PRIMARY RESEARCH PAPER

The spectral responses of a submerged plant Vallisneriaspiralis with varying biomass using spectroradiometer

Lin Yuan Æ Li-Quan Zhang

Received: 4 January 2006 / Revised: 21 October 2006 / Accepted: 28 October 2006 /Published online: 1 February 2007� Springer Science+Business Media B.V. 2007

Abstract The relationship between land features

and their spectral characteristics is a key for the

interpretation of remote sensing images. This study

was designed to investigate the spectral responses

of Vallisneria spiralis, a common submerged

aquatic plant in Shanghai, with varying biomass

both in the laboratory and in the Middle Lake

section of a field-scale constructed wetland, using a

FieldSpecTM Pro JR Field Portable Spectroradi-

ometer. The results showed that the reflectance

rate of V. spiralis increased with its increasing

biomass, and this was exhibited both at the visible

band (500–650 nm) and the near infrared band

(700–900 nm). The water environment influenced

the reflectance rate and the primary differences

between the laboratory and field results mainly

occurred at the near-infrared band (700–900 nm).

A regression analysis was carried out between the

biomass of V. spiralis and the reflectance rate at the

wavelengths of QuickBirdTM bands where the

biomass responded most strongly. The results of

this analysis showed a clear linear relationship by

which the biomass of V. spiralis could be quanti-

tatively deduced from the reflectance rate mea-

sured in situ. The implications of this observation,

in terms of the ability of hyperspectral remote

sensing to estimate and monitor the distribution

and dynamics of submerged aquatic vegetation on

a large scale, are discussed.

Keywords Submerged plant �Vallisneria spiralis �Biomass � Reflectance rate � spectroradiometer

Introduction

Submerged aquatic vegetation (SAV) is an

important component of aquatic ecosystems, as

it provides food and shelter for wildlife, and

habitat for spawning aquatic animals. SAV also

retain nitrogen and phosphorus, remove excess

nutrients and reduce the growth of algae (Jin,

2001). The reestablishment of submerged aquatic

vegetation has been recognized as a key measure

for restoring eutrophied lakes or rivers (Qiu et al.,

1997; Pu et al., 2001; Zhong et al., 2003).

Vallisneria spiralis is a completely submerged

plant commonly occurring in the lakes, ponds and

streams of the drainage area along the middle and

lower reaches of the Yangtze River, China. This

plant is now widely used as an ecological engi-

neering species for aquatic ecosystem restoration

in this region (Jin, 2001).

Handling editor: S. M. Thomaz

L. Yuan � L.-Q. Zhang (&)State Key Laboratory of Estuarine and CoastalResearch, Shanghai Key Laboratory for Urbanizationand Ecological Restoration, East China NormalUniversity, 3663 Zhongshan Road North,Shanghai 200062, Chinae-mail: lqzhang@sklec.ecnu.edu.cn

123

Hydrobiologia (2007) 579:291–299

DOI 10.1007/s10750-006-0444-1

Mapping the abundance and distribution of

aquatic biomass is essential to the management of

SAV. However, to map the distribution and

monitor the growth and dynamics of SAV on a

large scale is very labor intensive and time-

consuming due to the restriction of the water

environment. In the recent years, remote sensing

has become an important tool for vegetation

description and classification on a large scale

(Roughgarden et al., 1991). Unlike remote sensing

of terrestrial vegetation, however, the radiation

reflected from SAV must cross the air–water

interface. In addition, certain optically active

components in the water column, such as algal

chlorophyll and suspended contents, may alter the

spectral signal of the SAV. A number of research-

ers have investigated the potential of using remote

sensing approaches to provide timely data for the

mapping and monitoring of SAV (Orth & Moore,

1983; Welch et al., 1988; Nohara, 1991; Jensen,

1992; Jensen et al., 1993; Kirkman, 1996; Jakub-

auskas et al., 2000; Han & Rundquist, 2003;

Williams et al., 2003). Roy (1993) used Landsat

Thematic Mapper(TM) to assess seagrass biomass

in the southern Exuma Cays, and the spectral

characteristics of V. spiralis with varying coverage

were measured with a ground sensor/radiometer

(Yuan & Zhang, 2006), but the quantitative rela-

tionship between the biomass of SAV and its

spectral reflectance has yet to be fully explored.

The goal of this research was to investigate the

spectral characteristics of SAV with a view to

determining the utility of remotely sensed data in

mapping and monitoring its distribution and for

quantitative estimation. To address this goal, the

spectral responses of a major submerged plant (V.

spiralis) with varying biomass, both in the laboratory

and at a constructed wetland in Shanghai were

examined. A number of questions were considered.

First, does this submerged plant have its own unique

spectral signature? Second, can the biomass be

quantitatively estimated by the spectral reflectance

measured? Finally, what are the most important

factors influencing the spectral characteristics of

SAV? Identifying the relationship between the

biomass of SAV and its spectral characteristics is an

important first step to providing timely data for the

estimating, monitoring and managing of aquatic

biological productivity on a large scale.

Materials and methods

Materials

V. spiralis is a dominant perennial submerged

aquatic plant commonly occurring in the lakes,

ponds and streams of the Shanghai region. The

plant is an herbaceous and fast-growing species

with narrow, long leaves based on the stolons

procumbent in the sediments of the water body

(Yan, 1983). This species can remove excess

nutrients in the water body and has been widely

used as an appropriate ecological engineering

species for aquatic ecosystem restoration in this

region. V. spiralis was thus selected as the

experimental material in this study.

Laboratory experiments

A series of plastic boxes measuring 32 · 26 ·23 cm each, filled with 5 cm clear sands on the

bottom and 15 l of 10% Hoagland culture fluid

were prepared for the transplantation. Normal

and healthy individuals (each ca. 15 cm long and

with 3–4 leaves) of V. spiralis were transplanted

into each box, with coverage varying between 0%

and 80% in May 2005. Because the plants we

transplanted had similar heights, each box repre-

sented a varying biomass of V. spiralis. These

boxes were then transferred into growth cham-

bers with a 16/8 h light/dark regime

(9000 l · light intensity) at a constant tempera-

ture of 26�C for 40 days.

Field experiments

The field experiments were conducted out-doors

in natural sunlight at the Middle Lake (625 m2

surface area and average depth 90 cm) of a

constructed wetland in Mengqing Garden on the

southern bank of Suzhou Creek (30�23¢ N and

120�50¢ E), Shanghai. A series of plastic boxes

measuring 45 · 28 · 23 cm each, were filled with

5 cm of lake sediments. Normal and healthy

individuals of V. spiralis (each ca. 15 cm long

and with 3–4 leaves) were transplanted into each

box, with coverage varying between 0% and 80%,

this represented the varying biomass at the field

site in March 2005. The boxes were attached with

292 Hydrobiologia (2007) 579:291–299

123

nylon ropes and were lowered into the lake, thus

allowing the experimental plants to grow in a

natural condition.

Spectral data collection

The spectral measurements for each box with

varying biomass of V. spiralis were made in June

2005 using a FieldSpecTM Pro JR Field Portable

Spectroradiometer produced by ASD Inc. The

instrument collects data over the range

350~2,500 nm, with a sampling interval of

1.4 nm between 350–1,000 nm and 2 nm for the

region between 1,000 and 2,500 nm. The sensor

head used with the instrument had a full angle

field of view of 25 degrees. All measurements

were made from a nadir position, with the sensor

head located 0.5 m above the water surface,

giving an approximate sensor footprint of

0.22 m. The canopy of each box with varying

biomass of V. spiralis was always adjusted to be

approximately 10 cm below the water surface.

Twenty measurements were made for each box

and optimization of the ASD was carried out

approximately every 20 min, using a spectrolon

panel of known reflectance (20%).

In the laboratory experiment, the walls of the

box were covered with a black cotton cloth to

eliminate extraneous internal reflectance and a

50-W Tungsten-Quartz-Halogen lamp was used

as a source of artificial illumination. In the field

experiment, the measurements were made be-

tween 10:30 h and 14:30 h local time under

uniformly cloudless sunny weather conditions.

The concentration of chlorophyll and suspended

contents in the water body were 10.5 lg/l and

12.0 mg/l respectively during the spectral mea-

surements. At the same time as spectral measure-

ments were taken, the fresh weight (FW),

coverage and average height of V. spiralis for

each box were measured both in the laboratory

and in the field experiments. The biomass of

V. spiralis for each box was normalized to

g FW/m2 (Table 1).

Spectral data processing

The spectral data were processed to reflectance

values using the ViewSpecTM Pro 4.02 software

provided by ASD Inc. Twenty reflectance values

for each box with varying biomass of V. spiralis

were then averaged in order to eliminate any

potential influence of variations in illumination.

Due to an observed low signal-to-noise ratio at

wavelengths shorter than 400 nm and longer than

900 nm, only reflectance calculated between 400

and 900 nm was used. Since the study intended to

look at the potential application of airborne

sensors for biomass estimation, the averaged

reflectance values for each box were processed

to represent the four bands of QuickBirdTM, i.e.

blue light band (450–520 nm), green light band

(520–600 nm), red light band (630–690 nm) and

near infrared light band (760–900 nm).

Identifying the relationship between the vary-

ing biomass and its spectral characteristics is

essential in addressing the questions stated in the

introduction. The approach adopted here was

statistically based, using correlation and regres-

sion to analyze their quantitative relationship.

Table 1 The biomass, coverage and average height ofVallisneria spiralis in the laboratory and field experiments.B1–B6 and B1¢–B6¢ are the sample number with varying

biomass of V. spiralis measured in the laboratory exper-iment and field experiment, respectively

Laboratory Field

Sample No. Biomass(g/m2)

Coverage (%) Averageheight (cm)

Sample No Biomass(g/m2)

Coverage (%) Averageheight (cm)

B1 0 0 – B1¢ 0 0 –B2 250.68 20 19 B2¢ 257.68 20 23B3 496.08 30 20 B3¢ 518.48 30 28B4 926.76 50 17 B4¢ 944.08 50 20B5 1441.44 70 22 B5¢ 1497.76 70 23B6 2010.84 80 20 B6¢ 2054.56 80 27

Hydrobiologia (2007) 579:291–299 293

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The correlation and regression analyses were

implemented using SPSS11.0 and OriginLabTM

7.5 packages.

Results

Laboratory experiments

The spectral reflectance curves for the varying

biomass of V. spiralis in the laboratory experi-

ments formed a typical vegetation spectral curve,

with a small peak around 550 nm (the ‘green

peak’), higher reflectance beyond 700 nm (near-

infrared; NIR) than in the visible spectrum and

two low intensity absorption areas in the blue

band (between 400 and 500 nm) and red band

(around 675 nm) (Fig. 1). Figure 1 also depicts

the reflectance responses to the varying biomass

of V. spiralis, where it is seen that the reflectance

decreased in an orderly fashion with decreasing

biomass, especially in the NIR range, i.e. from

about 9% of reflectance with high biomass to 6%

of reflectance with low biomass; the pattern was

less obvious below 700 nm. It is also noticed that

the reflectance curves for the 0g biomass treat-

ment (100% water without any submerged plant)

did not show a typical vegetation spectral curve,

although it also had higher reflectance in the NIR

band. Therefore, the reflectance response of V.

spiralis with varying biomass was mainly observed

around 575 nm and in the NIR.

To understand the strength of the association

between the biomass of V. spiralis and the

spectral reflectance, the Pearson product-mo-

ment correlation coefficient r was estimated. A

strong positive correlation (P < 0.05) was found

at roughly the wavelength range of 400–421 nm

and 722–900 nm, with the highest correlation

coefficient around 887 nm (r = 0.967, P < 0.01)

(Fig. 2). This indicated that the higher the

biomass of V. spiralis, the stronger the reflec-

tance was at those wavelengths. Therefore,

regression analysis between the biomass of V.

spiralis and the reflectance at 887 nm and at the

band 4 (760–900 nm) of QuickBirdTM were

performed. A clear pattern of increasing reflec-

tance across an increase in biomass from left to

right can be identified (Fig. 3). The Pearson

product-moment correlation between the vary-

ing biomass of V. spiralis and its reflectance at

887 nm was highly significant (R2 = 0.9278;

P < 0.01) (Fig. 3a). The correlation between

the varying biomass of V. spiralis and its

reflectance at the band 4 of QuickBirdTM was

also highly significant (R2 = 0.9009; P < 0.05)

(Fig. 3b).

Field experiments

A similar pattern was observed in the field exper-

iments, i.e. as the biomass of V. spiralis increased,

the reflectance increased around 550 nm and

beyond 700 nm, and formed absorption areas

400 500 600 700 800 9000

2

4

6

8

10

Ref

lect

ance

(%

)

Wavelength (nm)

B1

B2

B3

B4

B5

B6

Fig. 1 Reflectance of Vallisneria spiralis with varyingbiomass in the laboratory experiments (the legends ofB1–B6 see Table 1)

400 500 600 700 800 900

-0.4

0.0

0.4

0.8

1.2

r a=0.05 a=0.01

Co

rrel

atio

n c

oef

fici

ent

( r)

Wavelength (nm)

Fig. 2 Correlation coefficients between the Vallisneriaspiralis of varying biomass and their reflectance measuredin the laboratory

294 Hydrobiologia (2007) 579:291–299

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between 400–500 nm and around 675 nm (Fig. 4).

However, the reflectance maximum around

550 nm (the ‘green peak’) were much higher than

that in the clear water of the laboratory experi-

ments and the ‘NIR plateau’ evolved into two

peaks with one around 720 nm and the other

around 830 nm.

Figure 5 depicts the Pearson product-moment

correlation coefficient r between the biomass of V.

spiralis and spectral reflectance in the field exper-

iments. A strong positive correlation with biomass

was found at the wavelength between 436–700 nm

(P < 0.01) and 835–900 nm (P < 0.05), with the

highest correlations at 533 nm (r = 0.986) in the

visible band and at 895 nm (r = 0.878) in the NIR.

A clear pattern of increasing reflectance with an

increase in biomass from left to right could be

identified at 533 nm (R2 = 0.9391, P < 0.01) and

895 nm (R2 = 0.741) (Fig. 6a, b).

To explore the potential of using current

operational satellite sensors for the estimation

and monitoring of SAV, regression analyses

were performed between the varying biomass of

V. spiralis and the four bands of QuickBirdTM.

A clear linear relationship was found for the

blue light band (450–520 nm, R2 = 0.9696,

P < 0.01), the green light band (520–600 nm,

R2 = 0.9605, P < 0.01), the red light band (630–

690 nm, R2 = 0.986, P < 0.01) and the NIR band

(760–900 nm, R2 = 0.8186, P < 0.05)(Fig. 6c–f).

y = 0.0016x + 3.6361

R2 = 0.9278

2

3

4

5

6

7

8

-200 300 800 1300 1800 2300

Biomass (g/m2)

Ref

lect

ance

(%

)y = 0.0014x + 4.7543

R2 = 0.9009

4

5

6

7

8

-200 300 800 1300 1800 2300

Biomass (g/m2)

(a) (b)

Fig. 3 Regression analysis between the biomass of Vallisneria spiralis and their reflectance measured in the laboratory: (a)at 830 nm and (b) at the Quick Bird band 4 (760–900 nm)

400 500 600 700 800 900

2

3

4

5

6

7

Ref

lect

ance

(%

)

Wavelength (nm)

B1' B

2' B

3'

B4' B

5' B

6'

Fig. 4 Reflectance of Vallisneria spiralis with varyingbiomass in the field experiments (the legends of B1

¢ –B6¢

see Table 1)

400 500 600 700 800 9000.5

0.6

0.7

0.8

0.9

1.0

r a=0.05 a=0.01

Co

rrel

atio

n c

oef

fici

ent

(r)

Wavelength (nm)

Fig. 5 Correlation coefficients between the Vallisneriaspiralis of varying biomass and their reflectance measuredin the field

Hydrobiologia (2007) 579:291–299 295

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Discussion

Spectral characteristics of Vallisneria spiralis

with varying biomass

A generally positive correlation between vegeta-

tion biomass, usually measured by leaf area index

(LAI) or coverage, and NIR reflectance has been

observed for common terrestrial vegetation (Gao

& Zhang, 2006). Yuan & Zhang (2006) reported

that the reflectance rate of V. spiralis increased

with its increasing coverage at the visible band

and the NIR band. Zhang (1998) used a Landsat

5TM image to assess the total biomass of SAV in

Honghu Lake, Hubei Province, China, and found

that submerged vegetation biomass had a good

linear relationship with spectral reflectance. Our

results both from the laboratory and field exper-

iments for the SAV plant V. spiralis displayed a

typical vegetation spectral curve, which showed

similar trends to those studies.

The reflectance of SAV in the visible bands

and in the NIR was less than 10%, which

was relatively low compared to the common

y = 0.0006x + 3.5189R2 = 0.9391

2

3

4

5

6

-200 300 800 1300 1800 2300

Ref

lect

ance

(%

)

y = 0.0008x + 2.6224R2 = 0.741

1

2

3

4

5

-200 300 800 1300 1800 2300

y = 0.0058x + 3.0335R2 = 0.9696

2.7

2.9

3.1

3.3

3.5

3.7

-200 300 800 1300 1800 2300

Ref

lect

ance

(%

)

y = 0.0183x + 3.5574R2 = 0.9605

3

3.5

4

4.5

5

5.5

-200 300 800 1300 1800 2300

y =0.0006x + 3.4378R2 = 0.986

3

3.5

4

4.5

5

-200 300 800 1300 1800 2300

Biomass (g/m2)

Ref

lect

ance

(%

)

y = 0.0167x + 3.5614R2 = 0.8186

3

3.5

4

4.5

5

5.5

-200 300 800 1300 1800 2300

Biomass (g/m 2)

(a) (b)

(c) (d)

(e) (f)

Fig. 6 Regression analysis between the biomass of Vallis-neria spiralis and their reflectance measured in the field:(a) at 533 nm, (b) at 895 nm, (c) at the Quick Bird band 1

(450–520 nm), (d) at the Quick Bird band 2 (520–600 nm),(e) at the Quick Bird band 3 (630–690 nm), and (f) at theQuick Bird band 4 (760–900 nm)

296 Hydrobiologia (2007) 579:291–299

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terrestrial vegetation and could be attributed to

the facts that the radiation reflected from SAV

must cross the air–water interface. However, a

clear linear relationship between varying biomass

of V. spiralis and their spectral reflectance at the

wavelength between 500–650nm and at the NIR

(700–900 nm) could be identified in our research.

Therefore, this approach demonstrated a poten-

tial of measuring quantitatively the distribution

and biomass of SAV by deducing from the

reflectance rate measured in situ.

Comparison between the laboratory and field

experiments

The primary differences in the spectral signature

between the laboratory and field experiments were

the higher reflectance maximum around 550 nm

(the ‘green peak’), the lower ‘red edge’ near

700 nm and the two peak NIR ‘plateau’ in the field

experiments. In general, variation in spectral sig-

nature is determined by factors such as canopy

physiognomy and physiology, the proportion of

green plant material in the canopy, the orientation

of individual leaves, the characteristics and degree

of visibility of the substrate and the amount of

canopy shadow (Armitage et al., 2004). In this case,

the plant material was the same and the structure of

the canopies was very similar, thus the differences

could be attributed to clear water in the laboratory

experiments versus algal-laden water in the field

experiments. The algal chlorophyll and other

suspended contents in the field water could prob-

ably change the reflectance at the visible bands and

the NIR (Han & Rundquist, 2003). Therefore, care

must be taken when the spectral signature of SAV

measured in the laboratory is to be used in the field.

Further work is needed to understand the impacts

of the water environment on the composite spec-

tral signature of SAV.

Implications for current operational satellite

and airborne systems

The restoration and reconstruction of SAV is now

a required action for restoring eutrophied lakes or

rivers in China. However, mapping the distribu-

tion and monitoring the growth and dynamics of

SAV on a large scale is very labor intensive and

time-consuming, e.g. large lakes, difficulties in

accessing sites and getting enough sites for reli-

able data, etc. A number of researchers have

investigated the potential of using current oper-

ational airborne systems for the mapping and

monitoring of SAV. A Landsat 5TM image was

used to assess the total biomass of SAV in

Honghu Lake, by deducing from the relationship

between SAV biomass and its spectral data

measured at a series of sampling sites (Zhang,

1998). In that study, the weighed SAV biomass

was set into a tank filled with the lake water and

the spectral reflectance was measured, in a

manner similar to our laboratory experiments.

As mentioned previously, the structure and shape

of the SAV canopy as well as the water environ-

ment conditions might be different in this mea-

surement, and the ground based in situ spectral

data could greatly improve the overall accuracy of

SAV biomass assessment.

Roy (1993) used Landsat Thematic Map-

per(TM) to assess the seagrass biomass in the

southern Exuma Cays, Bahamas. The TM bands

were transformed to minimize the depth-dependent

variance in the bottom reflectance signal. Williams

et al. (2003) applied airborne hyperspectral remote

sensing imagery for automated mapping of SAV in

the tidal Potomac River, Maryland, USA, by

developing a spectral library database containing

ground-based and airborne sensor spectra. The

depth of the absorption feature at a specific

wavelength could be used to identify two species

of SAV. With the advent of satellite sensors such as

QuickBirdTM and IkonosTM, which have spatial

resolutions similar to airborne systems, work by

Schmidt and Skidmore (2003) has indicated that

using greater spectral resolution in the form of

hyperspectral data may produce better results.

In this research, the ground-based spectral

signatures of V. spiralis with varying biomass

were measured both in the laboratory and field

conditions. The clear pattern of linear relation-

ships between the biomass of V. spiralis and their

spectral reflectance at the wavelengths of Quick-

BirdTM bands will be useful with the current and

forthcoming space-based hyperspectral remote

sensing systems to map SAV distributions and

abundance, and to estimate the biomass of SAV

in a shallow water body.

Hydrobiologia (2007) 579:291–299 297

123

In conclusion, this research has only investi-

gated a single species of SAV and a limited

sample of aquatic environments in Shanghai.

Identifying the relationships between the biomass

of SAV and their spectral characteristics is an

important first step to providing timely data for

the mapping, monitoring, estimating and manag-

ing of SAV on a large scale. Further work on

other types of SAV using multi-seasonal spectral

data, and investigating the impact of the aquatic

environment and water quality on the composite

spectral signature of SAV is necessary in order to

determine whether similar patterns emerge. Fur-

thermore, future work must also consider a

comparative analysis with hyperspectral airborne

remote sensing systems such as CASI, AVIRIS,

PHI and OMIS, or hyperspectral satellite remote

sensing system such as Hyperion for the tested

areas, although these hyperspectral airborne or

satellite data are usually difficult to acquire at the

moment.

Acknowledgements The authors would like to thankmembers of the Ecological Section of the State KeyLaboratory of Estuarine and Coastal Research, East ChinaNormal University, for their assistance with the collectionof the field data. We also thank Professor Bruce Anderson,Queen’s University, Canada for valuable comments andlinguistic checking. The research has been funded by thekey project of the Shanghai Scientific & TechnologicalCommittee (05DZ12009), National Key FundamentalResearch and Development Program (2003AA601020)and the state’s 10th five-year ‘‘211 Project’’ – supportedkey academic discipline program of ECNU.

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