Multispectral imaging (MSI) data collected at multiple angles over shallow water provide analysts with a unique
perspective of bathymetry in coastal areas. Observations taken by DigitalGlobe’s WorldView-2 (WV-2) sensor acquired
at 39 different view angles on 30 July 2011 were used to determine the effect of acquisition angle on bathymetry
derivation. The site used for this study was Kailua Bay (on the windward side of the island of Oahu). Satellite azimuth
and elevation for these data ranged from 18.8 to 185.8 degrees and 24.9 (forward-looking) to 24.5 (backward-looking)
degrees (respectively) with 90 degrees representing a nadir view. Bathymetry were derived directly from the WV-2
radiance data using a band ratio approach. Comparison of results to LiDAR-derived bathymetry showed that varying
view angle impact the quality of the inferred bathymetry. Derived and reference bathymetry have a higher correlation as
images are acquired closer to nadir. The band combination utilized for depth derivation also has an effect on derived
bathymetry. Four band combinations were compared, and the Blue and Green combination provided the best results.
Multispectral imaging (MSI) data acquired at different view angles provide an analyst with a unique view into shallow
water. Observations from DigitalGlobe's WorldView-2 (WV-2) sensor, acquired in 39 images in one orbital pass on 30
July 2011, are being analyzed to determine bathymetry along the windward side of the Oahu coastline. Satellite azimuth
and elevation range from 18.8 to 185.8 degrees, and 24.9 (forward-looking) to 24.9 (backward-looking) degrees with 90
degrees representing a nadir view (respectively). WV-2's eight multispectral bands provide depth information
(especially using the Blue, Green, and Yellow bands), as well as information about bottom type and surface glint (using
the Red and NIR bands). Bathymetric analyses of the optical data will be compared to LiDAR-derived bathymetry in
future work. This research shows the impact of varying view angle on inferred bathymetry and discusses the differences
between angle acquisitions.
Nearshore depths for Waimanalo Beach, HI, are extracted from optical imagery, taken by the WorldView-2 (WV-2)
satellite on 31 March 2011, by means of automated Wave Kinematics Bathymetry (WKB). Two sets of three sequential
images taken at intervals of about 10 seconds are used for the analyses herein. Water depths are calculated using a
computer program that registers the images, estimates the currents, and then uses the linear dispersion relationship for
surface gravity waves to estimate depth. Depths are generated from close to shore out to about 20 meters depth.
Comparisons with SHOALS Light Detection and Ranging (LiDAR) bathymetry values show WKB depths are accurate
to about half a meter, with R2 values of 90%, and are frequently in the range of 10 to 20 percent relative error for depths
ranging from 2 to 16 meters.
Observations taken from DigitalGlobe's WorldView-2 (WV-2) sensor were analyzed for bottom-type and bathymetry for
data taken at Guam and Tinian in late February and early March of 2010. Classification of bottom type was done using
supervised and unsupervised classification techniques. All eight of the multispectral bands were used. The supervised
classification worked well based on ground truth collected on site. Bathymetric analysis was done using LiDAR-derived
bathymetry in comparison with the multispectral imagery (MSI) data. The Red Edge (705-745 nm) band was used to
correct for glint and general surface reflectance in the Blue (450-510 nm), Green (510-580 nm), and Yellow (585-625
nm) bands. For the Guam beach analyzed here, it was determined that the Green and Yellow bands were most effective
for determining depth between 2.5 and 20 m. The Blue band was less effective. Shallow water with coral could also be
identified.
Multispectral imagery (MSI) taken with high-spatial resolution systems provides a powerful tool for mapping kelp
in water. MSI are not always available, however, and there are systems which provide only panchromatic imagery
which would be useful to exploit for the purpose of mapping kelp. Kelp mapping with MSI is generally done by use
of the standard Normalized Difference Vegetation Index (NDVI). In broadband panchromatic imagery, the kelp
appears brighter than the water because of the strong response of vegetation in the NIR, and can be reliably detected
by means of a simple threshold; overall brightness is generally proportional to the NDVI. Confusion is caused by
other bright pixels in the image, including sun glint. This research seeks to find ways of mitigating the number of
false alarms using spatial image processing techniques. Methods developed in this research can be applied to other
water target detection tasks.
The WorldView-2 sensor, to be launched mid-2009, will have 8 MSI bands - 4 standard MSI spectral channels and an
additional 4 non-traditional bands. Hyperspectral data from the AURORA sensor (from the former Advanced Power
Technologies, Inc. (APTI)) was used to simulate the spectral response of the WorldView-2 Sensor and DigitalGlobe's 4-
band QuickBird system. A bandpass filter method was used to simulate the spectral response of the sensors. The
resulting simulated images were analyzed to determine possible uses of the additional bands available with the
WorldView-2 sensor. Particular attention is given to littoral (shallow water) applications. The overall classification
accuracy for the simulated QuickBird scene was 89%, and 94% for the simulated WorldView-2 scene.
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