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Applied and Environmental Microbiology, November 2001, p. 5267-5272, Vol. 67, No. 11
0099-2240/01/$04.00+0 DOI: 10.1128/AEM.67.11.5267-5272.2001
Copyright © 2001, American Society for Microbiology. All rights reserved.
Detection and Quantification of Snow Algae with
an Airborne Imaging Spectrometer
Thomas H.
Painter,1,*
Brian
Duval,2
William H.
Thomas,3
Maria
Mendez,3
Sara
Heintzelman,3 and
Jeff
Dozier4
Institute for Computational Earth System Science,
University of California, Santa Barbara,
California1; Massachusetts Department of
Environmental Protection, Worcester,
Massachusetts2; Scripps Institution of
Oceanography, La Jolla, California3; and
Donald Bren School of Environmental Science and Management,
University of California, Santa Barbara,
California4
Received 22 March 2001/Accepted 20 August 2001
 |
ABSTRACT |
We describe spectral reflectance measurements of snow containing
the snow alga Chlamydomonas nivalis and a model to retrieve snow algal concentrations from airborne imaging spectrometer data. Because cells of C. nivalis absorb at specific wavelengths
in regions indicative of carotenoids (astaxanthin esters, lutein, 
-carotene) and chlorophylls a and b,
the spectral signature of snow containing C. nivalis is
distinct from that of snow without algae. The spectral reflectance of
snow containing C. nivalis is separable from that of snow
without algae due to carotenoid absorption in the wavelength range from
0.4 to 0.58 µm and chlorophyll a and b
absorption in the wavelength range from 0.6 to 0.7 µm. The integral
of the scaled chlorophyll a and b absorption
feature (I0.68) varies with algal concentration
(Ca). Using the relationship Ca = 81019.2 I0.68 + 845.2, we inverted Airborne Visible Infrared Imaging Spectrometer
reflectance data collected in the Tioga Pass region of the Sierra
Nevada in California to determine algal concentration. For the
5.5-km2 region imaged, the mean algal concentration was
1,306 cells ml
1, the standard deviation was 1,740 cells
ml1, and the coefficient of variation was 1.33. The
retrieved spatial distribution was consistent with observations made in
the field. From the spatial estimates of algal concentration, we
calculated a total imaged algal biomass of 16.55 kg for the
0.495-km2 snow-covered area, which gave an areal biomass
concentration of 0.033 g/m2.
| |
INTRODUCTION |
Seasonal snowfields in semiarid
regions, such as the western United States, provide a habitat for
microbial life (9), as well as the primary regional
freshwater supply. Snow can host an abundant microbial community
supported by phytoplankton collectively termed snow algae
(7, 8). Microbial processes, such as heterotrophy (1), photosynthesis (10), and nutrient
cycling (9), occur in melting snow and are important
factors in estimating carbon budgets and CO2 flux
(16). However, few carbon flow models consider these
activities in the snow meltwater column. Additionally, variations in
snow algal biomass and species composition may reflect regional environmental or climate changes (21).
While there have been numerous reports that have quantified snow algae
at the plot scale in terms of cells per milliliter (11, 19,
21), there have been no direct estimates of algal biomass at the
snowfield or watershed scale. This is primarily because standing crops
of snow algae are not uniformly distributed, making biomass estimates
problematic. Remote sensing through imaging spectroscopy offers the
capacity to analyze spectral reflectance features that are related to
snow algal concentration at a spatial resolution commensurate with the
spatial variability of surface cover in alpine basins.
Imaging spectrometers, such as the National Air and Space
Administration/Jet Propulsion Laboratory Airborne Visible Infrared Imaging Spectrometer (AVIRIS), have improved the ability of remote sensing to quantify surface cover properties (5). The use
of airborne imaging spectroscopy to estimate phytoplankton abundance has provided valuable information concerning algal population dynamics
and primary production in freshwater and marine water (13).
Chlamydomonas nivalis is the most prevalent alga found in
snowfields in the Sierra Nevada of California (18). During
a bloom, nonmotile algal resting spores (aplanospores) of C. nivalis impart a deep red color to snow. Germination that
results in motile (flagellated) cells of C. nivalis occurs
only in saturated snow since the algae require liquid water to move
around snow grains and position themselves vertically according to
irradiance levels and spectral composition (6). C. nivalis spores are spherical and have radii that range from 20 to
50 µm. During a bloom, most cells lie near the snow-air interface,
but cells can be found down to a depth of 10 cm (18). In
the Sierra Nevada of California, snow algae are found in old, wet
snowfields at elevations over 3,000 m (18). Thomas and
Duval (19) demonstrated that there is a significant
negative correlation between snow albedo and algal cell concentration
but also found that decreases in albedo due to algal snow did
not contribute to a significant decrease in the mean albedo of snowfields.
This work addresses multiple unexplored issues: (i) to document and
analyze the reflectance spectrum of algal snow and its relationship to
algal concentration, (ii) to develop a model that relates algal
concentration to the reflectance spectrum of algal snow, and (iii) to
demonstrate the ability of remote sensing with an imaging spectrometer
to detect and quantify the spatial distribution of algal concentrations
in alpine snow. We addressed these problems with data collected during
a field campaign in the Sierra Nevada of California in the summer of
2000. These data include direct measurements of snow algal
concentration (in cells per milliter), snow spectral reflectance, and
high-spatial-resolution data from the AVIRIS.
| |
MATERIALS AND METHODS |
Site.
The field site used in this work lies just outside the
eastern boundary of Yosemite National Park in California near Tioga Pass (37°55'N, 119°16'W). Snowfields on the east flank of Mt. Conness have an annual algal bloom that some of us have studied for
many years. The elevation in the Mt. Conness basin ranges from 3,050 to
3,800 m.
The site lies entirely above timberline, and the surface cover consists
of granite slabs, tundra-covered soils, and willows. The interannual
mean snow water equivalent on April 1 at the nearby California
Cooperative Snow Survey Saddlebag Lake site is 1.01 m. While the
Sierra Nevada is considered to have a predominantly maritime snow
regime, some regions on the eastern side exhibit intermountain
characteristics. Snow algae in this region usually begin a red bloom in
late May to early June (18, 19). In the summer of 2000, when the data presented here were collected, the algal bloom began
before June 20.
Snow spectral reflectance.
We measured the spectral
reflectance of snow in the field with an Analytical Spectral Devices
(Boulder, Colo.) FR field spectroradiometer. The FR
spectroradiometer records digital numbers for 2,151 spectral bands
across the wavelength range from 0.35 to 2.5 µm in a dynamic range
that is automatically optimized for current light conditions.
For each snow target, we collected 10 spectra of a near-100%
reflectance Spectralon white panel (Labsphere, New Sutton, N.H.)
laid
parallel to the surface and immediately after collected 10
spectra of
the snow surface. Both sets of measurements were made
with a view angle
that is normal to the surface in order to maintain
consistency in
bidirectional reflectance across all samples. Spectral
reflectance
(
R
) is calculated as:
|
(1)
|
where DN
snow, is the digital number obtained from
the snow target at wavelength
, DN
white, is the
spectral digital
number obtained from the white reflectance standard,
and WRC
is the calibration coefficient for the white
reflectance standard.
The mean of each target's 10
R spectra was the average spectrum
for the
target. Measured as described above and assuming that
the white
reflectance standard is a Lambertian target,
R is the equivalent of the nadir
bidirectional reflectance factor
for the given solar geometry
(
15).
Algal concentration.
We determined algal concentrations
(Ca) by collecting snow samples in the field and
processing them in the laboratory. Snow samples were collected in
Whirl-Pak bags with a 100-ml capacity. The samples were returned in an
ice chest at the end of the day to the Sierra Nevada Aquatic Research
Laboratory at Mammoth Lakes, Calif. Algal cells (always red spores of
C. nivalis) were counted microscopically at the laboratory.
We collected 26 snow samples for which we measured spectral
reflectance; 13 of these samples were samples of algal snow (which were
observed to be reddish), and 13 were samples of alga-free snow (which
had no red coloration). The errors inherent in this method for
determining algal concentrations may be on the order of 10 to 20%, and
for low concentrations the errors may be more than 20%
(17).
Imaging spectrometer data.
We used image data from the
NASA/JPL AVIRIS collected over the study site on 19 July 2000. The
AVIRIS measures reflected radiance in 224 bands across the wavelength
range from 0.4 to 2.5 µm at 0.01-µm spectral resolution and a
1-mrad field of view. The usual platform for AVIRIS is the National
Aeronautics and Space Administration ER-2 that flies at 20 km,
producing a nominal spatial resolution of 20 m. For our
acquisition, the AVIRIS was mounted on a Twin Otter airplane flying at
4.4 km. At the mean surface elevation of 3.2 km, the spatial resolution
was ~1.2 m.
The AVIRIS data were delivered as calibrated radiance data with units
of microwatts per square centimeter per nanometer per
steradian. AVIRIS
in year 2000 had a signal-to-noise ratio of
900 to 1,100 and a noise
equivalent change in radiance (NE
L)
of 0.005 to 0.010 µW/cm
2/nm/sr in the wavelength range from 0.6 to 0.75 µm. For this noise
equivalent change in radiance, the noise
equivalent change in
reflectance (NE
R) is ~0.1%. We
retrieved apparent surface reflectance
data from the calibrated
radiance data with a nonlinear least-squares
model that fits water
vapor absorption in the AVIRIS data (
4).
Apparent surface
reflectance (AS
R) is defined as:
|
(2)
|
where
LAVI, is the AVIRIS-measured
radiance at wavelength
and
E is the
irradiance on a level surface at the mean
surface elevation. Green et
al. (
4) retrieved AS
R from
AVIRIS data with the following equation:
![<UP>AS</UP>R<SUB><UP>&lgr;</UP></SUB><UP> = 1/</UP>{[(F<SUB><UP>0</UP></SUB><IT>T<SUB>d</SUB>T<SUB>u</SUB></IT><UP>/&pgr;</UP>)<UP>/</UP>(L<SUB><UP>AVI,&lgr;</UP></SUB><UP> − </UP><IT>F</IT><SUB><UP>0</UP></SUB><IT>r<SUB>a</SUB></IT><UP>/&pgr;</UP>)]<UP> + </UP><IT>S</IT>}](/content/vol67/issue11/fulltext/5267/img003.gif)
|
(3)
|
where
F0 is the exoatmospheric solar
irradiance,
Td is the downward direct and
diffuse transmittance of the atmosphere,
Tu is
the upward total atmospheric transmittance to the AVIRIS,
LAVI, is the total upwelling spectral
radiance at the AVIRIS,
ra is
the atmospheric
reflectance, and
S is the albedo of the atmosphere
above the
surface. This model is run as a series of FORTRAN 77
programs.
| |
RESULTS |
Snow spectral reflectance.
Figure
1 shows the spectral reflectance data for
alga-free snow and algal snow. The alga-free reflectance spectrum
exhibits high, convex reflectance in the visible wavelengths (0.4 µm 
0.7 µm), moderate reflectance in the
wavelength range 0.7 µm 1.4 µm, and very low
reflectance in the wavelength range 1.4 µm 2.5 µm (20). The algal snow reflectance spectrum has
moderate, concave reflectance in the visible wavelengths due to
absorption by carotenoids (0.4 µm 0.64 µm) and
a local reflectance minimum at a wavelength of about 0.68 µm due to
chlorophyll a-chlorophyll b absorption. Both
reflectance spectra have local reflectance minima that correspond to
ice absorption features (Fig. 1). The carotenoid and chlorophyll
absorption features provide leverage to detect algal snow. However, at
lower algal concentrations (less than about 5,000 cells
ml1) the carotenoid feature can resemble the effects of
dirt on the reflectance spectrum of snow and thus confound a model for
detecting snow algae. Because the chlorophyll absorption feature at a
wavelength of about 0.68 µm is uniquely biological, we analyzed this
feature with respect to algal concentration.
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FIG. 1.
Spectral reflectance measurements for (nearly) alga-free
snow (white) and algal snow (red), measured near Mt. Conness in
California with an Analytical Spectral Devices FR field
spectroradiometer. The alga-free snow had an algal concentration of 450 cells ml1, and the algal snow had an algal concentration
of 21,000 cells ml1. Carotenoid absorption and
chlorophyll absorption are indicated by a at wavelengths of
approximately 0.55 and 0.68 µm, respectively. Ice absorption is
indicated by i at wavelengths of approximately 0.81, 0.9, 1.03, 1.26, 1.5, and 2.0 µm.
|
|
Figure
2 shows the reflectance spectra of
snow samples with different algal concentrations, normalized
by their maximum reflectance
values. The 0.68-µm absorption feature
properly consists of chlorophyll
a absorption near a
wavelength of about 0.68 µm and chlorophyll
b absorption
near a wavelength of about 0.65 µm, dominated by
the former. The
depth and breadth of the 0.68-µm absorption feature
increased as the
algal concentration increased. We analyzed this
absorption feature with
a technique introduced by Clark and Roush
(
2) for mineral
applications and used by Nolin and Dozier (
12)
to retrieve
snow grain size. The method relates the integral of
the absorption
feature, scaled by its continuum spectrum, to the
physical parameter,
in this case the algal concentration. The
continuum spectrum is given
by the interpolated linear spectrum
between the peaks at the ends of
the absorption feature. The scaled
integral is:
|
(4)
|
where
Rcont, is the reflectance of the
continuum spectrum at wavelength
and
Rsnow, is the reflectance of the
snow
spectrum at wavelength
. Scaling by the inverse of the continuum
reflectance accounts for changes in irradiance and thereby gives
an
accurate measure of the relative absorption. The shoulders
of this
asymmetric absorption band lie near 0.63 and 0.70 µm.
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FIG. 2.
Spectral reflectance of algal snow, normalized by the
maxima of the respective spectra, for different algal concentrations.
The depth and breadth of the absorption feature at 0.68 µm increase
as Ca increases.
|
|
Figure
3 shows a plot of
Ca versus
I0.68 for 23 of
the field spectral reflectance measurements and snow samples. The other
three measurements have
I0.68 values that lie
well above the range
of
I0.68 values retrieved
from AVIRIS data, and these measurements
indicate that there is a
nonlinear relationship between
Ca and
I0.68. We did not use these data in developing
the model because
26 points are not sufficient to develop a
statistically significant
nonlinear model, and the points lying in the
range of
I0.68 values
retrieved from the AVIRIS
data (0
I0.68 0.25) may be fit
with
a linear model. This model is given by:
|
(5)
|
with
R2 = 0.93. The residual standard
error for this regression was 2,124 cells ml
1, and the
residuals were approximately normally distributed. The
mean of all root
mean squared (RMS) of all field spectra about
their respective
means was 1.4% for all wavelengths.
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FIG. 3.
Lab-sampled algal cell concentration (in cells per
milliliter) plotted against the integral of the continuum-scaled 0.68 µm chlorophyll absorption feature. Equation 5 is included on the
figure.
|
|
The limits of detection and quantification of algal concentrations
with AVIRIS data rely on the NE
R of AVIRIS, the atmospheric
correction, spectral calibration, and the residual standard error
of
the algal model. As described above, the NE
R of AVIRIS in
the
wavelength range 0.6 µm
0.8 µm is on the order
of 0.1%.
A greater source of error is the atmospheric correction and
spectral
calibration. Field spectra of calibration targets showed that
the mismatch between the atmospheric conditions and the atmospheric
model parameters contributed absolute errors in reflectance of
1 to
5%. In order to remove spectral noise introduced by the atmospheric
correction, we scaled all AVIRIS spectra by the ratio of a calibration
field spectrum to its associated AVIRIS spectrum (
3).
Field
spectra collected with the Analytical Spectral Devices FR are
less noisy than atmospherically corrected AVIRIS spectra, and
thus,
scaling by the ratio of the two improves the precision of
the resultant
spectra. The RMS difference between the field spectrum
and the AVIRIS
apparent surface reflectance spectrum was 1.3%,
and the mean spectral
difference was
0.2%. Therefore, because
the two spectra were
calculated for the same geometry, we consider
the spectra to be
approximately equal in reflectance accuracy.
The bidirectional
reflectance from Spectralon panels can deviate
several percent from
Lambertian (
14). Because this deviation
has little
spectral structure (R. O. Green, unpublished data),
field spectra
may be underestimated by several percent across
much of the spectrum.
Given the agreement between the AVIRIS apparent
surface reflectance
spectrum and the field spectrum, we assumed
that a reasonable estimate
for the maximum AVIRIS reflectance
inaccuracy, calculated as described
above, is at most a systematic
2%.
Algal concentration and biomass.
Figure
4 shows an AVIRIS radiance image
from the Mt. Conness region. Each AVIRIS band was converted to apparent
surface reflectance by using the method described above. AVIRIS surface
reflectance spectra are shown in Fig. 5.
The results of applying equation 5 to the AVIRIS apparent surface
reflectance data are shown in Fig. 6.
This image is a one-ninth subset of the total flight line, which
consisted of 815 samples and 4,608 lines. The vast majority of
snowfields in this image were on north-facing slopes. The data showed
that there were contiguous patches of algal snow
(Ca, >2,500 cells ml1), and the
highest concentrations occurred near the feet of snowfields. This is
consistent with observations made in the field in the summer of 2000 and previous observations (18, 19). The region of algal
snow in the lower left portion of Fig. 6 had inferred concentrations of 5,000 < Ca < 8,000 cells
ml1. Many small patches of snow are dominated by algal
concentrations greater than 4,000 cells ml1 (center right
and lower right in Fig. 6). The mean Ca for the imaged region was 1,305.7 cells ml1, the standard
deviation was 1,739.9 cells ml1, and the range was 0 to
34,848 cells ml1. The coefficient of variation is
1,739.9/1,305.7 or 1.33. The maximum value retrieved is beyond the
range of apparent validity of equation 5 and is likely an underestimate
given what may be an exponential relationship between
Ca and I0.68 for higher
I0.68 values. Because there were only 17 of
3,755,520 pixels (0.00045%) for which I0.68 was
beyond the range of equation 5, we consider this underestimate to be
insignificant. The total snow-covered area in the image is 0.495 km2, about 9% of the total 5.5-km2 imaged
area. While at a scale of less than 0.5 m2 the algal
concentration in snow may be more than 50,000 cells ml1,
the patchy spatial distribution of snow algae (19) results in a significantly lower concentration when values are
integrated over a 1.44-m2 area. This explains the
concentrations retrieved with AVIRIS data that were lower than the
concentrations measured in the field. No explicit ground truth
analysis was performed to validate the results because our
primary effort was devoted to collecting field spectra and snow samples
for model development. In the future, we will perform an explicit
ground truth analysis to validate the algal concentration results.
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FIG. 4.
AVIRIS radiance image with a band center wavelength of
0.635 µm near Mt. Conness in California. The image was acquired on 19 July 2000. The AVIRIS pixel size is 1.2 m. The masked areas along
the left and right edges accommodate the spatial span of the data
necessary for georectification. Georectification was performed to
correct for changes in the airplane attitude. Snowfields are the
apparent white patches that are spectrally distinct from the other
surface cover, which is predominantly exposed granite slab.
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|
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FIG. 5.
AVIRIS spectra for alga-free snow and algal snow
extracted from the image shown in Fig. 4. The algal concentrations
retrieved with equation 5 for these spectra were 273 and 7,321 cells
ml1, respectively.
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FIG. 6.
Snow algal concentration (in cells per milliliter)
retrieved from AVIRIS apparent surface reflectance data with the model
described with equation 5. Snow-free areas were masked from analysis
and are black. The maximum concentration on the scale bar is lower than
the maximum for the data in order to enhance the visibility of the
spatial distribution. The concentrations in the region where algal
concentrations are higher in the lower left portion of the figure lie
in the range 5,000 < Ca < 8,000 cells
ml1.
|
|
We analyzed the limits of detection for snow algal concentrations. The
limits of detection rely on the accuracy of the AVIRIS
spectral
reflectance, which is assumed to have a maximum value
of 2%. We added
a systematic error of 2% relative reflectance
to AVIRIS spectra and
determined the effect on the retrieved algal
concentrations. The mean
difference in algal concentrations was
68 cells ml
1, and
the RMS difference was 162 cells ml
1. On the basis of the
configuration of equation
5, the mean and
RMS difference for a
systematic reflectance error of 2% should
be zero. However,
digitization errors result in concentration
errors. Hence, since the
residual mean error for the model described
by equation
5 was 2,124 cells ml
1 and the quantization error for a
systematic error of 2% was 162
cells ml
1, the detection
limit for algal concentrations with the AVIRIS
is ~2,300 cells
ml
1. The greatest contribution to the error in estimates
comes from
the regression resulting in equation
5 and in turn from the
estimates
of algal concentrations. Errors in
Ca
of 10 to 20% and larger
errors for low
Ca are
on the order of the residual standard error,
2,100 cells
ml
1. The error in
Ca due to the
NE
R of the AVIRIS, 0.1%, was determined
to be 150 cells
ml
1. Hence, with improved estimates of algal
concentrations from
laboratory measurements and, in turn, a more robust
relationship
between
Ca and
I0.68 with a lower residual standard error, the
AVIRIS would theoretically have the capacity to map algal concentration
with 1-order-of-magnitude-greater
resolution.
By assuming that all of the algal biomass was in the top 10 cm of snow
and that our estimates of algal concentrations represented
the mean for
the top 10 cm (
18), we estimated the total imaged
biomass
(
Ba) as follows:
|
(6)
|
where
A is the mean AVIRIS pixel area,
d is
the depth of the snow in which the algae lie,
s is the snow density
(in kilograms per cubic
meter),
Ns is the number of snow-covered
pixels
in the AVIRIS image,
ma is the mass of an algal
cell (in
kilograms), and
w is the density of
liquid
water.
For the AVIRIS flight line which we used,
Ca was
1,305.71 cells ml
1,
A was (1.2)
2
m
2,
d was 0.1 m,
s was 456 kg m
3 (measured in
the field),
Ns was 344,838 pixels,
ma was 0.00056
µg (
18), and
w was 1,000 kg m
3, which gave a
Ba of 16.55 kg. Therefore, the spatial
concentration
was 0.0334 g/m
2
(
Ba/0.495 km
2).
| |
DISCUSSION |
We demonstrated the capacity of imaging spectroscopy to map the
spatial distribution of snow algal concentration. The spectral reflectance signature of algal snow exhibits absorption by carotenoids for wavelengths where 0.6 µm and absorption by chlorophyll a and chlorophyll b for the wavelength range 0.63 µm 0.70 µm. From field spectral reflectance
measurements, we developed a linear model relating algal concentration
to the scaled integral of the chlorophyll absorption feature. The
spatial distribution of snow algal concentrations was mapped by
applying this linear model to reflectance data acquired for the east
drainages of Mt. Conness near Tioga Pass in California with the AVIRIS.
The mean inferred algal concentration was 1,305.7 cells
ml1, the standard deviation was 1,739.93 cells
ml1, the minimum concentration was 0 cells
ml1, and the maximum concentration was 34,848 cells
ml1. Assuming that the algal biomass was in the top 10 cm
of the snowpack, we estimated that the total imaged algal biomass was 16.55 kg. The total snow-covered area was 0.495 km2,
so the areal biomass concentration was 0.033 g/m2.
Combining this areal biomass concentration with maps of snow-covered area from other remote sensing instruments covering larger regions could facilitate broad-scale estimates of total algal biomass in the
snow cover.
Acquiring snow algal biomass data by airborne imaging spectroscopy
could have several applications for exploring the effects of UV light
on biological systems. For instance, these data may be used to show
that (i) a loss or altitudinal shift of UV-sensitive alpine snow algae
may indicate changing environmental conditions (5); (ii)
due to their capacity to increase pigmentation and antioxidant
production in response to UV light (4), snow algae may
serve as indicators of UV stress that are more sensitive than other
polar-alpine communities (13); (iii) changes in snow algal biochemistry, population density, and distribution can be compared with
UV light measurements or regional or global warming trends; and (iv)
snow algal abundance may correlate with on-the-ground UV measurements
in alpine and polar regions most affected by column ozone loss
(22). Once a database of snow algal concentrations and
pigment levels is established, it may be possible to detect UV-enhanced
changes by using hyperspectral remote sensing data.
We acquired more AVIRIS data for the same sites in the summer of 2001. Using these data, we may begin to analyze the interannual variability
of the spatial distribution of snow algae. We will address the
relationships of algal concentration to topographic variables and snow
physical properties. In the field, we will measure the spatial
variability of snow alga concentration at the subpixel scale and
analyze the relationship between algal concentration and dirt concentration.
Yoshimura et al. (22) used algal layers for dating ice
cores in Himalayan glaciers. In their work they assumed there was uniform spatial distribution each year. The model described in this
paper should allow us to analyze AVIRIS imagery from the years 2000 and
2001 in order to characterize the spatiotemporal dynamics of snow algal
concentrations and, in turn, assess the validity of the assumption made
by Yoshimura et al. (22).
In regions like California, where melting snow provides most of the
drinking and agricultural water, consideration of the snowpack
microbial biota is important in monitoring runoff and water quality
within a watershed. Because snow algae and bacteria are closely
associated physically and metabolically (19) and because
red snow can be diuretic if it is ingested (7), water quality can be affected by snow biota. Hence, the model presented here
may contribute to monitoring of water resources that rely on alpine snowpacks.
| |
ACKNOWLEDGMENTS |
Funds for this research came from NASA grant NAG5-4814 (EOS IDS
"Hydrology, Hydrochemistry, and Remote Sensing in Seasonally Snow
Covered Alpine Drainage Basins") and a Scripps Vetlesen grant ("Variations in Sierra Nevada Snow Algae Abundances and Nutrient Chemistry in Relation to Global Change").
We thank Dan Dawson of the Sierra Nevada Aquatic Research Laboratory in
Mammoth Lakes, Calif., for assistance. We thank Maura Longden, District
Ranger for Tuolumne Meadows in Yosemite National Park, Calif., for
being a generous hostess. Finally, we thank two anonymous reviewers for
their comments and suggestions. We thank B. Greg Mitchell of Scripps
for suggesting initially that it might be possible to detect snow algae
with AVIRIS.
| |
FOOTNOTES |
*
Corresponding author. Mailing address: ICESS, 6th Floor
Ellison Hall, University of California, Santa Barbara, CA 93106. Phone: (805) 893-8116. Fax: (425) 740-9260. E-mail:
painter{at}icess.ucsb.edu.
| |
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Clark, R. N., and T. L. Roush.
1984.
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Applied and Environmental Microbiology, November 2001, p. 5267-5272, Vol. 67, No. 11
0099-2240/01/$04.00+0 DOI: 10.1128/AEM.67.11.5267-5272.2001
Copyright © 2001, American Society for Microbiology. All rights reserved.
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Abstract
Introduction
