Proposal: Tahoe Research Supported by SNPLMA 2010
I. Title Page (1 page maximum)
Title:
Remote Sensing of Lake Tahoe’s Near Shore Environment
Subtheme this proposal is
responding to 4b
Sub theme 4b: Identifying environmental indicators and developing
approaches for monitoring and evaluation.
Principal Investigator and
Receiving Institution
Erin Lee Hestir
University of California Davis
One Shields Ave., Davis, CA 95616
Phone: (530) 752-5092
Fax: (530) 752-1552
Email: elhestir@ucdavis.edu
Geoffrey Schladow
University of California Davis
One Shields Ave., Davis, CA 95616
Phone: (530) 752-3942
Fax: (530) 754-9141
Email: gschladow@ucdavis.edu
Shane Romsos
Tahoe Regional Planning Agency
PO Box 5310, Stateline, NV 89449
Phone: (775) 588-4547
Fax: (775) 588-4527
Email: sromsos@trpa.org
Daniel Sussman
Lahontan Water Quality Board
2501 Lake Tahoe Blvd., South Lake Tahoe, CA 96150
Phone: (530) 542-5466
Email: dsussman@waterboards.ca.gov
Wendy Ernst
University of California Davis
One Shields Ave., Davis, CA 95616
1580 Research Park Dr. Ste 300, Davis, CA 95618
Phone: (530) 754-7982
Fax: (530) 754-8229
Email: wibernst@ucdavis.edu
$ 198,970.00
$0
Co-Principal Investigator
Agency Collaborator
Grants Contact Person
Funding requested:
Total cost share (value of
financial and in-kind
contributions):
1
Proposal: Tahoe Research Supported by SNPLMA 2010
II. Proposal Narrative
ABSTRACT
The goal of this research is to use remotely sensed data to retrieve fine sediment, chlorophyll, and colored
dissolved organic matter (CDOM) concentrations from the water column in the near shore, and to map the
distribution of periphyton (attached algae), aquatic macrophytes (submerged plants), clam beds in the
near shore of Lake Tahoe and variations in sediment type. High spatial resolution multispectral satellite
imagery, moderate spatial resolution multispectral satellite imagery, and airborne hyperspectral imagery
will be used. We will investigate both empirical and model-driven methods to map fine sediment,
chlorophyll, and CDOM concentration, macrophyte communities, clam bed, periphyton, and substrate
type. The empirical approach will first classify the optically shallow near shore into the different bottom
classes using the field data and spectral library first to train and then (independently) validate the
classifier. This analysis allows the development of statistical correlations (e.g. regression modeling)
whereby reflectance information can be used to predict the probability of the concentration of water
quality constituents above a particular bottom type. Upon successful development, the statistical model
can then be used to predict water quality in each image pixel given the reflectance value of that pixel. The
second approach will use a radiative transfer model that simulates remote sensing reflectance of water
given inputs of different aquatic optical properties. One of the key deliverables of the project is a cost
benefit analysis of remote sensing approaches for monitoring the near shore environment and a manual
for implementing remote sensing analysis for monitoring the near shore environment.
JUSTIFICATION STATEMENT
Accurately measuring and monitoring the status and trends of environmental indicators in the Lake Tahoe
Basin is a critical management need. However, appropriate evaluation of these indicators is often limited
temporally and spatially. Remote sensing technologies are widely recognized globally as a useful tool in
addressing this need; they provide synoptic (wall-to-wall) measurements of reflected electromagnetic
radiation, providing scientists with the information needed for a range of environmental analyses. By
providing synoptic measurements, remote sensing analyses can characterize the spatial variability of an
environmental indicator, supplying the information needed to establish in situ monitoring programs.
When coupled together, in situ and remote sensing data offer a powerful means of identifying, measuring,
and monitoring the status and trends of environmental indicators.
The value of remote sensing technologies for evaluation and monitoring in the Lake Tahoe Basin has also
been widely recognized, and an increasing number of remote sensing datasets are being acquired over the
basin. NASA has been operating a remote sensing validation site at Lake Tahoe for over a decade;
Schladow et al. (2010) are currently finalizing a compilation of satellite remote sensing data repository,
funded under an earlier SNPLMA round; and recently the Tahoe Regional Planning Agency (TRPA) and
US Geological Survey have purchased high spatial resolution satellite imagery of the Tahoe Basin.
Remote sensing can be successfully used for studying the near shore environment of Lake Tahoe and
holds great potential as an environmental indicator of both the current status and of past and future trends.
Recent investigations by Schladow et al. (2010) highlight the potential of satellite remote sensing for
characterizing spatial and temporal variability in water clarity in Lake Tahoe, and the usefulness of
coupled in situ and remote sensing datasets for investigating near-shore and mid-lake hydrodynamics
(Schladow et al. 2004; Steissberg et al. 2005a,b). The sensitivity of reflected light to sediments, gelbstoff
(the “yellow matter” characteristic of colored dissolved organic matter), and chlorophyll in visible and
near-infrared wavelengths makes passive optical remote sensing an ideal tool to measure turbidity,
estimate fine suspended particle, colored dissolved organic matter (CDOM), and chlorophyll
concentrations, and characterize the near shore benthos.
The purpose of this project is to expand on the remote sensing research in the near-shore of Lake Tahoe in
order to investigate methods to 1) quantify and map the distribution of suspended fine sediments, 2)
2
Proposal: Tahoe Research Supported by SNPLMA 2010
chlorophyll, and 3) CDOM in the water column, and map the bottom-type and bottom characteristics of
the near-shore to quantify the spatial extent of 4) periphyton, 5) aquatic plants (macrophytes), 6) invasive
clam beds, 7) substrate of different sediment classes. By coupling the recently acquired and historical
archives of remote sensing datasets with existing and new in situ data, we will be able to provide spatially
explicit maps of these variables, as well as an analysis of how the specifications (spatial and spectral
resolution) of the different remote sensing datasets impact the accuracy of the measurements. We will
then use this information, coupled with consideration of funding limitations and regulatory requirements,
to develop a remote sensing monitoring plan for the near-shore environment as well as recommendations
for locations of in situ monitoring stations. This proposal addresses subtheme 4b: “Indentifying
environmental indicators and developing approaches for monitoring and evaluation,” addresses a
component of subtheme 2a, “Understanding the impacts of aquatic invasive species” by creating a
regional monitoring plan for invasive macrophytes and clams, and will inform subtheme 4c,
“Development of robust methods for evaluating fine sediment concentrations and loads” by providing a
regional characterization of near shore spatial and seasonal variability.
BACKGROUND AND PROBLEM STATEMENT
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 sediment particles (Jassby et al. 1996, Swift et al. 2006). This one 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 been turned toward
the nearshore zone, despite the fact that this is the part of the lake that most people see and interact with.
The near shore environment of Lake Tahoe, the littoral zone of the lake where light penetrates to the
bottom, serves as an important environmental indicator for the health of the lake as the primary interface
between anthropogenic disturbance and lake environmental health (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 aquatic invasive species (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).
Remote sensing of near shore environmental indicators
Monitoring the status and trends of near shore suspended matter, 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
3
Proposal: Tahoe Research Supported by SNPLMA 2010
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, we will expand upon this research in order to
retrieve spatially explicit maps of fine sediment, CDOM, and chlorophyll in the water column, and the
distribution of periphyton, aquatic macrophyte and clam beds. We will leverage existing remote sensing
datasets, in situ monitoring data, and will collect GPS locations and bottom reflectance of the target
bottom types. We will provide estimates of the agreement between in situ and remote sensing-derived fine
sediment, chlorophyll, and CDOM concentrations, and will provide an assessment of how such estimates,
as well as mapped bottom types vary between different remote sensor types of varying spatial and spectral
resolutions in order to inform future monitoring plans that can most accurately characterize the near shore
environment using the most strategic combination of remote sensing and in situ data.
GOAL
The goal of this research is to use remotely sensed data to retrieve fine sediment, 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.
OBJECTIVES
1. Create a spectral library of bottom reflectance in the near shore that includes GPS locations and in situ
bottom reflectance spectra of different densities of clam beds, different densities and species of aquatic
macrophytes and periphyton, and sediment grain sizes.
2. Develop an algorithm for deriving fine sediment, chlorophyll, and CDOM concentration in the near
shore water column from remote sensing imagery, and assess the agreement between in-situ monitoring
data.
3. Develop an algorithm to map the near shore distribution (and quantitatively assess the agreement
between field data) of
a. Periphyton
b. Aquatic macrophyte species
c. Clam beds
d. Benthic sediment grain size
4. Assess the stability of developed algorithms and accuracy of detection of target environmental
indicators when applied to three remote sensing datasets of varying spatial and spectral resolutions.
5. Perform a cost-benefit analysis of remote sensing techniques and datasets for near shore and small lake
environmental indicator monitoring for status and trends.
6. Create a procedural remote sensing monitoring a plan for near shore environmental indicators.
HYPOTHESES
We hypothesize that remote sensing data can be used to retrieve water column fine suspended particle,
chlorophyll, and CDOM concentrations and can be used to create accurate maps of the distribution of
bottom type, including the distribution of periphyton, macrophyte species, and clam beds.
Sub-hypothesis 1: Empirical approaches (e.g. relating in situ measurements with remote sensing
reflectance) will retrieve the most accurate maps. However, statistical relationships may be scene
dependent. These maps however, will provide spatially explicit insights into water quality gradients and
patterns not measured with corresponding in situ measurements, such as onshore flow and other
disturbance events.
Sub-hypothesis 2: Physics-based approaches using model inversion of existing bio-optical and radiative
transfer models provide modeling methods that can retrieve water quality and benthic maps independent
of scene-specific conditions. These provide a powerful technique for monitoring including potential
transferability across sensors and retrospective analyses. However, these models must be well
4
Proposal: Tahoe Research Supported by SNPLMA 2010
constructed, calibrated, and validated.
Sub-hypothesis 3: The accuracy of water quality constituent concentrations and benthic maps is dependent
on the spatial and spectral resolution of the remote sensing data. Small and moderate (<15m) pixel sizes,
with numerous spectral bands will result in the highest accuracy. Fewer spectral bands, especially from
400-900nm, and larger pixel sizes may result in successful detection but with lowered accuracy.
APPROACH, METHODOLOGY, & GEOGRAPHIC LOCATION
Field data campaign and spectral library building methods and location
We will use the existing Lake Tahoe near shore submerged substrate map (Figure 1) (Metz et al. 2006)
and input from our advisory committee (see below) to create an a priori stratified random sample of preselected locations around the lake’s near shore. By stratifying the sample, we ensure that relatively rare
cover classes (e.g. clam beds) are sampled during the field campaign. We will randomly select >100 point
locations of each of the different bottom types from the map, and will navigate to those locations via boat
using differential global positioning systems (DGPS). Field crews will record the bottom substrate type,
percent cover, estimate the length and width of the patch, record species composition of macrophytes (if
present), and take a Seabird profile (temperature, conductivity, light transmission, PAR attenuation,
chlorophyll, turbidity, dissolved oxygen). For one in ten of each sampling point in each class type, a
sample will be collected for later particle size distribution analysis and dissolved organic matter.
In order to map the bottom types (periphyton, macrophyte species and clam beds, and sediment types), we
must first determine spectral separability of these different types. That is, we must first determine whether
there are significant differences in reflectance between these different bottom types. In situ benthic
reflectance of each of these types at several locations will be measured using a submersible
spectroradiometer with a fiber optic cable that measures radiance by SCUBA diver. Dr. Susan Ustin will
provide her extensive expertise in field spectroscopy, and we will follow her recommended protocol for
underwater reflectance measurements in optically shallow waters (Goodman and Ustin 2002, Goodman
2004). Simultaneously, in situ surface (remote sensing) reflectance will be measured from a boat using a
second spectroradiometer. GPS locations and site attributes listed above will be recorded. 20 replicate
measurements will be made at 4 sites located within a subset of the UC Davis periphyton survey stations
(Figure 2). Dr. Erin Hestir recently concluded surface reflectance measurements to retrieve aquatic optical
properties in the Sacramento-San Joaquin River Delta of CA, and will provide expertise and field
protocols for this project.
Remote sensing imagery & no-cost data contributions
We will use high spatial resolution multispectral satellite imagery, moderate spatial resolution
multispectral imagery, and airborne hyperspectral imagery to retrieve water column fine sediment
chlorophyll and CDOM concentrations and benthic maps. In the summer of 2010, the TRPA and US
Geological Survey acquired Worldview-2 satellite imagery (~2 meter spatial resolution, 8 spectral bands
including a coastal band from 400-450nm) over the Tahoe Basin extending into the near shore. As a costshare contribution to this proposed project, Dr. Susan Ustin, in collaboration with researchers at NASA
Ames Research Center in November 2010, will provide radiometrically corrected pre-processed data at no
additional cost to TRPA. As part of the same project, Dr. Ustin, Dr. Hestir, and NASA Ames Research
Center will acquire, pre-process, and radiometrically correct airborne hyperspectral data which will be
flown over Lake Tahoe in Fall 2010 and again in Spring 2010 with a final spatial resolution of 5 and 15
meters. The airborne hyperspectral sensor is a prototype for NASA’s planned HyspIRI hyperspectral
satellite mission (HyspIRI Group 2010). This processed data will also be provided at no cost to this
proposed project. We estimate the value of this contribution to be $25,000. Landsat Thematic Mapper
(TM) satellite imagery is available freely for download from the US Geological Survey, and will be preprocessed and radiometrically calibrated during the course of this project. We will obtain cloud-free
Landsat imagery for dates nearest the Worldview-2 and airborne hyperspectral overflights.
5
Proposal: Tahoe Research Supported by SNPLMA 2010
Remote sensing of aquatic optical properties and bathymetric classification
The information contained in remote sensing imagery can be used to accurately quantify water quality
constituents, such as fine sediment concentration, as well as analyze bottom properties in optically
shallow waters (Lee et al. 2001, Lee and Carder 2002, Mobley et al. 2005, Albert and Gege 2006).
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) (Figure 3). Three primary constituents
contribute to total suspended matter: 1) suspended and dissolved sediment, 2) phytoplankton, and 3)
dissolved organic matter, all of which are highly reflective from 400-1000 nm (Holden & LeDrew, 2001).
There are two primary methods to estimate suspended matter and its constituents and analyze bottom
properties (Figure 4). 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. These in turn can be used to
classify the bottom type (e.g. macrophyte species, periphyton, sand, cobble, etc.), and estimate
concentrations of total suspended solids in the water column (Lee et al. 2001).
We will investigate both empirical and model-driven methods to map fine sediment, chlorophyll, and
CDOM concentrations, macrophyte communities, clam bed, periphyton, and substrate type. Figure 4
outlines the approach of both methodologies. The empirical approach will first classify the optically
shallow near shore into the different bottom classes using the field data and spectral library first to train
and then (independently) validate the classifier. We will then correlate field reflectance spectra and
derived spectral diagnostic indexes with field measured water quality constituent concentrations. This
analysis allows the development of statistical correlations (e.g. regression modeling) whereby reflectance
information can be used to predict the probability of the concentration of each water quality constituent
above a particular bottom type. Upon successful development, the statistical model can then be used to
predict water quality in each image pixel given the reflectance value of that pixel. The second approach
will use a radiative transfer model (Hydrolight/Ecolight 5.0) that simulates remote sensing reflectance of
water given inputs of different aquatic optical properties (Mobley 2010). By running the model in the
“reverse” direction, we will derive the aquatic optical properties, bottom depth and albedo from remote
sensing reflectance. The existing bio-optical model for Lake Tahoe (Swift et al. 2006) can then be used to
estimate fine sediment, chlorophyll, and CDOM concentration from the optical properties. The spectral
library and field data collected during the field campaign will be used with the bottom albedo to create
maps of bottom type.
Accuracy assessment across remote sensing datasets & cost benefit analysis
We will apply both methodologies to the three datasets: 1) Worldview multispectral satellite imagery, 2)
Landsat multispectral satellite imagery, and 3) airborne hyperspectral imagery. We will assess the
agreement between the field data and maps of water quality constituent concentrations, macrophyte
species, clam beds, periphtyon, and substrate type. We will collaborate with ongoing research and
monitoring efforts to assess whether these maps relate with periphyton and macrophyte biomass, and
clam abundance. Using the results of the different accuracy assessments of the two techniques across the
different datasets, we will perform a cost-benefit analysis for the use of remote sensing for monitoring
near shore environmental indicators. We will consider:
a) The accuracy of detection and characterization of near shore environmental indicators
b) The estimated cost of remote sensing data acquisition
c) The estimated amount of time and expertise needed for image analysis
d) The estimated quantity of in situ data needed for calibration and/or validation
e) The temporal frequency of the data (e.g. can Landsat TM be used as a temporal “bridge” for
6
Proposal: Tahoe Research Supported by SNPLMA 2010
trend evaluation between less frequent, higher resolution remote sensing acquisitions that can
be used for status evaluation? Will NASA’s HyspIRI mission provide the data needed to
successfully monitor near shore environmental indicator status and trends?
Small lakes supplement
The synoptic nature of remote sensing measurements provides information about the entire Lake Tahoe
Basin, not only Lake Tahoe itself. There is increasing interest in the status and trends of aquatic invasive
species and water clarity in the surrounding small lakes in the Tahoe Basin. However, these lakes have
not yet been surveyed for macrophytes or clams, due primarily to their remote nature. As an addendum to
this proposal, we can apply the techniques developed in this proposal to the remote sensing datasets in
order to estimate the status of these indicators in Tahoe Basin small lakes. Field survey will need to be
conducted to validate this work. We have not included this in our budgeted scope of work; however, this
small lake component could be accomplished for approximately $42,000. We can provide a precise
budget for this supplement if the work is desired.
RELATIONSHIP TO PREVIOUS AND CURRENT RELEVANT RESEARCH & MONITORING
Remote Sensing of Lake Tahoe’s Near Shore: Advisory Committee
In order to ensure that our research design, analyses and results are informed by current knowledge and
understanding of the near shore environment, the following experts will provide supporting roles as
members of an advisory committee to this project:
a) Dr. Lars Anderson, USDA Agricultural Research Service and University of California Davis.
Expert in biology, ecology, and management of aquatic weeds in Lake Tahoe.
b) Mr. Scott Hackley, M.S., Tahoe Environmental Research Center, University of California, Davis.
Expert in lake periphtyon & nutrient status.
c) Dr. Marion Wittmann, Tahoe Environmental Research Center, University of California, Davis.
Expert in aquatic invasive species ecology and dispersal, and Asian clams in Lake Tahoe.
d) Dr. Simon Hook, NASA Jet Propulsion Laboratory. Expert in remote sensing, optical radiometry
and instrument calibration at Lake Tahoe.
Remote sensing of submerged aquatic vegetation in the Sacramento-San Joaquin River Delta
Drs. Hestir, Ustin, and Greenberg at the University of California Davis Center for Spatial Technologies
and Remote Sensing (CSTARS) have experience mapping aquatic macrophytes. They successfully
monitored submerged aquatic vegetation in the highly turbid Sacramento-San Joaquin River Delta using
airborne hyperspectral imagery from 2004-2008 (Hestir et al. 2008, Santos et al. 2009, Hestir et al. 2010).
Through the development of a machine-learning algorithm Hestir et al. (2008) were able to successfully
map five years of 48 flightlines per year airborne imagery using training data from only a single year,
obtaining equal rates of detection at every depth (>10ft) where they were observed. Furthermore,
researchers at CSTARS have demonstrated that several aquatic macrophytes are spectrally distinct
(Figure 5), and can be successfully mapped to the species level in the turbid waters of the Delta (Santos et
al. 2010) (Figure 6). They have developed several automated procedures and image batch-processing
techniques specific to aquatic remote sensing that can be leveraged in this study.
Remote sensing mapping and monitoring of turbidity and Microcystis in the upper San Francisco Estuary
Currently Drs. Hestir & Ustin, in collaboration with the US Geological Survey and the CA Dept. of Water
Resources are investigating the use of field spectroscopy, in situ water optical property measurements and
water sampling to derive turbidity and toxic blue-green algae (Microcystis aeruginosa) from Landsat
multispectral remote sensing imagery. The field and image analysis procedures, and automated
methodology that are developed during the course of these investigations will be leveraged in this study.
Remote sensing to measure Lake Tahoe’s clarity
7
Proposal: Tahoe Research Supported by SNPLMA 2010
Dr Schladow and Dr Steissberg (UC Davis TERC) and Dr Hook (NASA-JPL) are currently finalizing a
SNPLMA funded project aimed at using remote sensing to quantify changes in lake-wide distributions of
Secchi depth and chlorophyll distribution. The large pixel size have limited the application to areas
outside the near-shore, however, there will be considerable benefit to this project based on what was
learned.
Asian clams (Corbicula fuminea) and other invasive species in Lake Tahoe
Dr Schladow is part of the scientific team that has been working on the detection and control of Asian
clam in Lake Tahoe (he is PI on several projects). All information from those studies are available for use
in this project, along with assistance from TERC staff with particular expertise. He is also the PI on
USACE funded project conducting a baseline assessment of benthic species and developing
recommendations for future assessments.
Periphyton in Lake Tahoe’s Near Shore
TERC has had a long-term program on measuring periphyton at Lake Tahoe. All those data are available
to this project, along with assistance from TERC staff with particular expertise.
Near Shore Studies
Dr Schladow is the PI of a multidisciplinary project (with collaborators from UNR and DRI) targeted on
the interactions that occur in the nearshore. The project includes measurements of the hydrodynamics,
fish populations, in situ turbidity and chlorophyll measurements and periphyton. Those data are available
to this project. Finally Dr Schladow is part of the team working on the directed action looking at the
evaluation of nearshore ecology and aesthetics. The results of this proposal are crucial to those
deliberations, as remote sensing potentially has an important role to play in future monitoring.
STRATEGY FOR ENGAGING WITH MANAGERS & OBTAINING PERMITS
No permits are required for the proposed research.
At the onset of the project, a half-day workshop will be convened with key agency representatives to
better define those deliverables that are of highest priority. The project team will meet with the agency
representatives approximately 9-month later to present preliminary results and get agency feedback. The
project team will also participate in other forums to engage with managers. This would include
participation in the biennial Tahoe Science Symposium.
DELIVERABLES
1. Spectral library of Lake Tahoe near shore bottom types. This includes aquatic macrophyte
species, algal species, substrate types, and clam beds of varying density. A spectral library
provides the spectral reflectance (from 400-2500nm), or signature, of target materials to be
detected in remote sensing imagery. If targets are spectrally distinct, then it may possible to
identify them remote sensing datasets. These data, and associated GPS locations and field
attributes, will be available for download from the web.
2. Digital maps (raster images or shapefiles) of fine suspended sediment, chlorophyll, and CDOM
concentrations, aquatic macrophyte species, periphyton, clam beds, and substrate types
successfully modeled and mapped with accuracy estimates for each one. Following peer-review,
these will be hosted on the California Geospatial Clearinghouse (www.atlas.ca.gov) California
Spatial Information Library (CaSIL).
3. Cost benefit analysis of remote sensing approaches and technologies for monitoring the near
shore environment.
4. Procedural manual of identified successful technique for implementing remote sensing analysis
for monitoring the near shore environment.
8
Proposal: Tahoe Research Supported by SNPLMA 2010
III. Schedule of major milestones/deliverables
Projects should not expect to begin before June 2011. Note that it is the responsibility of the
project proponent to coordinate with appropriate agency representatives or partners and secure
any agreements or approvals necessary prior to initiating research. Be sure to include adequate
time for submitting draft deliverables for review, responding to reviews, and submitting final
deliverables.
Milestone/Deliverables Start Date End Date Description
Prepare progress
September September Brief progress report to Tahoe Science
reports
1, 2011
30, 2011 Program October 1st, 2011. Describes
results from field campaign.
Field measurements
July 2011 Oct, 2011 Spectral readings and water quality
sampling
Prepare progress
reports
December
1, 2011
Prepare progress
reports
March 1,
2012
Prepare progress
reports
June 1,
2012
Annual
accomplishment report
September
1, 2012
Prepare progress
reports
December
1, 2012
Annual
accomplishment report
September
1, 2013
Final accomplishment
report
December
1, 2013
December Brief progress report to Tahoe Science
30, 2011 Program January 1st, 2011. Describes
progress with image acquisition, and preprocessing
March 30, Brief progress report to Tahoe Science
2012 Program April 1st, 2011. Describes
progress with algorithm development.
June30, Brief progress report to Tahoe Science
2012 Program April 1st, 2011. Describes
progress with algorithm development,
preliminary results.
September Annual summary of accomplishments.
30, 2012 Includes response to input from advisory
committee.
December Brief progress report to Tahoe Science
30, 2012 Program January 1st, 2012. Describes
progress with algorithm implementation,
and cost-benefit analysis
September Annual summary of accomplishments.
30, 2013 Includes response to input from advisory
committee.
December Final summary of accomplishments.
31, 2013 Includes shapefiles of maps created,
copies of manuscripts in preparation or in
print, cost benefit analysis of remote
sensing techniques and technologies for
evaluating and monitoring near shore
environmental indicators, and procedural
manual for implementing recommended
technique.
9
Proposal: Tahoe Research Supported by SNPLMA 2010
IV. Literature cited/References (Up to 2 pages)
Albert, A. and P. Gege. 2006. Inversion of irradiance and remote sensing reflectance in shallow
water between 400 and 800 nm for calculations of water and bottom properties. Appl.
Opt. 45:2331-2343.
Brando, V. and A. Dekker. 2003. Satellite hyperspectral remote sensing for estimating estuarine
and coastal water quality. IEEE Transactions on Geoscience and Remote Sensing
41:1378-1387.
Goodman, J. 2004. Hyperspectral remote sensing of coral reefs: deriving bathymetry, aquatic
optical properties and a benthic spectral unmixing classification using AVIRIS data in the
Hawaiian Islands. University of California, Davis, Davis.
Goodman, J. and S. Ustin. 2002. Acquisition of underwater reflectance measurements as ground
truth.in 11th Jet Propulsion Lab Airborne Earth Science Workshop, Pasadena, CA.
Hackley, S., B. Allen, D. Hunter, and J. Reuter. 2010. Lake Tahoe Water Qulaity Investigations.
Tahoe Environmental Research Center, UC Davis, Davis, CA.
Herold, M., Metz, J. and Rosmos, J.S. 2007. Inferring littoral substrates, fishhabitats and fish
dynamics of Lake Tahoe using IKONOS data. Can. J. Remote Sensing, 33(5), 445-456.
Hestir, E., J. Greenberg, and S. Ustin. 2010. Classification trees for aquatic vegetation
community prediction from imaging spectroscopy. IEEE Journal of Special Topics in
Earth Observations and Remote Sensing In review.
Hestir, E., S. Khanna, M. Andrew, M. Santos, J. Viers, J. Greenberg, S. Rajapakse, and S. Ustin.
2008. Indentification of invasive vegetation using hyperpsectral remote sensing in the
California Delta ecosystem. Remote Sensing of Environment 112:4034-4047.
Holden, H. and E. LeDrew. 2001. Effects of the water column on hyperspectral reflectance of
submerged coral reef features. Bulletin of Marine Science 69(2): 685-699.
HyspIRI Group, J. P. L. 2010. NASA 2009 HyspIRI Science Workshop report. National
Aeronautics and Space Administration, Pasdena, CA.
Jassby, A., J. Reuter, and C. Goldman. 1996. Determining long-term water quality in the
presence of climate variability: Lake Tahoe (USA). . Canadian Journal of Fisheries
Sciences 60:1452-1461.
Lee, Z. and K. L. Carder. 2002. Effect of Spectral Band Numbers on the Retrieval of Water
Column and Bottom Properties from Ocean Color Data. Appl. Opt. 41:2191-2201.
Lee, Z., K. L. Carder, R. F. Chen, and T. G. Peacock. 2001. Properties of the water column and
bottom derived from Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data. J.
Geophys. Res. 106:11639-11651.
Mertes, L., M. Smith, and J. Adams. 1993. Estimating suspended sediment concentrations in
surface waters of the Amazon River wetlands from Landsat images. Remotes Sensing of
Environment 43:281-301.
Metz, M., M. Herold, and S. Romsos. 2006. Mapping fish habitats in Lake Tahoe for planning
and management., Donald Bren School of Environmental Science and Management,
University of California Santa Barbara, Santa Barbara, CA.
Mobley, C. D. 2010. Hydrolight 5 and Ecolight 5 Sequoia Scientific, Bellevue, WA.
Mobley, C. D., L. K. Sundman, C. O. Davis, J. H. Bowles, T. V. Downes, R. A. Leathers, M. J.
Montes, W. P. Bissett, D. D. R. Kohler, R. P. Reid, E. M. Louchard, and A. Gleason.
2005. Interpretation of hyperspectral remote-sensing imagery by spectrum matching and
look-up tables. Appl. Opt. 44:3576-3592.
Reuter, J., J. Thomas, and A. Heyvaert. 2009. An Integrated Science Plan for the Lake Tahoe
10
Proposal: Tahoe Research Supported by SNPLMA 2010
Basin: Conceptual Framework and Research Strategies. USDA, Forest Service, Pacific
Southwest Research Station, Albany, CA.
Santos, M., E. Hestir, S. Khanna, E. Duncan, and S. Ustin. 2010. Imaging spectroscopy
elucidates fucntional dissimilarity between native and non-native plant species in the
aquatic environment. Remote Sensing and Hydrology Symposium. The International
Commission on Remote Sensing (ICRS) Jackson Hole, WY.
Santos, M. J., S. Khanna, E. L. Hestir, M. E. Andrew, S. S. Rajapakse, J. A. Greenberg, L. W. J.
Anderson, and S. L. Ustin. 2009. Use of Hyperspectral Remote Sensing to Evaluate
Efficacy of Aquatic Plant Management. Invasive Plant Science and Management 2:216229.
Schladow, S. G., Palmarsson, S. O., Steissberg, T. E., Hook, S. J. and Prata, F. 2004. An
extraordinary upwelling in a deep, thermally stratified lake. Geophysical Research
Letters Vol 31, L15054.
Schladow, S. G., Steissberg, J. S. and Hook, S. J. 2010. Monitoring Past, Present, and Future
Water Quality Using Remote Sensing. SNPLMA Round 7, Final Report.
Steissberg, T. E., Hook S. J. and Schladow, S. G. 2005a. Measuring surface currents in lakes
with high spatial resolution thermal infrared imagery. Geophysical Research Letters,
Vol.32, L11402, doi:10.1029/2005GL022912
Steissberg, T. E., Hook, S. J. and Schladow, S. G. 2005b. Characterizing Partial Upwellings and
Surface Circulation at Lake Tahoe, California-Nevada, USA with thermal infrared
images. Remote Sensing of the Environment 99, 2-15.
Swift, T., J. Perez-Losada, S. G. Schladow, J. Reuter, A. Jassby, and C. Goldman. 2006. A
mechanistic clairty model of lake waters: Linking suspended matter characteristics
toclairyt. Aquatic Sciences 68.
Taylor, K., R. Susfalk, M. Shanafield, and G. Schladow. 2004. Near-shore clarity at Lake Tahoe:
status and causes of reduction., Desert Research Institute Division of Hydrologic
Sciences.
USACE. 2009. Lake Tahoe Region Aquatic Invasive Species Management Plan, CaliforniaNevada.
11
Proposal: Tahoe Research Supported by SNPLMA 2010
V. Figures
Figure 1. Lake Tahoe’s nearshore substrate map produced from 2002 IKONOS high spatial
resolution mutispectral satellite imagery ((from Herold, Metz and Rosmos 2007)). This map will be
used to create a priori randomly selected field data points to which researchers will navigate.
This data will divided for use as training and validation data for both the empirical (statistical)
and modelling approaches to characterize nearshore bottom type and water column fine
suspended particle, chlorohyll, and CDOM concentrations.
12
Proposal: Tahoe Research Supported by SNPLMA 2010
Figure 2. Locations of periphyton monitoring stations. TERC long-term periphyton monitoring
stations (indicated with a star and black text), SNPLMA So. Shore monitoring stations and
additional sites marked with red dots are expanded monitoring sites sampled once per year near
peak periphyton growth. In situ bottom and water surface reflectance will be collected by boat
and SCUBA survey at a sub-set of these locations.
13
Proposal: Tahoe Research Supported by SNPLMA 2010
Figure 3. Conceptual model of the interaction of light with water, and remote sensing of water.
14
Proposal: Tahoe Research Supported by SNPLMA 2010
Figure 4. Conceptual model of proposed work: remote sensing of Lake Tahoe’s near shore
environment. Two approaches will be tested. The first uses empirical methods to relate in situ
measurements with remote sensing reflectance to classify bottom types (periphyton, sediment
substrate classes, macrophyte species, and clam beds) and then predict total suspended solids in
the water column over each of the different bottom types. The second approach will use an
inverted radiative transfer model to solve for the bottom reflectance, depth, and water column
optical properties given each pixel’s reflectance. That information can be used in combination
with in situ data and Swift et al.’s (2006) bio-optical model to retrieve water column fine
suspended particle, chlorophyll, and CDOM concentrations and bottom type.
15
Proposal: Tahoe Research Supported by SNPLMA 2010
(a)
(b)
Figure 5. Principal Component Analysis of hand held spectrometer (ASD) and airborne
spectrometer (HyMap) spectra for the submersed aquatic plant species occurring in the
Sacramento-San Joaquin River Delta. (a) ASD measurements, and (b) HyMap measurements.
Native species are represented in green, non-native species in red, and water in blue. Note that
ASD does not include P. nodosus and turbid water, and that HyMap does not include S.
filiformis, and E. canadensis because we did not register pure patches in the field. Submersed
aquatic plant species have significantly different reflectace in certain regions of the reflected
emlectromagentic spectrum (P<0.0001), allowing discrimination between natives and nonnatives with 80% certainty, and species discrimination with 60% certainty.
16
Proposal: Tahoe Research Supported by SNPLMA 2010
(a)
(b)
Figure 6. Area of the Sacramento-San Joaquin Delta selected to apply the discriminant function
classification for submersed aquatic plant species detection in hyperspectral (HyMap) imagery:
(a) hyperspectral imagery overlaid with field data points of patches dominated by natives (green)
and non-natives (red); (b) distribution of C. demersum, E. densa, C. carolinensis, P. crispus, P.
nodosus, M. spicatum.
17