CORAL REEF MAPPING IN THE RED
SEA (HURGHADA, EGYPT) BASED ON
REMOTE SENSING
Presented by:
Justin Prosper
s0090444
Presentation outline
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Introduction
Reflectance model
Atmospheric correction
Ecological classification in general
Study area
Data collection
Methodologies (pre-processing and classification)
Post classification and results
Discussions and conclusions
Introduction
• Need for monitoring and assessment of coral reefs
habitats – for better understanding how they are
threatened and how to protect them
• Remote sensing can provide information (composition of
coral reefs, biophysical parameters of seas and oceans
and changes over time)
• Concentrate on the identification of bottom types (macroalgae, coral, sea-grass and corals on reef systems in the
Red Sea using Lansat7 ETM.
Pre-classification (reflectance model)
Before bottom type classification
Li = Lsi + (ai * Rbi )e -f ki z
(where Li: Radiance at sensor)
Lsi : Deep water radiance
ai : Wavelength-dependent constant accounting for atmospheric effects and water
surface reflection
Rbi : Bottom reflectance
f : Geometric factor accounting for path length trough water
Ki : Effective attenuation coefficient of water for band i, accounting for absorption by
water, phytoplankton, suspended particulates and DOM, and for scattering due to
turbidity
Z: Depth
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Intensity of light decreases exp with depth
The attenuation is wavelength dependent (increasing with longer wavelength)
So if depth increases, the signal will be more attenuated and the distinction between
classes will decrease.
Spectral radiance recorded at sensor will be dependent on reflectance and depth
Source: (Vaderstraete et al, 2004)
Water column
• Most important effect on signals for bottom type
classification is the interaction with water column
• Technique used for water column correction – depth
invariant bottom index
• Each pair of water penetrating bands has its index
calculated and combined to form basis for classification
• This method works only in clear water – coral reef in red
sea occur in clear nutrient-poor waters
Classifications
• The most appropriate method of classification for remote sensing in
tropical areas was agreed to be a combination of both
geomorphological and ecological
• For ecological, only a coarse classification is necessary. This is
based on 3 depth-invariant bottom indices. Texture layers to be
added to improve classification (distinction between corals and sea
grasses)
• Geomorphological classification is based on visual digitising of
different features
• Low resolution of Landsat leads to misclassification
• Contextual editing applied to improve accuracy (decision rules based
on literature on corals classification worldwide)
• The maps are then combined following a hierarchical classification
scheme
Study Area
Source: (Vaderstraete et al, 2004)
Data collection
Field surveys
• 2 field surveys were previously carried out
• 420 observations were made at sea with emphasis on
depth measurements, and bottom type observations
Coordinates of observations made using GPS
Satellite imagery
• Landsat ETM (2000)
Geo-referencing
• ILWIS (21 points collected during field survey used)
Pre-processing
Atmospheric correction
• The atmospheric correction is applied to the image
before the depth-invariant bottom index is applied.
• In deep water most light is absorbed. The signal
received at sensor is almost made up of atmospheric
path radiance and surface reflection.
• The mean deep water radiance at sensor can be used to
remove atmospheric effect and surface reflectance
(assuming uniformity in atmosphere and reflectance)
• An area where the depth is known to be more than 50m
is selected to determine the MDW radiance
Pre-processing
Water column
• Formula is linearised by transforming the atmospherically corrected
radiance using natural log (pixel value/depth)
• Manipulation of newly obtained formula to obtain bottom reflectance
• The unknown variables are obtained from the data itself. Sandy
bottom type is used as it can be easily be identified by interpreter.
Saturation and total absorption in one band should also be avoided
upon pixel selection (shallow and deep water not used)
• Selected pixel radiance plotted on bi-plot whereby slope gives the
relative amount of attenuation in each band
• Bottom types represented on such a bi-plot would have a similar
slope with the y-intersect used as an index of bottom type
independent of depth
Classification
Ecological classification
• Coarse resolution – coarse level classification
• Supervised, maximum classification performed
Source: (Vaderstraete et al, 2004)
Classification
Geomorphological classification
Source: (Vaderstraete et al, 2004)
Post classification
Contextual editing
• 3 decision rules applied to improve ecological classification
• For e.g.: If the ecological class “sea grass dominated” occurs on the
geomorphological class “fore reef”, it is replaced by the ecology
class “coral dominated”
Masks
• As for misclassification on both land and in water, two masks were
applied
• Land: same as one as used for calculating the depth invariant
bottom indices
• Deep water: defined during geomorphological classification by areas
with no significant reflection of the sea bed were noticed.
Results-Effects of water column
Source: (Vaderstraete et al, 2004)
• Comparisons of the graphs shows the effects of the water
column correction
• Exponential relationship is observed between depth and raw
radiance
• Depth is however almost independent with DIB indices
Ecological classification before contextual editing
Ecological classification after contextual editing
Geomorphological classification
Hierarchical classification
Source: (Vaderstraete et al, 2004)
Discussion
• When water column correction is applied, map accuracy is
improved significantly for Landsat TM bottom type
classification
• Texture layers combined with depth-invariant bottom indices
improves the resulting classification map
• Accuracy can be improved by using independent set
observations concentrating on bottom types
• Classification of bottom types cannot be 100% accurate due
to the complexity of bottom types and unclear ecological
habitat boundary lines
• Main problems include limitation of classification accuracy
due to slope and aspect of benthic topography, the benthic
community having remarkable spectral similarities and time
between field surveys.
Source: (Vaderstraete et al, 2004)
Conclusion
• Constraints with classification when using Landsat7 ETM and
datasets include assumptions made depending on methods
used, low resolution of sensor and similarity on spectral
characteristics of bottom types.
• Possibility of making coarse level ecological and
geomorphological classification of the coral reefs.
• Improvement of classification by applying water column
correction technique, integration of texture layers in supervised
classification and finally by doing post classification contextual
editing.
• The integration of both afore mentioned classification can be
merged into a hierarchical classification scheme giving a higher
descriptive resolution.
Source: (Vaderstraete et al, 2004)
References
Main reference
Vanderstaete. T, Goossens. R and Ghabour. T. (2004). Coral reef Habitat
mapping in the Red Sea (Hurghada, Egypt) based on remote sensing. EARSeL
eProceedings. pp 191 – 207.
Photo
http://reflet.via.ecp.fr/~plongee/photos/photos.php?lieu=Photos%20de%20karin
e&year=Mer%20rouge%202005
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