Remote Sensing of Lake Tahoe's Near Shore Environment
Shohei Watanabe1,2, Erin L. Hestir (Co-PI)1*, Susan L. Ustin1, S. Geoffrey Schladow (PI)2
1
Center for Spatial Technologies and Remote Sensing, Department of Land, Air and Water
Resources, University of California, Davis, One Shields Avenue, Davis, CA 95616
2
Tahoe Environmental Research Center and Department of Civil and Environmental Engineering,
University of California, Davis, One Shields Avenue, Davis, CA 95616
Prepared for:
Tiffany van Huysen, Tahoe Science Program, Pacific Southwest Research Station, Tahoe Center
for Environmental Science, 291 Country Club Dr., Incline Village, NV 89451
i
Table of Contents
Executive Summary ............................................................................................................ iv
List of Figures ..................................................................................................................... vi
List of deliverables .............................................................................................................. vii
Acknowledgements ............................................................................................................. viii
1. Introduction ................................................................................................................... 1
1.1 Background ................................................................................................................... 1
1.1.1 Environmental indicators in the near shore environment ...................................... 1
1.1.2 Remote sensing of near shore environmental indicators ....................................... 1
1.2. Scope of work .............................................................................................................. 2
2. Methods.......................................................................................................................... 3
2.1 Field campaigns ............................................................................................................ 3
2.1.1 In situ spectroscopic measurements ....................................................................... 3
2.1.1.1 Above-water spectroscopy .............................................................................. 5
2.1.1.2 Underwater spectroscopy ................................................................................ 6
2.1.2 GPS reference points.............................................................................................. 7
2.1.3 Inherent optical properties of optical components in the near-shore area ............. 8
2.2. Radiative Transfer modeling........................................................................................ 10
2.2.1 Inherent optical properties of Tahoe Water ........................................................... 11
2.2.2 Radiative transfer modeling by HydroLight® ....................................................... 12
2.2.2.1 Test of substrate separability over a range of depth ...................................... 12
2.2.2.2 Cross validation between above-water and underwater measurements .......... 13
2.3 Satellite image analyses ................................................................................................ 13
2.3.1 Satellite image correction and calibrations ............................................................ 14
2.3.2 Bathymetry map validation .................................................................................... 14
2.3.3 Image transformation and substrate mapping ........................................................ 15
3. Results and Discussions ................................................................................................ 17
3.1 In situ spectroscopic measurements .............................................................................. 17
3.1.1 Above-water spectroscopy ..................................................................................... 17
3.1.2 Underwater spectroscopy ....................................................................................... 17
3.1.3 Inherent optical properties of optical components in the near-shore area ............. 20
3.2 Radiative transfer modeling .......................................................................................... 23
3.3 Satellite Remote Sensing .............................................................................................. 25
3.3.1 Cross comparison of reflectance spectra................................................................ 25
3.3.2 Spectra at GPS reference points ............................................................................. 26
3.3.3 Image transformation by the minimum noise fraction (MNF) and subsequent application
of the mixture tuned matched function (MTMF). ............................................................... 28
3.3.4 Macrophyte mapping in Southeast of the lake ....................................................... 29
4. Conclusions and Recommendations ............................................................................ 31
5. References ...................................................................................................................... 32
ii
Appendix ............................................................................................................................ 37
iii
Executive Summary
The goal of this research was to investigate the extent to which remotely sensed data could be
used to retrieve fine particles, chlorophyll, and CDOM concentrations from the water column in
the near shore, and to map the distribution of periphyton (attached algae), aquatic macrophytes
(submerged plants), and clam beds in the near shore of Lake Tahoe. If fully feasible, this would
provide a powerful and cost-effective methodology for longterm monitoring of the nearshore.
To accomplish this it was first necessary to create a spectral library of bottom reflectance in the
near shore that included GPS locations and in situ bottom reflectance spectra of different
densities of clam beds, different densities and species of aquatic macrophytes and periphyton,
and different densities of sediment grain sizes. This has in large part been accomplished.
Averaged spectra for different substrate types showed distinct spectral shapes, indicating they
may be separable from reflectance spectra at substrate surface level where the influence of the
water can be ignored. One challenge is for sand, the most common substrate type, where a range
of sand colors (and reflectance) were found to exist.
An algorithm for deriving fine sediment, chlorophyll, and CDOM concentration in the water
column in the near shore water regions using remote sensing imagery, proved to be very difficult.
Absorption analyses showed that the values of optically significant components are extremely
low relative to the values found in other inland water bodies, and the available instruments (US
Geological Survey, Sacramento) frequently had issues with samples below their levels. Spatial
variation and absolute concentrations in the absorption spectra of each component was minimal
in the sampled part of the lake, suggesting that it would be difficult to evaluate the concentration
of each component from the current generation of remotely sensed data products. Detection of
low concentration material would be feasible but difficult by remote sensing, especially in the
situation where we need to separate signal of bottom substrate and constituents in the water. Also,
low variability in constituents would make it difficult to develop empirical models where
significant statistical variability is required. Optical characterization of those components by
applying more sensitive laboratory instruments would provide useful information for future
remote sensing in the near-shore area.
Algorithms for underwater substrate detection were developed but their application proved to be
difficult due to influence (and lack of resolution) of water-borne constituents above the bottom
surface as discussed previously, even though the lake water is very clear. Discrimination
between sand and macrophyte would be feasible until the bottom depth reaches approximately
10 m. Detection of rocks from sand would also be feasible in a similar depth range. Clam beds
usually exhibit a patchy distribution in sand, and hence the possibility of separation would be
low due to the similarity in spectral shape between sand and clam beds, and site to site variability
of spectra within these substrate types. Separation of rocks from macrophyte would also be
feasible. In general, with present technology, it appears that only larger patches of macrophytes
are sufficiently distinguishable from remotely sensed datasets. It will require higher sensitivity
and high spectral resolution data (e.g., hyperspectral airborne sensor, PRISM by NASA-JPL) for
other substrate types to be distinguishable.
The development of a cost-benefit analysis for using remote sensing for nearshore monitoring is
not meaningful at the present time. The lack of adequate discrimination of different bottom types
iv
with current remote sensing products and the noise inherent in the in-water constituent
concentrations means that the full suite of benefits are not presently available.
v
List of Figures
Figure 1. Map of sampling sites for the in situ spectrometer measurements ......................
Figure 2. Photography of selected target substrates from above-water surface .................
Figure 3. Photography of above-water surface spectrometer measurement .......................
Figure 4. Photography of underwater spectrometer measurement .....................................
Figure 5. Map of sampled GPS reference points ................................................................
Figure 6. Sample sites for inherent optical property characterization conducted on Sep 26, 2012
Figure 7. Conceptial model of remote sensing of water and submersed bottom substrates
Figure 8. The profile of spectral absorption and scattering coefficient of Lake Tahoe ......
Figure 9. Schematic diagram of the optical forward modeling with HydroLight® ...........
Figure 10. Change of spectra (at sand) by glint correction .................................................
Figure 11. Comparison of depth at GPS reference points measured by an echo sounder on boat
and USGS SHOALS ...........................................................................................................
Figure 12. Above-water reflectance spectra measured by a ASD ......................................
Figure 13. Reflectance spectrea of bottom substrates measured by underwater spectrometer
Figure 14. Averaged reflectance spectra of bottom substrates measured by underwater
spectrometer ........................................................................................................................
Figure 15. Component specific absorption .........................................................................
Figure 16. Result of the radiative transfer modeling ..........................................................
Figure 17. Comparison of reflectance spectra obtained by three different methods at all 16 sites
where in situ radiometry was conducted .............................................................................
Figure 18. Remotely sensed reflectance value at 605 nm (Band 4 of WorldView2) against depth
for each separately captured satellite image .......................................................................
Figures 19. The result of image transformation by the minimum noise fraction (MNF) and
subsequent application of the mixture tuned matched function (MTMF) on the WorldView2
image of south west region in the depth range of 2-4 m .....................................................
Figure 20. Result of macrophyte mapping at Southeast shore of Lake Tahoe ...................
vi
List of deliverables
1. Above-water reflectance spectra collected at in situ spectroscopic sites (n = 16).
File name: D1_Above_Water_Reflectance.csv
Format: CSV file (628KB)
2. Underwater reflectance spectra of bottom substrates collected at in situ spectroscopic sites (n =
16).
File name: D2_Underwater_Reflectance.csv
Format: CSV file (100KB)
3. Averaged reflectance spectra of four substrate types (sand, rocks, macrophyte, clam beds).
File name: D3_Underwater_Reflectance_Averaged.csv
Format: CSV file (29KB)
4. Result of macrophyte distribution mapping in Southeast of the lake.
File name: D4_Classification_ShapeFile
Format: Shape file (four files in a folder, 12.1MB)
vii
Acknowledgements
This research was supported by an agreement from the USDA Forest Service Pacific Southwest
Research Station. It was supported in part using funds provided by the Bureau of Land
Management through the sale of public lands as authorized by the Southern Nevada Public Land
Management Act. The UC Davis Tahoe Environmental Research Center also provided partial
support for the research.
We wish to thank Brant Allen for his overall guidance with field work, and in particular
captaining the research vessel and leading the required diving effort. We also thank Katie Webb
for scuba diving support. George Scheer and Sean Hogan are thanked for their assistance with
the field work and satellite data processing. Brian Bergamaschi and Michael Saur at USGS
Sacramento loaned the project instruments and assistance for field optical measurements and
laboratory sample analyses. Warwick Vincent, Laval University, Canada, for provided optical
property data from Lake Tahoe for the radiative transfer modeling.
viii
1. Introduction
1.1 Background
1.1.1 Environmental indicators in the near shore environment
Lake Tahoe is an ultra-oligotrophic, deep, large lake in the Sierra Nevada internationally
known for its clear blue waters. Lake clarity has been decreasing over the past four decades due
primarily to an increase in the load of fine particles [Jassby et al., 2003; Swift et al., 2006]. This
indicator of Tahoe’s condition has tended to dominate the public’s view of Lake Tahoe and even
the attention of the management agencies. It has only been in the last few years that attention has
finally turned toward the near-shore zone, despite the fact that this is the part of the lake that
most people see and interact with.
The condition of the near shore environment of Lake Tahoe, the littoral zone of the lake
where light penetrates to the bottom, serves as an important indicator of the health of the lake as
it is the primary interface between anthropogenic disturbance and the lake [Reuter et al., 2009].
In the near shore, water clarity, periphyton (attached algal growth), native plants and aquatic
invasive species (AIS) are all important environmental indicators for the lake. Water clarity in
the near shore is controlled largely by suspended mineralogical particles, and varies spatially in
response to onshore development and runoff [Taylor et al., 2004]. Water clarity in much of the
measured near-shore has been classified as impaired (e.g. reduced water clarity), and is an
important indicator of lake aesthetic quality [Taylor et al., 2004]. In the near shore, periphyton
abundance is used as an environmental indicator for the status of nutrient limitation, but is an
aesthetic quality in its own right. Algal growth bioassays measured since the 1960s, and more
recent surveys of algal growth around the lake provide a record of the nutrient status of the near
shore [Hackley et al., 2010]. However, these measurements are made at only a few survey
stations around the lake. The spread of AIS serve as an indicator for the ecological health of the
near shore. AIS can displace native species, resulting in a decrease in biodiversity and a shift in
ecosystem structure. Furthermore, AIS cause significant harm to human recreation and economic
activities. In Lake Tahoe, it is estimated that new or expanding AIS could have a combined
economic impact of $22.4 million per year [USACE, 2009]. Invasive aquatic macrophytes
including Eurasian watermilfoil (Myriophyllum spicatum) and curlyleaf pondweed (Potamogeton
crispus) have been spreading around the near shore over the past 15-20 years, as are beds of
Asian clams (Corbicula fluminea), primarily as a result of increased boat traffic [USACE, 2009].
1.1.2 Remote sensing of near shore environmental indicators
Monitoring the status and trends of near shore suspended particle, periphyton, and plant
and clam beds using traditional in situ methods is difficult because these environmental
indicators vary spatially and a statistically significant number of samples requires extensive and
costly field sampling. Survey stations have been established for all of these environmental
indicators, yet it is unclear how well these stations characterize the near shore and capture the
effects of onshore events on these indicators. Recently, remote sensing combined with in situ
monitoring has successfully characterized the spatial variability of water clarity in the lake
[Schladow et al., 2010].
In this study, our initial aim was to expand this research in order to retrieve spatially
explicit maps of fine particles, colored dissolved organic matter (CDOM), and chlorophyll in the
water column, and the distribution of periphyton, aquatic macrophyte and clam beds. We
collected GPS locations and bottom reflectance of the target bottom types, and applied existing
remote sensing datasets, to evaluate feasibility of remote sensing of those targets, and then
1
provided a mapped bottom type in the southwest of the lake. We further provided
recommendations for future monitoring of such targets based on what have been learned from
the present research.
1.2. Scope of work
The goal of this research was to investigate the extent to which remotely sensed data
could be used to retrieve fine particles, chlorophyll, and CDOM concentrations from the water
column in the near shore, and to map the distribution of periphyton (attached algae), aquatic
macrophytes (submerged plants), and clam beds in the near shore of Lake Tahoe. If fully feasible,
this would provide a powerful and cost-effective methodology for longterm monitoring of the
nearshore.
2
2. Methods
2.1 Field campaigns
The first step of bottom type mapping is evaluation of spectral separability of different
types of substrates. It is required to determine whether there are significant differences in
reflectance spectra among different bottom types of interests. It also important to collect
reference points for calibration/validation of mapping of remote sensing measurements. The field
data acquisition was conducted on 13, 15 and 19 September, 2011, to develop spectral libraries
of the target substrate types and also to collect GPS reference locations at near shore areas of
Lake Tahoe. Spectroscopic data acquisition was conducted both above the water surface from a
boat and above the substrate in the water by divers at 16 sites covering a range of substrate types
of the near-shore environment. GPS reference point collection was also conducted at 518 sites to
cover the entire lake periphery. Another field campaign was conducted on September 26, 2012 to
characterize the inherent optical properties of optically significant components in near-shore area
of the lake. Five locations around the lake were sampled. Detailed methods are given below.
2.1.1 In situ spectroscopic measurements
The spectral reflectance of target substrates was characterized at 16 sites around the lake
on 13, 15 and 19 September, 2011 (Fig. 1 & 2). The sites covered various types of substrates
representing the substrate condition of the lake (sand, boulders, bedrock, Eurasian water milfoil,
Richardson's Pondweed, Curly-leaf Pondweed, and clam beds). Both above-water measurements
from the deck of the boat and underwater measurements by divers were simultaneously
conducted at each site. Depth of the sampling site was measured by the depth sounder installed
on the boat. A spectral library for the substrate types was collected between 10:00 am and 3:00
pm, when sun angles and surface conditions were optimal. Target locations were selected with
the criteria of being homogeneous and representative of the substrate types which are observable
from water surface (Fig. 2). Macrophyte beds were classified by the dominant species in the
patch. For each target type, target spectra were acquired from 3 to 4 regions of the lake, with the
exception of bedrock, which was rare in the lake and was spectrally similar to granite boulders.
Periphyton, which are usually at their maximum growth in spring, were not sampled due to
limited availability of instruments and personnel in the spring of 2012.
3
Figure 1. Map of sampling sites for the in situ spectrometer measurements.
4
Figure 2. Photos of selected target substrates from above the water surface: a. sand; b.
rocks (boulders); c. macrophyte (Richardson Pondweed); d. clam beds (Photos by G. Scheer
and S. Hogan).
2.1.1.1 Above-water spectroscopy
The spectral reflectance of target substrates at above-water surface was characterized
with a FieldSpec 4 spectroradiometer (ASD Inc., Boulder, CO) following the standard methods
described in NASA's Ocean optics protocol (Fig. 3) [Mueller et al., 2002]. Firstly, a spectrum of
a Spectralon® panel was collected as reference of ambient light (Lref(λ)). Secondly, water surface
(Ltarget(λ)) was measured at at azimuth angle of 90° from the sun and nadir angle of 45° from the
surface. Thirdly, sky (Lsky(λ)) wass measured at the same azimuth angle and zenith angle of 45°
from sky. This series of three measurements was repeated 10 times at each site. Photographs of
the surface and sky were taken at similar azimuth and view angles as the ASD measurements to
document surface and sky conditions.
The 10 spectra for Spectralon, water surface, and sky were individually averaged, and
then the spectral reflectance at water surface was calculated as:
𝑅!" λ =
!!"#$%! ! !!∙!!"# !
eq. 1
!!"# !
where, ρ is the reflectance factor defining the total skylight reflected from the water surface
estimated as 0.028 [Mobley, 1999].
5
Figure 3. Photography of above-water surface spectrometer measurement. Sean Hogan was
holding the optical sensor over water surface to collect spectra (Photo by G. Scheer).
2.1.1.2 Underwater spectroscopy
The underwater spectroscopic measurements by scuba divers were conducted to obtain
spectral reflectance of target substrates (Fig. 4). They were used to develop a spectral library of
each substrate type and for the subsequent radiative transfer modeling (discussed below). The
measurements were conducted with a GER 1500 field portable spectroradiometer (Spectra Vista
Co., Poughkeepsie, NY) equipped with a custom made housing for underwater deployment
[Goodman, 2004]. Prior to every spectral collection, upwelling radiance from a Spectralon
reference panel were was. The panel was then removed and 5-10 spectra of upwelling radiance
from the selected target were acquired. The measurements were taken 30 cm above the target at
nadir angle. Also, the measurements were acquired using the body of the instrument operator to
produce a shadow that completely covered the surface being measured in order to minimize light
fluctuations due to the surface wave focusing effect. Detailed methods and further discussion
regarding underwater measurements are given elsewhere [Goodman, 2004].
6
Figure 4. Photography of underwater spectrometer measurement (Photo by B. Allen).
2.1.2 GPS reference points
Reference reference points were collected around the near-shore area of the entire lake.
At each point, the dominant bottom substrate type, GPS coordinates and water depth were
recorded. A total of 518 points were collected. The selected substrate types were not evenly
distributed throughout the near shore environment, however, the GPS sample positions were as
evenly distributed as possible (Fig. 5). Also, the reference points for each substrate type were
collected at a variety of depths.
7
Figure 5. Map of sampled GPS reference points (n = 518).
2.1.3 Inherent optical properties of the optical components in the near-shore area
The analyses of inherent optical properties of optically significant components in the lake
water were conducted to determine the feasibility of quantification of such components in near
shore area from remotely sensed data. Surface water was taken on 26 September, 2012, at five
sites representing different substrate conditions around the south shore plus one pelagic site. The
sites were at Sugarpine point (sand), DL Bliss (rocks), Tahoe Keys1 (macrophyte), Tahoe Keys2
(macrophyte), Marla Bay (clam beds), and buoy site TB3 (pelagic site, as reference) (Fig. 6).
8
Water samples were immediately filtered through Whatman GF/F filters and the filters were
stored frozen until laboratory analyses were performed. The filtrate was subsequently filtered
through Millipore Nitrocellulose membrane filters with a pore size of 0.22 µm. This membrane
filtrate is stored in amber glass bottle and refrigerated until laboratory analyses.
Figure 6. Sample sites for inherent optical property characterization collected on 26 September,
2012. Green points indicate sampled points: 1. Sugarpine point (sand), 2. DL Bliss (rocks), 3.
Tahoe Key1 (macrophyte), 4. Tahoe Key2 (macrophyte), 5. Marla Bay (clam beds), 6. TB3
(pelagic site, as reference).
9
For analysis of colored dissolved organic matter (CDOM) the absorbance (A(λ),
dimensionless) of the filtrate against Milli-Q® water was measured from 200 to 850 nm at 1 nm
intervals (spectral slit width 2 nm) in a 10 cm quartz cuvette using a Cary 300 dual beam
spectrophotometer (Varian Inc., USA). The absorption coefficients of CDOM (aCDOM(λ), m-1)
were calculated as:
aCDOM (λ ) =
2.303 A(λ )
L
eq. 2
where L is the path length of the cuvette (Beer’s law). The absorbance of particulate matter was
measured by the wet filter technique (quantitative filter technique) in the same
spectrophotometer as above following Mitchell et al. [2002]. The absorbance of non-algal
particles was measured by extracting pigments with methanol [Kishino et al., 1985; Mitchell et
al., 2002]. The absorption coefficients of total and non-algal particulate matter (ap(λ) and aNAP(λ)
in m-1, respectively) were calculated as:
a p (λ ) or a NAP (λ ) =
2.303A(λ ) ⋅ T
β ⋅V
eq. 3
where T is the filtered area, V is filtered volume, and β is the path length amplification factor
[Mitchell et al., 2002]. A constant value for β = 2 was used after Roesler [1998]. The absorption
coefficient of algal particles (aΦ(λ), m-1) was obtained by subtracting aNAP(λ) from ap(λ). The
above laboratory analyses were conducted at the United States Geological Survey (USGS) in
Sacramento.
2.2. Radiative Transfer modeling
The information contained in remote sensing imagery can potentially be used to quantify
water quality constituents, such as suspended particle concentrations, as well as to infer bottom
properties in optically shallow waters [Albert and Gege, 2006; Lee and Carder, 2002; Lee et al.,
2001; Mobley et al., 2005]. Apparent upwelling irradiance (what the sensors effectively measure),
is a function of atmospheric properties and solar inputs, as well as the upwelling irradiance at the
surface (a function itself of the reflection off the bottom and constituents in the water column),
and the reflection off the water surface due to both direct sunlight and diffuse skylight [Mertes et
al., 1993] (Fig. 7). Assuming atmospheric properties, solar input, the reflection off the water
surface as constant in the system, what we measure as reflectance on water surface is a function
only of reflectance characteristics of bottom and optical properties of water (i.e., optical
characteristics and concentrations of constituents in the water column). These in turn can be
estimated from remotely sensed data off the water surface [Lee et al., 2001].
There are two primary methods to estimate constituents in water and analyze bottom
properties. The first uses empirical models that develop statistical relations between measured
field data and image data [Brando and Dekker, 2003]. The second approach uses physical
modeling of light through the atmosphere, water column, surface and bottom to simultaneously
obtain water backscatter and absorption coefficient, bottom depth, and bottom reflectance [Semianalytical approach, Lee et al., 1998]. The latter approach is particularly useful to give a
fundamental understanding of behavior of reflectance at water surface. The radiative transfer
model is a well developed model describing transmission of electromagnetic radiation in a
10
medium. There is commercially available software computing visible light transmission,
specifically in natural water, with input of ambient conditions, bottom reflectance spectra, and
water characteristic, etc.
Figure 7. Conceptial model of remote sensing of water and submersed bottom substrates.
2.2.1 Inherent optical properties of Tahoe Water
The radiative transfer modeling requires knowledge of the inherent optical properties of
water, such as the spectral absorption and scattering coefficients (a(λ) and b(λ), respectively) and
ambient conditions (irradiance, wave conditions, solar angle etc.). The absorption and beam
attenuation coefficients, excluding those of pure-water, for surface water samples taken at the
LTP site (at-w(λ) and ct-w(λ) in m-1, respectively) at Lake Tahoe were measured using an AC-S in
situ spectrophotometer (25 cm path length, WET Labs Inc., Philomath, OR) on the laboratory
bench [Watanabe et al., 2011]. The pure-water calibration values obtained immediately before
the sampling period were subtracted from the raw readings [Pegau et al., 2003; Sullivan et al.,
2006; Twardowski et al., 1999]. The temperature correction was applied by using the spectral
correction coefficients obtained specifically for this instrument. A null point correction was
applied by subtracting the absorption coefficient at the longest wavelength measured by the
instrument (751.7 nm), as in Pegau et al. [2003].
The measured spectral ranges of the instrument were 403.0-751.7 nm and 401.6-750.8
nm for at-w(λ) and ct-w(λ), respectively. The obtained spectra were linearly interpolated for the
11
range 403 to 700 nm at 1 nm intervals, and were also extrapolated to 400 nm by fitting the
measured values from 403 to 410 nm to the exponential model as:
aCDOM (λ ) = aCDOM (λ0 ) ⋅ e − SCDOM ( λ −λ0 )
eq. 4
The scattering coefficients of suspended particulate matter (bp(λ), m-1) were then obtained by
subtracting at-w(λ) from ct-w(λ). Obtained spectra are shown in the Fig. 8. These variables were
measured and provided by a research group from Laval University, Canada, who conducted their
field observation from 13 to 15 September 2011.
Figure 8. The profile of spectral absorption and scattering coefficient of Lake Tahoe (provided
by Warwick F. Vincent, Laval University, Canada)
2.2.2 Radiative transfer modeling by HydroLight®
2.2.2.1 Test of substrate separability over a range of depths
The radiative transfer modeling was conducted to understand the behavior of the shape of
the reflectance spectra with water depth for four representative substrate types (sand, rocks,
macrophyte, clam beds). HydroLight® (Sequoia Inc., Bellevue, WA), a commercially available
software product, computes radiance distribution and related quantities, including reflectance,
from the input of water absorption, scattering, sky conditions and bottom boundary conditions
12
(Fig. 9). We applied averaged spectra of four substrate types as the bottom boundary condition
(see Fig. 14), and at-w(λ) and bp (λ) described above as the spectral absorption and scattering
coefficients of lake water (Fig. 8). The absorption and scattering coefficients of pure water was
estimated by the built in module. The backscattering coefficient was estimated by the FournierForand phase function with the backscattering ratio of 0.008 by built in module. The downward
irradiance at the water surface was estimated through a built in module from the input of date
and time (3PM, Sep 14, 2011), and latitude of the lake (39° N, 120° E). The ambient conditions
were set to be clear sky with no cloud, calm water surface with no wind. The calculations were
conducted for water depths ranging from 1 to 10m at 1m intervals, and for 15 m and 20m.
Figure 9. Schematic diagram of the optical forward modeling with HydroLight® (Radiative
Transfer modeling)
2.2.2.2 Cross validation between above-water and underwater measurements
Similar modeling was conducted to estimate above-water reflectance at 16 sites where
above-water and bottom surface spectroscopy were taken. The bottom reflectance of each site
and depth measured by depth sounder on the boat was applied. And recorded time of sample was
applied to estimate downward irradiance. Other inputs (e.g., absorption and scattering) were the
same as above.
2.3 Satellite image analyses
Satellite imagery data analyses were conducted to map the bottom substrate condition of
near shore area. High spatial resolution multispectral satellite imagery taken by WorldView2
13
(DigitalGlove Inc., Longmont, CO) have been applied. The imagery has a spatial resolution of
0.5 m for panchromatic and 2 m for multispectral bands. The multispectral sensor has 8 bands:
coastal (400-500 nm), blue (450-510 nm), green (510-580 nm), yellow (585-625 nm), red (630690 nm), red edge (705-745 nm), NIR-1 (770-895 nm), NIR-2 (860-900nm).
2.3.1 Satellite image correction and calibrations
Four images were used to cover the entire near shore area of the lake. The images were
acquired on Aug 11, 2011 (southeast and northeast corners of the lake), Aug 22, 2010
(northwest), and Sep 10, 2010 (southwest). The images were orthorectified to reduce terrain
distortions, and amothpherically corrected by ATCOR2. Also, the image was pan-sharpened to
obtain multispectral images with 0.5 m spatial resolution. Wave motion of water surface can be
significant source of observational error for remote imagery. It is usually observed as glint
yielding high noise. The method introduced by Hedley et al. [2005] was applied to the images
and successfully reduced the noise and produced reasonably stable spectra (Fig. 10).
Figure 10. Change of spectra (at sand) by glint correction [Hedley et al., 2005].
2.3.2 Bathymetric map validation
The depth of the lake bottom (bathymetry) is necessary for image analysis. We utilized
data from USGS's SHOALS survey. The SHOALS data and depth recorded at GPS reference
points were compared, and showed reasonable agreement with the boat echo sounder (Fig. 11).
Relatively large errors were found at the edge of steep slopes. The bathymetric information was
used to extract satellite images for target depth range (e.g., extracting image where depth ranges
from 2 to 4 m).
14
Figure 11. Comparison of depth at GPS reference points measured by an echo sounder on boat
and USGS SHOALS data. Solid line is the linear regression line and dashed line one-by-one
relationship.
2.3.3 Image transformation and substrate mapping
Further treatment of the remotely sensed images is required for data with noise
interfering simple end member classification. Our data still shows moderate level of noise caused
by wave motion of water surface, even after the reduction of noise by glint correction above
[Hedley et al., 2005]. The minimum noise fraction (MNF) transformation is used to determine
the inherent dimensionality of image data, to segregate noise in the data, and to reduce the
computational requirements for subsequent processing [see, Boardman and Kruse, 1994]. The
MNF transform as modified from Green et al. [1988] and implemented in ENVI is essentially
two cascaded Principal Components transformations. The first transformation, based on an
estimated noise covariance matrix, decorrelates and rescales the noise in the data. This first step
results in transformed data in which the noise has unit variance and no band-to-band correlations.
The second step is a standard Principal Components transformation of the noise-whitened data.
For the purposes of further spectral processing, the inherent dimensionality of the data is
determined by examination of the final eigenvalues and the associated images. Matched filtering
is based on well-known signal processing methodologies. It maximizes the response of a known
end member and suppresses the response of the composite unknown background, thus
15
"matching" the known signature [Chen and Reed, 1987; Harsanyi and Chang, 1994; Stocker et
al., 1990; Yu et al., 1993]. It provides a rapid means of detecting specific minerals based on
matches to specific library or image end member spectra. This technique produces images with
significantly less computation. Matched filter results are presented as gray-scale images with
values from 0 to 1.0, which provide a means of estimating relative degree of match to the
reference spectrum (where 1.0 is a perfect match). MTMF Constrains the Matched Filtering as
mixtures of the composite unknown background and the known target. These image
transformation and unmixing technique were applied to the image of Southeast of the lake to
classify macrophyte bed from sand in the depth range from 2 to 4m.
16
3. Results and Discussions
3.1 In situ spectroscopic measurements
3.1.1 Above-water spectroscopy
The above-water surface reflectance measurements gives the base information for
validating image correction of remotely sensed data and results of radiative transfer modeling. In
the present research, the reflectance spectra were in the range from 0 to 5 % (Fig. 12), which is
typical for above-water surface measurement of clear shallow water bodies [e.g., Dekker et al.,
2011; Goodman and Ustin, 2007]. All spectra from near-shore are were distinct from that from
the pelagic site (sold gray line in Fig. 12), indicating the above-water spectra should contain
information of bottom substrate to some degree [Albert and Gege, 2006; Lee and Carder, 2002;
Lee et al., 2001; Mobley et al., 2005]. Variation in spectral shapes were observed among target
types, and even within the same substrate category. These above-water surface reflectance
spectra are a function of bottom reflectance and optical condition of water (including depth) as
discussed above (Section 2.2) [Mertes et al., 1993; Mobley, 1994]. Hence, it is normal to see
such spectral variation where the measurements were taken with variety of depth and also with
various bottom substrate reflectance (discussed below). These spectra are provided as a
deliverable (as XXXX.csv).
3.1.2 Underwater spectroscopy
Underwater measurement of reflectance spectra of bottom substrate directly gives us
reflection properties of each substrate type by minimizing the influence of the optical properties
of water, as it is measured at very close to the surface (ca. 30 cm, Fig. 4). The measurements give
the base information for assessment of the spectral separability of the substrate types, and for the
radiative transfer modeling. The present result showed that each substrate types shows somewhat
distinct spectral shapes (Fig. 13); however, considerable variation within same substrate
categories were observed. Note that, these measurements exhibited a measurement error due to
variation of ambient light with in water caused by wave motion at surface [Goodman, 2004] (The
data are summarized in Appendix 1).
17
Figure 12. Above-water reflectance spectra measured by an ASD spectrometer at: a. sand, b.
rocks, c. macrophyte, and d. clam beds. Sample site and depth are noted in the legends. Gray line
is the reflectance spectra at off shore site where no bottom substrate effect is observed, shown as
a reference (Provided by W.F. Vincent, Laval University, Canada)
Sand showed an increasing spectral value with wavelength with a shoulder around 600
nm. The absolute reflectance values were higher than those of rocks and macrophytes (Fig. 13a).
This is consistent with the results observed in other aquatic environments [e.g., Dekker et al.,
2011; Goodman and Ustin, 2007; Maritorena et al., 2002]. Within particular substrates
variations were high, suggesting that the color of sand can be different from site to site so that
classification of sand by applying single set of "standard end member" may not be feasible
[Dekker et al., 2011].
Rocks (boulder and bed rocks) exhibited lower reflectance with fairly stable spectral
shape among the 5 measurements (Fig. 13b). Spectral shapes were quite similar increasing from
ca. 3% at 400 nm to 5-15% at 580 nm. There were peaks at 580 nm and 630 nm. We sampled
both boulder (cobble) and bedrock sites, but there was no distinct difference in spectra among
grain size of rocks; thus, we have decided to deal with these substrate types as a single category
"rocks."
18
Macrophytes showed the most distinct spectral shapes from other substrate types (Fig.
13c). There were two different shapes observed: one as simple slope increasing from 400 nm to
650 nm as shown by sites 7, 8 and 11, and another as mound like shape peaking around 580 nm
as shown by sites 9, 12 and 13. The differences may be associated with species composition of
the plants in the patch or the physiological condition of plants [Dekker et al., 2011; Maritorena
et al., 2002]. However, it is difficult to evaluate such factors from the current data. The
depression of the curve at 680 nm, coinciding with the photosynthetic pigment absorption peak,
is significant for this substrate class except for the spectrum obtained at site 7.
Clam beds showed similar a spectral shape to that of sand, but with a steeper slope in the
range from 500-600 nm suggesting higher absorption in blue part of the spectrum (Fig. 13d). The
spectrum from the site 14 showed significant peaks at 580 and 630 nm (site 14), but that is not
seen at the site 10. Variation between the two observations was large. Due to the variation,
further field data acquisition would be recommended for better characterization of the spectral
features of this substrate class.
Figure 13. Reflectance spectra of bottom substrates measured by underwater spectrometer: a.
sand, b. rocks, c. macrophyte, and d. clam beds. Depth of sampled site is noted in the legends.
19
Obtained spectra for each substrate type were averaged (observation at site 9, 12, and 13
were used for macrophyte, and that at 10 was taken for clam beds) for further comparison of
spectral shape among substrate types, and subsequent radiative transfer modeling analyses (Fig.
14). These averaged spectra showed distinct spectral shapes as discussed above, indicating they
may be separable from reflectance spectra at substrate surface level where the influence of the
water can be ignored.
Figure 14. Averaged reflectance spectra of bottom substrates measured by underwater
spectrometer. Yellow line stands for sand, red boulder, green macrophyte, and pink clam beds.
3.1.3 Inherent optical properties of optical components in the near-shore area
The result of the field campaign conducted on Sep 26, 2012 to characterize inherent
optical properties of optically significant components in the near-shore area of Lake Tahoe
showed instrumental limitation of the measurements and more importantly severe limitation of
further quantification of such components by remotely sensed data. The absorption coefficient of
colored dissolved organic matter (aCDOM(λ)) exhibited noisy spectra for all six observations (Fig.
15, red lines). Such spectra of natural water bodies normally show a smooth exponential curve
which can be characterized by an exponential model [e.g., Bricaud et al., 1981] as:
20
aCDOM (λ ) = aCDOM (λ0 ) ⋅ e − SCDOM ( λ −λ0 )
eq. 5
The exponential slope parameter (SCDOM in equation 5) is often used to characterized the shape of
the slope and evaluate compositional variation of organic carbon pool [Stedmon and Markager,
2001; Zepp et al., 2008]. Obtained spectra, however, did not fit well to the model due to the high
noise. Thus, characterization of CDOM spectra was not feasible. The high noise is likely due to
the sensitivity of the spectrophotometer used, which was not appropriate for water with such low
concentrations of CDOM [Swift et al., 2006]. Application of a spectrophotometer with higher
sensitivity or newer technique applied in oceanic studies (e.g., UltraPath, World Precision
Instruments Inc., Sarasota, FL, USA, as in Bricaud et al. [2010]) where organic carbon
concentrations are similarly low as Lake Tahoe would be required for future research.
The absorption coefficient of non-algal particles (aNAP(λ)) also show noisy spectra (Fig.
15, brown lines); however, the noise level is smaller than that for aCDOM(λ) spectra. The
absorption coefficient at 440nm (aNAP(440)) ranged from 0.0052 - 0.0212 m-1 with a mean of
0.0094 m-1 and the coefficient of variation (CV) of 67%. The values are quite low relative to
other inland water bodies [Watanabe et al., 2011, and references therein]. The values were
similarly low at Sugar Pine Point, D.L. Bliss, Marla Bay, and TB3, 0.0052, 0.0052, 0.0067, and
0.0064 m-1 respectively, but two of Tahoe Keys sites showed higher values (0.0212 and 0.0166
m-1, for sites 3 and 4 respectively). Higher non-algal absorption in Tahoe Keys could be
associated with resuspension of particles from bottom sediments, and also incoming urban runoff
containing higher particle concentrations. It was quite interesting to see the values were similar
at other near-shore area and pelagic site TB3. It may be possible that the absorption by non-algal
suspended particles would not exhibit much variation throughout the lake except for the Tahoe
Keys sites.
The absorption coefficient of algal particles (aΦ(λ)) showed similar noise level as that of
the aNAP(λ) spectra; however, the shape of the spectra can reasonably be analyzed. The value at
440 nm (aΦ(440)) ranged narrowly from 0.0152 to 0.0199 m-1 with a mean of 0.0176 m-1 and CV
of 9%. This is at low end of previously reported for oceanic studies [Bricaud et al., 1995], and
exceptionally low for inland water bodies [Watanabe et al., 2011, and references therein]. The
shape of the spectra was also quite similar among sites indicating there was little to no variation
in optical quality of this component throughout the lake. There were no quantitative or
qualitative variability of algal pigment absorption found from this experiment.
These absorption analyses showed that the values of optically significant components
(CDOM, NAP, algal particles) are extremely low relative to the values found in other inland
water bodies [e.g., Effler et al., 2012; Gallegos et al., 2008; O'Donnell et al., 2010; Perkins et al.,
2009; Watanabe et al., 2011]. It is not surprising for Lake Tahoe where an ultra-oligotrophic
condition is maintained. In particular CDOM absorption, which was measured on filtered water,
was lower than the detection limit of the instrument used here. Spatial variation in the absorption
spectra of each component was minimal in the sampled part of the lake. These results, exhibiting
low concentrations and low variability of components, indicates that it would be difficult to
evaluate the concentration of each component from remotely sensed data. Detection of low
concentration material would be feasible but difficult by remote sensing, especially in the
situation where we need to separate signal of bottom substrate and constituents in the water. Also,
low variability in constituents would make it difficult to develop empirical models where
significant statistical variability is required. The field campaign we conduced collected small
numbers of samples due to limitation of instrument availability and personnel. Further optical
21
characterization of those components by applying more sensitive instruments with more spatial
and temporal variation may provide useful information for future remote sensing of those
components in the near-shore area.
Figure 15. Component specific absorption spectra of 1. Sugar Pine Point, 2. D.L. Bliss, 3. Tahoe
Keys 1, 4. Tahoe Keys 2, 5. Marla Bay, and 6. TB3. Red line indicates the absorption coefficient
of colored dissolved organic matter (aCDOM(λ)), brown of non-algal particles (aNAP(λ)), and green
of algal pigments (aΦ(λ)).
22
3.2 Radiative transfer modeling
The result of radiative transfer modeling clearly showed the change in the shape of
reflectance spectra with water depth (Fig. 16). All substrate types became darker (lower
reflectance) and the shape of the spectra changed as water became deeper. The rate of such
change largely depends on optical conditions of the water body [Goodman and Ustin, 2007;
Vahtmae et al., 2006]. In Lake Tahoe's case, sand was always brightest at any depth, and rocks
were darkest after clam beds and macrophyte. When water was shallow, all substrate types
showed distinct shapes and were much brighter than deep water sites (Fig. 16, blue lines),
suggesting the logical separation would be feasible from optical characteristics when water is
shallow. As water became deeper, the spectra of sand and clam beds become similar, and rocks
and macrophyte become similar as well (4-10 m). When the water depth reached 15 m,
macrophyte and rocks were not noticeably different from the deep water spectra. Sand and clam
beds were still slightly different from others, but they showed a highly similar spectral shape. At
20 m, all substrate types exhibited almost the same spectra as deep water, meaning that the signal
of the bottom substrate is not detectable.
Underwater substrate detection would be difficult due to influence of water above the
bottom surface as has been documented and discussed elsewhere [e.g., Goodman and Ustin,
2007; Maritorena et al., 2002; Mobley, 1994; Vahtmae et al., 2006]. The results from Lake
Tahoe's near-shore area showed similar difficulties even though the lake water is very clear for
an inland water body. This result indicated that discrimination between sand and macrophyte
would be feasible until the bottom depth reaches approximately 10 m. Detection of rocks from
sand would also be feasible in a similar depth range. Clam beds usually exhibit a patchy
distribution in sand, and hence the possibility of separation would be low due to the similarity in
spectral shape between sand and clam beds, and site to site variability of spectra within these
substrate types (Fig. 13). Separation of rocks from macrophyte would be feasible since they have
different spectral shape in the spectral range from 500 and 600 nm. However, such detection
would not be feasible with remotely sensed data with low sensitivity and spectral resolution
(which was the case in satellite data analyses as discussed below), and it would require high
sensitivity and high spectral resolution data (e.g., hyperspectral airborne sensor, PRISM by
NASA-JPL). At depths greater than 15 m, everything would be obscured by water.
23
Figure 16. Result of the radiative transfer modeling. Reflectance spectra at the water surface was
modeled for different water depths using Hydrolight with input of bottom reflectance, optical
properties of water, incident radiation. Yellow line represents sand, red boulder, green
macrophyte, and pink clam beds. The blue line is from the off shore reference (TB3) with no
bottom reflectance.
24
3.3 Satellite Remote Sensing
3.3.1 Cross comparison of reflectance spectra
The reflectance spectra at each sampling site for in situ spectroscopy (n=16) were
extracted from the corrected WorldView 2 images. The spectra were then compared with the
above-water spectroscopic measurements and the modeled above-water reflectance spectra based
on underwater measurements (Fig. 17). The result showed that there was reasonable agreement
among three observations at some sites (e.g., Site 1&4, Fig. 17), but discrepancies were found in
the other sites (e.g., Site 11&14). The difference between in situ measurements and the RTM
modeled values were reasonably small at a majority of the sites but larger discrepancies were
found at shallow sites with macrophyte (Site 9, 12, &13), and sand (Site 2) and clam beds (Site
14 & 15). Multiple reasons can be assumed for this discrepancy. First, in situ reflectance
measurements from the water surface would incorporate error associated with the reflection off
the water surface due to both direct sunlight and diffuse skylight, correction for which would be
difficult to be evaluated [Mobley, 1994]. Second, the underwater reflectance measurements of
substrates would also incorporate error (discussed above), which resulted in the error in RTM
modeled value. Third, optical properties in water, which was important for RTM modeling and
considered to be constant, would be variable especially in shallower sites where re-suspension of
particles from bottom causing higher turbidity would be high. The observed discrepancy would
be resulted from the mixture of these errors.
Although the satellite data showed reasonable agreement with in situ measurements at a
few sites (e.g., site 4, 5, & 10), overestimation was common in the others. The error was
especially noticeable in the coastal (400-500 nm) and blue (450-510 nm) bands. This error may
be derived from error in the atmospheric correction method which is not optimized for a water
surface [IOCCG, 2010]. The application and evaluation of atmospheric corrections adopted for
water surface data (e.g., TAFKAA, Montes & Gao, 2004) should be considered for future studies.
Furthermore, detailed spectral shape, which would be critical for some substrate classifications
(discussed above), were not well described due to spectral resolution of the imager. The result
showed that developing the robust method for bottom substrate classification applicable to the
whole lake on remotely sensed data seems to be difficult due to presented error came from
various sources as discussed above. Further analyses on spectral separability of WorldView2
data are given in the next section.
25
Figure 17. Comparison of reflectance spectra obtained by three different methods at all 16 sites
where in situ radiometry was conducted. Solid line represents in situ above-water spectrometer
measurements, dashed line estimated spectra by radiative transfer modeling (Hydrolight), and
solid line with dots remotely obtained spectra by World View2.
3.3.2 Spectra at GPS reference points
The reflectance spectra at all the GPS reference points were extracted from the
WorldView2 images to further examine feasibility of detection of bottom types. Bottom
substrate types were not evenly distributed in the lake; Northwest, Northeast, and Southwest only
have sand and rocks as mentioned earlier (see, Fig. 5). There should be patches of macrophyte
[Gamble et al., 2013] and clam beds (e.g., at the mouth of Emerald Bay) in those area, however,
they were not captured when GPS point samplings were conducted and could not be evaluated.
26
Image of Southeast of the lake includes all four substrate types. Each substrate showed its own
depth range; sand and rocks were distributed in a wide range of depth (1 to 8m), but macrophyte
only in 2 to 4m, and clam beds in 5 to 8m.
When reflectance of each band was plotted against depth of the sites (e.g. Band 4, Fig.
18), it showed a clear trend with depth [Miecznik and Grabowska, 2012]. In the Northwest,
Northeast, and Southwest of the lake, sand and rocks were found in a similar continuum with the
depth. This result indicates that discrimination of those substrates was not feasible from the
image, due to similarity of the reflectance signal captured by the satellite imager. The similarity
of measured reflectance would come from variation in reflectance among each substrate type
(Fig. 13) and also measurement error. In the Southeast of the lake, rocks and clams showed
similar reflectance values as those of sand, suggesting discrimination of those substrates was not
feasible from the image. macrophyte, however, showed distinct values from sand in their
distributing range, indicating that macrophyte detection in this image may be conducted with
reasonable chance. In conclusion, image analyses would be feasible only on the image of
Southeast of the lake, between macrophyte and sand in the depth range from 2 to 4 m. The image
analyses below was conducted based on this results
Figure 18. Remotely sensed reflectance value at 605 nm (Band 4 of WorldView2) against depth
for each separately captured satellite image: a. Northwest, b. Northeast, c. Southwest, and d.
Southeast (detailed map information is given in Fig. 5). Colors of dots represent type of bottom
substrate at each GPS reference point (see legend).
27
3.3.3 Image transformation by the minimum noise fraction (MNF) and subsequent application of
the mixture tuned matched function (MTMF)
The MTMF band values at GPS reference points of WorldView2 image transformed by
MNF and MTMF were extracted and plotted in Figure 19. The results showed clear continuum
where macrophyte showed lower values of Band 1 (less than -0.5) and higher values of Band 2
(more than 0.6). Although, some macrophyte points were in the range of sand, it is encouraging
for further mapping indicating the threshold value for mapping can be set by using either band
value. In the present research, macrophyte detection would be feasible at 75% success rate by
setting the band 1 of -0.5 as threshold. The mapping result is presented in the next section.
Figures 19. The result of image transformation by the minimum noise fraction (MNF) and
subsequent application of the mixture tuned matched function (MTMF) on the WorldView2
image of south west region in the depth range of 2-4 m. Extracted MTMF bands at GPS
reference points were plotted. Green points indicated Macrophyte and yellow indicated sand. The
vertical dashed line was drawn at Band 1 = -0.5, where 75% of macrophyte were included.
28
3.3.4 Macrophyte mapping in Southeast of the lake
Figure 20 shows the result of macrophyte mapping in Southeast of Lake Tahoe on the
MNF and MTMF transformed WorldView2 image. The classification was reasonably completed,
as the success rate was set to be 75 % based on MTMF result analysis (Fig. 19). The result in
front of Tahoe keys showed an example of successful mapping (Fig. 20, Sub-image 1). The
major misclassification occurred on dark colored sand, and the deeper part of the lake where
reflectance was lower (Fig. 20, Sub-image 2). Moored boats were also misclassified as
macrophyte (Fig. 20, Sub-image 3). Dark sand misclassification would particularly be critical,
since the area is relatively wide. Thus, future analyses are needed to understand occurrence of
color difference in sand, and characterization of spectral feature of such sand for improved
mapping results.
The results suggested that remote sensing of macrophyte distribution in Southwest of
Lake Tahoe would be feasible with reasonable accuracy by using high spatial resolution multi
waveband satellite images (WorldView-2). This result suggested that tracking of historical
macrophyte distribution could be conducted by using the data archive of such type of satellite
sensor (e.g., LandSat data archive, USGS-NASA). Spatial resolution of LandSat sensors (30 m)
could be an issue for detailed mapping of small patches of macrophyte beds. Also, sensor
sensitivity for water surface reflectance would be hard for images for older sensors [Kutser et al.,
2005], and those need to be evaluated in future research. Also, it would be recommended to
apply high spectral resolution, high sensitive airborne sensor adopted to water analyses (e.g.,
PRISM, NASA-JPL) would be appropriate instead of using satellite data. The result of this
classification is provided as shape file (xxx.shp).
29
Figure 20. Result of macrophyte mapping at Southeast shore of Lake Tahoe. The original image used for the MNF-MTMF
transformation is place at top left. The boxed area in the original image are magnified as sub-image 1-3. Pixels classified as
macrophyte were colored in red in the sub-images. Stars in sub-images indicate GPS reference points: Green - macrophyte, Yellow sand.
30
4. Conclusions and Recommendations
The results of in situ spectroscopy and radiative transfer modeling altogether indicated
that detection of bottom substrate type from remotely sensed imagery would be feasible at
shallower sites in Lake Tahoe's near-shore area. Even though there was variation in reflectance
spectra within each substrate types, each substrate types exhibited somewhat distinct shapes, and
distinction was conserved until depth reaches ca.10 m. In the high spatial resolution multiwavelength satellite image, however, separation of macrophyte from sand in Southeast of the
lake was only feasible mapping with reasonable accuracy. A major source of error would be low
reflectivity of water surface which make it difficult to obtain reflectance spectra with low error
by using remote sensing imagers, the majority of which have been developed for observing
terrestrial environments where reflectivity of surfaces are much higher. Another possible source
of error was the spectral resolution of the sensor. As shown in Figure 16, substrate detection
would rely on spectral differences in a certain waveband, requiring data of a hyperspecral nature
rather than the multi-wavelength data available through WorldView-2 that was available for the
present study. Therefore, future application of data from a hyperspectral imager with a highly
sensitive sensor specifically developed for aquatic environment measurements (e.g., PRISM,
NASA-JPL) would be far better suited for future observations aiming at accurate mapping in the
near-shore area. In addition to such instruments, development of a new advanced technique such
as an autonomous submersible imager, which can run over the substrate with consistent distance
from the surface (e.g., 1 m), would be useful for substrate mapping by minimizing influence of
water on reflectance spectra. Such system would especially be useful for clam bed detection in
sandy bottom.
Estimation of the concentrations of optically significant components in water of the lake
proved to be difficult due to low concentration and low variability. Colored dissolved organic
matter (CDOM), non-algal suspended solid, and algal particle all showed extremely low level of
absorption relative to other natural water bodies. CDOM was almost at detection limit of the
instrument and could not be quantified reliably. Also, spatial variability was quite low among our
samples, in that statistical model development was not feasible. For future analyses, better
analytical equipment (a more sensitive spectrophotometer and an integrating sphere) to
characterize the inherent optical properties is needed to extract fundamental information of the
optical characteristics of those components in lake water. Also, spatial and temporal variability
of such IOPs are needed to be studied with larger number of samples.
31
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Appendix
Appendix I. Substrate reflectance measured with submersible spectrometer by divers at 16 sites.
Solid line shows averaged value over 10 measurements and broken lines show the range of
plus/minus 1 standard deviation.
37