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et al (1998) used CASI (Compact Airborne Spectrographic
Imager) for mapping coral reefs habitats of the Turks and
Caicos Islands (British West Indies). The study suggested that
CASI is capable of mapping benthic habitat classes with a high
level of accuracy. However, airborne hyperspectral data is not
commonly used due to the large expense involved in acquiring
such information (Holden & LeDrew, 1998).
Many satellite sensors have also been recognized as useful tool
to monitor and map CRE. Satellite sensors cover large
geographic areas and have good temporal coverage in most
areas of interest (Dekker et al, 2001). This can enhance the
understanding of coral reef ecosystems and their threats, by
providing spatio-temporal data on reef ecosystems and the
environmental conditions influencing them (Eakin et al, 2010).
NASA has developed a baseline of global reef maps that can be
a foundation for future more detailed scientific investigation
(NASA, 20112).
The common multispectral sensors like those on Landsat,
SPOT, IKONOS, QuickBird and WorldView 2 have been used
in mapping and monitoring coral reef ecosystems for some time
(Mumby et al, 1997; Mumby and Edwards, 2002; Fonseca et al,
2010; Kerr, 2010). However, the accuracy of identifying coral
reef features has been limited due to the spectral and spatial
resolutions of these sensors.
The development of Hyperspectral sensors has improved the
multispectral capabilities by dealing with narrow spectral bands
over a contiguous spectral range (Chang, 2003). Increasing
number of bands has increased the number of coral reef
substrate classes that can be discriminated (Kutser et al, 2006).
For example, Kutser et al (2006) investigated the suitability of
the Hyperion satellite sensor for mapping coral reef benthic
substrates in Cairns reef, in the northern section of the
Australian Great Barrier Reef (GBR). The results suggested the
capability of Hyperspectral data to discriminate and map bottom
type and water depth. However, the spatial resolution of satellite
based Hyperspectral sensors remains an issue to be solved in the
future (Cetin, 2004).
Monitoring the changes in coral reef ecosystem using remote
sensing data can be a cost effective and time efficient of coral
reef management (Mumby et al, 1999; Maeder et al, 2002). A
number of researchers used time-serious data to detect changes
in overall reflectance that can be attributed to major changes in
coral reef ecosystems using remote sensing techniques
(Andréfouët et al, 2001; Dustan et al, 2001; Palandro et al,
2003; Sterckx et al, 2005; Scopélitis et al, 2010). However, the
spectral and spatial resolutions of the multiple temporal
imageries can be limiting factors in the application of the
method. In addition, the temporal texture associated with areas
of CRE degradation can be highly variable.
Therefore, the adequacy and accuracy of remote identification
for mapping and monitoring coral reefs ecosystems remains
unclear due to technical constraints. Thus, the actual challenge
is to achieve both high spectral and high spatial resolutions in
order to have more accurate image data to discriminate coral
reef ecosystems whilst still maintaining the benefits accruing
from the use of operational spaceborne systems.
2. IMAGE FUSION AND LIMITATIONS
In many applications of remote sensing, such as in coral reef
ecosystem mapping, images with high accuracy both spectrally
and spatially are required (Kutser et al, 2006; Eakin el at, 2010).
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
To achieve this outcome, image fusion methods, or pan-
sharping, can potentially efficiently and accurately provide
images with high spectral and high spatial resolutions (Zhang,
2008). There are few research studies undertaken on mapping
and monitoring coral reef ecosystems using image fusion
techniques. Hanaizumi et al (2008) used the pan-sharping
method based on multiple regression analysis to enhance
satellite imagery from FORMOSAT-2 at Ishigaki and Ryukyu
coral reef ecosystems in Japan. The pan-sharping method was
implemented pixel-by-pixel and incorporated the panchromatic
information without distorting the original spectral information.
The brightness components of each lower spatial resolution
multispectral band were replaced with higher spatial resolution
values derived from the panchromatic band. The spatial
resolution of the multispectral band was improved from 8x8 m
to 2x2 m and the method produced a visual improvement to
coral reef imagery.
Ninsawat and Tripathi (2003) used the Intensity-Hue-Saturation
(IHS) method to merge multispectral data from LISS-III
(23.5m) with the panchromatic data (5.8m) from the same
satellite (Indian Remote Sensing satellite IRS-1D), to generate
multispectral and higher spatial resolution image data of Phi Phi
Island, Thailand. The study was aimed to map coral reef
ecosystems and to identify the spectral distinction between
healthy and dead coral. The fused image and depth invariant
index based on classified field data generated a classification of
coral reef ecosystem type image without any unclassified pixel.
The study confirmed the capability of (IHS) method to provide
better description of coral reef ecosystem; thereby the coral reef
ecosystem can be more accurately classified.
In this study it is proposed to utilize MS imagery from
WorldView 2 (2m spatial resolution) and hyperspectral imagery
from Hyperion (30m spatial resolution). The spectral
correspondence between these sensors is shown in table |.
Tablel: Spectral Correspondence between Hyperion and
WorldView 2 sensors
WV2 Wavelength | Hyperion | No of
MS (nm) Bands Hyperion
Band bands
No.
1 400 - 450 5-10 6
2 450 - 510 11-16 6
3 510 - 580 17-23 7
4 585 - 625 24 - 27 4
However, the pan-sharpening process of the Hyperspectral
bands (30m) with the first four Multispectral bands of
WorldView2 imagery (2m) is potentially problematic due to the
extreme 15:1 ratio of spatial resolutions. In addition the large
number of Hyperspectral bands corresponding to each WV2 MS
band (Table 1) might not allow fusion methods to be easily
applied (Garzelli et al, 2010). The outcome of applying standard
pan-sharpening methods may contain artifacts that reduce the
quality of the fused product. However, Ling et al (2006) suggest
that even with spatial resolution ratios between 10:1 and 30:1,
the fused image still presents information which is more
interpretable than the original image. Whilst standard methods
will be applied, this study will develop a specific fusion model
to as best as possible preserve the spectral and spatial
information of the input images (see 3.5).
