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Remote Sensing Data Acquisitions and Processing for 

Vistula Lagoon Ecosystem Management 

I. M. Mróz (1) , M. Mleczko (1) , K. Osińska-Skotak (2) , D. Durand (3) , M. Szumilo (1) , J. Kosakowski (1) 

1 

University of Warmia and Mazury in Olsztyn, ul.Oczapowskiego 2, 10-957 Olsztyn, Poland 

2 

Warsaw University of Technology, Plac Politechniki 1, 00-660 Warsaw, Poland 

3 NIVA – Water Research Institute, Oslo-Bergen, Norway 

Abstract-The paper presents a part of the research project 

“VISLA” concerning establishing of a spatial and environmental 

information system for Vistula Lagoon in Poland. An important 

set of satellite images has been collected during last 3 years for 

this water body and surrounding land. The images are processed 

not only in order to extract well known water quality parameters 

but also in the perspective of their use as a source of information 

for land use/ land cover updating. The innovative aspect of the 

project is implementing ecological modeling for this water body 

using GEMSS approach and use remote sensing data at the later 

stage to verification of output results from the model. 

I. INTRODUCTION 

The ability to map and monitor ecological phenomena over 

large spatial extents has become a focus of research in the 

context of increasing awareness of human activities and 

environmental change. Research efforts in support of 

sustainable ecosystem management have focused on 

characterizing ecosystem condition and understanding how 

natural and anthropogenic processes affect ecosystem 

functioning [10, 11]. Coastal aquatic ecosystems are often 

perceived as a complex and “difficult” area from a 

management perspective due to their dynamic nature and joint 

management by multiple local, regional and national level 

government institutions and agencies. Common activities on 

these areas range from resource use (fishing, tourism), 

recreation (boating, swimming) and urban development 

(housing, port, industrial, commercial) to natural functions 

(habitat, shoreline stabilization, flood reduction, nutrient sinks) 

Environmental management in coastal zones is frequently 

represented by term “three-M” approach: Mapping, 

Monitoring and Modeling. Mapping – surveys or inventories 

conducted to determine the presence and locations of features, 

Monitoring – time series analyses enabling changes to be 

mapped and measured, Modeling – understanding and 

mathematical description how an environmental system, or 

one of its components, operates [11]. The objective of this 

paper is to present a part of multidisciplinary research project 

“VISLA” concerning a geoinformation system building for 

assessment of ecological vulnerability and sustainable 

development of ecologically valuable Vistula Lagoon (VL) 

ecosystem. This project covers all “three-M” aspects of 

management. It is carried out in the framework of the Polish 

Norwegian Research Fund and consists in integration of 

multisource databases like local and regional GIS bases, 

remotely sensed data and water quality parameters extracted 

from in situ measurements. One of the major challenges of the 

project is implementation of mathematical modeling of VL 

water ecosystem processes based on GEMSS model solutions 

[5]. The second one is to join together land information 

databases (governmental and regional) existing for adjacent 

communes and include the results of several water quality 

measurement campaigns in a common VL_GIS system 

serving local communities and decision makers as a support in 

their management activity. One of important data sources 

about lagoon’s water quality and features of adjacent land 

areas is remote sensing. 

II. LAND AND WATER INFORMATION SYSTEM FOR VISTULA 

LAGOON 

A. Study area 

The study area is located on the Polish part of Vistula 

Lagoon (828 km 2 area, 2.6 m mean depth), the estuary of 

Baltic Sea. The hydrological regime of Vistula Lagoon is 

dependent on the inflow of sea waters. Even 80% of water is 

coming from Baltic Sea by Pilawa Strait. The major 

phenomenon causing permanent disturbance of ecosystem 

balance is an excessive primary productivity of phytoplankton, 

particularly regular harmful algae blooms [12]. The main 

sources of nutrients are originating from unmanaged properly 

water treatment in the catchment area and from the 

resuspension from bottom sediment. The trophic indices are 

characteristic for the state of strong eutrophy and even 

hypertrophy. The main consequence of the state described 

above is a significant impoverishment of biodiversity and a 

lowering of the production other, except phytoplankton, 

ecological associations including fishes and macrophytes. 

More, Cyanophytes and bacteria, as well as trace metals and 

organic pollutants coming into Lagoon from the sludge, cause 

severe toxic effects, which practically eliminate this water 

body from effective fisheries, recreation and water sports 

activities. 

B. Integrated approach for VL_GIS establishing 

The tasks of communes and counties in ecological 

management are very often supported by governmental or 

regional GIS –based tools. For Vistula Lagoon Area Of 

Interest (VL AOI) there are many different databases, 

generally branch specific, organized or not as GIS maps or

layers. Some of them cover only land areas, others cover 

whole territory including water bodies and rivers. For ex. 

Regional Spatial Information System (RSIS) managed by 

geodesy and cartography regional offices, detailed geological 

maps in paper, digital raster or vector form, ecological maps 

of NATURA 2000 areas, Geochemical atlas of Vistula 

Lagoon, Corine Land Cover 2006 database, hydrographic map 

of Poland, Digital Terrain Models, etc. RSIS is a main set of 

information about topography and man-made infrastructure. 

On the other hand there exist numerous ecological databases, 

official, administrative, governmental, often offering long 

time series of parameters’ values concerning water quality in 

lakes, rivers and streams. But these measurement results (e.g. 

sediment transport, salinity, temperature, chemical and 

biological components) are often stored in simple 

alphanumerical tables or in the spreadsheets in different 

institutions. In some cases administrative limits are the 

“firewalls” disabling data and information exchange between 

adjacent administrative units without any agreement from 

headquarter. One of the purposes of mentioned project is 

complementing distributed ecological databases with in situ 

measured water quality parameters of the Vistula Lagoon 

using own equipment and following own procedures. The next 

step in creation of Common VL_GIS is establishing 

geodatabases in ArcGIS environment permitting bi-directional 

interoperability between water specific and land specific 

features. It means e.g. taking into account water quality 

parameters when elaboration of derivative thematic maps for 

land areas management. And backward - taking into account 

land features and properties when planning management 

actions concerning improvement of water quality in the lagoon. 

The VISLA project seems to be an initial research platform for 

joining together dispersed and distributed sources of data 

about land characteristics and water quality, converting them 

into coherent GIS system and complementing them with 

satellite images bringing updated information on water optical 

properties (on main water quality parameters) as well as on 

land use/land cover changes. The innovative aspect of the 

project is implementing ecological modeling for this water 

body using GEMSS approach [5]. Fig.1, 2 and 3show three 

mock-ups of the project structure and interrelationships 

between its components. Functional mock-up, GIS area mockup 

and Remote Sensing contribution mock-up. 

Figure 2. GIS area mock-up 

Figure 3. Remote Sensing area mock-up 

Figure 1. Functional mock-up 

III. SATELLITE IMAGES DATABASE 

Many satellite images have been registered for this area 

during last three years (2008, 2009 and 2010). 

ENVISAT/MERIS and CHRIS/PROBA served as source of 

information on water parameters, others like Landsat5 TM, 

SPOT XS, DMCII, both for water body inspection and 

surrounding land areas mapping The extent of water body and 

the needs of study were the main factors influencing the 

choice of satellite systems and sensors used for observing 

lagoon and surrounding land areas. Before this study start we 

expected to have an access to Earth Observing 1 satellite and 

its sensor ALI (Advanced Land Imager). We had at our

disposal some images acquired by this sensor during 2006 and 

2007 seasons and we appreciated its relatively high spatial 

resolution (30m MS and 10m Panchro) and spectral bands in 

blue region of spectrum. Since 2008 the clouds free images 

have not been available and programmed acquisitions have 

been even impossible. Therefore the main sources of satellite 

images became “old” Landsat 5 TM system with 30m spatial 

resolution and ENVISAT/MERIS sensor largely used for 

ocean and coastal waters study, but characterized by about 

300m spatial resolution, not very well suited for Vistula 

Lagoon “detailed” area mapping. The third sensor used in the 

project is CHRIS imaging spectrometer flying on PROBA 

platform. CHRIS, i.e. the Compact High Resolution Imaging 

Spectrometer is specially designed for environmental 

investigations. Moving on the orbit of the average altitude of 

600 km, CHRIS acquires images on a swath of the 

approximate width of 14 km with the average spatial 

resolution of 18×18 m. This value is slightly changing as a 

result of variations in the orbit altitude, which varies between 

561 and 681 km, with the nominal scanner field of view of 

1.3 o . CHRIS images are normally acquired as a set of five 

scenes of the same area of the Earth’s surface recorded along 

track, for different view angles, in an „almost-real” time. 

CHRIS acquires images for the spectral range between 410 

nm and 1050 nm; it is possible to achieve 63 spectral bands 

with the spatial resolution of 36×36 m or 18 spectral bands 

with the so-called, full spatial resolution, i.e. 18×18 m. In the 

case of higher spatial resolution it is possible to acquire 

images in the following modes: „Land/Aerosols” (18 bands 

marked as L), „Water” (18 bands marked as W), 

„Chlorophyll” (18 bands marked as C) and „Land” (36 bands 

marked as H, acquired for a half of the nominal band of 

scanning). In the case of data acquisition dedicated to 

investigations of water, CHRIS acquires more visible spectral 

bands than in the case of data registration for the needs of land 

investigations. In the case of CHRIS the images cover small 

area and are used for water parameters extraction only. For 

this reason the acquisitions were scheduled close to field 

campaigns days (cruises) on two separate smaller areas of 

interest (Vistula Lagoon East and West). Only MERIS and 

CHRIS acquisitions could be anticipated or programmed. The 

Landsat 5 TM images were available from the archives, much 

later than the day of acquisition. Thus in order to complement 

the set of images of high spatial resolution and to fill temporal 

gaps resulting from clouds cover we decided to purchase also 

ALOS/AVNIR-2, SPOT-4 XS and SPOT-5 HRG images. 

TABLE I 

SPECTRAL BANDS AND SPATIAL RESOLUTION OF THE SENSORS 

Sensor type 

Spectral 

bands 

Wavelength 

[µm] 

Spatial 

resolution 

[m] 

Landsat 5 TM Panchrom. 0.520 - 0.900 15 

1 0.450 - 0.515 30 

2 0.525 - 0.605 30 

3 0.630 - 0.690 30 

4 0.775 - 0.900 30 

5 1.550 - 1.750 30 

7 2.090 - 2.350 30 

6a 10.40 - 12.50 60 

6b 10.40 - 12.50 60 

CHRIS/PROBA 

Mode 2 

W1 0.406 - 0.415 18 

W2 0.438 - 0.447 18 

W3 0.486 - 0.495 18 

W4 0.505 - 0.515 18 

W5 0.526 - 0.534 18 

W6 0.556 - 0.566 18 

W7 0.566 - 0.577 18 

W8 0.585 - 0.596 18 

W9 0.618 - 0.627 18 

W10 0.646 - 0.656 18 

W11 0.666 - 0.677 18 

W12 0.677 - 0.683 18 

W13 0.683 - 0.689 18 

W14 0.700 - 0.712 18 

W15 0.752 - 0.759 18 

W16 0.773 - 0.788 18 

W17 0.863 - 0.881 18 

W18 1.003 - 1.036 18 

SPOT 4 

Monospectral 

0.610 - 0.680 10 

B1 0.500 - 0.590 20 

B2 0.610 - 0.680 20 

B3 0.780 - 0.890 20 

B4 1.580 - 1.750 20 

SPOT 5 Panchrom. 0.480 - 0.710 2.5 - 5 

B1 0.500 - 0.590 10 

B2 0.610 - 0.680 10 

B3 0.780 - 0.890 10 

B4 1.580 - 1.750 20 

ALOS/AVNIR 1 0.420 - 0.500 10 

2 0.520 - 0.600 10 

3 0.610 - 0.690 10 

4 0.760 - 0.890 10 

ENVISAT/ 

MERIS 

1 0.403 - 0.423 300 

2 0.433 - 0.453 300 

3 0.480 - 0.500 300 

4 0.500 - 0.520 300 

5 0.550 - 0.570 300 

6 0.610 - 0.630 300 

7 0.655 - 0.675 300 

8 0.674 - 0.689 300 

9 0.699 - 0.719 300 

10 0.746 - 0.761 300 

11 0.757 - 0.764 300 

12 0.764 - 0.794 300 

13 0.845 - 0.885 300 

14 0.875 - 0.895 300 

15 0.890 - 0.910 300 

DMC NiSAT 0 0.770 - 0.900 32 

1 0.630 - 0.690 32 

2 0.520 - 0.600 32 

EO-1/ALI Panchrom. 0.480 - 0.690 10 

MS - 1' 0.433 - 0.453 30 

MS - 1 0.450 - 0.515 30 

MS - 2 0.525 - 0.605 30 

MS - 3 0.630 - 0.690 30 

MS - 4 0.775 - 0.805 30 

MS - 4' 0.845 - 0.890 30 

MS - 5' 1.200 - 1.300 30 

MS - 5 1.550 - 1.750 30 

MS - 7 2.080 - 2.350 30

TABLE II 

MULTITEMPORAL DATASET OF SATELLITE IMAGES 

Acquisition 

Date 

Sensor 

Acquisition mode 

2008 

2008-04-26 Landsat 5 TM Multispectral 

2008-05-26 Cruise 


2008-06-02 SPOT4 Multispectral 


2008-06-13 ALOS/AVNIR-2 Multispectral 

2008-06-17 Cruise 

2008-06-17 CHRIS/PROBA Superspectral 








2008-08-18 

Cruise 

CHRIS/PROBA 

Superspectral 

2008-08-19 

Cruise 

CHRIS/PROBA 

Superspectral 


2008-09-16 Cruise 


2008-09-24 DMC NiSAT Multispectral 

2009 


2009-04-21 CHRIS / PROBA Superspectral 

2009-04-23 Cruise 

2009-04-24 Envisat/MERIS Superspectral 




2009-05-26 Cruise 







2009-06-15 Cruise 






2009-07-21 Cruise 




2009-08-12 Cruise 





2009-09-01 SPOT-5/ HRG Multispectral 



2009-09-23 Cruise 



Acquisition 

Date 

Sensor 

Acquisition mode 

2010 

2010-03-11 Envisat/MERIS superspectral 














A. MERIS 

IV. SATELLITE IMAGES PROCESSING 

Taking into account effective short revisit time of 

ENVISAT/MERIS system it gives us the most frequent 

information about main water quality parameters and their 

temporal changes. MERIS data was provided free of charge by 

the European Space Agency under the Cat.1 Project. Products: 

MER_FR_1P (Full Resolution Level 1 with spatial resolution 

of 290x260m, and MER_FR_2P (Full Resolution Geophysical 

Product Level 2) were made available by ESA on the Internet 

via Near Real Time processing service. Level 1 datasets have 

been used for color compositions elaboration and Level 2 

standard products have been sources of water quality 

parameters. The algorithms used in geophysical product 

preparation, including the atmospheric correction for case 1 

waters and its extension to case 2 are based on the MERIS 

Advanced Theoretical Basis Documents available from ESA 

Website.(http://envisat.esa.int/instruments/meris/pdf/ [2]. The 

processing of MERIS pixels classified as “water pixels” 

provides 8 quantitative and 5 qualitative products as well as 

flags relevant to the quality of all products [4]. Three main 

water parameters have been extracted from MERIS Level 2 

standard quantitative products in native measurements units 

and converted to: Algal Pigment Index 2 (Chl-a) in [mg/m3], 

TSM – Total Suspended Matter in [g/m3] and Yellow 

Substance Absorption (or ODOC) coefficient remaining in 

[1/m] absorption unit. As Vistula Lagoon is classified as Case 

2 waters and observed as turbid water reservoir the masks of 

“Sediment dominated Case_2 water” have been also extracted 

from Level 2 Product for each acquisition. The detailed results 

of MERIS data processing were presented during ESA 

Hyperspectral Workshop 2010 in Frascati [7].

B. CHRIS/PROBA 

CHRIS/ PROBA images were processed according to 

successive steps: 

1. Metadata analysis and extraction of information needed 

for radiometric and atmospheric corrections of satellite 

images. 

2. Radiometric and atmospheric correction. Noise reduction. 

(The atmospheric correction of the CHRIS/PROBA 

satellite data was made using ATCOR 2/3 model - version 

6.4.) 

3. Water quality parameters extraction. (This was made 

from radiometric, atmospheric and geometrically 

corrected images. 

Figure.4. MERIS FRS Level 1 image acquired on 2009/04/24 (7,5,2, bands 

as RGB) merged with Landsat5 TM image (3,2,1, bands as RGB channels). 

Figure 5. Example of “Sediment dominated Case_2 water” mask. 

The following water quality parameters were extracted 

using empirical and statistical relationships between CHRIS 

measured spectral reflectance and in situ measurements 

(regression based): Secchi disc depth, Suspended solids, 

Chlorophyll-a and Pheophytins. An attempt of Total 

Phosphorus and Total Nitrogen extraction was also made. 

Those results are not yet ready to publish. Generally the 

relationships between in situ measurements and remotely 

sensed values of parameters for single acquisitions were weak. 

Low values of coefficient of determination were noted. This 

observation is due to the small gradient in water optical 

properties observed on the Polish side of the lagoon. It is 

clearly visible on many color compositions made from MERIS, 

CHRIS and other sensors during observation period. Only 

common analysis of all available images showed significant 

empirical relationships confirmed by statistical coefficient of 

determination. The strongest relationships have been obtained 

for Secchi Disk Depth, Total Suspended Matter and 

Chlorophyll-a. 

The masks of uncertain pixel values for these 3 quantitative 

parameters have been also extracted from metadata records. 

Figure 6. Examples of “uncertain pixel values” mask 

Figure 7. Georeferenced quicklooks of CHRIS images taken on Polish part 

of the Vistula Lagoon.

22.07.2008 18.08.2008 

Figure 8. The relationship of Secchi Disk Depth [m] and A 679-755 nm calculated 

on CHRIS/ PROBA satellite data acquired for Vistula Lagoon AOI. 

21.04.2009 04.06.2009 

06.06.2009 

Figure 9. The relationship of total suspended matter [mg/l] and A 560-590 nm 

calculated on CHRIS/ PROBA satellite data acquired for Vistula Lagoon AOI. 

Figure.11. Spatial distribution of Secchi Disk Depth [m] for Vistula 

Lagoon AOI. 

C. Other sensors –Landsat5 TM, SPOT-4&5, ALOS/AVNIR- 

2, DMC NiSAT. 

Figure10. The relationship of chlorophyll-a [micrograms/l] content and index 

706 nm / 674 nm calculated on CHRIS/ PROBA satellite data acquired for 

Vistula Lagoon AOI. 

These systems/sensors play several roles in the project. The 

first one is a source of data in water parameters extraction 

following empirical and statistical approach similar to CHRIS 

case. Of course the main indicator of water state calculated 

from wide visible bands is turbidity index. Better spatial 

resolution of these images in VNIR bands comparing with 

MERIS ones gives much more precision in water vegetation 

mapping. Multitemporal set of L5 TM images acquired in 

2008 and 2009 year, supported by SPOT HRG image 

permitted updating and refinement of Corine Land Cover 2006 

(CLC 2006) thematic layer for Polish side of the AOI. An 

important effort was also made in elaboration of this layer for

V. CONCLUDING REMARKS 

The presented 3-years VISLA project gave an opportunity 

to collect first time an important set of different satellite 

images for Vistula Lagoon water ecosystem and surrounding 

land. This dataset complements well other (GIS and in situ 

measurements) components of the VL_GIS. The images 

acquired as geophysical products or processed to thematic 

layers are useful for better spatial and temporal description of 

the water quality parameters of the lagoon. At the same time 

the images can be used for land information updating, 

particularly on the Russian side of AOI. The remotely sensed 

data is still under processing and its later use will be linked to 

the GEMSS model output results. All processed data are made 

available for interested parties at the visla.uwm.edu.pl server 

via WEB page (in Polish, but soon in English), or FTP service 

for registered users. 

ACKNOWLEDGMENT 

Figure.12. SPOT-5 HRG image converted into pseudo-realistic colors 

orthophotomap with CLC 2006 vector overlay (near Kaliningrad – Russia). 

Russian part of the AOI. CLC 2006 vector data available for 

Polish side, methodological description of its creation and 

many color composition covering large, transboundary area 

permitted reliable expanding of this map on non directly 

accessible terrains for field works. And the last but not least 

from PR point of view is joining together high spatial 

resolution TM images for representing land areas and 

oversampled MERIS images used only for representing of 

water body surface by water/non water masking. An example 

of this kind of image is given on the figure 13. 

Figure.13. An example of MERIS / TM merged image complemented with 

watermarks. 

This work has been carried out in the framework of the 

VISLA project financed by the Polish-Norwegian Research 

Fund. MERIS satellite data was provided by the European 

Space Agency free of charge. Peter Fletcher is thanked for his 

efforts in ensuring that the CHRIS PROBA data of Vistula 

Lagoon were collected during the periods of the water 

sampling. 

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