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Integrating
SONAR, LIDAR, GPS and CASI on Prince Edward Island: Towards
a seamless coastal DEM
Gavin K. Manson 1,
Tracy L. Lynds 1, Yann G. Morel 2,
Steven M. Solomon 1, Donald L. Forbes 1,
Herb Ripley 2, Kimberly A. Wahl 3,
Tim L. Webster 4
1 Geological Survey of Canada
(Atlantic), PO Box 1006, Dartmouth, NS, B2Y 4A2
2
Hyperspectral
Data International, 7071 Bayers Rd., Suite 119, Halifax, NS, B3L 2C2
3
O?Ceirin
Digital Geographics, PO Box 51, Lawrencetown, NS, B0S 1M0
4
Centre of
Geographic Sciences, 50 Elliot Rd., Lawrencetown, NS, B0S 1M0
In
the study area on the North Shore of Prince Edward Island, the coastal
zone can be considered to extend from a landward limit of sand dune
sedimentation to a seaward limit of storm-induced sand mobilisation.
Construction of a seamless digital elevation model (DEM) across this
zone is important for the interpretation of coastal geology and
geomorphology, detection and measurement of coastal change, storm-surge
flood-hazard mapping and as a base for dynamic coastal modeling. Due to
the wide range of environments present within the coastal zone, various
technologies are required to build a seamless, full coverage DEM.
We
use airborne terrestrial LIDAR at low tide to map the landward portion
of the coastal zone and various SONAR systems to chart bathymetry of
the seaward portion out to depths of 30 m or more. The acoustic methods
include swath multi-beam systems (EM1000 in >10 m water depth
and EM3000 in >4 m depth) and 12-channel sweep or dual-frequency
single-beam systems in shallow water. In selected inter-tidal areas,
elevation data are collected using dual-phase differential GPS in
real-time kinematic mode. Even with the use of all these systems,
bathymetric coverage typically does not extend far enough landward to
seamlessly meet the terrestrial LIDAR. Where shallow water sweep or
high-density single beam SONAR bathymetric mapping is not conducted, a
gap in the DEM extends from the intertidal zone to 10 m water depth.
In
this study, we tested the application of hyperspectral CASI data and
the Self-Calibrated Spectral Supervised Shallow-water Modeler (4SM)
method for filling this near-shore gap. The 4SM method utilizes the
variable attenuation by water of differing wavelengths of visible
light, along with a band-ratioing technique to estimate attenuation
coefficients and derive bottom depths from geometrically and
radiometrically corrected CASI imagery. Single-beam echo-sounding data
collected and used to fine-tune and validate the derived bathymetry
demonstrate excellent accuracy of the CASI bathymetry in some areas and
problems with the technique in others where the assumptions of the
method are inapplicable. These include constant bottom characteristics
and water properties.
The integration of CASI with SONAR, LiDAR and
GPS data shows considerable promise for filling the nearshore gap and
represents a relatively fast and low-cost method for the development of
a seamless coastal DEM.
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NOTES
AND COMMENTS BY Y. MOREL
AN EXERCISE IN CALIBRATION
AND SEA-TRUTHING
This dataset lacks a real coverage of optically
deep waters
Sea truth was acquired one year after CASI imagery.
Optical modeling results reported above by Manson et al.
were first obtained prior any field data was communicated to Y. Morel.
See the results and location of
sea truth data: ns-E_results.jpg
TOWARDS QUASI-ABSOLUTE OPTICAL
CALIBRATION: K[nir]
SEA TRUTH PROFILES AND THE LAG DEPOSITS ARTIFACT
COMPUTED
SPECTRAL BOTTOM REFLECTANCE
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TOWARDS QUASI-ABSOLUTE OPTICAL CALIBRATION:
K[nir]
By application of an input
K[799nm]=4.56 m -1 in the -KK argument of the
command line ,
a CoefZ=1.33 is proposed by the calibration process
in
calibration diagram ns-E calibration.
The details are as follows:
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Left alone, initial
calibration through Jerlov's water type yields K[799nm]=6.075 m -1:
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Left alone, this
initial calibration yields a sea truth regression for Line6100 of
ZC=0.02+0.75*ZR (N=167; r 2=0.92; RMS=0.94 m)
ns-E
RegressZZ Line6100 1.
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This suggestthat a
value of CoefZ=1/0.75=1.33 is in order.
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==> The application of a
value of CoefZ=1.33brings about an excellent fit for
Line6100: ZC=0.03+1.00*ZR (N=167; r 2=0.92;
RMS=0.36 m) ns-E RegressZZ Line6100 2, as well as for most of the
whole seatruth dataset ns-E RegressZZ ALL.
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We can
now derive an operational value for K[799nm]=6.075/1.33=4.56 m -1,
which is virtually twice the absorption coefficient for pure water at
this wavelength.
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Therefore
we can accept that K[799nm]=4.56 m -1is
a very satisfactory operational value.
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Using
this last value as a seed value to the complete set of Ki/Kj ratios
observed in the image, we can then derive the whole series of spectral
operational K values for the whole CASI bandsetting.
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This
suggest that, using published values for the
absorption coefficient of pure water fin the Near InfraRed
region of the spectrum, it might turn out that a quasi-absolute optical
calibration of shallow water optical modeling may be achieved without
the use of any field data.
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SEATRUTH
PROFILES AND THE LAG DEPOSITS ARTIFACT(?)
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The sea
truth dataset was acquired in summer 2001, one year after the
CASI imagery.
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Major
storms have affected the scene between those two data collection
exercises, which have affected the location and the shape of the sand
waves.
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Sand
waves lie over a bed of red-rich lag deposit.
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In the
sea truth profiles ns-E_ProfileZZ ,
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the
computed depths (colored profile) across the dark patch ns-E_lag_deposits
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appears
to have been badly underestimated (Line6400 to Line6900),
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as
the trough between sand waves is shown to be flat and shallow at approx
3.5 m in the computed depths
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while
sea truth (black profile) shows depths ranging from 4 to 6 m one year
later.
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These
"faulty pixels" pixels exhibit radiance levels well above deep water
radiances in the 650-670 nm range: ns-E_650nm
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This
radiance level is ~7 DNs above deep water radiance in 8-bits scaled
bands at 650 nm.
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Maximum
depth of bottom detection for bright sands does not exceed ~5-4 m over
this wavelength range,
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even much less for a dark bottom.
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Applying
a depth 1-2 m deeper would result in unacceptable extremely
bright computed bottom reflectances
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whereas it is clear that the bottom
exhibits distinctly dark features: see ns-E_R where a raw RGB view is shown
on left, and a "water column corrected" RGB view is shown on the right.
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Note in this last image that most bright
bottoms exhibit a greenish computed bottom spectral signature.
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It
is therefore likely that genuine bottom detection actually
occurs,
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whether
because the bottom has really changed since sea truth data acquisition
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or
because of the growth of algae/seaweeds , in which case the top of the
canopy is detected instead of the true bottom.
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COMPUTED SPECTRAL BOTTOM REFLECTANCE
This shows that the water-column corrected
radiances are available for thematic classification.
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Scatter
plot of spectral bottom
reflectance inside the red polygon ns-E_blue_versus_red
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Left :
TOA rawradiances Ls
[490]
versus Ls[625].
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Right:BOA
normalized water column corrected radiances LB[490] versus LB[625].
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Five
BOA normalized water column corrected spectral bottom reflectance
signatures are illustrated in ns-E_spectral_bottom_signatures
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for
10 spectral bands ranging from ch_46=490 nm to ch_55=690 nm
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for
five distinct targets (black, red, blue, green, yellow).
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