LAKE SEVAN

High-resolution satellite remote sensing of littoral vegetationof Lake Sevan (Armenia) as a basis for monitoringand assessment

Jorg Heblinski • Klaus Schmieder •

Thomas Heege • Thomas Kwaku Agyemang •

Hovik Sayadyan • Lilit Vardanyan

Published online: 30 November 2010

� Springer Science+Business Media B.V. 2010

Abstract Physics-based remote sensing in littoral

environments for ecological monitoring and assess-

ment is a challenging task that depends on adequate

atmospheric conditions during data acquisition, sen-

sor capabilities and correction of signal disturbances

associated with water surface and water column.

Airborne hyper-spectral scanners offer higher poten-

tial than satellite sensors for wetland monitoring and

assessment. However, application in remote areas is

often limited by national restrictions, time and high

costs compared to satellite data. In this study, we

tested the potential of the commercial, high-resolu-

tion multi-spectral satellite QuickBird for monitoring

littoral zones of Lake Sevan (Armenia). We present a

classification procedure that uses a physics-based

image processing system (MIP) and GIS tools for

calculating spatial metrics. We focused on classifi-

cation of littoral sediment coverage over three

consecutive years (2006–2008) to document changes

in vegetation structure associated with a rise in water

levels. We describe a spectral unmixing algorithm for

basic classification and a supervised algorithm for

mapping vegetation types. Atmospheric aerosol

retrieval, lake-specific parameterisation and valida-

tion of classifications were supported by underwater

spectral measurements in the respective seasons.

Results revealed accurate classification of submersed

aquatic vegetation and sediment structures in the

littoral zone, documenting spatial vegetation dynam-

ics induced by water level fluctuations and inter-

annual variations in phytoplankton blooms. The data

prove the cost-effective applicability of satellite

remote-sensing approaches for high-resolution map-

ping in space and time of lake littoral zones playing a

Guest editor: Martin A. Stapanian / QuickBird satellite imagery

as a tool for restoration and rehabilitation of Lake Sevan,

Armenia

J. Heblinski (&)

Brauhausgasse 1, 82205 Gilching, Germany

e-mail: mail@joerg-heblinski.de

T. Heege

EOMAP GmbH & Co. KG, Friedrichshafener Str. 1,

82205 Gilching, Germany

e-mail: heege@eomap.de

K. Schmieder � T. K. Agyemang

Institute for Landscape and Plant Ecology,

University of Hohenheim, August-v.-Hartmann-Str. 3,

70599 Stuttgart, Germany

e-mail: schmied@uni-hohenheim.de

T. K. Agyemang

e-mail: agyemang@uni-hohenheim.de

H. Sayadyan

Department of Forestry and Agro-Ecology, State Agrarian

University, 74 Teryan, 375079 Yerevan, Armenia

e-mail: hovik_s@yahoo.com

L. Vardanyan

Vanevan University, #19, Gulbenkyan 29A,

Yerevan, Armenia

e-mail: livag777@yahoo.com

123

Hydrobiologia (2011) 661:97–111

DOI 10.1007/s10750-010-0466-6

major role in lake ecosystem functioning. Such

approaches could be used for monitoring wetlands

anywhere in the world.

Keywords Remote sensing � Littoral vegetation �Water level fluctuations � Spectral unmixing �Inversion � QuickBird

Introduction

Submerged macrophytes perform important ecologi-

cal functions: offering refuge to zooplankton, zoo-

benthos and fish populations; self-purification of the

shallow water zone; and reduction of small-scale

turbulence. Various studies underlined the importance

of structural aspects such as species diversity and

spatial complexity (patchiness) of vegetation struc-

tures for fish populations (Wilcox, 1992; Weaver

et al., 1997; Petr, 2000). Submerged macrophytes also

protect juvenile fish against predators whose catching

efficiency decreases with increasing habitat structural

complexity (Diehl, 1993). Since they can affect fish

populations, submerged macrophytes are not only

ecologically, but also economically important. Chick

& McIvor (1994) report that from the fish’s perspec-

tive, the littoral zone can be perceived as a mosaic of

underwater plants. However, water level fluctuations

strongly influence wetland vegetation patterns,

regardless of seasonal fluctuations (Strang & Dienst,

2004), annual fluctuations (Strang & Dienst, 2004;

Wilcox & Xie, 2007; Wilcox & Nichols, 2008) or

extreme events (Schmieder et al., 2002; Ostendorp

et al., 2003; Dienst et al., 2004).

The water levels of Lake Sevan (Armenia) have

changed considerably over the last few decades.

Anthropogenic activities caused a drop in the lake

level by nearly 20 m between 1940 and 1960. This

decrease in water level was accompanied by serious

ecological consequences (Babayan et al., 2006). In

recent years, the water level rose by nearly 2 m

(Karibyan, 2007), which again may have caused

changes in littoral vegetation structures, affecting

their ecological functions. This increased the need to

document recent changes and predict future changes

of vegetation structures due to any further rise in

water levels.

Large scale mapping and characterisation of

submersed vegetation patterns using remote sensing

and GIS techniques offer many opportunities to

develop ecological assessment tools, such as struc-

tural diversity of littoral vegetation and habitat

suitability models (Woithon & Schmieder, 2004;

Schmieder et al., 2010). These can be used for

monitoring changes and supporting management

decision applications.

Remote sensing image classification is a challeng-

ing task because images often contain information

redundancy, noise, or lack of spectral separation (Lu

& Weng, 2007). In land coverage classification,

remote sensing is accepted as a common technique.

Classification of water constituents is more ambi-

tious, but mapping of macrophytes, sea grasses,

sediments, coral reefs and other ground features is

possible if the water column is sufficiently transpar-

ent, and a sufficient amount of light reaches the

ground and is reflected back out of the water (Dekker

et al., 2001). Govender et al. (2007) reviewed

applications of hyper- and multi-spectral remote

sensing in vegetation and water resources studies.

Becker et al. (2005, 2007) derived optimal band

combinations from hyper-spectral scanner data and

optimal spatial resolutions for wetland vegetation

classification. They recommended an optimal spatial

resolution of 2 m and a minimum of 7 m, with

strategically located bands in the VIS–NIR wave-

length region. During the last decade, physics-based,

automatic classification methods were further devel-

oped by Heege et al. (2004) using spectrally and

spatially high-resolution images and physics-based

classification procedures. Pinnel (2007) analysed the

spectral signatures of various submersed species in

different lakes of the Alpine forelands, which formed

the basis for improved classification algorithms and

assessment tools (Schmieder et al., 2010).

The use of high-resolution images will hopefully

fulfil the needs of environmental monitoring tasks in

areas of high structural diversity. However, in remote

areas, availability of airborne hyper-spectral scanner

data is limited, and flight campaigns from foreign

countries and logistic efforts are restricted and costly.

Sawaya et al. (2003) obtained good results (80%

accuracy) for aquatic vegetation surveys from high-

resolution QuickBird and IKONOS satellite imagery.

Thus, despite spectral limitations, we tested multi-

spectral QuickBird satellite images, highly resolved

in space and time, for their suitability to monitor

littoral vegetation of Lake Sevan (Armenia)

98 Hydrobiologia (2011) 661:97–111

123

associated with water level fluctuations and other

environmental changes.

Materials and methods

Study site

Lake Sevan is the largest freshwater lake in the

Transcaucasus region (Fig. 1). The alpine catchment

of Lake Sevan results in characteristic annual water

level fluctuations with low water during winter and

flood peak in July due to snow melt (Fig. 2). Extensive

use of water for irrigation and hydroelectric power in

the twentieth century caused lowering of the lake level

by nearly 20 m (Chilingaryan et al., 2002; Jenderedjian

et al., 2005; Babayan et al., 2006). This not only

reduced the total lake surface area by 12%, the average

depth by 34.2% and the lake volume by 42.2%, but

particularly affected the littoral zone, seriously affect-

ing its ecological functions. The recently adopted

Sevan rehabilitation programme envisages rising lake

levels, which increased by nearly 2 m between 2003

and 2007 (Karibyan, 2007). Over the 3 years investi-

gated (2006–2008), the water level rose by 0.62 m

(Karibyan, 2007; MES-Armenia, 2008, Fig. 2).

In addition, extended inputs of nutrients from

municipal point sources and non-point sources due to

agriculture and soil erosion from over exploited

pastures in the catchment area increased the trophic

state of Lake Sevan over the last few decades, making

the lake highly eutrophic. Blooms of blue–green

bacteria (Cyanobacteria) during the summer season

are regular, and limit the development of submersed

macrophyte vegetation (Babayan et al., 2006).

The site chosen for this study (Gavaraget) is situated

in the Minor Sevan, the northern part of the lake at

the western shore (Fig. 1). It is the rearmost part of a

wide bay with a more or less unstructured shoreline

not directly influenced by the discharging river

Gavaraget. The littoral zone in Gavaraget gently

declines towards the open water region, with sub-

mersed vegetation of different coverage (Fig. 3A–C)

and extended reeds and bush vegetation (Fig. 3D–F).

Fig. 1 Lake Sevan,

overview (Landsat ETM?

20 August 2005) with

region of interest,

Gavaraget bay (outlined bya white box) shown in the

inset map down left(QuickBird of 16 August

2007). Lake Sevan, darkblue area in main image, is

located in Transcaucasus

region of Armenia (insetmap, top right). Hash markson the margins represent

UTM coordinates Zone

38N, WGS 84. (Color figure

online)

Hydrobiologia (2011) 661:97–111 99

123

Fig. 2 Water levels of

Lake Sevan, daily data

plotted until 19 November

2008 (Karibyan, 2007;

MES-Armenia, 2008),

period of ground truthing

(lilac bands) and days of

satellite data acquisition,

red dot with the data in red.

Months of the year as a

single letter are along the

x-axis, sampling was in

September 2006, then in

July for 2007 and 2008. The

plots demonstrate the

characteristic low levels in

winter and high levels in

summer due to snowmelt,

but the overall picture is of

increasing levels each year.

(Color figure online)

Fig. 3 Submersed

vegetation coverage classes:

A sparse, B moderate and

C dense. D Overview and

E, F emersed vegetation of

Gavaraget bay. Photographs

taken during ground truth

measurements on 23 July

2007. (Color figure online)

100 Hydrobiologia (2011) 661:97–111

123

Remote sensing data

The QuickBird satellite is an on demand working

multi-spectral sensor, and collected the highest multi-

spectral resolution imagery commercially available—

during the term of the project. The spatial resolution is

up to 61 cm Ground Sample Distance (GSD) at nadir

in panchromatic and 2.44 m GSD at nadir in multi-

spectral mode. QuickBird image bands lie at central

wavelengths of 480 nm (Blue), 560 nm (Green),

660 nm (Red), 780 nm (NIR) and 672.5 nm (PAN)

(Digital Globe, 2009 (1)).

For this study we used QuickBird images of the

Gavaraget region over three consecutive years. The

images were collected on 18 August 2006, 16 August

2007 and 23 August 2008. The spatial resolution of

the images represented 0.70/2.8 m in ‘ortho-ready

product level’ (Level 2A) with nearest neighbour

pixel re-sampling and off-nadir viewing angles of

5.2� (2008), 24.33� (2007) and 18.81� (2006). The

satellite data provider DigitalGlobe assures a geo-

location accuracy of 23 m CE90%, excluding any

topographic displacement and off-nadir viewing

(Digital Globe, 2009 (2)). Weather conditions during

remote sensing observations were good, with no

clouds and little sun glint on the water surface. All

the images represent littoral vegetation biomass at

peak state in August. The water transparency

(turbidity, waves) was excellent in 2007, but some

areas had re-suspended sediments in 2006. In

contrast, the 2008 water transparency was extremely

reduced by a blue–green bacteria bloom, with Secchi

depths around 1.5 m only measured during a field

campaign in July 2008.

Ground truth data

During the field campaigns in September 2006, July/

August 2007 and July 2008, we used the ’Radiation

Measurement Sensor with Enhanced Spectral Reso-

lution’ (RAMSES, TriOS Optical Sensors GmbH,

Oldenburg) for ground truth measurements of spec-

tral signatures of littoral vegetation and sediment

patches in shallow water areas, and for spectral

measurements in the pelagial area. The RAMSES

sensor represents a combination of radiance and

irradiance sensors for optical measurements in water.

The system applied in this study consisted of three

sensors, one measuring the up-welling radiance (Lu),

one measuring the up-welling irradiance (Eu) and one

measuring the down-welling irradiance (Ed).

For measurements in the water column, we placed

the sensor at a distance of 2 m from the boat. The

sensors were orientated with the best possible

object–sensor–sun geometry beyond shading influ-

ences. We measured spectral reflectance curves of 30

homogeneous patches of macrophyte or sediment in

2008 to verify classification results. We recorded the

spectra from station equivalent measurements

directly above (R(0?)) and below (R(0-)) the water’s

surface and directly above the top of target (R(b)) in

shallow water areas (Table 1). During a vessel trip in

pelagial areas, we measured reflectance directly

below (R(0-)) the water’s surface and in 2 m water

depth (R(2-)) to determine parameters of atmosphere

and water body as an input for the processing

algorithm (Table 2).

In addition, a dark current measurement was

collected to correct the sensor’s internal noise signal.

The radiometric instruments were linked to a GPS

(Navilock, NL-302U, Berlin Germany) and a Tough-

book CF-30 (Panasonic, Osaka, Japan) to record the

measurements and location of the respective site and

corresponding vegetation patches (see Agyemang

et al., this issue). By diving at measurement points,

we sampled submerged plants and checked the

percentage of vegetation coverage of the patch

measured by visual interpretation. We distinguished

between sparse (\30%), moderate (30–70%) and

dense coverage ([70%) (Fig. 3A–C). The results of

measurements and additional observations (station,

GPS-location, date, time, substrate, vegetation type,

sample, type of spectral measurement, sensor depth,

Secchi depth, water depth, clouds, waves, winds)

were recorded directly in the field (Table 1).

To test the plausibility of the classification results,

the modelled ground reflectance from QuickBird was

compared with ground reflectance measured with

RAMSES during field campaigns. The respective

field measurements of water depth (plumb; Table 1)

were used to check the plausibility of the generated

bathymetric map. The use of GPS during field

activities guaranteed the identification of measure-

ment point location in geo-referenced image data.

Hydrobiologia (2011) 661:97–111 101

123

Image processing

The pre-processing of the raw data was performed

using ENVI (ITT VIS, Boulder, CO, USA) image

analysis software. The pre-processing involved radio-

metric calibration and orthorectification. The images

were radiometrically calibrated and orthorectified

using the calibration coefficients and orthorectifica-

tion parameters delivered by the provider. The

images were rectified to the mean elevation of the

water level during a field trip in July 2007, i.e.

1899 m above the Baltic Sea level (Karibyan, 2007,

pers. comm.). All the images were geo-referenced to

the UTM system, Zone 38 N, WGS-84.

To improve image quality for classification, the

images were corrected for atmosphere, air–water

interface and water column effects using the Modular

Inversion and Processing System (MIP) (EOMAP

GmbH & Co. KG, Gilching, Germany) (Kisselev

et al., 1995; Heege & Fischer, 2004; Miksa et al.,

2005). The physics-based system is designed to

evaluate hyper- and multi-spectral remote sensing

data. Its systematic structure and the independent

treatment of physically independent properties allow

efficient development and implementation of itera-

tively coupled algorithms. Algorithms are defined

independently from sensor and recording conditions.

To execute atmospheric correction with MIP, we

separated terrestrial and water-covered areas. Accord-

ing to the focus of interest, we used different databases

and masked the neglected area. The retrieval of water

constituents for optically deep and shallow natural

waters, as well as the retrieval of littoral ground

characteristics in shallow water areas of lakes and sea,

are major applications of MIP (Heege et al., 2003;

Miksa et al., 2005). Such retrieval is performed in

combination with retrieving atmospheric parameters

and, in specific cases, sun glitter correction algorithms

Table 1 Ground truth measurements in shallow water area Gavaraget (abridged)

Date Time

(local)

Location (UTM

38N, WGS-84)

Sample Measurement

type

Water

depth (m)

Secchi

depth (m)

Wind

force

Wave

height (m)

Cloud

coverage

X (m) Y (m)

20 July

2008

12:53 509310 4475460 P. pectinatus R(0-)/R(b) 1.7 Bottom

visible

Gentle

breeze

0.5–1.0 1/8 (only at

horizon)

25 July

2008

11:45 509275 4475469 P. pectinatus,

Chara sp.

R(0-)/R(b) 2.0 Bottom

visible

Light

breeze

0.2–0.5 0/8

20 July

2008

13:23 509226 4475556 Z. palustris,

Chara sp.

R(0-)/R(b) 1.8 Bottom

visible

Gentle

breeze

0.5–1.0 1/8 (only at

horizon)

20 July

2008

13:38 509153 4475488 Mud Area R(0-)/R(0?) 1.0 0.4 Gentle

breeze

0.5–1.0 1/8 (only at

horizon)

22 July

2008

15:47 509672 4474932 M. spicatum R(0-)/R(0?) 1.0 0.8 Gentle

breeze

0.5–1.0 1/8 (only at

horizon)

22 July

2008

15:31 509672 4474932 White Sand R(0-) 0.3 Bottom

visible

Light

breeze

0.2–0.5 1/8 (only at

horizon)

Table 2 Ground truth measurements in pelagial area used for retrieval of SIOP

Station Time

(local)

Location (UTM 38N, WGS-84) Measurement

type

Secchi

depth (m)

Wind force Wave

height (m)

Cloud coverage

X (m) Y (m)

1 11:15 502497 4488737 R(0-)/R(2-) 4.8 Calm 0 0/8

2 11:45 499266 4489201 R(0-)/R(2-) 5.0 Calm 0 0/8

3 12:30 497607 4485543 R(0-)/R(2-) 4.2 Light breeze 0.2–0.5 0/8

4 13:10 503002 4485081 R(0-)/R(2-) 3.5 Light breeze 0.2–0.5 1/8 (only at horizon)

5 14:50 513678 4475157 R(0-)/R(b) Bottom visible,

1.8 m depth

Light breeze 0.2–0.5 1/8 (only at horizon)

6 15:20 510717 4478459 R(0-)/R(2-) 4.5 Light breeze 0.2–0.5 1/8 (only at horizon)

102 Hydrobiologia (2011) 661:97–111

123

(Heege et al., 2003). The MIP software is an efficient

processing system for inversion of remote sensing data

from natural waters (Heege et al., 2003; Heege &

Fischer, 2004). The overall workflow of data process-

ing in MIP follows the structure presented in Fig. 4. An

azimuthally resolved radiative transfer model for a

multilayer atmosphere–ocean system with a flat water

surface is used for aerosol retrieval, sun glitter and

atmospheric correction modules. The radiative transfer

modules and database system in MIP was implemented

by Kisselev et al. (1995) and is based on the Finite

Element Method (FEM) (Bugarelli et al., 1999).

Radiative transfer calculations are stored in extensive

libraries independently from specific sensor and

recording conditions (system initialisation, necessary

only once). The reduction to concrete recording

conditions and adjusting algorithms to the sensor

specifications is supported automatically in MIP. The

libraries and analytical parameterisations of the under-

water light field form the search space for the retrieval

algorithm.

Water constituent concentrations are needed as

input for the water column correction process. The

water constituent concentrations were calculated

twice. The first approximation was carried out

according to Heege & Fischer (2004) over pelagial

areas. The second adjustment, over moderate deep

benthic substrates, was performed in the frame of the

littoral ground coverage and bathymetry retrieval.

The retrieval of littoral ground reflectance was

performed in shallow water areas. As input, we used

the retrieved water constituent concentrations and the

ground reflectance images. The transformation of

ground reflectance to the ground albedo was based on

the equations published by Albert & Mobley (2003).

The unknown input value of depth was calculated

iteratively in combination with the spectral unmixing

of the respective ground reflectance (Heege et al.,

2006). The unmixing produces littoral ground cover-

age of two to three main class components and the

residual error between the modelled ground reflec-

tance and the calculated reflectance. The final depth,

ground reflectance and ground coverage are achieved

at the minimum value of the residual error. The

potential mapping area is limited due to water

transparency and remote sensor sensitivity. Image

areas without reflection components from the lake’s

ground—the so-called optically deep-water areas—

cannot be mapped with this method. Again, we used

RAMSES measurements of the littoral ground reflec-

tance to validate and consolidate the configuration.

The created underwater digital surface model has a

vertical resolution of 0.1 m.

Based on the ground reflectance image, a spectra-

normalised image, we performed a detailed classifi-

cation of littoral ground coverage. Normalised spectra

are obligatory to summarise spectrally similar pixel in

one class. The final step of the thematic processing

classifies the ground reflectance due to the spectral

signature of different sediment types and macrophyte

Fig. 4 Flow chart of the MIP: A to calculate subsurface

reflectance, B for retrieval of littoral ground coverage and

bathymetric map and C for supervised classification for

underwater cases

Hydrobiologia (2011) 661:97–111 103

123

species using a Fuzzy Logic method including

assignment of individual probability functions for

each defined littoral ground coverage class. Spectra for

the littoral ground classification were derived by

extracting the spectral characteristics from different

areas all over the satellite scene. This was performed

by analysing the statistical variation of each class and

by inspecting the spectral overlaps between the

classes. According to the class-specific spectral fea-

tures, configuration settings for the Fuzzy Logic

discrimination of classes were established. The out-

puts of the littoral ground coverage classification are

class probabilities (in percent) for each coverage class

per pixel.

Calculation of spatial metrics

To summarise spatial changes and assess spatial

diversity, landscape metrics were calculated. This

approach attempts to record the spatial distribution of

landscape elements (patches) and their configuration to

generate explicit, spatially orientated parameters

(Blaschke, 2000). The underlying objective is to record

the structure of a landscape or an ecosystem on the

basis of area, shape, edge, diversity and topology

parameters.

Based on histogram analyses of classification

images, we calculated the surface area covered by

each class. The calculated area metrics are based on a

constant mask, which includes the largest extent of

water on the landside (2008) and the largest extent of

optically deepwater on the lakeside (2008). In addition

to these summarising area metrics, diversity metrics

(Shannon diversity index; and Interspersion and

Juxtaposition Index: IJI) were calculated to document

changes in class diversity and spatial diversity in

vegetation structures over the investigated years using

Fragstats Version 3.3 (McGarigal et al., 2002).

Results

Image processing: Sevan-specific inherent optical

properties

Using the underwater reflectance spectra from RAM-

SES measurements in pelagial areas (Fig. 5A), we

inverted water constituent concentrations and opti-

mised the specific absorption and scattering spectra of

the optical active water components (total suspended

matter, coloured dissolved organic material, phyto-

plankton/chlorophyll). These so-called Lake Sevan-

Specific Inherent Optical Properties (SIOP) (Fig. 5B)

were then applied to processing all the satellite scenes

of Lake Sevan and remarkably improved the

subsequent results.

Classifying multi-spectral satellite data

The basic classification of multi-spectral satellite data

of the three investigated years revealed two main

ground coverage classes: submerged vegetation and

sediments (Fig. 6A, example for 2007). Mixed pixels

contain both vegetation and sediments. Littoral

ground is not completely covered by vegetation in

the respective pixel. The colour gradient represents

the respective proportion of each class, with class

proportions in each pixel adding up to 100%.

The bathymetric map as a direct MIP output

represented a digital surface model of the lake

bottom. Using the example for 2007, with best

conditions compared to 2006 and 2008, the map was

created down to 3 m water column between water

surface and top of target (1896 m above Baltic Sea

level; Fig. 6B). In cases of sediment coverage, the

lake bottom elevation was determined by subtraction

of water level elevation and calculated water column.

Scattered water areas in the land part of the images

are caused by seepage water in small depressions or,

vice versa, dense emergent vegetation coverage

totally hiding the water surface.

The supervised classification procedure revealed

three types of littoral sediments, two types of muddy

areas, and up to six types of submersed vegetation

(Fig. 7). The littoral sediment types represented three

different light-pure sediments without vegetation

cover. Type 1 contained white sand, Type 2 con-

tained buff sand and Type 3 contained dark sand. The

two types of muddy were distinguished by differ-

ences in water clarity.

The six vegetation types (Fig. 7) represented the

littoral ground coverage by submersed macrophytes

(emersed macrophytes were classified separately, and

the data are not presented here). Type 1 was

characterised by sparse coverage (\30%) of low-

growing macrophytes (Chara sp. and Zannichellia

palustris) or sparse coverage (\30%) of high-grow-

ing macrophytes (Potamogeton pectinatus) mixed

104 Hydrobiologia (2011) 661:97–111

123

with buff sand ([70%). Type 2 had moderate

coverage (30–70%) of low-growing macrophytes

(Chara sp. and Z. palustris) mixed with buff sand.

Type 3 had dense coverage ([70%) of low-growing

Chara sp. and Z. palustris partly mixed with sparse

coverage (\30%) of high-growing species (mainly

P. pectinatus). Type 4 was characterised by dense

coverage ([70%) of high-growing plants (mainly

P. pectinatus). Type 5 had moderate coverage

(30–70%) of high-growing species (mainly P. pec-

tinatus, Myriophyllum spicatum and Ceratophyllum

demersum) sometimes mixed with low-growing mac-

rophytes (mainly Chara sp. and Z. palustris) or with

dark sand. Finally, Type 6 was characterised by

moderate coverage (30–70%) of low-growing mac-

rophytes (Chara sp. and Z. palustris), mixed with

dark sand, spiked with stumps of dead reed patches

and/or trees, and low water clarity.

Due to the water level rise of 0.62 m over the three

study years (Fig. 2), the class allocation changed (i.e.

some classes supervened or disappeared). However,

class definition remained constant, so that we could

calculate area metrics and monitor changes of

specific classes over the years. With the collected

results of the 3-year time series, we could document

the spatial distribution of the submerged vegetation

Fig. 5 A Calculated

subsurface irradiance

reflectance R- from

RAMSES measurements at

six pelagial stations (Stat.)

in Minor Sevan. B Lake

Sevan Specific Inherent

Optical Properties (SIOP):

Absorption a* of

phytoplankton (chl) and

Gelbstoff (y), scattering b*

of suspended matter (sm).

(Color figure online)

Hydrobiologia (2011) 661:97–111 105

123

and its spatial changes. In general, due to increasing

water levels, the landside increase of the inundated

area in the gently sloped littoral zone of the ROI

Gavaraget was clearly visible (Fig. 7). Further, a

lakeside decrease of the littoral zone due to reduced

water transparency (optically deep water masked out)

from 2007 to 2008 was apparent, which may have

affected the area metrics. Thus, the calculated area

metrics (Fig. 9) were based on a constant mask. The

water-covered area was largest in 2008 compared to

2007 and 2006, and land area was the opposite

(Fig. 8). The littoral area covered by submersed

Fig. 6 Ground coverage

map of Gavaraget region,

16 August 2007, derived

from QuickBird image

processing. A Derivation of

submersed vegetation with

colour gradient from blue(sediment) to green(vegetation) signifying per

cent ratio of these

components. B Bathymetric

map, colour gradient from

red (shallow areas, about

0.1 m water depth) to green(water depth about 3 m).

Optical deep water is

indicated in black and

terrestrial vegetation in

grey. Contour lines were

digitised from topographic

maps (1:50,000 scale) and

represent elevation above

Baltic Sea level. (Color

figure online)

106 Hydrobiologia (2011) 661:97–111

123

vegetation was largest in 2007, whereas the non-

vegetated littoral area was greatest in 2008. Cover

vegetation types 4 and 5 were the highest in 2007

(Fig. 9), whereas cover of type 3 patches were the

highest in 2008. Large patches vegetation type 1

occurred only in 2006. Patches of vegetation type 6

could only be detected in 2007.

The scores for the Shannon diversity index and IJI

index were the highest in 2007 (1.47 and 83.99,

respectively), compared to 2008 (1.24 and 83.31) and

2006 (0.96 and 81.02). These results indicated higher

class diversity and higher spatial diversity in 2007

compared to the other years. However, variation in

spatial diversity of submersed vegetation structures in

the investigated area was not high over the study

years.

The modelled ground reflectance curves from

QuickBird mirrored the ground reflectance curves

measured by RAMSES in the field (Fig. 10). These

results confirmed the high quality and reliability of

Fig. 7 Detailed littoral ground coverage, Gavaraget region, A 18 August 2006, B 16 August 2007, C 23 August 2008. What each

colour represents is indicated in the key on the right. (Color figure online)

Hydrobiologia (2011) 661:97–111 107

123

the classifications (compare Agyemang et al., this

issue).

Discussion

Remote sensing methods for determining macrophyte

cover in littoral zones of lakes are effective tools for

high-resolution mapping of large areas in space and

time, and thus useful for monitoring and ecological

assessment (Becker et al., 2005, 2007; Schmieder

et al., 2010). Although most applications in lake

littoral zones are based on airborne hyper-spectral

scanner data (Woithon & Schmieder 2004; Becker

et al., 2005, 2007; Schmieder et al., 2010), less cost

and effort are required to test commercially available,

operational satellite data with lower spectral resolu-

tion. We submit that the latter technique offers a

practical and cost-effective alternative for monitoring

and ecological assessment in lake littoral zones

(Sawaya et al., 2003), particularly in remote areas.

Based on multi-spectral data from the QuickBird

satellite sensor, a first classification step allowed us to

create high spatial resolution maps of submersed

vegetation cover and uncovered sediments in the

littoral zone of Lake Sevan. Without this step, Wolter

et al. (2005) only separated two submersed vegetation

density classes, and Sawaya et al. (2003) separated

four submersed vegetation classes differing in density

and growth type. In contrast, we could obtain a higher

resolution of six classes differing in density and

growth type, as well as monitor changes in submersed

vegetation structures over the three investigated

years.

However, vegetation patches need to be large and

adequately dense for successful identification. The

minimum dimension of the patches should at least be

twice the length of a pixel diagonal (Haberacker,

1991). For QuickBird images, with maximal

Fig. 8 Spatial changes in Land-Water-Coverage of ROI

Gavaraget during the investigation period 2006–2008. The ratios

of land (yellow) to water (blue) plotted as bar graphs, clearly show

an increase in water over the 3 years. (Color figure online)

Fig. 9 Spatial changes in

submersed vegetation

classes of ROI Gavaraget

during the investigation

period 2006–2008. Bargraph comparing each year

with respect to different

vegetation types as

described along the x-axis.

(Color figure online)

108 Hydrobiologia (2011) 661:97–111

123

available resolution of 2.4 m, vegetation patches

should be at least 6.8 m on a side. The patch density

and its homogeneity affect the reliability of the class

assignment of a patch. Mixed pixels are classified as a

separate class or have less probability of assignment.

The non-vegetated littoral area was the greatest in

2008 due to a blue–green bacteria bloom. This event

seriously affected the potential of submersed vegeta-

tion classification of deeper littoral areas, leading to a

higher proportion of optical deep water. On the other

hand, the bloom also affected the potential of

submersed macrophytes to form dense high growing

patches. Collectively, this suggests that shading by

phytoplankton has more effect on the high growing

P. pectinatus stands, whereas charophyte-dominated

vegetation seems able to grow well under limited

light conditions. In contrast, P. pectinatus has been

shown to be much more tolerant of high trophic

conditions than charophyte species (e.g. Schneider,

2007). We are unable to resolve this issue which is

suggested for future research.

Our results demonstrate the limitations of remote

sensing in mapping submersed vegetation in phyto-

plankton-dominated lakes. We can only speculate

about the reasons why the regular bloom of blue–

green bacteria did not occur during the summer of

2007. Exceptional weather conditions and increased

total lake volume (approximately 2500 million m3

over the last 6 years) might have inhibited the

development of blue–green bacteria. However,

during clear water conditions in 2007, submersed

macrophytes colonised larger littoral areas, forming

denser patches of increased biomass and higher

spatial diversity, and implying a higher capacity to

perform their ecological functions in the lake

ecosystem.

The low spectral resolution of QuickBird limits the

effectiveness of the classification algorithms and,

consequently, the quality of results and their use for

further applications. Due to the high bandwidth of the

sensor, spectral characteristics of objects can only be

detected at a general level (Sawaya et al., 2003).

However, for the proposed applications, the remote

sensing data quality was acceptable (compare

Agyemang et al., this issue). Sawaya et al. (2003)

also obtained reliable results in mapping littoral

vegetation from high-resolution IKONOS and Quick-

Bird satellites. Thus, particularly for remote areas,

such high-resolution satellites provide an alternative

to airborne hyper-spectral scanner data, especially

when new generations of such satellites offer the

minimum spectral capabilities defined by Becker et al.

(2005, 2007). However, so far only airborne hyper-

spectral scanner data in combination with in situ

measurements of spectral signatures allow higher

thematic resolution up to single species patch detec-

tion (Pinnel, 2007; Schmieder et al., 2010).

In addition to the usual limitations in aquatic

environments (cloud cover, sun glitter, sediment

resuspension, etc.), the retrieval of results in our

study was affected by water level changes (0.62 m

rise between 2006 and 2008) and subsequent varia-

tions in macrophyte spectral characteristics over the

years. In particular, we detected a landside extension

of muddy areas with moderate vegetation cover,

indicating a loss of former terrestrial vegetation.

Rapid water level rise in future years will have a

strong influence on the littoral vegetation. Slowly and

continuously increasing water levels will increase the

chances of macrophytes adapting to changing condi-

tions, thus maintaining their ecological functions

(Wilcox, 1992; Weaver et al., 1997; Petr, 2000).

Conclusions

A continuous environmental monitoring plan for

Lake Sevan using remote sensing techniques is

needed to document and visualise changes in a

Fig. 10 Comparison of reflectances R measured with RAMSES

(continuous lines) and calculated with MIP from QuickBird data

(dotted lines) for different ground coverage (P. pectinatus,

Chara sp., Z. palustris, mud area and white sand as colour coded

in the key top right) in Gavaraget region. (Color figure online)

Hydrobiologia (2011) 661:97–111 109

123

contemporary and cost-effective manner. The

described workflow, using the MIP, demonstrates

the feasibility of retrieving high quality results for

supporting decisions made by lake management

authorities. Although we used a pixel-based classifi-

cation approach here, MIP is open for advanced

object- and knowledge-based classification tech-

niques as described elsewhere (Im et al., 2007;

Lu & Weng, 2007).

An operational, lake-wide application of the

developed algorithms is practicable with little addi-

tional expense. Considering a project-focused acqui-

sition schedule with the provider and stable good

weather conditions, lake-wide coverage of Lake

Sevan could be achieved within five QuickBird

overpasses during 1 month. Once the general optical

properties of a water body (SIOP) are known, data

processing for the whole lake using MIP is not more

work-intensive than for a single scene, due to high

automation of the parameterised processing system.

The use of standardised analytical ground truth

methods can boost the retrieval process. Thus,

although this study covered only part of one lake in

Armenia, it has general applications to wetland

monitoring tasks around the world.

Acknowledgements We thank the SEMIS team and our

other Armenian partners for supporting the project activities.

A special thanks goes to the EOMAP company for support in

processing and parameterisation tasks. Finally, we gratefully

thank the VW Foundation for financial support of this study.

References

Agyemang, T. K., J. Heblinski, K. Schmieder, H. Sayadyan &

L. Vardanyan, 2010. Accuracy assessment of supervised

classification of submersed macrophytes: the case of the

Gavaraget region of Lake Sevan, Armenia. Hydrobiolo-

gia. doi:10.1007/s10750-010-0465-7.

Albert, A. & C. D. Mobley, 2003. An analytical model for

subsurface irradiance and remote sensing reflectance in

deep and shallow case-2 waters. Optics Express 11:

2873–2890 [available on internet at http://opticsexpress./

org/abstract.cfm?URI=OPEX-11-22-2873].

Babayan, A., S. Hakobyan, K. Jenderedjian, S. Muradyan &

M. Voscanov, 2006. Lake Sevan—Experience and Lessons

Learned Brief: 347–362 [available on internet at http://

www.ilec.or.jp/eg/lbmi/pdf/21_Lake_Sevan_27February

2006.pdf].

Becker, B. L., D. P. Lusch & J. Qi, 2005. Identifying optimal

spectral bands from in situ measurements of Great Lakes

coastal wetlands using second-derivative analysis. Remote

Sensing of Environment 97: 238–248.

Becker, B. L., D. P. Lusch & J. Qi, 2007. A classification-based

assessment of the optimal spectral and spatial resolutions

for Great Lakes coastal wetlands imagery. Remote Sens-

ing of Environment 108: 111–120.

Blaschke, T., 2000. Landscape metrics: Konzepte eines jungen

Ansatzes der Landschaftsokologie im Naturschutz. Archiv

fur Naturschutz & Landschaftsforschung 9: 267–299.

Bugarelli, B., V. Kisselev & L. Roberti, 1999. Radiative transfer

in the atmosphere ocean system: the finite-element method.

Applied Optics 38: 1530–1542.

Chick, J. H. & C. C. McIvor, 1994. Patterns in the abundance

and composition of fishes among beds of different mac-

rophytes: viewing a littoral zone as a landscape. Canadian

Journal of Fisheries and Aquatic Sciences 51: 2873–2882.

Chilingaryan, A. L., B. P. Mnatsakanyan, K. A. Aghababyan &

H. V. Toqmagyan, 2002. Hydrology of Rivers and Lakes

of Armenia. Yerevan, Armenia.

Dekker, A., V. Brando, J. Anstee, N. Pinnel, T. Kutser, H.

Hoogenboom, R. Pasterkamp, S. Peters, R. Vos, C. Olbert

& T. Malthus, 2001. Applications of imaging spectrom-

etry in inland, estuarine, coastal and ocean waters. In van

der Meer, F. D. & S. M. de Jong (eds), Imaging Spec-

trometry: Basic Principles and Prospective Applications,

Volume IV of Remote Sensing and Digital Image Pro-

cessing. Kluwer Academic Publishers, Dordrecht, The

Netherlands.

Diehl, S., 1993. Effects of habitat structure on resource avail-

ability, diet and growth of benthivorous perch, Percafluviatilis. OIKOS 67: 403–414.

Dienst, M., K. Schmieder & W. Ostendorp, 2004. Effects of

water level variations on the dynamics of the reed belts of

Lake Constance. Limnologica 34: 29–36.

Digital Globe, 2009 (1). QuickBird Product Description [avail-

able on internet at http://www.digitalglobe.com/index.php/

85/QuickBird, accessed 24 February 2009].

Digital Globe, 2009 (2). QuickBird Imagery Products, Prod-

uct Guide, Revision 5.0 [available on internet at http://

www.digitalglobe.com/index.php/6/DigitalGlobe?Products

, accessed 03 March 2009].

Govender, M., K. Chetty & H. Bulcock, 2007. A review of

hyperspectral remote sensing and its application in vege-

tation and water resources studies. Water SA 33: 145–151.

Haberacker, P., 1991. Digitale Bildverarbeitung: Grundlagen

und Anwendungen. Carl Hanser Verlag, Munchen, Wien.

Heege, T., A. Bogner & N. Pinnel, 2003. Mapping of sub-

merged aquatic vegetation with a physically based process

chain. Proceedings of Remote Sensing, SPIE—The

International Society for Optical Engineering, Vol. 5233.

CD-ROM Proceedings.

Heege, T., C. Hase, A. Bogner & N. Pinnel, 2004. Physikalisch

basierte Prozessierung multispektraler Fernerkundungsda-

ten von Binnengewassern. Laufener Seminarbeitrage, Ba-

yer. Akad. f. Naturschutz u. Landschaftspflege 03: 67–71.

Heege, T., P. Hausknecht & H. Kobryn, 2006. Hyperspectral

seafloor mapping and direct bathymetry calculation using

HyMap data from the Ningaloo reef and Rottnest Island

areas in Western Australia, CD-ROM. 13th ARSP Con-

ference, 20–24 November 2006, Canberra, Australia: 1–7.

110 Hydrobiologia (2011) 661:97–111

123

Heege, T. & J. Fischer, 2004. Mapping of water constituents in

Lake Constance using multispectral airborne scanner data

and a physically based processing scheme. Canadian

Journal Remote Sensing 30: 77–86.

Im, J., J. R. Jensen & J. A. Tullis, 2007. Object-based change

detection using correlation image analysis and image

segmentation. International Journal of Remote Sensing

29(2): 399–423.

Jenderedjian, K., A. Babayan, S. Hakobyan, S. Muradyan &

M. Voskanov, 2005. Managing Lakes and their Basins for

Sustainable Use: A Report for Lake Basin Managers and

Shareholders. ILEC Foundation, Kusatsu, Japan.

Karibyan, M., 2007. Head of Meteorological Station, Sevan,

Hydro-Meteorological Agency, Ministry of Nature Pro-

tection, Armenia. Personal Communication 4 August

2007.

Kisselev, V. B., L. Roberti & G. Perona, 1995. Finite-element

algorithm for radiative transfer in vertically inhomoge-

neous media: numerical scheme and applications. Applied

Optics 34: 8460–8471.

Lu, D. & Q. Weng, 2007. A survey of image classification

methods and techniques for improving classification per-

formance. International Journal of Remote Sensing 28(5):

823–870.

McGarigal, K., S. A. Cushman, M. C. Neel & E. Ene, 2002.

FRAGSTATS: Spatial Pattern Analysis Program for Cate-

gorical Maps [available on internet at http://www.umass.

edu/landeco/research/fragstats/fragstats.html, accessed March

2010].

MES-Armenia (Ministy of Emergency Situations), 2008.

Hydro-Meteorological Agency. Communication Novem-

ber 2008.

Miksa, S., T. Heege, V. Kisselev & P. Gege, 2005. Mapping

water constituents in Lake Constance using Chris/Proba.

Proceedings of the 3rd ESA Chris/Proba Workshop,

Volume ESA SP-593, June 2005, Frascati, Italy. ESRIN.

Ostendorp, W., M. Dienst & K. Schmieder, 2003. Disturbance

and rehabilitation of lakeside Phragmites reeds following

an extreme flood in Lake Constance (Germany). Hydro-

biologia 506–509: 687–695.

Petr, T., 2000. Interactions between fish and aquatic macro-

phytes in inland waters. A review. FAO Fisheries Tech-

nical Paper 396. FAO, Rome: 185 pp.

Pinnel, N., 2007. A Method for Mapping Submerged Macro-

phytes in Lakes using Hyperspectral Remote Sensing:

164 pp [available on internet at http://mediatum2.ub.

tum.de/node?id=604557].

Sawaya, K. E., L. G. Olmanson, N. J. Heinert, P. L. Brezonik &

M. E. Bauer, 2003. Extending satellite remote sensing to

local scales: land and water resource monitoring using

high-resolution imagery. Remote Sensing of Environment

88: 144–156.

Schmieder, K., M. Dienst & W. Ostendorp, 2002. Effects of the

extreme flood in 1999 on the spatial dynamics and stand

structure of the reed belts in Lake Constance. Limnologica

32: 131–146.

Schmieder, K., A. Woithon, T. Heege & N. Pinnel, 2010.

Remote sensing techniques and GIS modeling approaches

for monitoring and assessment of littoral vegetation at

Lake Constance, Germany. Verhandlungen International

Verein Limnologie 30(10): 1–3.

Schneider, S., 2007. Macrophyte trophic indicator values from

a European perspective. Limnologica 37(4): 281–289.

Strang, I. & M. Dienst, 2004. Effects of water level at Lake

Constance on the Deschampsietum rhenanae from 1989 to

2003. Limnologica 34: 22–28.

Weaver, M. J., J. J. Magnuson & M. K. Clayton, 1997. Distri-

bution of littoral fishes in structurally complex macro-

phytes. Canadian Journal of Fisheries and Aquatic Sciences

54: 2277–2289.

Wilcox, D. A., 1992. Implications for faunal habitat related to

altered macrophyte structure in regulated lakes in North-

ern Minnesota. Wetlands 12: 192–203.

Wilcox, D. A. & S. J. Nichols, 2008. The effects of water-level

fluctuations on vegetation in a Lake Huron wetland.

Wetlands 28(2): 487–501.

Wilcox, D. A. & Y. Xie, 2007. Predicting wetland plant

community responses to proposed water-level-regulation

plans for Lake Ontario: GIS-based modeling. Journal of

Great Lakes Research 33(4): 751–773.

Woithon, A. & K. Schmieder, 2004. Bruthabitatmodellierung

fur den Drosselrohrsanger (Acrocephalus arundinaceusL.) als Bestandteil eines integrativen Managementsystems

fur Seeufer. Limnologica 34: 132–139.

Wolter, P. T., C. A. Johnston & G. J. Niemi, 2005. Mapping

submerged aquatic vegetation in the US Great Lakes

using QuickBird satellite data. International Journal of

Remote Sensing 26(23): 5255–5274.

Hydrobiologia (2011) 661:97–111 111

123