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

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.

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 1984 - 2015 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

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

Classification Algorithm Training data NRI LWB polygon New image to classify Output DOY: 120 - 180 180 - 240 240 - 300 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

Case Study: Dunbar Slough 2014-09-19 2000-08-11 2007-07-14 1985-08-02 A B C D B A D C White pixel = JRC & CSSM predict water Blue pixel = Only CSSM predicts water

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

Validation Study JRC Method Error: Mean: -0.048 Median: -0.028 SD: 0.069 CSSM Method Error: Mean: -0.014 Median: -0.005 SD: 0.039

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 1997. 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

NRI Comparison

NRI Comparison

Lowest Residuals r = 0.00016 r = 0.00016 r = 0.00019 NRI CSSM JRC

Highest Residuals r = 0.28204 r = 0.21101 r = 0.19586 NRI CSSM JRC

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

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

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

Thank you!

References [1] Pekel, Jean-Franois, Andrew Cottam, Noel Gorelick, and Alan S Belward. 2016. “High- Resolution Mapping of Global Surface Water and Its Long-Term Changes.” Nature 540:418. http://dx.doi.org/10.1038/nature20584. U.S. Department of Agriculture. 2015. Summary Report: 2012 National Resources Inventory, Natural Resources Conservation Service, Washington, DC, and Center for Survey Statistics and Methodology, Iowa State University, Ames, Iowa. http://www.nrcs.usda.gov/technical/nri/12summary. Gorelick, Noel et al. 2017. “Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone.” Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2017.06.031. Landsat Imagery courtesy of the U.S. Geological Survey. NAIP Imagery courtesy of the U.S. Geological Survey.