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UPDATING WETLAND MAPS FOR RESOURCE MONITORING AND MANAGEMENT IN OKLAHOMA. Daniel Dvorett OKLAHOMA CONSERVATION COMMISSION WATER QUALITY DIVISION. Outline. Need for improved regional maps Automated Mapping Background/Methods Results Classification Hydrologic Attribution
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UPDATING WETLAND MAPS FOR RESOURCE MONITORING AND MANAGEMENT IN OKLAHOMA Daniel Dvorett OKLAHOMA CONSERVATION COMMISSION WATER QUALITY DIVISION
Outline • Need for improved regional maps • Automated Mapping Background/Methods • Results • Classification • Hydrologic Attribution • Manual vs. Automated Mapping • Field Verification • Conclusions and Future Projects
Applications • Understanding distribution and location of wetlands • Preliminary project planning • Status and trends • Restoration planning and prioritization • Landscape studies
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)
Need for Map Updates • NWI an amazing resource! BUT • Regional problems with accuracy include: • Map age • Temporary wetlands • Hydroperiod attribute 2005 2008
Need for Map Updates • Original NWI maps missed a large number of temporary wetlands in portions of the Central Great Plains of Oklahoma.
Automated Maps: Background • Advantages of Landsat for mapping temporary wetlands • High return interval • Moderate spatial resolution • Moderate spectral resolution • Available back to 1980s • Free!
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.
Methods: Study Area • Cimarron River Pleistocene Sand Dunes Ecoregion in Central Oklahoma • Semi-arid with abundance of ephemeral wetlands
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
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
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)
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
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
Results: Classification • Decision Tree Analysis was the best method. • B3 is red • B4 is near infra-red • B5 is shortwave infra-red
Results: Classification • Accuracy Assessment conducted using concurrent NAIP • 200 water pixels and 1,000 upland pixels • Water pixel if ≥25% of pixel was water
Classification: Results Wet 1 Image Wet 2 Images Wet 3 Images
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)
Results: Hydrologic Attributes • NWI maps have relatively poor hydrologic attribution. • Wetland hydrology drives ecosystem function: • Biological functions • Biogeochemical functions • Hydrological functions
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
Results: Hydrologic Attributes • Wetness Index as supplement for wetland maps
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
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.
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
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
Maps: Field Verification * Wetlands were removed if they were not visible from the road, lost to development or their status was undeterminable (farmed).
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
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
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
Questions? Dvorett D., C. Davis. and M. Papeᶊ (2016) Mapping and hydrologic attribution of temporary wetlands using recurrent Landsat imagery. Wetlands 36: 431-443