in time, due to tidal oscillations and, as a consequence, the soil can emerge or be completely submerged by a water layer several tens of centimetres deep; (v) the tidal network is very well developed in Venetian salt marshes, presenting creeks whose widths vary from several meters to a few centimetres. On the basis of these observations one can conclude that a spatial scale suitable for salt marsh vegetation studies should not exceed a few meters and, as a consequence, high spatial resolution remotely sensed data are needed. Given the very quick dynamics of water elevation (the period of the first tidal harmonic in the Venice lagoon is 12 h) data should be collected within a few tens of minutes over the entire area in order to allow for the assumption of homogenous conditions throughout the scene. In agreement with other authors (Gomarasca, 1997; Campbell, 1996) a spectral range covering the visible and the NIR is deemed most suitable for the study at hand and hyperspectral data (tens of channels) rather than multispectral ones are used to enable the discrimination of vegetation types which can be spectrally quite similar (Donoghue and Shennan, 1987; Dale et al., 1986; Alberotanza et al., 1999; Smith et al., 1998; Eastwood et al., 1997; Bajjouk et al., 1996). The first part of this paper describes, through evidence taken from field campaigns, the strong relationship between halophytes and soil topography in the Venice lagoon. These measurements are used to calibrate a model which uses halophytic vegetation as a morphological indicator of soil topography to estimate ground elevation from fractional cover values of vegetation types. The second part of the paper will focus on species recognition through airborne hyperspectral data and on the performance of a linear mixture model for detecting fractional cover values at a sub-pixel scale.

Fig. 1. Picture of salt marsh vegetation: the zonation.

S. Silvestri et al. / Physics and Chemistry of the Earth 28 (2003) 15–25

The development of a digital elevation model based on fractional cover values of halophytes is discussed at the end of the paper.

2. Study area description The Venice lagoon (Fig. 2), NE Italy, is ItalyÕs largest lagoon (550 km2 ) with a mean water depth of approximately 1.5 m (Rijstenbil et al., 1996). The tidal regime is semi-diurnal, with a complete tidal cycle every 12 h, and the maximum astronomical tidal excursion (at the inlets) is about 70 cm around mean sea level. From a structural point of view a lagoon environment may be divided into four main elements with different hydrodynamical and ecological characters: areas which are always submerged (the channels and the flat expanses around them), areas that emerge only during extremely low tides (tidal flats), areas which are flooded only during high tide (salt marshes and tidal creeks), and areas which are always emerged (islands). Salt marshes represent the transition between submerged and emerged environments and, in order to objectively identify them, they may be defined as areas which are colonised by halophytic vegetation, i.e. by vegetation which has adapted, to varying degrees, to very salty environments (e.g. Silvestri, 1997). Salt marshes currently cover an area of approximately 37 km2 in the Venice lagoon. Human activities have historically had a strong impact on the morphology of the lagoon: the rivers that once used to terminate in the lagoon have been diverted directly to the sea during the 15th and 16th centuries. Deep channels at the three entrances have been dredged and long jetties have been built; many channels and

17

creeks are continuously dredged to maintain their hydraulic efficiency. The lagoon as a whole is currently experiencing a strong erosional trend, particularly dangerous for the survival of salt marshes, which become a key element to understand the dynamics of intertidal environments. To better understand erosional processes and their causes it is useful to start from the observation of the most stable areas, which are located in the northern part of the tidal basin, and then to compare their characteristics to those of the rapidly degrading central and southern basins. The analyses presented in the following focus on a salt marsh selected in the northern, more stable, part of the Venice lagoon called San Lorenzo (Caniglia et al., 1997; Day et al., 1999). The marsh is surrounded by the San Lorenzo channel to the North, by the Gaggian channel to the West, by the San Felice channel to the South, and by the shallow area Palude del Tralo to the East (Fig. 2). The halophytic species that colonise the San Lorenzo marsh are quite limited in number, namely: Salicornia veneta (Sal), Spartina maritima (Sp), Limonium narbonense (Li), Sarcocornia fruticosa (Sa), Juncus maritimus (Ju), Puccinellia palustris (Pu), Inula crithmoides (In), Halimione portulacoides (Ha), Suaeda maritima (Su), Arthrocnemum macrostachyum (Ar), Aster tripolium (As) (nomenclature follows Caniglia et al. (1997)).

3. Materials and methods 3.1. The field campaigns The strong relation linking soil elevation and halophytes has been experimentally confirmed and

Fig. 2. The study area (San Lorenzo salt marsh) in the North basin of the Venice lagoon (Gauss Boaga geographic reference system).

18

S. Silvestri et al. / Physics and Chemistry of the Earth 28 (2003) 15–25

Fig. 3. Soil profile along the white transect signed in Fig. 2.

quantified through a preliminary topographic survey and is illustrated by the soil profile in Fig. 3 (identified by the white line in Fig. 2) along which measurements have been taken every 10 m. The sketch of Fig. 3 shows not only the relation between species and soil elevation, but also between vegetation and the structure of the marsh. For example, Inula crithmoides and Puccinellia palustris tend to grow along the edges of creeks and channels, usually more elevated, while Spartina maritima and Limonium narbonense are found in the inner areas, usually depressed and often near to salt pans. In October 1999 the elevation of the marsh soil was measured with a laser theodolite at randomly chosen sites uniformly distributed over the San Lorenzo salt marsh (Silvestri et al., 2000). At each site, the relative abundance of vegetation species has been evaluated over areas of 1 m2 using the Braun–Blanquet method (Pignatti, 1953; Mueller-Dombois and Ellenberg, 1974) and the coverage classes indicated in Table 1 (the estimated abundances of the species by definition sum up to one within each sampled area). This systematic survey allowed the determination of the relation between ground elevation and halophytic species on firm statistical grounds. Mean soil elevation values for each species and the relative standard deviations are shown in Fig. 4, Table 1 Coverage classes (Pignatti, 1953) Class

Percentage coverage

+ 1 2 3 4 5

Presence <1% 1–20% 21–40% 41–60% 61–80% 81–100%

Fig. 4. Mean soil elevation values for each halophytic species and relative standard deviations in the San Lorenzo salt marsh (Sp ¼ Spartina maritima; Li ¼ Limonium narbonense; Sa ¼ Sarcocornia fruticosa; Ju ¼ Juncus maritimus; Pu ¼ Puccinellia palustris; In ¼ Inula crithmoides; Su ¼ Suaeda maritima; Ar ¼ Arthrocnemum macrostachyum).

where it can be observed that Limonium narbonense and Puccinellia palustris are most sensitive to soil elevation, having standard deviations of 4.8 and 5.3 cm respectively, while the species showing the greater adaptability is Juncus maritimus. Spartina maritima is adapted to live in the lowest zones of the marsh, while Arthrocnemum macrostachyum has been found on the most elevated soils. 3.2. The remote sensing campaign An airborne hyperspectral campaign took place in the Venice lagoon on September the 18th 1998. The MIVIS sensor (DaedalusÕ multispectral infrared and visible imaging spectrometer) is a modular instrument with

S. Silvestri et al. / Physics and Chemistry of the Earth 28 (2003) 15–25 Table 2 MIVIS spectral characteristics (Bianchi et al., 1995) Spectrometer #

Bands

Lower limit (nm)

Upper limit (nm)

Band width (nm)

1 2 3 4

1–20 21–28 29–92 93–102

433 1150 1983 8180

833 1550 2478 12 700

20 50 9 340–540

four spectrometers whose characteristics are summarised in Table 2. It is an across-track scanner, with a field of view (FOV) of 71.1 and an instantaneous FOV of 2.0 mrad. The MIVIS records 755 pixel per line and the data are stored in 12 bits per pixel. During the flight over the Venice lagoon the aeroplane flew at a height of 1500 m, giving a geometric resolution of 3  3 m2 . During the flight, ancillary data have been collected at the ground, measuring the optical thickness of the atmosphere with a sunphotometer (CIMEL CE-318-2, five channels at the wavelengths 0.44, 0.67, 0.87, 0.94 and 1.02 lm, kindly offered by NERC––UK), which has been positioned on Torcello island, near the survey areas. 3.3. Data acquisition and pre-processing MIVIS data have first been radiometrically corrected using the calibration relation: Ri ¼ ðR0  Rr Þ=C; where Ri is the radiance value in W/(m2 sr nm), R0 is the digital number value measured by the instrument during the flight, Rr is the digital number measured on a reference panel within the instrument, and C is a calibration determined in the laboratory the day before the flight. The data have then been atmospherically corrected, taking into consideration the direct solar irradiance, the atmospheric irradiance, the interaction of the electromagnetic energy with the atmosphere (absorption, reflection and scattering) and the sensor geometry. Such correction is important to allow the comparison of remotely sensed data taken by different instruments, or with portable spectrometers. Moreover, this operation allows the creation of a spectral library useful to define the spectral characteristics of the targets. The atmospheric correction has been carried out by applying the 6S model (Second Simulation of the Signal Satellite in the Solar Spectrum, Vermote et al., 1995, 1997). The model simulates radiative processes occurring in the visible and NIR parts of the electromagnetic spectrum, and therefore it has been applied only to data collected with the first MIVIS spectrometer (in the range 433–833 nm). The flight was performed with optimal meteorological conditions (data available at the IVSLA website, IVSLA, 2002). The meteorological service of the Marco Polo international airport (Venice) supplied the data of horizontal visibility, which was higher than 10 km throughout the flight. The 6S model has been applied

19

using the Maritime aerosol model and the optical thickness data collected during the flight with the sunphotometer. When the atmospheric correction model is applied to satellite images, the distance of the instrument from the surface can be considered to be constant throughout the entire image, since the FOV of the satellite sensors is very small with respect to the satellite distance. On the contrary, when dealing with a sensor mounted on an aircraft, as is the case for the MIVIS, the optical path of an electromagnetic wave coming from the center of the flight line is considerably different from that coming from its edges. In the case of the flight with the MIVIS sensor at a height of 1500 m, since the FOV is 71, the distance between the first and the last pixel of a scan line is 1830 m, with an increment of optical thickness equal to 22%. Such path differences have been taken into account in the atmospheric correction of MIVIS data (see Silvestri (2000) for details). In order to complete the pre-processing operations and to rectify images for the comparison between ground and remote measurements, a warping transformation has been applied to MIVIS data (rotation, scaling and translation), using ground control points. A nearest neighbour resampling method has been used. Unfortunately, the data collected had not been previously geometrically corrected by the agency carrying out the flight to compensate for panorama distortions due to scanner geometry and effects introduced by aircraft position perturbations. Measurements of aircraft position were no longer available after the flight. Due to the lack of a proper geometric correction, the warping procedure gave an root-mean-square (RMS) error of 1.8 pixel. The last pre-processing operation was the application of a mask to exclude water areas from the analyses, detecting channels, creeks and pans with a classical supervised classification (spectral angle mapper classification, e.g. Kruse et al., 1993). Moreover, pixels with only soil have been isolated and masked, with the purpose of isolating vegetated areas only. 3.4. Sub-pixel data analysis Automatic classification of multi- and hyper-spectral data collected over a region is typically performed by applying a simple decision rule (like, for example, in the maximum likelihood method) based on statistics. This approach implies that each pixel is classified individually and assigned to a specific class. When the objects to be detected are small compared to the pixel size, the signal from a given pixel will be the result of a ‘‘mixture’’ of different spectral signatures. The linear mixture model (Settle and Drake, 1993) assumes that the reflectance of a pixel is a linear combination of the reflectance of each material (or end-member) present within the pixel.

20

S. Silvestri et al. / Physics and Chemistry of the Earth 28 (2003) 15–25

Considering that no multiple scattering occurs, i.e. that electromagnetic waves only interact with a single target, the reflectance, ri , in the ith spectral band may be expressed as: n X ri ¼ ðaij  fj Þ þ ei with i ¼ 1; . . . ; m ð1Þ j¼1

where m is the number of spectral bands, n is the number of end-members within the pixel, fj is the cover fraction for the jth end-member and aij is the jth end-member reflectance in band i. Once the spectral signatures of the n end-members are specified, a linear spectral unmixing procedure determines the relative abundances, for Pmeach pixel and each end-member, by minimizing e ¼ i¼1 e2i . Three unmixing procedures are possible: unconstrained, partially constrained and fully constrained unmixing (Metternicht and Fermont, 1998). The present work applies an unconstrained technique, in which physically impossible negative relative covers are accepted and no constraint is imposed to force the sum of cover fractions to sum to unity (Elmore et al., 2000). This method allows a control on the outcome of the unmixing procedure, which, ideally, should only produce positive relative abundances. The unmixing results are a series of matrices, containing the spatial distribution of relative covers for each end-member, plus the RMS error matrix: ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi rP m 2 i¼1 ei RMS ¼ m

ð2Þ

which is calculated for each image pixel. The RMS values should be low throughout the image and with a uniform, uncorrelated distribution. The success of an unmixing procedure heavily depends on the accuracy of the selection of end-member spectra. Several alternatives are possible (McGwire et al., 2000; Elmore et al., 2000; Metternicht and Fermont, 1998): • to select end-member pixels in the image and compute a mean spectrum representing each category; • to collect radiometric spectra with a portable instrument; • to select spectra from spectral libraries; • to compute a pixel purity index, PPI (Boardman et al., 1995); • to apply a principal component analysis, PCA (Lelong et al., 1998; Bajjouk et al., 1998). Unfortunately, no spectral libraries are available for halophytes, and the PPI and PCA applications did not yield adequate results for the selection of end-members. It was thus decided to select end-member spectra directly on the image, locating pixels in the areas with monospecific vegetation (determined through direct field

Fig. 5. Spectral signatures of end-member vegetations.

inspection). Within the area of interest, three pure endmembers were selected: Spartina marittima, Limonium narbonense and Sarcocornia fruticosa. An end-member for the mixture of vegetation types growing along the creek edges was also defined and identified as Edge vegetation. It should also be noticed that no soil endmember was introduced as no pixel containing just bare soil could be selected in the area of interest. The endmember reflectance spectra defined are shown in Fig. 5. Before applying the linear unmixing procedure it was found that not all MIVIS spectral bands were contributing useful information and a minimum noise fraction rotation was applied (Green et al., 1988). The procedure applies a rotation to the original data separating noise from the actual signal in the data. The transformed components are sorted in descending order on the basis of the information/noise ratio and the first five components were selected for use in subsequent analyses. The selection of the number of bands to be used in the unmixing was based on the requirement that the number of bands should be larger or equal to the number of endmembers plus ones in order to have a number of equations larger than the number of unknowns. 3.5. Soil elevation estimation: model development Given the strong relationship between halophytes and soil topography, a method for estimating soil elevation using vegetation species abundances was developed. Let us consider, as an example, three vegetation species V1, V2 and V3, growing within an area A and occupying sub-areas A1, A2 and A3 respectively. The model proposed assumes that mean soil elevation Z of the total area A is linearly dependent on the cover fraction nj ¼ Aj =A of the generic jth species as follows: Z¼

n X j¼1

hj nj

ð3Þ

S. Silvestri et al. / Physics and Chemistry of the Earth 28 (2003) 15–25

21

where  hj is the mean soil elevation of a monospecific population of species j. Eq. (3) also assumes that all the species colonising the marsh are known and that they cover the soil entirely (i.e. the abundances sum up to one). When vegetation density is very low and bare soil occupies most of the area A, the model is not applicable since no sensible mean elevation can be attributed to the soil category. Such areas have to be preliminarly identified and excluded from the analysis. The model calibration has been performed by randomly extracting 190 soil elevation measurements from the 240 data taken on the San Lorenzo marsh: the mean soil elevation  hj for each species was computed on the basis of this data subset. After calibration, Eq. (3) was applied to the 50 remaining measurements. The comparison between the estimated levels and those measured in the field are discussed in the following section.

4. Results and analyses Unmixing results are shown in Fig. 6, where (A)–(D) are grey-scale representations of the spatial distribution of the relative abundance of Spartina maritima, Limonium narbonense, Sarcocornia fruticosa and Edge vegetation respectively. It is interesting to note that the model correctly identifies low-lying inner marsh areas, where Spartina is predominant, while the edge areas, more elevated, are mostly covered by Sarcocornia and Edge vegetation. These results are in good accordance with the soil profile previously shown in Fig. 3. The RMS error of the unmixing, shown in image (E) of Fig. 6, is quite low and uniform throughout the entire scene. To better verify the accuracy of the unmixing, Table 3 shows the percentage of pixels with estimated relative abundance values smaller than 0 or larger than 1: as discussed above these are unphysical values and would not be present in the result of an ideally perfect unmixing. Spartina maritima and Sarcocornia fruticosa are characterised by the largest number of unphysical relative cover values, while Limonium narbonense is associated with the lowest number of erroneous relative abundance estimates. This is probably due to the fact that Spartina grows on very low-lying areas and water accumulations over the soil could have interfered with the spectral signatures. Sarcocornia, on the contrary, usually grows at higher elevations but is usually characterised by a low density and the presence of bare soil could be responsible for the unmixing errors. The results of the model for estimating soil elevations using vegetation species abundances are shown in Fig. 7, where the comparison between the estimated and measured soil elevations can be seen. The agreement between the two series of data is quite good: the interpolating line has a determination coefficient equal to 0.61. Nevertheless, it is important to underline that the

Fig. 6. Unmixing results represented in grey-scale images: (A) Spartina maritima, (B) Limonium narbonense, (C) Sarcocornia fruticosa, (D) Edge vegetation, (E) RMS value.

Table 3 Percentage of pixels with a value smaller than 0 or bigger than 1 End-member

% pixel 62 [0,1]

Spartina maritima Limonium narbonense Sarcocornia fruticosa Edge vegetatione

20.5 7.2 19.9 12.5

interpolating line does not intersect the origin. The model, in fact, tends to overestimate the elevation of depressed marsh areas, while it underestimates the elevation of higher areas. This is probably due to the fact that the ‘‘tails’’ of the distributions of soil elevation

22

S. Silvestri et al. / Physics and Chemistry of the Earth 28 (2003) 15–25 Table 4 Soil elevation value hj calculated for each end-member End-member

hj (cm)

Spartina maritima Limonium narbonense Sarcocornia fruticosa Vegetazione di bordo

14.6 20.6 28.0 32.0

Fig. 7. Comparison between the estimated and measured soil elevations.

cannot be accurately accounted for with a model that only makes use of mean values. Nevertheless, the standard deviation of the estimation error depends on elevation and its value is, on the average, 5.3 cm. It is interesting to note that such high accuracy in the estimation of the salt marsh elevation is comparable (or higher) to that obtained by use of current airborne laser altimeters. Huising and Gomes Pereira (1998) analysed the performances of a number of different laser altimeters (ALTM1020 TS and ALTM 1020 GG, Toposys, Fli-Map, and TopEye) and reported highly variable error standard deviations dependent on target characteristics. In relatively flat areas (which is the case of salt marshes) laser altimeters yield standard deviation values varying from 8 cm (Toposys) to 11 cm (ALTM1020 TS) when the area is not vegetated or is covered by rock, while in sandy areas or tidal areas covered by low vegetation the standard deviations are 13 cm for Toposys and 16 cm for ALTM1020 TS (Huising and Gomes Pereira, 1998). These measurement errors are quite large, particularly in a salt marsh environment: in the Venice lagoon the range of salt marsh soil elevation values is about 50 cm. In order to obtain a complete representation of the spatial distribution of vegetation and of soil elevation, unmixing results have been normalised into the [0,1] interval. Pixel mean elevation was then estimated through Eq. (3) by using the relative abundance values estimated through the unmixing procedure, i.e. nj ¼ fj . In the selected salt marsh area only four vegetation species were present and the soil elevation values hj of the four end-members are listed in Table 4. The digital elevation model obtained through this procedure is shown in Fig. 8 where lower soil levels are in purple and blue, while the highest values are represented in red. The results are in good agreement with field campaign measurements, as may be noticed by comparing observations along the two transects represented in Fig. 8.

Fig. 8. Digital elevation model (DEM) of a San Lorenzo salt marsh area.

Fig. 9. Comparison between observed and estimated soil elevations along the profile A.

Soil profiles are shown in Figs. 9 and 10, where it can be seen that the actual concave soil profile is well described by model results.

5. Discussion and conclusion Salt marsh topography is an important characteristic for understanding and modelling tidal environments and yet it is, in general and particularly in the lagoon of

S. Silvestri et al. / Physics and Chemistry of the Earth 28 (2003) 15–25

Fig. 10. Comparison between observed and estimated soil elevations along the profile B.

Venice, very difficult to observe because of access difficulties and frequent flooding. Further, very accurate observations are needed, because the range of soil elevations of interest is limited between zero and 50 cm above mean sea level. Extensive field measurement campaigns are expensive and difficult, while the accuracy of airborne laser altimetry has to be further examined for flat and vegetated environments. The work presented in this paper exploits the strong relation between vegetation and soil morphology to develop a procedure using halophytic species as an ecological indicator of salt marsh topography. In particular, it has been shown that in the Venice lagoon halophytes may be associated with narrow ranges of ground elevation and that almost all species preferably develop around a characteristic optimal elevation. A model has been developed which uses halophytic vegetation as a morphological indicator of soil topography to estimate ground elevation from fractional cover values of each vegetation type. The comparison between estimated and measured soil elevations (see Fig. 7) indicates that the estimation accuracy decreases both at low and high elevation values. This is probably due to the fact that the model only makes use of mean soil elevation values, and this produces a soil topography with no extremes. The application of an improved model, not simply based on mean soil elevation values but also on vegetation densities, will be considered in future approaches. In fact, Silvestri et al. (2000) have found that, on Venice salt marshes, more favourable environmental conditions produce an increase of vegetation density, and this aspect is strictly related to the soil elevation above mean sea level. The classical Braun–Blanquet method (Pignatti, 1953; Mueller-Dombois and Ellenberg, 1974), used during the field campaigns for the present research, does not provide vegetation density values but only estimates of vegetation relative abundances and, in particular, does not consider a relative cover for bare soil. An accurate description of vegetation density distribu-

23

tion over the marsh (using, for example, its relation to the well known vegetation index NDVI) would probably be useful in the characterisation of the topography. Despite the difficulties encountered, the advantage of the proposed approach is clear and consists in the possibility of coupling model results with analyses of remotely sensed data. It was shown, in fact, that airborne hyperspectral remote sensing is a suitable means for studying salt marsh vegetation and that spectral unmixing techniques may be used to retrieve vegetation cover fractions. One limitation that has been encountered is due to the high spatial variability of vegetation patterns. This produced difficulties in the end-member selection due to the small extension of salt marsh areas covered by monospecific populations. Moreover, the spectral response of species may be greatly influenced by visible soil or the presence of water, particularly in the Venice lagoon, where the tidal regime is semi-diurnal. Tidal propagation also induces an important variability in space. Given the large dimension of the Venice lagoon (550 km2 ), the tide reaches inner areas with considerable delay (on the order of a few hours), producing instantaneous water levels which are dependent on the distance from the inlets and on the hydrodynamic characteristics (e.g. frictional forces and water depths) of the paths travelled by the tidal wave. Such high hydrodynamic variability adds serious difficulties to the mapping of vegetation through remote sensing due to non-homogenous conditions within the scene which are reflected in a strong variability of spectral signatures. For these reasons the unmixing methodology presented may only be applied to relatively small scenes with few heterogeneities. Pixel mean soil elevation was estimated by using the relative covers inferred through a linear unmixing procedure. The method allows the construction of a digital elevation model of the salt marsh area whose overall structure is in good agreement with field observations. Soil elevation is correctly predicted to be higher along channels and creeks than in the inner marsh areas and the topography of experimental transects is well reproduced. The model for estimating the soil elevation can be applied to other sites within the Venice lagoon or even in other coastal areas of the world. The dependence of vegetation distribution on soil elevation has, in fact, often been noticed in many coastal areas (Pignatti, 1966; Scarton et al., 2000; Silvestri et al., 2000; Mahal and Park, 1976; Vince and Snow, 1984; Snow and Vince, 1984; Olff et al., 1988; Adam, 1990; Sanchez et al., 1996). It is interesting to note that the application of the presented approach could be easier in areas where the tidal excursion is larger than in Venice and where, as a consequence, the variability of salt marsh soil elevation is larger. Improvements of the model presented and its quantitative comparison with laser altimetry

24

S. Silvestri et al. / Physics and Chemistry of the Earth 28 (2003) 15–25

measurements is currently under way as part of a research project financed by the European Community (TIDE project). In conclusion, the advantages of the described approach for constructing a digital elevation maps (DEM) of salt marsh areas may be summarised as follows: (i) it allows high estimation accuracy (a few centimetres in the lagoon of Venice); (ii) it can be constructed very rapidly from frequently updated remotely sensed data; (iii) it is derived from an indicator, halophytic vegetation, which is extremely sensitive to space/time variations of soil elevation. These properties make vegetation an extremely useful morphologic indicator which may constitute the basis of an efficient salt marsh monitoring scheme.

Acknowledgements We thank Servizio Informativo––CVN––Magistrato alle Acque of Venice and its director Ing. Roberto Rosselli for supplying the MIVIS data used in this study. Funding from European Project TIDE (EVK3CT-2001-00064) and ASI project ‘‘Dinamica degli ambienti a marea’’ and CORILA is acknowledged.

References Adam, P., 1990. Saltmarsh Ecology. Cambridge University Press., pp. 461. Alberotanza, L., Brando, V.E., Ravagnan, G., Zandonella, A., 1999. Hyperspectral aerial images. A valuable tool for submerged vegetation recognition in the Orbetello Lagoons, Italy. Int. J. Remote Sensing 20 (3), 523–533. Bajjouk, T., Guillaumont, B., Populus, J., 1996. Application of airborne imaging spectrometry system data to intertidal seaweed classification and mapping. Hydrobiologia 326/327, 463–471. Bajjouk, T., Populus, J., Guillaumont, B., 1998. Quantification of subpixel cover fractions using principal component analysis and a linear programming method: application to the coastal zone of Roscoff (France). Remote Sensing Environ. 64, 153–165. Bianchi, R., Cavalli, R.M., Marino, C.M., Pignatti, S., 1995. MIVIS airborne hyperspectral remote sensing in Europe. ISSSR-1995. Available from . Boardman, J.W., Kruse, F.A., Green, R.O., 1995. Mapping target signatures via partial unmixing of AVIRIS data. In: Summaries, Fifth JPL Airborne Earth Science Workshop, vol. 1. JPL Publication 95-1, pp. 23–26. Campbell, J.B., 1996. Introduction to Remote Sensing, second ed. The Guilford Press, London, pp. 622. Caniglia, G., Contin, G., Fusco, M., Anoe, A., Zanaboni, A., 1997. Confronto su base vegetazionale tra due barene della laguna di Venezia. Fitosociologia 34, 111–119. Chapman, V.J., 1964. Coastal Vegetation. Pergamon Press, Oxford, the Macmillan Company, New York. Dale, P.E.R., Hulsman, K., Chandica, A.L., 1986. Classification of reflectance on colour aerial photographs and sub-tropical saltmarsh vegetation types. Int. J. Remote Sensing 7 (12), 1783–1788.

Day Jr., J.W., Rybczyk, J., Scarton, F., Rismondo, A., Are, D., Cecconi, G., 1999. Soil accretionary dynamics, sea-level rise and the survival of wetlands in Venice lagoon: a field and modelling approach. Estuar., Coastal Shelf Sci. 49, 1–20. Donoghue, D.N.M., Shennan, I., 1987. A preliminary assessment of Landsat TM imagery for mapping vegetation and sediment distribution in the Wash estuary. Int. J. Remote Sensing 8 (7), 1101–1108. Eastwood, J.A., Yates, M.G., Thomson, A.G., Fuller, R.M., 1997. The reliability of vegetation indices for monitoring saltmarsh vegetation cover. Int. J. Remote Sensing 18 (18), 3901–3907. Elmore, A.J., Mustard, J.F., Manning, S.J., Lobell, D.B., 2000. Quantifying vegetation change in semiarid environments. Precision and accuracy of spectral mixture analysis and the normalized difference vegetation index. Remote Sensing Environ. 73, 87–102. Gomarasca, M.A., 1997. Introduzione a Telerilevamento e GIS per la gestione delle risorse agricole e ambientali, ArteStampa, Daverio (Va), pp. 246. Green, A.A., Berman, M., Switzer, P., Craig, M.D., 1988. A Transformation for Ordering Multispectral Data in Terms of Image Quality with Implications for Noise Removal. IEEE Trans. Geosci. Remote Sensing 26 (1), 65–74. Huising, E.J., Gomes Pereira, L.M., 1998. Errors and accuracy estimates of laser data acquired by various laser scanning systems for topographic applications. J. Photogramm. Remote Sensing 53, 245–261. IVSLA, Istituto Veneto di Scienze, Lettere ed Arti, 2002. Banca Dati Ambientale sulla Laguna di Venezia. Available from . Kruse, F.A., Lefkoff, A.B., Boardman, J.W., Heidebrecht, K.B., Shapiro, A.T., Barloon, P.J., Goetz, A.F.H., 1993. The spectral image processing system (SIPS)––interactive visualization and analysis of imaging data. Remote Sensing Environ. 44, 145–163. Lelong, C.C.D., Pinet, P.C., Poilve, H., 1998. Hyperspectral imaging and stress mapping in agriculture: a case study on wheat in Beauce (France). Remote Sensing Environ. 66, 179–191. Mahal, B.E., Park, R.B., 1976. The ecotone between Spartina foliosa Trin. and Salicornia virginica L. in salt marshes of northern San Francisco Bay. II. Soil water and salinity. J. Ecol. 64, 793–809. McGwire, K., Minor, T., Fenstermaker, L., 2000. Hyperspectral mixture modeling for quantifying sparse vegetation cover in arid environments. Remote Sensing Environ. 72, 360–374. Metternicht, G.I., Fermont, A., 1998. Estimating erosion surface features by linear mixture modeling. Remote Sensing Environ. 64, 254–265. Mueller-Dombois, D., Ellenberg, H., 1974. Aims and Methods of Vegetation Ecology. John Wiley & Sons, New York. Olff, H., Bakker, J.P., Fresco, L.F.M., 1988. The effect of fluctuations in tidal inundation frequency on a salt-marsh vegetation. Vegetatio 78, 13–19. Pethick, J., 1984. An Introduction to Coastal Geomorphology. Arnold Pb, London. Phinn, S.R., Stow, D.A., Van Mouwerik, D., 1999. Remotely sensed estimates of vegetation structural characteristics in restored wetlands, Southern California. Photogramm. Eng. Remote Sensing 65 (4), 485–493. Pignatti, S., 1953. Introduzione allo studio fitosociologico della pianura veneta orientale con particolare riguardo alla vegetazione litoranea. Arch. Bot. 28 (4), 265–329; 29(1), 1–25; 29(2), 65–98. Pignatti, S., 1966. La vegetazione alofila della laguna veneta, Istituto Veneto di Scienze, Lettere ed Arti, Memorie, vol. XXXIIIFascicolo I, Venezia, pp. 174. Rijstenbil, J.W., Kamermans, P., Nienhuis, P.H., 1996. Sinthesis report. EUMAC. Sanchez, J.M., Izco, J., Medrano, M., 1996. Relationships between vegetation zonation and altitude in a salt-marsh system in northwest Spain. J. Veget. Sci. 7, 695–702.

S. Silvestri et al. / Physics and Chemistry of the Earth 28 (2003) 15–25 Scarton, F., Day, J.W., Rismondo, A., Cecconi, G., Ave, D., 2000. Effects of an intertidal sediment fence on sediment elevation and vegetation distribution in a Venice (Italy) lagoon salt marsh. Ecol. Eng. 16, 223–233. Settle, J.J., Drake, N.A., 1993. Linear mixing and the estimation of ground cover proportions. Int. J. Remote Sensing 14, 1159–1177. Silvestri, S., 1997. Le trasformazioni morfologiche della laguna di Venezia. Tesi di laurea, Universita CaÕ Foscari di Venezia. Silvestri, S., 2000. La vegetazione alofila quale indicatore morfologico negli ambienti a marea. Tesi di Dottorato in Modellistica dei Sistemi Ambientali, Universita di Padova. Silvestri, S., Marani, M., Rinaldo, A., Marani, A., 2000. Vegetazione alofila e morfologia lagunare. Atti dellÕIstituto Veneto di Scienze, Lettere ed Arti, Tomo CLVIII (1999–2000), Classe di scienze fisiche, matematiche e naturali. Silvestri, S., Marani, M., Settle, J., Benvenuto, F., Marani, A., 2002. Salt marsh vegetation radiometry: data analysis and scaling. Remote Sensing Environ. 2, 473–482.

25

Smith, G.M., Spencer, T., Murray, A.L., French, J.R., 1998. Assessing seasonal vegetation change in coastal wetlands with airborne remote sensing: an outline methodology. Mangroves Salt Marshes 2, 15–28. Snow, A.A., Vince, S.W., 1984. Plant zonation in an Alaskan salt marsh. II. An experimental study of the role of edaphic conditions.. J. Ecol. 72, 669. Vermote, E., Tanre, D., Deuze, J.L., Herman, M., Morcrette, J.J., 1995. Second simulation of the satellite signal in the solar spectrum (6S). Available from . Vermote, E.F., Tanre, D., Deuze, J.L., Herman, M., Morerette, J.J., 1997. Second simulation of the satellite signal in the solar spectrum, 6S: an overview. IEEE Trans. Geosci. Remote Sensing 35 (3), 675– 686. Vince, S.W., Snow, A.A., 1984. Plant zonation in an Alaskan salt marsh. I. Distribution, abundance and environmental factors. J. Ecol. 72, 651.