1. Conflation of aquatic habitat data for linking stream and landscape features Mindi Sheer, NOAA fisheries – Northwest Fisheries Science Center, Seattle Bernard Catalinotto – DES, Maryland

2. What is “GIS Data Conflation?” Combining attributes and arcs, polygons, or points of two GIS files to create a third, best-case data set.   The first dataset is the “source”   The second dataset is the “target”   The combination of source + target is the “result” SOURCE -GOOD ATTRIBUTES TARGET GOOD LINEWORK Conflation RESULT BEST ATTRIBUTES & LINEWORK

3. ► Automatically match corresponding arc nodes ► Automatically match corresponding arcs within user-defined distance ► Check and fix errors Conflation software requires three major steps: TARGET SOURCE

4. Objectives ► ► GIS data conflation   How conflation is applied to hydrographic datasets ► ► Watershed case study   Use of conflation   Habitat study results ► ► Benefits and “caveats” of conflating ► ► Recommendations

5. GIS Data Conflation - Example ► ► US Census Bureau:   Realigning 50 million TIGER file road & hydro arcs, 3200 counties Target – 1:6,000 & 1:2,000 (photogrammetry) Source – 1:100,000 DIME (1970)

6. Why conflate streams? ► ► Highly variable spatial representation of stream features ► ► Limitations in positional accuracy, density, and sinuousity of 100k streams, can result in inaccurate results Multiple methods & sources of stream hydrography

7. Stream Length 100k streams Stream density Stream sinuousity

8. Project Background ► ► The challenge: 1. Stream hydrography & land cover to correlate landscape & fine-scale stream morphology 2. Validation of DEM-based modeled stream ► ► Sources:   Oregon Dept. of Fish and Wildlife Surveys (1:100,000)   DEM hydro (1:24,000)

9. TARGET: DEM-derived 24k reach-segmented streams ► SOURCE: Oregon Department of Fish and Wildlife (ODFW) segmented field data

10. ► ► All source (survey data) successfully transferred ► ► Target DEM reaches were subdivided to reflect relative arc length of the habitat unit ► ► Small amount of stretching of arcs at the unit scale Conflation Results

11. Also… ► ► 10% of the data had “0” arc lengths (dyn segmentation) ► ► “0” length channels were secondary channels to the main stream (important as salmon rearing habitat)

12. Channel Complexity

13. Habitat Results ► ► Length differences (+ 9%):   1639 km (New)   1507 km(Survey)   85% of conflated stream units +/- 10 m   New lengths matched calibration info 0-5 m -5-0 m Difference in conflated length (m) Count (# arcs)

14. Watershed scale habitat variables

15. Model Validation - Gradient Field slope Model slope Molalla North Santiam

19. Conclusions ► ► Benefits ► ► Provides substantial benefits to ecological studies ► ► Allows automated and manual processing ► ► Data was validated effectively ► ► Results had higher confidence than if conflation had not been used ► ► Costs ► ► Conflation was performed at low cost for major project (80,000 features) ► ► Recommendations ► ► Recommend researchers consider using conflation on their multi-scale projects

20. Feel free to contact Us…. ► ► Mindi Sheer   NOAA   Mindi.Sheer@noaa.gov@noaa.gov   206-860-3428 ► ► Bernard Catalinotto   Data Enhancement Services, LLC   bcatalinotto@gisdes.com bcatalinotto@gisdes.com   301-717-1077