Use of Satellite Imagery for Water Clarity Assessment of … · 2015. 7. 13. · linked to a lake...
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Leif G. Olmanson Leif G. Olmanson Steve M. Steve M. Kloiber Kloiber Patrick L. Patrick L. Brezonik Brezonik Marvin E. Bauer Marvin E. Bauer University of Minnesota University of Minnesota Use of Satellite Imagery for Water Clarity Assessment of Minnesota’s 10,000 Lakes
Use of Satellite Imagery for Water Clarity Assessment of … · 2015. 7. 13. · linked to a lake polygon layer in a geographic information system (GIS) and water clarity maps are
Leif G. OlmansonLeif G. OlmansonSteve M.Steve M. KloiberKloiber
Patrick L.Patrick L. BrezonikBrezonikMarvin E. BauerMarvin E. Bauer
University of MinnesotaUniversity of Minnesota
Use of Satellite Imagery for Water Clarity Assessment of Minnesota’s 10,000 Lakes
Leif Olmanson
This slideshow first gives an introduction of the use of Landsat imagery for water quality assessments then summarizes the methods used for water clarity assessment and finally presents a water clarity assessment of over 500 Twin City (Minneapolis And St. Paul, Minnesota, U.S.A.) Metropolitan area.
Trophic States
Hypereutrophic Lake
Oligotrophic Lake
Leif Olmanson
These pictures show some of the differences in water clarity that we are interested in for these studies. The upper left picture of Lake Hallett in St. Peter MN represents an oligotrophic lake with clear water and the lower picture of Schwanz in Eagan, MN represents a hypereutrophic lake plagued with sever algal blooms.
1. Background information on satellite imagery
2. Methods used to assess water clarity
3. TCMA Assessment of Water Clarity
4. LakeBrowser Mapserver application
Outline
1. Synoptic coverage; one image covers ~12,000 mi2 (115 mi ×105 mi)
2. Landsat pixel size (30 m × 30 m) suitable for all but smallest lakes and provides information on in-lake variability.
3. Data based on well-understood optical principles.
4. Multispectral data allows measurement of several water characteristics.
5. Cost-effective for regional assessments
Why satellite imagery?
1. Management agencies need more data but lack funds for more monitoring.
2. Advances in computer hardware and software enables quick, semi-automated image processing.
3. Improvements in satellites and frequency of image collection.
4. Decrease in image cost for Landsat 7 images.
Why now?
Successfully launched April 15, 1999
http://landsat.gsfc.nasa.gov/
Landsat-7 and ETM+
Leif Olmanson
The cost of Landsat satellite imagery was substantially reduced with the introduction of Landsat 7. The $4400 cost per Landsat image was reduced to ~$600, which made it’s utilization for regional assessments much more cost effective.
Landsat Image
Leif Olmanson
This is a Landsat image centered over the Twin Cities Metropolitan Area (TCMA). This one Landsat image covers ~12,000 mi2 (115 mi ? 105 mi) area and includes spectral radiometric data for over two thousand lakes.
Landsat image centered over Lake Minnetonka
Leif Olmanson
When we zoom into the Lake Minnetonka area of this false color image we can see differences between lakes of different water clarity. We can also see differences in land cover with red area being green vegetation and developed areas show up as light green.
Landsat Water Only Image
Leif Olmanson
The first step of image processing for water clarity assessment is to mask off all terrestrial areas. This creates a water only image that will be used for further processing. Note that the differences in water clarity are even more pronounced in this image.
The next step is to conduct an unsupervised classification (a clustering algorithm which groups similar pixels together into classes) of the water only image to identify water that is affected by vegetation, bottom or terrestrial features that should be avoided when selecting representative samples from lakes. This graph shows the spectral radiometric signatures of ten classes of lake pixels that the computer software program identified. Classes that are affected by vegetation, bottom or terrestrial features can be identified by elevated response in TM Band 4 (NIR) and\or TM Band 5 (MIR) and the location where these pixels occur.
Unsupervised Classification Map
Leif Olmanson
For this Unsupervised Classification Map we have colored affected pixels in red hues and different water clarities in blues and greens. This map is used to identify representative lake sample locations. In the next slide we will zoom to Christmas Lake and identify a sample location.
Sample location for Christmas Lake
Leif Olmanson
To avoid all affected pixels a representative sample is extracted from the central part of the lake.
Landsat Band Wavelength (u m) Spectral Region Digital NumberTM-1 0.45 - 0.52 Blue 47.36TM-2 0.52 - 0.60 Green 15.08TM-3 0.63 - 0.69 Red 10.16TM-4 0.76 - 0.90 Near-infrared 6.12TM-5 1.55 - 1.75 Mid-infrared 2.32TM-6 10.40 - 12.5 Thermal-infrared 124.36TM-7 2.08 - 2.35 Mid-infrared 1.08
Spectral Sample Extracted fromChristmas Lake
Leif Olmanson
Samples for each lake are extracted from the Landsat image using an image processing software program. This table presents the spectral radiometric data that was extracted from Christmas Lake. For water clarity assessments we have found that Secchi disk transparency (SDT) correlates best with TM1/TM3 ratio and TM 1.
Regression analysis using ln(SDT) as dependent variableand TM1:TM3 and TM1 as Independent variables
Convert to Trophic State Index (TSI) TSI(SDT) = 60 - 14.41 ln(SDT)
Christmas Lake TSI(SDT) = 40
Build Statistical Model
Leif Olmanson
This slide shows how we build a model to infer TSI(SDT) from Landsat Images.
y = 1.0002x + 0.0815R2 = 0.80
SEE = 0.26
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Landsat TM Inferrred TSI(SDT)
Obs
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How does the statistical model compare toobserved secchi disk transparency?
Leif Olmanson
This graph shows that there is a very good relationship between Landsat inferred TSI(SDT) and observed field measurements of TSI(SDT).
Pixel-Level Water Clarity MapSeptember 7, 1998
Leif Olmanson
With the relationship we can map water clarity. This map is a pixel-level map where the relationship is used to model water clarity for every pixel. This map can be used to see inter-lake differences.
Lake-Level Water Clarity MapSeptember 7, 1998
Leif Olmanson
This is a lake-level water clarity map that is created by applying the model to samples collected from each lake. A spreadsheet program is used to calculate water clarity for each lake. The water clarity data is then linked to a lake polygon layer in a geographic information system (GIS) and water clarity maps are created. This method allows the data to be used in a database and can be used for other analyses. This is the method we used in the TCMA to create a database of water clarity.
1975 1983 1986 1991 1995 1998
1988Dry Year
1993Wet Year
Water Clarity Legend•Database of over 500 lakes•14 Images•10 Different Years•1973 - 1998
In the Twin Cities Metropolitan Area (TCMA) we developed a water clarity database of over 500 lakes using ten Landsat TM images and four MSS images of the Twin Cities Metropolitan Area from 1973-1998. Images were selected from years with relatively normal climatic conditions except for one dry year (1988) and one wet year (1993). Four images from the same year (1991) were selected to analyze seasonal variability, and TM and MSS images from the same day (September 4, 1991) were acquired to evaluate the compatibility of the two sensors for water clarity assessments.
64 Lakes had significant trends (13.3%)
33 Lakes (6.8%) increased water clarity
31 Lakes (6.4%)decreased water clarity
Lake Water Clarity Trends, 1973-1998
Leif Olmanson
The lake clarity data (expressed as TSI(SDT)) derived from this study were analyzed to determine whether significant water clarity trends exist for TCMA lakes. Of the lakes in the study, 482 lakes had at least five satellite based TSI(SD) measurements over the 25-year period. Sixty-four of the evaluated lakes had temporal trends. Water clarity increased for 33 lakes and decreased for 31 lakes. Over half (58%) of the lakes with apparent trends changed by 10 or more TSI units (equivalent to a doubling or halving of SDT) from 1973 to 1998.
R2 = 0.718
R2 = 0.7415
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1971 1976 1981 1986 1991 1996
TSI
(SD
T)
LandsatInferred
SummerAverage
Linear(LandsatInferred)Linear(SummerAverage)
Improving Water Clarity Trend - Coon Lake
Leif Olmanson
This graph shows a lake with improving water clarity. As you can see the Landsat inferred TSI(SDT) has a good relationship with field observed TSI(SDT). The Landsat inferred TSI(SDT) is slightly higher then the summer (June through Sept) average this is expected since we target a late summer (July 15 through Sept 15) index period when selecting Landsat imagery. The next slide shows the reason we use this index period.
General Seasonal Patterns
May June July Aug Sept Oct-100
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SDTChl a
Chl a and SDT trends normalized to growing season average by lake
Leif Olmanson
Looking at change in CHL A and SDT over the course of the growing season. A large database of chla and SDT measurements on TCMA lakes was analyzed to determine the consistency of seasonal patterns among lakes in the region (Anderle 1999; Brezonik et al. 2000; Stadelmann et al., submitted) and evaluate the reliability of inferences made from a few measurements (1-3) in a growing season. The database included 370 “lake-years” of measurements on 145 lakes obtained between 1986 and 1997. Distinct patterns were found for both variables, and both fit a sine function (Stadelmann et al., submitted). SDT starts high in spring and early summer and decreases to a minimum in mid to late summer, followed by an increase in early fall. The opposite pattern was observed for chla. Strong relationships were observed between the growing season mean SDT and minimum SDT and between growing season mean chla and maximum chla. An index period of late July to early September was found to be the optimal time for measuring trophic conditions in TCMA lakes. During his period temporal and in-lake variability is at a minimum for SDT. Statistical analysis of these data indicated that 1-3 observations within the index period are sufficient to estimate the growing season mean SDT to within 30%.
Leif Olmanson
% Difference = (Obs - Mean) / Mean
Lake Browser
Http://resac.gis.umn.edu/lakebrowser
Leif Olmanson
One of the keys to successful application of satellite remote sensing is to make data and information readily accessible to agencies and the public. One of the RESAC objectives is develop easy to use Internet-based tools for delivery of information products. The RESAC project has developed a MapServer application for accessing maps and statistics derived from classifications of lake water clarity. MapServer is an open source Web mapping tool in which applications are created by coding an HTML template file along with a MapServer configuration file (see, http://mapserver.gis.umn.edu/). Extensions include raster query and analysis and the ability to extract attribute data from relational databases. The initial data in the LakeBrowser are the 10 years of Landsat classifications of the lakes in the Twin Cities Metropolitan Area. Users can select the year and view the satellite-derived lake clarity map of all of the lakes in the area, zoom in to smaller areas, or query the data base for information on particular lakes. The LakeBrowser is linked to the Minnesota PCA/DNR's LakeFinder database for additional information such as lake perimeter, area, depth, water quality and clarity measurements, fish surveys, recreation opportunities, and topographic maps. An example of a map from LakeBrowser is shown. The LakeBrowser can be accessed at: http://resac.gis.umn.edu/lakebrowser.
Lake Browser Minnesota
Leif Olmanson
Our ~1990 Minnesota lake water clarity assessment is also available in the lakebrowser. The ~2000 Minnesota State-wide water clarity assessment is going through final editing and should be available soon.
Lake Browser – Lake information
Leif Olmanson
Lake Browser query of the database for information on White Bear Lake. Click on the View the 25-year trend…
Graphed data
Leif Olmanson
View the trend of available water data in our database. Click on the DNR link to find out more information about White Bear Lake.
Lake Browser – Links to DNR Web Site
Leif Olmanson
The LakeBrowser is linked to the Minnesota PCA/DNR's LakeFinder database for additional information such as lake perimeter, area, depth, water quality and clarity measurements, fish surveys, recreation opportunities, and topographic maps.
