1. Monitoring Surface Area Change in Iowa's Water Bodies
Charlie Labuzzetta
Center for Survey Statistics & Methodology
April 24th, 2018

2. Motivation
Annual estimates of large water body (LWB) surface area extent on non-Federal lands are recorded as part of the National Resources Inventory (NRI).
Landsat Mission satellite imagery provides opportunity to generate estimates of LWB surface area at higher temporal resolution.
Higher temporal resolution data may help identify seasonal variation and structural breaks in surface area extent of these LWB.
This method provides supplementary data for NRI surveys and validation.

3. Previous Methodology
European Commission’s Joint Research Centre (JRC) – Monthly Water History Database
Uses ”procedural sequential decision tree” [1]
Classifies available LANDSAT pixels monthly from
Land ( pixel contains <30 m2 open water )
Water ( pixel contains 30 m2 open water )
Estimated <1% false water detection, <5% water omission
JRC Water History Dataset

4. Google Earth Engine Provides quick access to Imagery
Select via drawing a polygon
Select via importing kml file
Highspeed server-based computing
Supports machine learning
Random Forest
Naïve Bayes
Unsupervised K-means Methods
Faster than Python

5. Classification Algorithm
Training data
NRI LWB polygon
New image to classify
Output
DOY:
Example:
NRI LWB for
Dunbar Slough Wetland Management Area
Landsat imagery within 800 m. buffered region around NRI LWB
Naïve Bayes classifier to predict water-type land cover in each image
Three images of an 800 m. buffered region around the centroid of the polygon are selected. JRC classifications with “cluster likelihood label permutations”* used as training labels.
*method not described here

6. Case Study: Dunbar Slough A B C D B A D C
White pixel = JRC & CSSM predict water
Blue pixel = Only CSSM predicts water

7. Validation Study
Three LWB from each of Iowa’s nine agricultural districts were randomly selected from the NRI database. A date for testing was randomly selected for each LWB from the available Landsat images passing the quality controls (<1% cloud cover, <1% missing data) and where a 1 m2 high resolution National Agriculture Imagery Program (NAIP) image was taken within the same month. Water body surface area was traced by hand in the NAIP imagery to calculate a validated proportion of water area to total area of the image.[5] Both our method, denoted CSSM, and the previous classifications by JRC were used to calculate predicted proportions. Errors for each method were quantified in comparison to the validated proportions.
NAIP image with hand-drawn polygon

8. Validation Study JRC Method Error: Mean: -0.048 Median: -0.028
SD:
CSSM Method Error:
Mean:
Median:
SD:

9. yijkl = μi + ⍺j + βk + 𝑒ijkl
NRI Comparison
Consider a simple linear model
yijkl = μi + ⍺j + βk + 𝑒ijkl
where yijkl denotes the lth measure of water surface area for the ith water body from the kth month of the jth year starting at Consider each to be categorical fixed effects with sum to zero constraints placed upon coefficients ⍺ and β.
Thus, the predicted mean surface area for the ith LWB is estimated as μi. We compare the estimated mean surface area to the median permanent surface area recorded by NRI, which we denote ni.
Consider ti to be the total area of the predicted region for water body i. Consider residuals of the form: ri = |ni – μi| / ti

10. NRI Comparison

11. NRI Comparison

12. Lowest Residuals
r =
r =
r =
NRI
CSSM
JRC

13. Highest Residuals
r =
r =
r =
NRI
CSSM
JRC

14. Beaver Creek / Des Moines River
August 23rd, 2003
Should there be more LWBs here than NRI records?
NRI
CSSM
JRC

15. Dead Lake / Mississippi River
May 23rd,
2003
June 19th,
2009
This area is highly variable due to the influence of the Mississippi River.
NRI
CSSM
JRC

16. Rush Lake
June 29th, 2002
Rush Lake seems to have been much drier than NRI reports…
NRI
CSSM
JRC

17. Thank you!

18. References
[1] Pekel, Jean-Franois, Andrew Cottam, Noel Gorelick, and Alan S Belward “High- Resolution Mapping of Global Surface Water and Its Long-Term Changes.” Nature 540:
U.S. Department of Agriculture Summary Report: 2012 National Resources Inventory, Natural Resources Conservation Service, Washington, DC, and Center for Survey Statistics and Methodology, Iowa State University, Ames, Iowa.
Gorelick, Noel et al “Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone.” Remote Sensing of Environment.
Landsat Imagery courtesy of the U.S. Geological Survey.
NAIP Imagery courtesy of the U.S. Geological Survey.