This document summarizes a study that used QuickBird satellite imagery to map benthic habitats in the Caribbean Sea. In situ depth and bottom type data were collected using a towed sensor platform. Atmospheric correction and bathymetry estimation techniques were applied to the satellite data. Bottom albedo was derived and used in a maximum likelihood classification to map seagrass, sand, coral, and deep water habitats. The classification achieved an overall accuracy of 81%. The high resolution QuickBird data helped discriminate basic benthic communities and has potential for monitoring coral reef health by detecting structural changes over time.

1. Anisa Aulia
Sabilah
C552190011
Benthic Habitat Mapping
in Tropical Marine
Environment Using
QuickBird Multispectral
Data
Deepak Mishra, et al. 2006

2. Introduction
Remote sensing has been
suggested as a potential tool
for monitoring coral-reef
ecosystems.
The Caribbean region has
abundant coral reefs. They are
being destroyed (nearly 2/3 are
threatened, primarily by human
activities (Wolanski et al., 2003;
Burke and Maidens, 2004)).
There is a critical need to
monitor the conditions of
these corals by
documenting the changing
conditions associated with
Caribbean coastal
ecosystems over time.
Satellite QuickBird has high
spatial resolutions and would be
deemed appropriate for
evaluating benthic habitats,
include coral reef.

3. Study Area
Carribean Sea
Half Moon Bay
Anthony’s Key
West End

4. Methods and Materials
In Situ Data Satellite Data

5. In Situ Data
b
A towed sensor platform (TheTowfish).a
c
d
TheTowfish was constructed to resist wave
action, so it moved through the water at a
relatively constant, horizontal position while
being towed at a speed of approximately 3
km/h.
Depths were logged continuously along
selected transects and a high-resolution “mini-
camera” imaged the bottom at the locations
where these readings were acquired.
TheTowfish was used to acquire the
bathymetric model validation data sets.

6. QuickBird Satellite
Acquisition on March
15 2004
485 nm (450 to 520
nm)
560 nm (520 to 600
nm)
660 nm (630 to 690 nm)
Satellite Data
Spatial Resolution 2.4 m
(multispectral) and 0.6
(panchromatic)

7. Satellite Data
1
2 3
4
DerivingBottomAlbedo
Atmospheric Correction
ShallowMarine
BathymetryEstimation
Image Classification

8. 1. Atmospheric Correction
0 65535
f
DN
Before
Correction
After
Correction
0 65535
f
DN
Noise
The total signal received at the
satellite altitude is dominated
by radiance contributed by
atmospheric scattering
processes.
Therefore, it is advisable to
correct for atmospheric effects.
Histogram adjustment method
(dark subtract)

9. 2. Shallow
Marine
Bathymetry
Estimation
Lyzenga (1978) :
Ki = a constant which includes solar irradiance;
Lxi = radiance observed over deep water;
rBi = bottom reflectance;
ki = effective attenuation coefficient of water;
f = a geometrical factor to account for path length
through water; and
z = water depth.
Principal Component Analysis
(PCA)
Which factor is most
highly correlated with
each component ???

10. 3. Deriving Bottom Albedo
Tabel 1. Absorption and backscattering coefficients Quickbird
image. These parameters were later used to derive a bottom albedo
image of the study area.
Rb-rx(λ) = Remote sensing reflectance from the bottom;
a(λ) = Total absorption coefficient in m;
bb(λ) = Backscattering coefficient in m;
H = Depth of water in m; and
ρ = Bottom albedo

11. 4. Image
Classification
Dense Seagrass
Mixed(seagrass,
sand,algae)
Mixed
(coral,sand)
Sand
Coral
Deep
Water
Maximum
Likelihood
(Supervised)
Training Area

12. Sandy Bay

13. Graphic
Infographic
Designed
Modern PowerPoint Presentation
Bathymetric image-map of
Roatan Island based on the
linear regression model along
with two enlarged insets of
(A) Half-Moon Bay and (B)
Anthony’s Key areas for
visualizing the detail depicted
by the bathymetric model.

14. Image showing bottom albedo
varying from 0 to 25 percent
with a enlarged inset (A) for
visualizing the details depicted
by the albedo image.

15. Color coded map showing
different Benthic Habitats off
the northwest coast of Roatan
Island, Honduras. This map
resulted from an supervised
classification of the bottom
albedo image. White circle
points represent areas where
photographs of the bottom
types were acquired by the
Towfish. The border color of
each photograph matches the
legend color assigned to a
particular class.

16. RESULTS OF AN ACCURACY ASSESSMENT BASED ON 383 UNDERWATER REFERENCE
POINTS
Overall Accuracy = 81%
Overall Kappa Coefficient = 0.774
383

17. Conclusi
on
Sand, seagrass, and coral reefs were although have
different spectral characteristics, in reality there can
be separable as homogenous pixels.
Additional classes could be identified and validated
with an extensive field campaign acquiring substantial
samples to enable statistical evaluation for each class.
The QuickBird image data produced improved
classification accuracies, and discrimination between
basic benthic communities.
Further research should focus on using the high
spatial resolution data to address certain key issues
that are of vital interest to the biological community,
especially the detection of stress effects and structural
changes.

18. Thank you