Patterns and dynamics of coastal
waters in multi-temporal satellite
images: support to water quality
monitoring in the Archipelago Sea,
Finland
Anne Erkkila, Risto Kalliola, 2003
University of Turku, Finland
Contents
Introduction
Materials and Methods
The Study Area
Landsat TM/ETM +
Landsat Images and Image Acquisition conditions
Image Processing
Spectral Image Analyses
Results
Surface Water Reflectance Patterns
Permanency of Average Water Conditions
Discussion
Surface Water Dynamics
Implications for Water Quality Monitoring
Conclusions
Acknowledgements
Introduction
• Located in the Archipelago Sea
• Highly dynamic flow regime
• A methodology to monitor
water quality is needed
• Helped through analysing 6
Landsat TM/ETM + images
• Combined with monitored
data to classify Secchi disk
depth and Chlorophyll-A
The Study Area
• The Archipelago Sea
• Located in the Northern
Baltic, just off the coast of
Finland
• ´Archipelago´ meaning a large
group of Islands
• Creates highly dynamic flow
regimes
• Large touristic and
recreational interests
• Water Quality is affected
through agriculture and fish
farming
Landsat TM/ETM +
• Landsat Thematic Mapper (TM) is a multispectral scanning
radiometer.
• The Landsat Enhanced Thematic Mapper (ETM) was
introduced with Landsat 7.
• ETM data cover the visible, near-infrared, shortwave, and
thermal infrared spectral bands of the electromagnetic
spectrum.
• Landsat's Global Survey Mission is to establish and execute a
data acquisition strategy that ensures repetitive acquisition
of observations over the Earth's land mass, coastal
boundaries, and coral reefs.
• Launch Dates / Status:
Landsat-7: 15 April 1999 – operational
Orbit Height: 705 km
Orbit Type: Landsat-6/7: sun-synchronous polar
Repeat Cycle: 16 days
Resolution: 15 m panchromatic; 30 m multispectral
Swath Width: 185 km
Onboard Sensors provided under TPM:
+ TM (Thematic Mapper) on board Landsat-5
+ ETM+ (Enhanced Thematic Mapper Plus) on board
Landsat-7
Landsat Images and Image Acquisition
conditions
• 6 Landsat TM/ETM +
images from the late
1990´s
• Conditions:
– High Summer
– Cloud Free!!
– Dates close to the water
quality measurements
(although in reality this
was not possible)
• Wind data taken from
the nearest metrological
stations
Image processing
• Performed at the University of Turku using ERDAS IMAGINE
8.4 software
• Rectified to the Finnish national grid
• RMS error below 0.5, but small errors found in the
topography – complex shoreline
• No atmospheric corrections performed due to high
turbidity (yellow substances)
• Masks were created to analyse only the water – all 6 masks
were summed to create an independent mask
• Focal mean filtering was performed to reduce image noise
Spectral Image Analysis
• First studied through visual
interpretation of grey-scale images
of all TM/ETM + bands 1-3
– Visual portion of the electromagnetic
spectrum
– A result of spectral reflectance
caused by underwater adsorption
and scattering processes
– Affected by suspended solids, with
Chlorophyll-a and other humus
absorbing different wavelengths
Surface Water Reflectance Patterns
• The images show pronounced spatial
patterns in surface water reflectance
• Particularly: Mainland coast and the
shores of the largest Island
• Low reflectance for the open sea,
some rounded fluid patterns (10k
wide) can be attributed to algal drifts
• Mixing can be seen in the archipelago
• Different dynamic forms can be seen
in each image
• Comparisons with Thermal infrared to
check correlation – none found
Permanancy of Average Water
Conditions
• Generally stable
patterns of water
turbidity
• Gradient towards the
open sea
• Some discernable
algal bloom
• Other factors such as
inorganic suspensions
may cause turbidity,
but these are
considered negligible
Surface Water Dynamics
• Visualisations show a variety of non-persistent currents
• Dynamic surface waters, driven by winds, tides and a
fragmented geometry
• Highly capricious in nature!! – no existence of
permanent flow patterns
• However, there is a general consistency with the spatial
distribution of water quality
• Correlation with human activities
Implications for Water Quality
Modelling
• Regular monitoring is carried out in the
region, but the sites are limited in number
• Correlation between the unsupervised
classification and measured data is shown
• You could increase the number of samples,
but due to hydrodynamics and seasonality
this may not accurately define the area
• Cloud cover issues, 70% of year
• There is a cost/benefit ratio
• Solution: Coupled with airborne surveillance,
field sampling, water quality ad
hydrodynamic modelling
Conclusions
• Data from Landsat TM/ETM + are capable of
expressing significant patterns of dynamic surface
waters of the Archipelago Sea
• Flow patterns are discernable from IR images
• Effective water quality modelling requires a large
spatial view – offered by Landsat etc
• Images can be integrated into a water quality
monitoring and forecasting system
• How?? - Coupled with airborne surveillance, field
sampling, water quality ad hydrodynamic
modelling
Acknowledgements
• The Maj and Tor Nessling Foundation
• The Southwest Finland Regional Environment
Centre
• FIBRE
Thanks for your
Attention