UPDATING WETLAND MAPS FOR RESOURCE MONITORING AND MANAGEMENT IN OKLAHOMA

1. UPDATING WETLAND MAPS FOR RESOURCE MONITORING AND MANAGEMENT IN OKLAHOMA Daniel Dvorett OKLAHOMA CONSERVATION COMMISSION WATER QUALITY DIVISION

2. Outline • Need for improved regional maps • Automated Mapping Background/Methods • Results • Classification • Hydrologic Attribution • Manual vs. Automated Mapping • Field Verification • Conclusions and Future Projects

3. Applications • Understanding distribution and location of wetlands • Preliminary project planning • Status and trends • Restoration planning and prioritization • Landscape studies

4. Need for Map Updates • National Wetlands Inventory (NWI) • High altitude and single date • 1980’s base imagery in Oklahoma • Digitized and freely available for most of U.S. • Wetlands classified by landscape position, vegetation structure and water regime (hydroperiod)

5. Need for Map Updates • NWI an amazing resource! BUT • Regional problems with accuracy include: • Map age • Temporary wetlands • Hydroperiod attribute 2005 2008

6. Need for Map Updates • Original NWI maps missed a large number of temporary wetlands in portions of the Central Great Plains of Oklahoma.

7. Automated Maps: Background • Advantages of Landsat for mapping temporary wetlands • High return interval • Moderate spatial resolution • Moderate spectral resolution • Available back to 1980s • Free!

8. Automated Maps: Background • Other studies have used 1-2 multi-spectral satellite images to map wetlands with relatively good accuracy.* • For temporary wetlands 1-2 images is likely still insufficient and tells us little about wetland hydrology. • Once an accurate classification method is developed it is easy to apply to additional satellite images. *Baker C, Lawrence R, Montagne C, Patten D (2006) Mapping wetlands and riparian areas using Landsat ETM+ imagery and decision-tree- based models. Wetlands 26:465-474 Maxa M, Bolstad P (2009) Mapping northern wetlands with high resolution satellite images and LiDAR. Wetlands 29:248-260.

9. Methods: Study Area • Cimarron River Pleistocene Sand Dunes Ecoregion in Central Oklahoma • Semi-arid with abundance of ephemeral wetlands

10. Methods: Imagery • 54 Landsat images from 18 years (1994-2011) • 3 images per year • Classify water and upland pixels • Aggregate classified images • Frequency and duration of inundation for each pixel • Wetlands are pixels inundated in >25% of years

11. Methods: Image Processing • Only images with <10% cloud cover selected and no “popcorn clouds” • Classification of multiple images requires radiometric correction and normalization • Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) • Atmospheric correction • Differences between LE5 and LT7 • Changes in sensor calibration over time • Illumination and observation angles

12. Methods: Classification • Completed in ENVI 5.2 (Exelis Visual Information Solutions) • Training Data • Five classes (Water, urban, crop, grass and forest ) • 5/4/2008 NAIP Imagery and concurrent Landsat image • Classification Methods • Maximum Likelihood • Decision Tree • Manual Threshold (B5 and B5-B3) • Accuracy assessment conducted using concurrent NAIP for LE5 (2008) and LT7 (2010)

13. Methods: Manual Map Update • Wet year base imagery: NAIP 2008 • Followed NWI guidelines • Ancillary data from additional aerial images (2003,2005,2010),USGS topos and NRCS soil maps

14. Results: Classification • Accuracy of all methods was excellent on May imagery • Kappa ranged from 0.84 to 0.93 • Accuracy declined in early spring and fall • Evergreen stress in fall • Crop plantings in spring • Training data was added from Mar., Jun., and Oct. of 2008 • Reran classification with expanded training dataset

15. Results: Classification • Decision Tree Analysis was the best method. • B3 is red • B4 is near infra-red • B5 is shortwave infra-red

16. Results: Classification • Accuracy Assessment conducted using concurrent NAIP • 200 water pixels and 1,000 upland pixels • Water pixel if ≥25% of pixel was water

17. Classification: Results Wet 1 Image Wet 2 Images Wet 3 Images

18. Results: Automated Map • Wet 5/18 or >25% of years included in final map • 3,156 wetland basins (718 more than NWI and only 34% agreement)

19. Results: Hydrologic Attributes • NWI maps have relatively poor hydrologic attribution. • Wetland hydrology drives ecosystem function: • Biological functions • Biogeochemical functions • Hydrological functions

20. Results: Hydrologic Attributes • Frequency of inundation • 25-50% of years • 51-80% of years • 81-99% of years • 100% of years • Average hydroperiod when inundated: • Temporary (1-1.5 images) • Seasonal (1.6-2.5 images) • Semi-permanent (2.6-2.9 images) • Permanent (3 images every year) Frequency of Inundation Duration of Inundation

21. Results: Hydrologic Attributes • Wetness Index as supplement for wetland maps

22. Results: Hydrologic Attributes • Polygon volume tool in ARCMAP • Combined Landsat with high resolution LIDAR elevation data • Volume and depth calculations for actual wetland boundary or during any inundation event Wetland: Volume: 51,915 m3 Max Depth: 2.6 m Average Depth: 1.6 m Water Extent March 2008 Volume: 116,699 m3 Max Depth: 3.3 mAverage Depth: 2.0 m

23. Results: Manual Maps • Updated Maps: 6,531 total polygons • Original NWI: 2,868 total polygons • Of wetlands mapped both manually and through the automated protocol, only 41% were mapped from original NWI.

24. Results: Manual and Automated • 4,054 Unique manual wetlands • Small or thin wetlands • Riparian wetlands • Errors of commission • 589 Unique automated wetlands • Temporary hydroperiod • Basins that received less rainfall • Erroneous sites

25. Results: Manual and Automated

26. Results: Field Verification • Roadside assessment of: • 30 automated wetlands, • 30 manual wetlands, and • 30 wetlands mapped through both protocols Unique Automated Wetland: 5/4/08 Aerial Unique Automated Wetland: Field Visit

27. Maps: Field Verification * Wetlands were removed if they were not visible from the road, lost to development or their status was undeterminable (farmed).

28. Conclusions • Multi-date Landsat classification can be a valuable supplement to wetland mapping and provide improved identification and hydrologic attribution. • Important to consider the implications of: • Seasonal impacts • Radiometric correction and normalization • Classification method • Wetland Size

29. Future Directions • Inaccuracies in floodplain wetland boundaries due to channel incision and seasonality of floods • Select Landsat images that coincide with flood events (stream gauges) • Will start mapping along the Salt Fork of the Arkansas and Canadian Rivers this fall

30. Acknowledgements • US Environmental Protection Agency • 104(b)(3) Wetland Program Development Grant • Collaborators • Craig Davis, Mona Papeᶊ, Bryan Murray • Technicians • Bill Hiatt and Anthony Thornton

31. Questions? Dvorett D., C. Davis. and M. Papeᶊ (2016) Mapping and hydrologic attribution of temporary wetlands using recurrent Landsat imagery. Wetlands 36: 431-443