Continental Shelf Research 31 (2011) S26–S35

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Continental Shelf Research

0278-43

doi:10.1

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journal homepage: www.elsevier.com/locate/csr

Bio-physical characterization of sediment stability in mudflats using remotesensing: A laboratory experiment

Stefanie Adam a,�, Annelies De Backer b,1, Aaike De Wever c, Koen Sabbe c, Erik A. Toorman a,Magda Vincx b, Jaak Monbaliu a

a Hydraulics Laboratory, Department of Civil Engineering, Katholieke Universiteit Leuven, K.U.Leuven postbus: 02448 Kasteelpark Arenberg 40, 3001 Heverlee, Belgiumb Universiteit Gent, Department of Biology, Marine Biology Section, Krijgslaan 281 S8, 9000 Ghent, Belgiumc Universiteit Gent, Department of Biology, Laboratory of Protistology and Aquatic Ecology, Krijgslaan 281 S8, 9000 Ghent, Belgium

a r t i c l e i n f o

Article history:

Received 24 November 2008

Received in revised form

15 September 2009

Accepted 16 December 2009Available online 24 December 2009

Keywords:

Tidal flats

Surficial sediment stability

Bio-physical indicators

Remote sensing

Spectral reflectance

43/$ - see front matter & 2009 Elsevier Ltd. A

016/j.csr.2009.12.008

esponding author. Tel.: +32 16 32 16 52; fax:

ail addresses: stefanieadam4@gmail.com (S.

nderen.be (A. De Backer), aaike.dewever@ug

ugent.be (K. Sabbe), Erik.toorman@bwk.ku

incx@ugent.be (M. Vincx), jaak.monbaliu@bw

rrent address: Institute for agricultural an

s, Ankerstraat 1, B-8400 Oostende, Belgium.

a b s t r a c t

Mudflats are important for coastal zone ecosystems by providing wildlife habitat and by acting as

natural sea defenses that serve to dissipate tidal and wave energy. Geomorphological models of these

intertidal flats and estuaries require site-specific bio-physicochemical sediment parameters as input.

Hyperspectral remote sensing can be used as a tool providing synoptic maps of these properties.

However, the interpretation of hyperspectral remotely sensed images over mudflats is only possible if

the appropriate bio-geophysical or empirical models for information extraction are available. Therefore,

the objective of this paper was to model the effects of varying sediment properties on the reflectance in

laboratory conditions.

The methodology consisted of (i) hyperspectral measurements of sediment mixtures with varying

physical and biological characteristics in laboratory conditions, (ii) determination and quantification of

specific absorption features and (iii) regression between the absorption features and physical

parameters.

In laboratory conditions, quantification of clay in dry sediment, moisture in unsaturated sediment

and chlorophyll a in sediment mixtures was achieved with coefficients of determination (r2) of 0.98,

0.90 and of 0.96 using the scaled band area of absorption features at 2204, 1450 and 673 nm,

respectively. Additionally, the water absorption at 1190 nm was identified as suitable to predict

moisture content in very wet sediment and preliminary results showed the potential of hyperspectral

signals to assess the effect of bioturbation on sediment properties.

Future work will consider the applicability of this methodology in field situations to relate bio-

physical sediment parameters to the hyperspectral signal.

& 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Mudflats are important for coastal zone ecosystems byproviding wildlife habitats and by acting as natural sea defensesthat serve to dissipate tidal and wave energy. The erodibility ofcohesive sediments on these mudflats is dependent on theinteraction between the physical and biological processes ofstabilization and destabilization (Paterson et al., 2000; deBrouwer et al., 2000; Andersen, 2001; Amos et al., 2004).

ll rights reserved.

+32 16 32 19 89.

Adam), annelies.debacker@

ent.be (A. De Wever), koen.

leuven.be (E.A. Toorman),

k.kuleuven.be (J. Monbaliu).

d fisheries research, ILVO-

Physico-chemical properties which affect the erodibility ofintertidal sediments are, among others, grain size distributionand moisture content (Dyer, 1986; Berlamont et al., 1993;Mitchener and Torfs, 1996; Aberle et al., 2004; Winterwerp andvan Kesteren, 2004). An important biological destabilizer ofsediments is the macrofaunal species Corophium volutator (amud shrimp), an abundant deposit and/or filter feeding species inintertidal mudflats (Meadows and Reid, 1966; Le Hir et al., 2007).Its influence on sediment stability is related to grazing onmicrophytobenthos (Gerdol and Hughes, 1994; Grant and Daborn,1994), active resuspension (de Deckere et al., 2000) andbioturbation, thereby changing the physical sediment properties(Rhoads, 1974; Aller, 1982; Hall, 1994; Jones et al., 1994;De Backer et al., 2009) and bottom roughness (Meadows andReid, 1966; de Deckere et al., 2001). Microphytobenthos isknown to stabilize the sediment by gluing the sediment grainstogether and by the formation of biofilms (Yallop et al., 1994;

S. Adam et al. / Continental Shelf Research 31 (2011) S26–S35 S27

Miller et al., 1996; De Brouwer et al., 2005; Le Hir et al., 2007). Inspite of the scale and economic/ecological importance of inter-tidal zones, the dynamics and behaviour of fine sediments and thebiota living in this region are poorly understood. The generalpaucity of data on intertidal mudflats is primarily due to physicaldifficulty of working in soft mud and the vast scale of manymudflats. Remote sensing offers a means to acquire the necessarydata to study these large mudflats (van der Wal et al., 2004;Forster and Jesus, 2006; Murphy et al., 2008). In previousresearch, remotely sensed images were used to characterizeintertidal sediments (Thomson et al., 1998; Rainey et al., 2003;Deronde et al., 2006). Sediment and vegetation types wereclassified, and the sand and mud content were successfullyquantified by Rainey et al. (2003). The significant amount of fielddata necessary to calibrate the empirical models and the site andimage dependence of these models are disadvantages of theproposed methods. Unsupervised classification avoids calibrationof models, but requires field data to label the obtained clusters(Adam et al., 2006). More accurate and interpretable results canonly be obtained by improving the knowledge about physicallybased or empirical models, which explain the influence ofbiological and physical properties on the hyperspectral signal(Schaepman et al., 2009).

Since clay, water and chlorophyll a (estimator of microphyto-benthos biomass) show specific absorption features, theseabsorption features were explored for the quantification of thesesubstances. Absorption features of liquid water at 975, 1200 and1750 nm have been used for the quantification of relative watercontent in vegetation (Penuelas et al., 1993; Pu et al., 2003).Specific absorption features of clay are widely used for identifyingclay minerals in geological applications (Hunt, 1977; van derMeer and Bakker, 1998) and a recent study (Lagacherie et al.,2008) showed the capability of the absorption feature at around2206 nm to predict clay content in dry soil samples, both in thelaboratory and in the field. Microphytobenthos on mudflats hasbeen intensively studied using hyperspectral remote sensing andthe absorption of chlorophyll a at around 673 nm, both inlaboratory (Meleder et al., 2003; Combe et al., 2005) and in thefield (Hakvoort et al., 1997; Carr�ere et al., 2004; Murphy et al.,2005a).

In this paper, absorption features were used to assesschlorophyll a, clay and relative moisture content in sedimentmixtures in laboratory conditions. Additionally, an exploratoryexperiment to estimate the influence of bioturbation by Coro-

phium volutator on the reflectance of sediment was performed.

Table 1Overview of the number of measurements related to the assessment of the clay

and moisture content.

Clay contentFine sand+clay n=98

Medium sand+clay n=95

Total n=193

Moisture contentFine sand+water n=496

Fine sand+clay+water n=389

Total n=885

Moisture content limited to RMCo20%Fine sand+water n=240

Fine sand+clay+water n=257

Total n=497

2. Methodology

2.1. Hyperspectral measurements in laboratory conditions

Hyperspectral measurements were acquired at 50 cm height,nadir looking (vertical downward) with a field of view of 11 usingthe ASD Field Spec Pro JR. spectrometer. A small area of 0.6 cm2

was sampled with these settings. The light source is a tungstenhalogen 50 W Ushio lamp, inside a Lowel assembly of 12.7 cmdiameter. The lamp, which produces 1250 lumen, covers theelectromagnetic spectrum in the region of 350–2500 nm andprovides a sufficiently strong signal for the detector (Biliouriset al., 2007). The ASD spectrometer records the reflectance in thevisible (VIS), near infra-red (NIR) and short wave infra-red (SWIR)region of the spectrum (350 till 2500 nm). The spectral resolutionwas 3 nm for the region 350–1000 nm and 10 nm for the 1000–2500 nm region. Calibration was performed using a Spectralons

panel (0.30 m*0.30 m Labsphere, North Sutton, USA), which has aquasi Lambertian reflectance higher than 99% from 400 to

1500 nm and higher than 95% from 1500 to 2500 nm. About30 min after the calibration, the reflectance of the Spectralons

panel had not changed, except for small deviations situated at theextreme upper and lower ends of spectrum. This indicated thatthe instrument and the light source were stable enough forcalibration to be performed every 30 min. All the measurements,except the measurements to assess the influence of Corophium

volutator and chlorophyll a content, were performed in a ‘darkroom’, where everything was painted in dull black to avoid diffuselight influencing the reflectance measurements.

Four sets of experiments were carried out:(1) with varying sand fractions, (2) with varying clay and

moisture content, (3) with and without bioturbation byCorophium volutator, and (4) with different densities of biofilm.

For experiments 1 and 2, the sediment used was collected on aBelgian intertidal zone located at the outlet into the North Sea ofthe IJzer river, and was sieved into fractions of medium (250–500mm), fine (125–250mm) and very fine (63–125mm) sand. Fineand medium sand being the most abundant sand fractions, wereused for the experiments. For experiment 1, measurements on thepure sand fractions and on mixtures of medium and fine sandwere carried out. For experiment 2, a commercial illite claywithout organic matter and water was used to prepare thesediment mixtures. Firstly, the clay fraction was graduallyincreased in the fine sand and in the medium sand. Secondly,fine sand and mixtures of fine sand and clay were saturated withwater. The moist samples were then heated on a warm plate toensure homogeneous and gradual drying. The samples wereregularly weighted, reflectance measured, and relative moisturecontent (RMC) calculated as: 100((SW�DW)/SW) with SW=sam-ple weight, and DW=weight of dry sample. All the sedimentmixtures were prepared in small dishes with a height of 0.5 cmand painted in black to minimize their reflectance. An overview ofthe measurements of experiments 1 and 2 is given in Table 1.

For experiment 3, two reservoirs were filled with a sedimentmixture of 50% clay and 50% sand, and water was added tillsaturation. Corophium volutator was added to one of the reservoirswhere it was allowed to bioturbate the sediment for 12 h.Afterwards, the water was bottom drained. Spectral measure-ments of the sediment in the two reservoirs were taken every30 min till 4.5 h after draining. After the spectral measurement, asmall sediment sample (0–5 cm deep) was taken for moisturecontent analysis.

Finally, for experiment 4, sediment was collected from thePaulinapolder in the Westerschelde estuary, The Netherlands. Anatural sediment was used to include the spectral variation innatural mudflat sediment. Afterwards, the sediment was washedover a 1 mm sieve, homogenized, dried at 110 1C and 100 ml ofsediment was poured in each of 27 custom-made containers.Sediments were moisturized with 60 ml of Guillard’s (F/2) marine

Fig. 1. Measures that quantify absorption features. Rb is the reflectance at maximum absorption; Rc is the reflectance out of absorption feature.

Fig. 2. Spectra of medium (dashed line) and fine (full line) sand in the visible light.

S. Adam et al. / Continental Shelf Research 31 (2011) S26–S35S28

water enrichment solution (Sigma) (Guillard and Lorenzen, 1972), astandard medium suitable for cultivating marine diatom species,and left in an incubator for 24 h. After removal of the upper waterfilm and floating sediment particles, 15 ml of new culture mediumwas added before inoculation. Nine containers were inoculated (atapproximately 400,000 cells/ml) with a Cylindrotheca closterium

culture and nine were inoculated (at the same concentration) oneweek later in order to obtain an intermediate biofilm density. TheC. closterium strain was isolated from Paulinapolder, on March 21,2007. The fast growing and abundant Cylindrotheca was selectedto realise a high density biofilm under laboratory conditions.Mixed or natural assemblages were not realised as (1) thesewould have drastically changed in community composition aftertwo weeks of growth, and (2) large differences in biofilm densitieswould have resulted as the final community composition is likelyto differ between replicates. The containers were incubated in aculture room at 18 1C with cool white fluorescent light at 90mmolphotons m�2 s�1 and a 12 h/12 h light cycle. The culture mediumwas refreshed every two days during the first 4 days and everyday thereafter. Measurements were performed after 14 days. Afterthe spectral measurements, three contact cores per containerwere taken for pigment analysis using a 25 mm diameter corer.The top 2 mm of the sediment was frozen using liquid nitrogenand chlorophyll a content was determined by HPLC analysis.

2.2. Quantification of specific absorption features

If absorption features are present, the degree of absorption canbe characterized by the ratio between the reflectance atmaximum absorption Rb and the reflectance out of the absorptionfeature Rc (Fig. 1). It is assumed that Rc is not influenced by thematerial causing the absorption dip. Since the objective is tocompare absorption amounts, a normalization technique,continuum removal, was applied. This continuum consists ofstraight-line segments that connect local maxima in thespectrum. It is then removed by dividing it into the reflectancespectrum (Clark and Roush, 1984) in order to compare absorptionfeatures from a common baseline. The continuum was calculatedpreferably between the local maxima of the absorption feature ofinterest. If there is no local maximum, the shoulder of the dip or apre-defined wavelength was considered for continuum removal(see also Section 3).

2.3. Regression analysis

The relation of the bio-physical sediment parameter to theratio, scaled band depth or scaled band area of the absorptionfeatures was explored by minimizing the mean squared error ofthe residuals. The model parameters were estimated and thegoodness of fit expressed as R-squared value (coefficient ofdetermination) and the root mean square error (RMSE), calculatedas the mean of the absolute error between observed and modeledvalues (RMSE). If a model developed on a different data set wasapplied, the applicability of that model was expressed in terms ofthe root mean square error of prediction (RMSEP), calculated asthe mean of the absolute error between observed and predictedvalues.

3. Results

3.1. Varying grain size distribution of sand fraction

The reflectance of medium and fine sand is significantlydifferent (po0.01) in the green, red (Fig. 2) and short waveinfrared light. The smaller the grain size, the higher thereflectance, because the light scattering surface area increaseswhen the grain size decreases (Baumgardner et al., 1985). Though

S. Adam et al. / Continental Shelf Research 31 (2011) S26–S35 S29

significant, the differences were very small. When the mediumsand content in a fine/medium sand mixture was increased from 0to 100%, the reflectance in the red, green or short wave infrareddid not decrease gradually (Fig. 3), as could be expected under theassumption of linear mixing. Therefore, assessment of the grainsize distribution of sandy sediment is, even in laboratoryconditions, very difficult.

Spectra of medium and fine sand were not significantlydifferent in the near infrared or the slope in the visible light.

3.2. Varying clay and moisture content

Typical spectra of fine sand with clay and moist sand areshown in Fig. 4.

The spectrum of fine sand with clay shows absorption featuresat around 1420, 1950 and 2204 nm. It was decided to use theabsorption feature at 2204 nm to quantify clay content, since thisdip is caused by Al-OH present in the clay mineral (Yang et al.,2000), while the spectral absorption features at around 1420 and1950 nm originate from light absorption by molecular water inthe minerals due to vibrational processes (Hunt, 1977; Yang et al.,2000). The first maximum for continuum removal was found asthe local maximum between 2125 and 2204 nm, since thewavelength of maximum reflectance is dependent on the claycontent, while the second maximum was defined as 2265 nm.

Fig. 3. Reflectance in the SWIR (+), red (&) and green (*) light of fine/medium

sand mixtures with varying medium sand content. For interpretation of the

references to colour in this figure legend, the reader is referred to the web version

of this article.

Fig. 4. Spectrum of dry fine sand with clay (66% b

The spectrum of moist fine sand shows clear water absorptionfeatures at 1450 and 1950 nm. Very moist sediment also absorbslight at around 970 and 1190 nm, but these features disappearwhen the moisture content becomes lower than 25% and 20%,respectively. To quantify moisture content, the water absorptionfeatures at around 1190 and 1450 nm were used. The continuumswere calculated between on the one side, the local maxima in theinterval 1090–1190 nm and in the interval 1300–1450 nm, and onthe other side 1275 and 1680 nm for the absorption feature at1190 and 1450 nm, respectively. The absorption feature of waterat 970 nm was not suitable to quantify the moisture content, sinceit was found to be influenced by the junction between the firstand second sensor of the ASD spectrometer at 976 nm. Theabsorption feature at 1950 nm saturated at low moisturecontents.

The relation of clay content to the measures of the clayabsorption feature at 2204 nm appeared to be a 4th orderpolynomial. Fig. 5 shows the clay content related to the scaledband depth of the absorption feature (as example) by: clay

content(%)=(1.5�105)x4+(�5.4�104)x3+(7.4�103)x2�306x+4,

where x is the scaled band depth. The relation between absorptionat 2204 nm and clay content in a mixture of medium and finesand was also successfully applied to predict the clay content inpure medium sand (RMSEP=1.5 wt% clay) or pure fine sand(RMSEP=1.1 wt% clay) (Fig. 6), i.e. the sand grain size does notaffect clay absorption (Table 2). The best fit between absorption at1450 nm and moisture content in all the sediment samplesconsists of second-order polynomials for RMC lower or higher

y weight) and of moist fine sand (RMC=27%).

Fig. 5. Clay content versus the scaled band depth of the absorption feature at

2204 nm.

Fig. 6. Measured clay content versus the predicted clay content using the relation between clay absorption and clay content in fine and medium sand.

Fig. 7. Relative moisture content versus the scaled band area of the water absorption feature at 1450 nm (left) and at 1190 nm (right).

Table 2Overview of R-squared values between clay or relative moisture content (RMC) and measures of the absorption features at 2204 and 1450 nm, respectively.

R-squared values

CLAY ratio_2204 nm depth_2204 nm area_2204 nm

Fine sand (n=98) 0.99 0.99 0.99

Medium sand (n=95) 0.99 0.98 0.99

Fine+medium sand (all, n=193) 0.98 0.98 0.98

RMC (o20%) ratio_1450nm depth_1450nm area_1450nm

Fine sand (n=240) 0.93 0.94 0.95

Fine sand+clay (n=257) 0.85 0.86 0.87

Fine sand and fine sand +clay (all, n=497) 0.89 0.89 0.90

depth=scaled band depth, area=scaled band area.

S. Adam et al. / Continental Shelf Research 31 (2011) S26–S35S30

than 20% (Fig. 7, scaled band area as example). At the highmoisture contents, there is a lot of scatter. During theexperiments, it was observed that a thin surface layer of wateron the sediment was visible for RMC larger than 720%, whichexplains the differences in water absorption. For RMC higher than

20%, the absorption at 1190 nm was more suitable to quantifyRMC (Fig. 7).

Moisture absorption at 1450 nm is dependent on the grain size(Table 2). Firstly, different relations were found between moistureabsorption and moisture content in fine sand or in a fine sand/clay

Fig. 8. Relations between absorption at 1450 nm and RMC in moist fine sand (left) and in moist fine sand/clay mixtures (right).

Fig. 9. Observed RMC (o20%) versus predicted RMC in fine sand/clay mixtures

using the model between absorption at 1450 nm and moisture content for moist

fine sand.

Fig. 11. Continuum removed spectra of a fine sand with 17% clay by weight with

varying moisture content at the clay absorption feature at 2204 nm.

–5 0 5 10 15 20 250

5

10

15

20

Predicted clay content (%)

Mea

sure

d cl

ay c

onte

nt (%

)

Fig. 12. Measured clay content versus predicted clay content for moist fine sand/

clay mixtures.

Fig. 10. Observed RMC (420%) versus predicted RMC in fine sand/clay mixtures

using the model between absorption at 1190 nm and moisture content for moist

fine sand.

S. Adam et al. / Continental Shelf Research 31 (2011) S26–S35 S31

Table 3Difference in moisture content (0–5 cm deep) between sediment with and without

bioturbation. Positive values indicate higher moisture contents in the bioturbated

sediment.

Time after drainage 300 1 h 1 h300 2 h 2 h300 3 h 3 h300 4 h 4 h300

D RMC (%) 1.9 2.2 1.3 1.8 3.2 2.6 2.5 1.4 0.6

500 1000 1500 2000 25000

0.2

0.4

0.6

0.8

Wavelength (nm)

Ref

lect

ance

( )

Fig. 13. Spectra of sediment between 3 and 4.5 h after drainage without (grey) and

with (black dots) bioturbation by Corophium volutator. The values between 1000

and 1300 nm were removed, because the reflectance in this region was very noisy

due to a malfunctioning second sensor of the ASD.

0 2 4 6 8 10 12 140

20

40

60

80

100

Chl

orop

hyll

a co

nten

t (m

g/m

2 )

Scaled band area

r2 = 0.96

Fig. 14. Chlorophyll a content versus the scaled band area of the chlorophyll a

absorption feature at 673 nm.

S. Adam et al. / Continental Shelf Research 31 (2011) S26–S35S32

mixture, RMC(%)=0.007x2�0.016x+1.918 and RMC(%)=0.003

x2+0.183x+0.276, respectively, where x is the scaled band area(Fig. 8). And secondly, applying the fine sand model on the finesand/clay mixtures gives a large error of prediction of 12.5% RMC(Fig. 9), although the models for the separate fractions have errorslower than 2.5% RMC. The difference in relation between theabsorption at 1450 nm and the moisture content for sedimentwith or without clay can be explained by clay hydroxyl groupsabsorbing light in the 1420 nm region and thereby influencing thedip centered at 1450 nm.

Moisture absorption at 1190 nm is less dependent on grain sizethan absorption by water at 1450 nm. The models relating RMChigher than 20% to the scaled band area are very similar for finesand and fine sand/clay mixtures: RMC(%)=�0.073x2+1.97x+17.6and RMC(%)=�0.078x2+1.78x+17.1, respectively, and the error ofprediction of the moisture content in sand/clay mixtures using thesand model is only 2.7% RMC (Fig. 10).

Prediction of clay content in moist sediment is hindered due todecreasing light absorption by clay with increasing moisturecontent (Fig. 11). Nevertheless, clay content in moist, butunsaturated, sediment could be quantified in laboratoryconditions using the scaled band area of the clay absorptionfeature at 2204 nm (area_clay) and the scaled band area of thewater absorption feature at 1450 nm (area_water), leading to:

clay contentð%Þ ¼�2:51þ2:53 area_clayþ0:12 area_water;

with R2=0.87. The mean error of prediction is 6.6% clay by weight,which is a large error compared to the average clay content (6.0%)and standard deviation (4.5%). Fig. 12 shows an overestimation forlow clay contents and a large underestimation for high claycontents.

3.3. Bioturbation by Corophium volutator

Clear differences were observed between sediment with andwithout bioturbation by Corophium volutator. Bioturbation resultsin higher turbidity of the overlying water column and in theappearance of a fluffy sediment top layer. After drainage, thesediment surface of the reservoir with bioturbation remainedmoist for a longer period, which was also noticed in the moisturecontent of the sediment samples (Table 3).

The spectra with bioturbation show higher reflectance in theVIS and NIR, and deeper water absorption features at 1450 nmthan the spectra without bioturbation (Fig. 13). Due toexperimental difficulties, i.e. a defect of the second sensor of theASD spectrometer (976–1775 nm) resulting in low signal-to-noiseratios in this region of the spectrum, and experimentalshortcomings (insufficient intensity of the light source, diffuselight, limited drainage and a lack of repeated sampling due to thesmall number of reservoirs), these effects were not quantified.Nevertheless, these preliminary results justify a follow-upexperiment with more aquaria and a better experimental setup.

3.4. Diatom biofilms

The chlorophyll a content ranged from 0 to 80 mg/m2, with amean value of 25.1 mg/m2. These values are typical for fieldsituations in early spring. The spectra with chlorophyll a show aclear dip in the red light and more specifically at 673 nm. Thecontinuum was calculated between the local maximum in theinterval 650–670 and 700 nm. Fig. 14 shows the chlorophyll a

content versus the scaled band area of the absorption feature at673 nm. The coefficient of determination for the second-orderregression line is 0.96.

4. Discussion

4.1. Grain size

In laboratory conditions, a significantly (po0.01) highervisible and short wave infrared reflectance was measured for fine(125–250mm) sand compared to medium sand (250–500mm).This is caused by the higher surface area available for lightscattering in fine sand (Baumgardner et al., 1985). However, thedifference is very small, probably too small to be of use in field

S. Adam et al. / Continental Shelf Research 31 (2011) S26–S35 S33

applications. To our knowledge, no satisfying results have beenreported to assess the sand grain size in field conditions usinghyperspectral remote sensing.

4.2. Clay content

Absorption by clay at 2204 nm is a good estimator of claycontent in dry sediment in laboratory conditions. Moreover, thegrain size of the sand fractions in the sand/clay mixtures does notaffect the relation between absorption and clay content. Furtherresearch will be performed to assess its ability in field conditions.However, since clay and moisture content in intertidal sedimentare positively correlated (Kromkamp et al., 2006; Adam, 2009), itis expected that the clay absorption dip is obliterated by moistureabsorption, as already shown by Lobell and Asner (2002) andWhiting et al. (2004) in soils and illustrated in this study forintertidal sediments (Section 3.2, last paragraph). Since increasingmoisture leads to a decrease in albedo, Whiting et al. (2004)suggest to search for methods to correct the effects of soilmoisture in hyperspectral images.

Murphy (1995) used simulated spectra under the assumptionof linear mixing to find that dead and live vegetation alsoinfluence clay absorption. There is no vegetation cover onintertidal mudflats, but algal mats are often present, so it shouldbe investigated if these algal mats influence clay absorption. Sincespectral mixing is often non-linear, measurements on realsediments are preferred to simulated spectra.

4.3. Moisture content

Low moisture content (o20%) was well correlated with theabsorption at 1450 nm, but a large scatter was observed for highermoisture contents. This change in behaviour of reflectance forincreasing moisture content was also observed by Lobell andAsner (2002), Weidong et al. (2002) and Whiting et al. (2004) andcan be explained by a change in the scattering processes due tothe distribution of water within the pores and around theparticles at increasing moisture contents.

To develop a general model for moisture quantification inunsaturated sediment using 1450 nm absorption, the effect ofabsorption at 1420 nm by hydroxyl groups adsorbed on clayminerals should be removed. This was shown in this study, sincethe moisture model in fine sand was not suitable to predictmoisture content in fine sand/clay mixtures (Section 3.2, Figs. 8and 9). As long as the effect of clay cannot be removed from waterabsorption, sediment specific relations between moisture contentand absorption at 1450 nm need to be calibrated.

At around 20% RMC, absorption at 1190 nm becomes clearlyvisible, and this absorption dip was more suitable to estimatehigh moisture contents. Interestingly, the absorption at 1190 nmin very wet sediment, is not influenced by clay content, which canbe important in tidal flats since clay and moisture content arepositively correlated (Paterson et al., 2000; Adam, 2009). Moreresearch is needed to find out if the absorption at 1190 nm is aspecific estimator for moisture content in very wet sediment andif it can be applied in the field and on hyperspectral imagery.

4.4. Bioturbation by Corophium volutator

Preliminary results in this study show that remote sensingmight be capable of determining the effects of bioturbation.Bioturbation by macrobenthic species results in a sediment fluffytop layer and increased bottom roughness, causing increasedsediment resuspension (de Deckere et al., 2003; Orvain et al.,2004). Corophium alters, through its activities, sediment proper-

ties by changing the porosity, water content, grain size distribu-tion, and chemistry of the sediment (Jones and Jago, 1993; Gerdoland Hughes, 1994; Pelegri and Blackburn, 1994; Limia andRaffaelli, 1997; Mermillod-Blondin et al., 2004). Even thoughbioturbation is recognized as a major process influencing thestructure and function of sediment environments (Lohrer et al.,2004; Meysman et al., 2006), contradicting results of these effectsof bioturbation are found in the literature. Remote sensingpossibly opens new perspectives to study the short- and long-term effects of bioturbation in laboratory conditions in a non-destructive and efficient way. In a follow-up experiment thephysical impact by Corophium volutator on the water content andgrain size have been examined using conventional, destructivetechniques. However, it has also been investigated whetherphysical changes caused by bioturbation influence the hyper-spectral signal. The paper reporting on this experiment is in press(De Backer et al. 2009).

In previous studies (Yates et al., 1993; van der Wal et al., 2004,2008), remote sensing has been used to predict macrobenthosdistribution on an intertidal flat by relating maps of sedimentproperties to macrobenthos distribution, which is an appropriateapproach, since there is no specific signature for macrobenthos.

4.5. Microphytobenthos biomass

A high correlation was observed between absorption at673 nm and chlorophyll a content in mg/m2, which was shownto be the more appropriate unit for remote sensing applicationsthan chlorophyll a content expressed in mg/kg (Murphy et al.,2005b). Saturation was not observed at chlorophyll a contents of80 mg/m2, and this is consistent with Meleder et al. (2003) whoobserved saturation at 673 nm at 100 mg/m2 in laboratoryconditions. Murphy et al. (2005a) found a ratio in visible bands(R526/R647) to be the best predictor of chlorophyll a content inAustralian mudflats, but the composition of Australian intertidalsediment is different (Murphy et al., 2008), and therefore, otherindices might perform better in Western-European mudflats. Wehave used the scaled band area at 673 nm. This was found to besuitable for chlorophyll a estimation in the field by Carr�ere et al.(2004).

It is well known that the relation between absorption depth tochlorophyll a content should approximate an exponential func-tion (Beer Lambert-like law). Carr�ere et al. (2004) found this typeof relation and assumed a linear function between scaled bandarea and chlorophyll a concentration. However, the relationsshown in this study do not show exponential behaviour, as alsofound by Murphy et al. (2005a). A possible explanation is theintimate mixing of chlorophyll a with sediment, water, organicmatter, etc.

In the field, correlations between reflectance and chlorophyll a

are lower (Carr�ere et al., 2004; Murphy et al., 2005a; Adam, 2009),since reflectance is also influenced by grain size distribution,macroalgae, moisture content, specular sun glint effects, organicmatter content, and species composition (Murphy et al., 2005a, b;Kromkamp et al., 2006). Additionally, microalgal biofilms oftenexhibit high spatial variability with patches ranging from 4 to191 cm2 (Jesus et al., 2005; Murphy et al., 2008), so groundtruthing for remote sensing studies should be performed on aspatial scale relevant to the process of spectral reflectance fromthe sediment (Paterson et al., 1998). Another consideration forfield remote sensing of microphytobenthos is the verticalmigration of microphytobenthos (Jesus et al., 2006) and theoptical depth (Kromkamp et al., 2006), which determine themoment and the depth of sediment sampling.

S. Adam et al. / Continental Shelf Research 31 (2011) S26–S35S34

It should be noted that all these measurements wereperformed in controlled laboratory conditions. In natural condi-tions, reflectance of the sediment will not only be influenced bymoisture, chlorophyll a, sand and clay content, but also by thepresence of organic matter, iron oxides, illumination conditions,surface slope, etc. (Lillesand and Kiefer, 2000). Hence, the need forfurther experiments to assess the validity of extrapolatinglaboratory models to field situations and to estimate the influenceof varying sediment properties on the relation between chlor-ophyll a content and hyperspectral signals, is clear. Moreover,hyperspectral airborne and satellite sensors acquiring images ofintertidal flats have lower spectral resolutions than the analyticalspectral device spectrometer, leading to a decrease in thepotential of remote sensing to predict these bio-physical sedimentproperties. If the spatial gradients are very high, e.g. the highpatchiness of MPB on intertidal mudflats, the lower spatialresolution of this imagery (from 1 m by 1 m for airborne to 30 mby 30 m for satellite images) will also lead to a decreased accuracyfor predicting bio-physical sediment properties.

5. Conclusion and outlook

This paper shows results, of which some are preliminary overthe influence of bioturbation, clay, moisture and chlorophyll a

content on the reflectance of surface sediment in laboratoryconditions. In the laboratory, quantitative measures of absorptionat 673, 1450 and 2204 nm are highly correlated with, respectively,chlorophyll a, water content in unsaturated sediment, and claycontent in dry sediment. It was demonstrated that the relationbetween clay content in dry sand and absorption at 2204 nm isnot dependent on the sand grain size. However, the effect ofmoisture absorption should be removed to assess the clay contentin moist sediment. In unsaturated sediment, the absorption at1450 nm is influenced by the presence of clay, since hydroxylgroups in the clay mineral absorb light at 1420 nm.

Further investigation on the use of absorption features withvarying moisture content and clay content is needed. Addition-ally, the developed methods need to be tested on in situmeasurements with simultaneous sediment sampling and analy-sis. It is expected that the accuracy will decrease due to the spatialscale and environmental factors such as microphytobenthos,macroalgae, microrelief, macrofaunal species, etc.

The changes due to bioturbation could be detected in thehyperspectral signal. Further research is needed to assess thepotential of remote sensing for long term and non-destructivemeasurements of bioturbation in laboratory conditions.

Acknowledgements

The CISS-project is funded by the Research Foundation—

Flanders (FWO Vlaanderen) under contract no. G0480.05. Thefourth author’s position as research associate is financed by theKULeuven Special Research Fund.

The TideSed, SedOptics and ALGASED projects are supportedby the Belgian Federal Science Policy Office in the frame of theSTEREO program—project 043, 072 and 109.

Finally, the authors are grateful to three anonymous refereesfor their useful remarks.

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