A number of institutions, including the Naval Research Laboratory (NRL), have developed look up tables for remote retrieval of bathymetry and in-water optical properties from hyperspectral imagery (HSI) [6]. For bathymetry retrieval, the lower limit is the very shallow water case (here defined as \u3c 2m), a depth zone which is not well resolved by many existing bathymetric LIDAR sensors, such as SHOALS [4]. The ability to rapidly model these shallow water depths from HSI directly has potential benefits for combined HSI/LIDAR systems such as the Compact Hydrographic Airborne Rapid Total Survey (CHARTS) [10]. In this study, we focused on the validation of a near infra-red feature, corresponding to a local minimum in absorption (and therefore a local peak in reflectance), which can be correlated directly to bathymetry with a high degree of confidence. Compared to other VNIR wavelengths, this particular near-IR feature corresponds to a peak in the correlation with depth in this very shallow water regime, and this is a spectral range where reflectance depends primarily on water depth (water absorption) and bottom type, with suspended constituents playing a secondary role

Bachmann, Charles M

Fusina, Robert A

Goode, Wesley

Hill, Victoria

Korwan, Daniel

McIlhany, Kevin

Montes, Marcos J

Nichols, C Reid

Parrish, Christopher

Schwarzschild, A.

Sellars, Jon

Truitt, Barry

Weidemann, Alan

Woodward, Patrick

Zimmerman, Richard

English

University of New Hampshire

University of New Hampshire
University of New Hampshire Scholars' Repository
Center for Coastal and Ocean Mapping Center for Coastal and Ocean Mapping
7-2008
Very Shallow Water Bathymetry Retrieval from
Hyperspectral Imagery at the Virginia Coast
Reserve (VCR'07) Multi-Sensor Campaign
Charles M. Bachmann
U.S. Naval Research Laboratory
Marcos J. Montes
U.S. Naval Research Laboratory
Robert A. Fusina
U.S. Naval Research Laboratory
Christopher Parrish
University of New Hampshire, Durham
Jon Sellars
NOAA
See next page for additional authors
Follow this and additional works at: https://scholars.unh.edu/ccom
Part of the Computer Sciences Commons, and the Oceanography and Atmospheric Sciences and
Meteorology Commons
This Conference Proceeding is brought to you for free and open access by the Center for Coastal and Ocean Mapping at University of New Hampshire
Scholars' Repository. It has been accepted for inclusion in Center for Coastal and Ocean Mapping by an authorized administrator of University of New
Hampshire Scholars' Repository. For more information, please contact nicole.hentz@unh.edu.
Recommended Citation
C. M. Bachmann et al., "Very shallow water Bathymetry retrieval from Hyperspectral imagery at the Virginia Coast reserve (VCR’07)
multi-sensor campaign," IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008.
Authors
Charles M. Bachmann, Marcos J. Montes, Robert A. Fusina, Christopher Parrish, Jon Sellars, Alan
Weidemann, Wesley Goode, C Reid Nichols, Patrick Woodward, Kevin McIlhany, Victoria Hill, Richard
Zimmerman, Daniel Korwan, Barry Truitt, and A. Schwarzschild
This conference proceeding is available at University of New Hampshire Scholars' Repository: https://scholars.unh.edu/ccom/444
VERY SHALLOWWATER BATHYMETRY RETRIEVAL FROMHYPERSPECTRAL
IMAGERY AT THE VIRGINIA COAST RESERVE (VCR’07) MULTI-SENSOR CAMPAIGN
†Charles M. Bachmann, †Marcos J. Montes, †Robert A. Fusina, ††Christopher Parrish, ††Jon Sellars
‡Alan Weidemann, ‡Wesley Goode, *C. Reid Nichols, *Patrick Woodward, #Kevin McIlhany
++Victoria Hill, ++Richard Zimmerman, †Daniel Korwan, **Barry Truitt, +Arthur Schwarzschild
†Naval Research Laboratory, Remote Sensing Division, Washington, DC 20375 (bachmann@nrl.navy.mil)
††NOAA NOS, Silver Spring, MD
‡ Naval Resarch Laboratory, Oceanography Division, Stennis, MS
*Marine Information Resources Corporation, Ellicott City, MD
# US Naval Academy, Physics Department, Annapolis, MD
++Old Dominion University
**The Nature Conservancy, Nassawaddox, VA
+University of Virginia, Dept. of Environmental Science, Charlottesville, VA
ABSTRACT
A number of institutions, including the Naval Research Laboratory (NRL), have devel-
oped look up tables for remote retrieval of bathymetry and in-water optical properties
from hyperspectral imagery (HSI) [6]. For bathymetry retrieval, the lower limit is the
very shallow water case (here defined as < 2m), a depth zone which is not well re-
solved by many existing bathymetric LIDAR sensors, such as SHOALS [4]. The ability
to rapidly model these shallow water depths from HSI directly has potential benefits
for combined HSI/LIDAR systems such as the Compact Hydrographic Airborne Rapid
Total Survey (CHARTS) [10]. In this study, we focused on the validation of a near
infra-red feature, corresponding to a local minimum in absorption (and therefore a local
peak in reflectance), which can be correlated directly to bathymetry with a high degree
of confidence. Compared to other VNIR wavelengths, this particular near-IR feature
corresponds to a peak in the correlation with depth in this very shallow water regime,
and this is a spectral range where reflectance depends primarily on water depth (water
absorption) and bottom type, with suspended constituents playing a secondary role.
Index Terms— hyperspectral, shallow-water bathymetry, bottom type, IOP,
Hydrolight, Virginia Coast Reserve
1. INTRODUCTION AND BACKGROUND
At visible and near infra-red (VNIR) wavelengths, reflectance
is determined by a variety of factors including water depth,
bottom type, and the presence of suspended constituents such
as color-dissolved organic matter (CDOM), suspended sedi-
ments, chlorophyll, phytoplankton, and other suspended con-
stituents [6]. Thus the general problem of retrieving depth
The NRL authors gratefully acknowledge platform support from the Office of Naval Research. This paper was
funded by the National Geospatial-Intelligence Agency (NGA). The opinons, views, and conclusions found within the
paper do not necessarily reflect those of the U.S. Dept. of Defense or the NGA.
as well as other water properties has been approached using
spectral look-up tables [7] in which a forward radiative trans-
fer model such as Hydrolight [8] is executed repeatedly with
varying water column properties, depth, and bottom types.
To be comprehensive, these look-up tables must be large and
may need to be tuned to specific coastal types because of vari-
ability in bottom types and water properties which may vary
significantly with coastal type. An attractive feature of hy-
perspectral data is the enhanced capability to simultaneously
serve a multitude of uses, in addition to bathymetry retrieval.
Light Detection and Ranging (LIDAR) has also been widely
used to retrieve bathymetric data. LIDAR has the advantage
that it is an active sensor, which may provide greater accu-
racy in deeper waters, however, LIDAR systems such as the
Scanning Hydrographic Operational Airborne Lidar Survey
(SHOALS) [4] must be flown at very low altitude with a rel-
atively small swath compared to typical airborne hyperspec-
tral sensors. In the very shallow water regime (less than 2m
depth), LIDAR systems often do not provide a reliable re-
trieval, failing to resolve the difference between the bottom
and surface returns. In this paper, we focus on this very shal-
low water regime, specifically on the retrieval of bathymetry
from a spectral range where a relatively simple model for re-
flectance can be assumed. Compared to the reflectance at vis-
ible wavelengths, where all contributions from the water col-
umn must be carefully considered, near infra-red wavelength
reflectance (beyond 800nm) is primarily dependent on water
absorption and depth, along with bottom reflectance, with wa-
ter column constituents playing a secondary role.
II - 125978-1-4244-2808-3/08/$25.00 ©2008 IEEE IGARSS 2008
Fig. 1. (Top) Parramore Island in situ spectrometry site on salt marsh tidal
creek bank with muddy bottom: (left) beginning (near high tide), (right)
end (near low tide) of measurement cycle. (Bottom right) Spectral sam-
ples at depths varying from 89cm to 0cm (wet substrate). (Bottom left)
ln(R810nm) vs d (depth in cm).
2. VIRGINIA COAST RESERVE CAMPAIGN
In September 2007, NRL and a number of collaborating in-
stitutions undertook a multi-sensor airborne data collection
and in situ validation campaign at the Virginia Coast Reserve
(VCR’07). Airborne, in-water, and land based measure-
ments took place over the course of the 2 1
2
week exercise and
covered a 95 km stretch of barrier islands, coastal lagoons,
tidal flats, and mainland marsh systems. NRL flew three
sensors on a Twin Otter aircraft: a CASI-1500 visible and
near infra-red (VNIR) HSI sensor, a Surface Optics InGaAs
SWIR HSI sensor operating in the 0.9-1.7 μm spectral re-
gion, and single channel mid-wave IR camera. This paper
focuses on the CASI imagery taken during the experiment,
and the in situ validation data collected to study the NIR
feature described above (other littoral focus areas studied
during the VCR’07 experiment included the remote retrieval
of soil bearing strength [2], biomass retrieval, land-cover
mapping [1], as well as seagrass and oyster reef monitoring).
For the very shallow bathymetry study, field validation ef-
forts used Analytical Spectral Devices (ASD) spectrometers
positioned on a tripod in a shallow cove behindWreck Island,
VA, and in the shallow stretches of a muddy tidal creek and
salt marsh system in the northern end of Parramore Island,
VA. Depth and ASD spectral measurements were recorded
on a falling tide until the substrate was completely exposed,
allowing an in situ bottom reflectance to be obtained in the
measurement suite. Bottom reflectance was also measured by
divers using an underwater spectrometer at other locations.
Previously, during field work in October 2005, ASD measure-
ments had been collected in the shallow waters on the edge of
a salt marsh system in the northern end of Smith Island. Dur-
Fig. 2. Wreck Island in situ spectrometry site with oyster bed bottom in a
shallow cove. (Top) Site photos. (Bottom right)Spectral samples at depths
varying from 86cm to 0cm (wet substrate); (bottom left) ln(R810nm) vs d
(depth in cm) and associated regression.
ing VCR’07, Kinematic GPS data were taken in the inter-tidal
region on the northern end of Wreck Island to support prod-
uct validation. In addition, depth soundings were taken in the
shallow waters behind the island and in the adjacent chan-
nel separating Wreck from Cobb Island. At the conclusion of
the study, the NASA EAARL LIDAR took data in conjunc-
tion with the NRL sensor suite, albeit during a period when
weather conditions were not optimal, and turbidity was high.
Figures 1 and 2 show the measured spectral curves for
each depth collected during the measurement cycle at Par-
ramore and Wreck Islands respectively, along with in situ
photos and the regression at the 810nm feature. A similar
regression was carried out for data taken at Smith Island, VA
in October 2005 for a sand/silt bottom on the watery margin
between beach and salt marsh zones. As described earlier
the focus was on the longer wavelengths of the near infra-red
spectrum, but the full range spectrum from 0.35-2.5 microns
was also collected. Note that the visible and near infra-red
are the portions of the spectrum usually used in water appli-
cations, since the reflectance beyond a micron is near zero,
except when constituents in the water column are fully mixed
to the surface layer. The visible spectrum shows a relatively
broad peak near 810nm, corresponding to a local minimum
in the liquid water absorption [3]. There is a similar peak
near 1.076 microns, which is of a similar origin, though less
pronounced character. To assess the relative merits of wave-
length selection at these longer wavelengths and confirm the
optimality of the choice of the 810nm feature, we computed
an R2 goodness of fit between a linear regression of ln(Rλ)
and d, where Rλ is the reflectance at wavelength λ, and d is
the depth in cm. Figure 3 shows plots of this feature across
the full spectrum for each of the two sites, with enlargements
of the VNIR portion of the spectrum. The best correlation oc-
curs near two peaks, one at 810nm and the other near 720nm.
II - 126
Fig. 3. R2 fit of ln(Rλ) and d for: (left column) Muddy bottom, Parramore
Island, (right, column)oyster bed bottom, Wreck Island. (Top row) 0.35-2.5
μm, (bottom row) enlargement of 0.35-1.07 μm.
Wavelengths between these two peaks would work well also,
but the best fit with R2 values near 0.89 (muddy bottom)
and 0.98 (oyster shell bed bottom) for Parramore Island and
Wreck Island respectively are near these two wavelengths. In
the VNIR spectrum below 800nm, the R2 fit for the oyster
shell bottom case (Wreck Island), R2 never exceeds ∼ 0.81
(at 384nm) compared to the peaks at 721nm and 809nmwhere
R2 ∼ 0.98, while for the muddy bottom case (Parramore Is-
land), the R2 never exceeds ∼ 0.53 (at 409nm), compared to
the peaks at 725nm (R2 = 0.891) and 811nm (R2 = 0.900).
Spectral stability and high R2 of the 810nm feature for both
dark and bright bottom cases, demonstrate its optimality. In
contrast, at visible wavelengths, the best R2 is not at a stable
location and is only moderately good for bright bottoms.
3. HYDROLIGHTMODELS
To demonstrate that varying concentrations of suspended con-
stituents does not significantly impact the reflectance at 810
nm but indeed is a large contributing factor to the shorter
wavelength returns, we have run a series of models using
Hydrolight[8]. This model allows us to vary the concentra-
tion of constituents, bottom type, depth, (etc) and look at the
contribution to remote sensing return as a function of wave-
length. In this Section, we show that varying suspended con-
stituents is indeed expected to make only a small difference
near 810nm, while at shorter wavelengths in the visible, these
changes have a significant impact that can not be ignored in
modeling. Figure 5 illustrates this point for a case in which
total suspended sediment (TSS) is variedwhile all other inher-
ent optical properties (IOP’s) are held fixed and the depth is
constant for a specific bottom type (in this case, the field spec-
trum of our bottom substrate reflectance taken for the oys-
Fig. 4. (Top) NRL CASI-1500 image of Wreck Island (9:45am line). (Mid-
dle) Retrieved depth using the 810nm regressions for each bottom type deter-
mined in preprocessing from the spectral libraries. Depth is quantized in the
color scale, but retrieval is continuous.
II - 127
Fig. 5. Simulated reflectance using Hydrolight, for varying TSS, with all
other IOP’s held fixed, at depths: (left) 0.4m, (right) 0.8m.
ter bed depth vs ASD profiles at Wreck Island described ear-
lier). We provide two examples, one at a fixed depth of 0.4m
and the other at 0.8m. Hydrolight reflectances are shown for
TSS = 2,3,and 5 g
m3
. Note the much larger variation in re-
flectance at wavelengths below 700nm compared to longer
wavelengths. This example supports our earlier contention
that suspended constituents make a secondary contribution at
wavelengths such as 810nm and 725nm, which are the ideal
wavelength ranges for regressions between the logarithm of
reflectance and depth as illustrated in Figure 3.
4. BATHYMETRY RETRIEVAL
During VCR’07, the NRL CASI-1500 acquired imagery over
Wreck Island at eight different times. Two of these scenes
were acquired on the same day and during the sampling win-
dow for in situ spectral sampling onWreck Island on Septem-
ber 13, 2007. The first of these scenes was acquired on a
falling tide roughly 15 minutes prior to the first ASD spectral
sampling (Figure 4). Although various atmospheric correc-
tion models were explored using the NRL Tafkaa atmospheric
correction program [9], an empirical line calibration was ul-
timately used, aligning the spectral data from the CASI with
ASD data taken both in the water (dark pixel) for the depth vs
ASD profiles and those that were being acquired for substrate
studies [2] on the beach nearby (bright pixels).
Data were initially segmented using scene based regions
of interest designed to separate out the deep water pixels from
shallow water pixels. This step involved maximum likelihood
classification to trim off deep water pixels from the scene. For
the remaining shallowwater data, prior to applying the 810nm
regressions, the data were partitioned by bottom type using a
spectral angle mapper [5] criterion which matched the CASI
imagery pixels to the closest ASD profile found in the in situ
profiles taken by us during VCR’07 and in October 2005 as
just described. During this phase of the processing, a moder-
ate angle was sufficient to generate a reasonable delineation
of bottom types in the scenes. Three bottom types described
earlier, oyster bed, muddy bottom, and sand/silt mixture were
used and were based on the ASD vs depth profile spectral li-
braries obtained at Parramore, Wreck, and Smith Islands. Re-
trievals using the ln(Rλ) vs d regressions for the 810nm for
each bottom were applied separately to the data after sege-
mentation by bottom type. The resulting depth retrieval in
centimeters is shown in Figure 4. Although the bathymetry
retrieval is real-valued, in Figure 4, the retrieval has been
quantized (see the accompanying color bar) to better high-
light bathymetric structure. Note that the retrieval is applied
successfully in the shallow cove, the shallow ocean side in
the surf zone, as well as in the salt marsh and tidal creeks in
the western portion of the image. The salt marsh vegetation
is dominated by Spartina alterniflora, an erectophile canopy
which allows for significant substrate exposure, and when in-
undated, a significant fraction of the pixel appears as water in
a nadir view. This allows the 810nm retrieval to be used suc-
cessfully in these regions via the muddy bottom regression.
5. CONCLUSIONS
We have shown the potential of a near infra-red feature cen-
tered near 810nm for retrieving very shallowwater bathymetry
(< 2m depth). Because this portion of the spectrum is pre-
dominantly influenced by water absorption (depth) and the
reflectance of the bottom, effects of suspended constituents
have only a secondary contribution, allowing us to use a set
of simple regressions based on bottom type, rather than a full
radiative transfer model in the very shallow water regime.
Operational LIDAR systems such as SHOALS do not pro-
duce reliable retrievals in the very shallow water regime, and
in joint HSI/LIDAR platforms such as CHARTS, the new
approach could fill a gap in bathymetric retrieval.
6. REFERENCES
[1] C. M. Bachmann, M. H. Bettenhausen, R. A. Fusina, T. F. Donato, A. L. Russ, J. Burke, G. M. Lamela, W.
Joseph Rhea, B. R. Truitt, J. H. Porter, “A Credit Assignment Approach to Fusing Classifiers of Multi-Season
Hyperspectral Imagery,” IEEE Trans. Geosci. Remote Sensing, vol. 41, no. 11, pp. 2488-2499, 2003.
[2] C M. Bachmann, C. R. Nichols, M. J. Montes, R.-R. Li, P. Woodward, R. A. Fusina, W. Chen, M. Crawford, W.
Kim, V. Mishra, J. Monty, K. McIlhany, D. Korwan, D. Miller, E. Bennert, G. Smith, D. Gillis, G. Marmorino, J.
Sellars, C. Parrish, A. Schwarzschild, B. Truitt, “Remote Sensing Retrieval of Substrate Bearing Strength from
Hyperspectral Imagery at the Virginia Coast Reserve (VCR’07) Multi-Sensor Campaign,” Proc. IGARSS’08.
[3] J. A. Curcio, C. C. Petty, 1951. “The Near Infrared AbsorptionSpectrum of LiquidWater,” J. Optical Soc. Amer.,
Vol. 41, No. 5, pp. 302-304.
[4] S. Ebrite, B. Pope, W. J. Lillycrop, “A Multi-Agency Solution for Coastal Surveys - SHOALS in the Pacific,”
Oceans 2001 Proceedings, Vol. 2, 2001, pp. 1204-1211.
[5] F. A. Kruse, A. B. Lefkoff, J. W. Boardman, K. B. Heidebrecht, A. T. Shapiro, P. J. Barloon, A. F. H. Goetz,
“The Spectral Image Processing System (SIPS) - Interactive Visualization and Analysis of Imaging Spectrometer
Data,,” Remote Sens. Environ,, Vol. 44, pp. 145-163, 1993.
[6] C. D. Mobley, Light and Water: Radiative Transfer in Natural Waters, Academic Press, San Diego, 1994.
[7] C. D. Mobley, W. P. Bissett, J. H. Bowles, C. O. Davis, T. V. Downes, A. Gleason, D. D. R. Kohler, R. A.
Leathers, E. M. Lochard, M. J. Montes, R. P. Reid, L. K. Sundman, 2005. “ Interpretation of hyperspectral
remote-sensing imagery via spectrum matching and look-up tables, Applied Optics,” 44(17): 3576-3592.
[8] C. D. Mobley, L. K. Sundman, 2000. Hydrolight 4.1 Technical Documentation. Seattle: Sequoia Scientific, Inc.
[9] M. J. Montes, B.-C. Gao, C. O. Davis, “NRL Atmospheric Correction Algorithms for Oceans: Tafkaa User’s
Guide,” Naval Research Laboratory Memorandum Report 7230–04-8760,March 2004.
[10] R. A. Smith, J. L. Irish, M. Q. Smith, 2000. “Airborne LIDAR and Airborne Hyperspectral Imagery: A Fusion
of Two Proven Sensors for Improved Hydrographic Surveying,” Proceedings of the Canadian Hydrographis
Conference 2000.
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Very Shallow Water Bathymetry Retrieval from Hyperspectral Imagery at the Virginia Coast Reserve (VCR\u2707) Multi-Sensor Campaign

UNH Scholars' Repository

University of New HampshireUniversity of New Hampshire Scholars' RepositoryCenter for Coastal and Ocean Mapping Center for Coastal and Ocean Mapping7-2008Very Shallow Water Bathymetry Retrieval fromHyperspectral Imagery at the Virginia CoastReserve (VCR'07) Multi-Sensor CampaignCharles M. BachmannU.S. Naval Research LaboratoryMarcos J. MontesU.S. Naval Research LaboratoryRobert A. FusinaU.S. Naval Research LaboratoryChristopher ParrishUniversity of New Hampshire, DurhamJon SellarsNOAASee next page for additional authorsFollow this and additional works at: https://scholars.unh.edu/ccomPart of the Computer Sciences Commons, and the Oceanography and Atmospheric Sciences andMeteorology CommonsThis Conference Proceeding is brought to you for free and open access by the Center for Coastal and Ocean Mapping at University of New HampshireScholars' Repository. It has been accepted for inclusion in Center for Coastal and Ocean Mapping by an authorized administrator of University of NewHampshire Scholars' Repository. For more information, please contact nicole.hentz@unh.edu.Recommended CitationC. M. Bachmann et al., "Very shallow water Bathymetry retrieval from Hyperspectral imagery at the Virginia Coast reserve (VCR’07)multi-sensor campaign," IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008.AuthorsCharles M. Bachmann, Marcos J. Montes, Robert A. Fusina, Christopher Parrish, Jon Sellars, AlanWeidemann, Wesley Goode, C Reid Nichols, Patrick Woodward, Kevin McIlhany, Victoria Hill, RichardZimmerman, Daniel Korwan, Barry Truitt, and A. SchwarzschildThis conference proceeding is available at University of New Hampshire Scholars' Repository: https://scholars.unh.edu/ccom/444VERY SHALLOWWATER BATHYMETRY RETRIEVAL FROMHYPERSPECTRALIMAGERY AT THE VIRGINIA COAST RESERVE (VCR’07) MULTI-SENSOR CAMPAIGN†Charles M. Bachmann, †Marcos J. Montes, †Robert A. Fusina, ††Christopher Parrish, ††Jon Sellars‡Alan Weidemann, ‡Wesley Goode, *C. Reid Nichols, *Patrick Woodward, #Kevin McIlhany++Victoria Hill, ++Richard Zimmerman, †Daniel Korwan, **Barry Truitt, +Arthur Schwarzschild†Naval Research Laboratory, Remote Sensing Division, Washington, DC 20375 (bachmann@nrl.navy.mil)††NOAA NOS, Silver Spring, MD‡ Naval Resarch Laboratory, Oceanography Division, Stennis, MS*Marine Information Resources Corporation, Ellicott City, MD# US Naval Academy, Physics Department, Annapolis, MD++Old Dominion University**The Nature Conservancy, Nassawaddox, VA+University of Virginia, Dept. of Environmental Science, Charlottesville, VAABSTRACTA number of institutions, including the Naval Research Laboratory (NRL), have devel-oped look up tables for remote retrieval of bathymetry and in-water optical propertiesfrom hyperspectral imagery (HSI) [6]. For bathymetry retrieval, the lower limit is thevery shallow water case (here defined as < 2m), a depth zone which is not well re-solved by many existing bathymetric LIDAR sensors, such as SHOALS [4]. The abilityto rapidly model these shallow water depths from HSI directly has potential benefitsfor combined HSI/LIDAR systems such as the Compact Hydrographic Airborne RapidTotal Survey (CHARTS) [10]. In this study, we focused on the validation of a nearinfra-red feature, corresponding to a local minimum in absorption (and therefore a localpeak in reflectance), which can be correlated directly to bathymetry with a high degreeof confidence. Compared to other VNIR wavelengths, this particular near-IR featurecorresponds to a peak in the correlation with depth in this very shallow water regime,and this is a spectral range where reflectance depends primarily on water depth (waterabsorption) and bottom type, with suspended constituents playing a secondary role.Index Terms— hyperspectral, shallow-water bathymetry, bottom type, IOP,Hydrolight, Virginia Coast Reserve1. INTRODUCTION AND BACKGROUNDAt visible and near infra-red (VNIR) wavelengths, reflectanceis determined by a variety of factors including water depth,bottom type, and the presence of suspended constituents suchas color-dissolved organic matter (CDOM), suspended sedi-ments, chlorophyll, phytoplankton, and other suspended con-stituents [6]. Thus the general problem of retrieving depthThe NRL authors gratefully acknowledge platform support from the Office of Naval Research. This paper wasfunded by the National Geospatial-Intelligence Agency (NGA). The opinons, views, and conclusions found within thepaper do not necessarily reflect those of the U.S. Dept. of Defense or the NGA.as well as other water properties has been approached usingspectral look-up tables [7] in which a forward radiative trans-fer model such as Hydrolight [8] is executed repeatedly withvarying water column properties, depth, and bottom types.To be comprehensive, these look-up tables must be large andmay need to be tuned to specific coastal types because of vari-ability in bottom types and water properties which may varysignificantly with coastal type. An attractive feature of hy-perspectral data is the enhanced capability to simultaneouslyserve a multitude of uses, in addition to bathymetry retrieval.Light Detection and Ranging (LIDAR) has also been widelyused to retrieve bathymetric data. LIDAR has the advantagethat it is an active sensor, which may provide greater accu-racy in deeper waters, however, LIDAR systems such as theScanning Hydrographic Operational Airborne Lidar Survey(SHOALS) [4] must be flown at very low altitude with a rel-atively small swath compared to typical airborne hyperspec-tral sensors. In the very shallow water regime (less than 2mdepth), LIDAR systems often do not provide a reliable re-trieval, failing to resolve the difference between the bottomand surface returns. In this paper, we focus on this very shal-low water regime, specifically on the retrieval of bathymetryfrom a spectral range where a relatively simple model for re-flectance can be assumed. Compared to the reflectance at vis-ible wavelengths, where all contributions from the water col-umn must be carefully considered, near infra-red wavelengthreflectance (beyond 800nm) is primarily dependent on waterabsorption and depth, along with bottom reflectance, with wa-ter column constituents playing a secondary role.II - 125978-1-4244-2808-3/08/$25.00 ©2008 IEEE IGARSS 2008Fig. 1. (Top) Parramore Island in situ spectrometry site on salt marsh tidalcreek bank with muddy bottom: (left) beginning (near high tide), (right)end (near low tide) of measurement cycle. (Bottom right) Spectral sam-ples at depths varying from 89cm to 0cm (wet substrate). (Bottom left)ln(R810nm) vs d (depth in cm).2. VIRGINIA COAST RESERVE CAMPAIGNIn September 2007, NRL and a number of collaborating in-stitutions undertook a multi-sensor airborne data collectionand in situ validation campaign at the Virginia Coast Reserve(VCR’07). Airborne, in-water, and land based measure-ments took place over the course of the 2 12week exercise andcovered a 95 km stretch of barrier islands, coastal lagoons,tidal flats, and mainland marsh systems. NRL flew threesensors on a Twin Otter aircraft: a CASI-1500 visible andnear infra-red (VNIR) HSI sensor, a Surface Optics InGaAsSWIR HSI sensor operating in the 0.9-1.7 μm spectral re-gion, and single channel mid-wave IR camera. This paperfocuses on the CASI imagery taken during the experiment,and the in situ validation data collected to study the NIRfeature described above (other littoral focus areas studiedduring the VCR’07 experiment included the remote retrievalof soil bearing strength [2], biomass retrieval, land-covermapping [1], as well as seagrass and oyster reef monitoring).For the very shallow bathymetry study, field validation ef-forts used Analytical Spectral Devices (ASD) spectrometerspositioned on a tripod in a shallow cove behindWreck Island,VA, and in the shallow stretches of a muddy tidal creek andsalt marsh system in the northern end of Parramore Island,VA. Depth and ASD spectral measurements were recordedon a falling tide until the substrate was completely exposed,allowing an in situ bottom reflectance to be obtained in themeasurement suite. Bottom reflectance was also measured bydivers using an underwater spectrometer at other locations.Previously, during field work in October 2005, ASD measure-ments had been collected in the shallow waters on the edge ofa salt marsh system in the northern end of Smith Island. Dur-Fig. 2. Wreck Island in situ spectrometry site with oyster bed bottom in ashallow cove. (Top) Site photos. (Bottom right)Spectral samples at depthsvarying from 86cm to 0cm (wet substrate); (bottom left) ln(R810nm) vs d(depth in cm) and associated regression.ing VCR’07, Kinematic GPS data were taken in the inter-tidalregion on the northern end of Wreck Island to support prod-uct validation. In addition, depth soundings were taken in theshallow waters behind the island and in the adjacent chan-nel separating Wreck from Cobb Island. At the conclusion ofthe study, the NASA EAARL LIDAR took data in conjunc-tion with the NRL sensor suite, albeit during a period whenweather conditions were not optimal, and turbidity was high.Figures 1 and 2 show the measured spectral curves foreach depth collected during the measurement cycle at Par-ramore and Wreck Islands respectively, along with in situphotos and the regression at the 810nm feature. A similarregression was carried out for data taken at Smith Island, VAin October 2005 for a sand/silt bottom on the watery marginbetween beach and salt marsh zones. As described earlierthe focus was on the longer wavelengths of the near infra-redspectrum, but the full range spectrum from 0.35-2.5 micronswas also collected. Note that the visible and near infra-redare the portions of the spectrum usually used in water appli-cations, since the reflectance beyond a micron is near zero,except when constituents in the water column are fully mixedto the surface layer. The visible spectrum shows a relativelybroad peak near 810nm, corresponding to a local minimumin the liquid water absorption [3]. There is a similar peaknear 1.076 microns, which is of a similar origin, though lesspronounced character. To assess the relative merits of wave-length selection at these longer wavelengths and confirm theoptimality of the choice of the 810nm feature, we computedan R2 goodness of fit between a linear regression of ln(Rλ)and d, where Rλ is the reflectance at wavelength λ, and d isthe depth in cm. Figure 3 shows plots of this feature acrossthe full spectrum for each of the two sites, with enlargementsof the VNIR portion of the spectrum. The best correlation oc-curs near two peaks, one at 810nm and the other near 720nm.II - 126Fig. 3. R2 fit of ln(Rλ) and d for: (left column) Muddy bottom, ParramoreIsland, (right, column)oyster bed bottom, Wreck Island. (Top row) 0.35-2.5μm, (bottom row) enlargement of 0.35-1.07 μm.Wavelengths between these two peaks would work well also,but the best fit with R2 values near 0.89 (muddy bottom)and 0.98 (oyster shell bed bottom) for Parramore Island andWreck Island respectively are near these two wavelengths. Inthe VNIR spectrum below 800nm, the R2 fit for the oystershell bottom case (Wreck Island), R2 never exceeds ∼ 0.81(at 384nm) compared to the peaks at 721nm and 809nmwhereR2 ∼ 0.98, while for the muddy bottom case (Parramore Is-land), the R2 never exceeds ∼ 0.53 (at 409nm), compared tothe peaks at 725nm (R2 = 0.891) and 811nm (R2 = 0.900).Spectral stability and high R2 of the 810nm feature for bothdark and bright bottom cases, demonstrate its optimality. Incontrast, at visible wavelengths, the best R2 is not at a stablelocation and is only moderately good for bright bottoms.3. HYDROLIGHTMODELSTo demonstrate that varying concentrations of suspended con-stituents does not significantly impact the reflectance at 810nm but indeed is a large contributing factor to the shorterwavelength returns, we have run a series of models usingHydrolight[8]. This model allows us to vary the concentra-tion of constituents, bottom type, depth, (etc) and look at thecontribution to remote sensing return as a function of wave-length. In this Section, we show that varying suspended con-stituents is indeed expected to make only a small differencenear 810nm, while at shorter wavelengths in the visible, thesechanges have a significant impact that can not be ignored inmodeling. Figure 5 illustrates this point for a case in whichtotal suspended sediment (TSS) is variedwhile all other inher-ent optical properties (IOP’s) are held fixed and the depth isconstant for a specific bottom type (in this case, the field spec-trum of our bottom substrate reflectance taken for the oys-Fig. 4. (Top) NRL CASI-1500 image of Wreck Island (9:45am line). (Mid-dle) Retrieved depth using the 810nm regressions for each bottom type deter-mined in preprocessing from the spectral libraries. Depth is quantized in thecolor scale, but retrieval is continuous.II - 127Fig. 5. Simulated reflectance using Hydrolight, for varying TSS, with allother IOP’s held fixed, at depths: (left) 0.4m, (right) 0.8m.ter bed depth vs ASD profiles at Wreck Island described ear-lier). We provide two examples, one at a fixed depth of 0.4mand the other at 0.8m. Hydrolight reflectances are shown forTSS = 2,3,and 5 gm3. Note the much larger variation in re-flectance at wavelengths below 700nm compared to longerwavelengths. This example supports our earlier contentionthat suspended constituents make a secondary contribution atwavelengths such as 810nm and 725nm, which are the idealwavelength ranges for regressions between the logarithm ofreflectance and depth as illustrated in Figure 3.4. BATHYMETRY RETRIEVALDuring VCR’07, the NRL CASI-1500 acquired imagery overWreck Island at eight different times. Two of these sceneswere acquired on the same day and during the sampling win-dow for in situ spectral sampling onWreck Island on Septem-ber 13, 2007. The first of these scenes was acquired on afalling tide roughly 15 minutes prior to the first ASD spectralsampling (Figure 4). Although various atmospheric correc-tion models were explored using the NRL Tafkaa atmosphericcorrection program [9], an empirical line calibration was ul-timately used, aligning the spectral data from the CASI withASD data taken both in the water (dark pixel) for the depth vsASD profiles and those that were being acquired for substratestudies [2] on the beach nearby (bright pixels).Data were initially segmented using scene based regionsof interest designed to separate out the deep water pixels fromshallow water pixels. This step involved maximum likelihoodclassification to trim off deep water pixels from the scene. Forthe remaining shallowwater data, prior to applying the 810nmregressions, the data were partitioned by bottom type using aspectral angle mapper [5] criterion which matched the CASIimagery pixels to the closest ASD profile found in the in situprofiles taken by us during VCR’07 and in October 2005 asjust described. During this phase of the processing, a moder-ate angle was sufficient to generate a reasonable delineationof bottom types in the scenes. Three bottom types describedearlier, oyster bed, muddy bottom, and sand/silt mixture wereused and were based on the ASD vs depth profile spectral li-braries obtained at Parramore, Wreck, and Smith Islands. Re-trievals using the ln(Rλ) vs d regressions for the 810nm foreach bottom were applied separately to the data after sege-mentation by bottom type. The resulting depth retrieval incentimeters is shown in Figure 4. Although the bathymetryretrieval is real-valued, in Figure 4, the retrieval has beenquantized (see the accompanying color bar) to better high-light bathymetric structure. Note that the retrieval is appliedsuccessfully in the shallow cove, the shallow ocean side inthe surf zone, as well as in the salt marsh and tidal creeks inthe western portion of the image. The salt marsh vegetationis dominated by Spartina alterniflora, an erectophile canopywhich allows for significant substrate exposure, and when in-undated, a significant fraction of the pixel appears as water ina nadir view. This allows the 810nm retrieval to be used suc-cessfully in these regions via the muddy bottom regression.5. CONCLUSIONSWe have shown the potential of a near infra-red feature cen-tered near 810nm for retrieving very shallowwater bathymetry(< 2m depth). Because this portion of the spectrum is pre-dominantly influenced by water absorption (depth) and thereflectance of the bottom, effects of suspended constituentshave only a secondary contribution, allowing us to use a setof simple regressions based on bottom type, rather than a fullradiative transfer model in the very shallow water regime.Operational LIDAR systems such as SHOALS do not pro-duce reliable retrievals in the very shallow water regime, andin joint HSI/LIDAR platforms such as CHARTS, the newapproach could fill a gap in bathymetric retrieval.6. REFERENCES[1] C. M. Bachmann, M. H. Bettenhausen, R. A. Fusina, T. F. Donato, A. L. Russ, J. Burke, G. M. Lamela, W.Joseph Rhea, B. R. Truitt, J. H. Porter, “A Credit Assignment Approach to Fusing Classifiers of Multi-SeasonHyperspectral Imagery,” IEEE Trans. Geosci. Remote Sensing, vol. 41, no. 11, pp. 2488-2499, 2003.[2] C M. Bachmann, C. R. Nichols, M. J. Montes, R.-R. Li, P. Woodward, R. A. Fusina, W. Chen, M. Crawford, W.Kim, V. Mishra, J. Monty, K. McIlhany, D. Korwan, D. Miller, E. Bennert, G. Smith, D. Gillis, G. Marmorino, J.Sellars, C. Parrish, A. Schwarzschild, B. Truitt, “Remote Sensing Retrieval of Substrate Bearing Strength fromHyperspectral Imagery at the Virginia Coast Reserve (VCR’07) Multi-Sensor Campaign,” Proc. IGARSS’08.[3] J. A. Curcio, C. C. Petty, 1951. “The Near Infrared AbsorptionSpectrum of LiquidWater,” J. Optical Soc. Amer.,Vol. 41, No. 5, pp. 302-304.[4] S. Ebrite, B. Pope, W. J. Lillycrop, “A Multi-Agency Solution for Coastal Surveys - SHOALS in the Pacific,”Oceans 2001 Proceedings, Vol. 2, 2001, pp. 1204-1211.[5] F. A. Kruse, A. B. Lefkoff, J. W. Boardman, K. B. Heidebrecht, A. T. Shapiro, P. J. Barloon, A. F. H. Goetz,“The Spectral Image Processing System (SIPS) - Interactive Visualization and Analysis of Imaging SpectrometerData,,” Remote Sens. Environ,, Vol. 44, pp. 145-163, 1993.[6] C. D. Mobley, Light and Water: Radiative Transfer in Natural Waters, Academic Press, San Diego, 1994.[7] C. D. Mobley, W. P. Bissett, J. H. Bowles, C. O. Davis, T. V. Downes, A. Gleason, D. D. R. Kohler, R. A.Leathers, E. M. Lochard, M. J. Montes, R. P. Reid, L. K. Sundman, 2005. “ Interpretation of hyperspectralremote-sensing imagery via spectrum matching and look-up tables, Applied Optics,” 44(17): 3576-3592.[8] C. D. Mobley, L. K. Sundman, 2000. Hydrolight 4.1 Technical Documentation. Seattle: Sequoia Scientific, Inc.[9] M. J. Montes, B.-C. Gao, C. O. Davis, “NRL Atmospheric Correction Algorithms for Oceans: Tafkaa User’sGuide,” Naval Research Laboratory Memorandum Report 7230–04-8760,March 2004.[10] R. A. Smith, J. L. Irish, M. Q. Smith, 2000. “Airborne LIDAR and Airborne Hyperspectral Imagery: A Fusionof Two Proven Sensors for Improved Hydrographic Surveying,” Proceedings of the Canadian HydrographisConference 2000.II - 128

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Very Shallow Water Bathymetry Retrieval from Hyperspectral Imagery at the Virginia Coast Reserve (VCR\u2707) Multi-Sensor Campaign

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