1. Classification of Tidal Wetland Communities Using Multi-temporal, Single Season Quickbird Imagery Emily Wilson, Sandy Prisloe, James Hurd & Dan Civco Center for Land Use Education and Research University of Connecticut Martha Gilmore Department of Earth and Environmental Sciences Wesleyan University ASPRS 2006 Reno, NV

2. © 2006 University of Connecticut 2 Project Participants Project Lead and Image Classification Collection and Analysis of Field Spectra Funding Support and Quickbird Image Acquisition Institute for the Application of Geospatial Technology

3. © 2006 University of Connecticut 3 Outline The problem The need for mapping Methods –Image Acquisition –Measuring Spectral Differences –Field Data Collection –Image Segmentation and Classification Preliminary results

4. © 2006 University of Connecticut 4 The Problem: A Highly Invasive Plant Very aggressive Forms dense monocultures Displaces indigenous vegetation Does not support rich mix of wildlife Spreads rapidly Physically changes marshes Phragmites australis

5. © 2006 University of Connecticut 5 The Problem: A Highly Invasive Plant 1968 1994 Upper Island marsh, Old Lyme, Connecticut

6. © 2006 University of Connecticut 6 The Problem: A Highly Invasive Plant 1968 1994 Lord Cove marsh, Lyme, Connecticut

7. © 2006 University of Connecticut 7 Phragmites Eradication Efforts Connecticut DEP, The Nature Conservancy and others have undergone extreme and costly eradication efforts –Mulching or stomping - Herbicide application

8. © 2006 University of Connecticut 8 Phragmites Eradication Example Photo courtesy Joel Stocker Lord Cove

9. © 2006 University of Connecticut 9 Phragmites Eradication Example Treated and Mulched

10. © 2006 University of Connecticut 10 Phragmites Eradication Example After Treatment

11. © 2006 University of Connecticut 11 Phragmites Eradication Example After Treatment

12. © 2006 University of Connecticut 12 Phragmites Eradication Example After Treatment

13. © 2006 University of Connecticut 13 The Problem Very little baseline information No long-term monitoring, especially over large areas

14. © 2006 University of Connecticut 14 Overall Project Goals Identify and map coastal wetlands with multi-spectral moderate resolution imagery Identify and map Phragmites australis –Multi-spectral high-res imagery Quickbird satellite imagery 2.5m resolution ADS40 airborne imagery 0.5m resolution –Lidar data Map other tidal marsh plant communities as possible Tidal Wetland Classification from Landsat Imagery using an Integrated Pixel-based ad Object-based Classification Approach James Hurd, Thursday, May 4 1:30-3:00pm Use of Lidar Data to Aid in Discriminating and Mapping Plant Communities in Tidal Marshes of the Lower Connecticut River Sandy Prisloe, Thursday, May 4 1:30-3:00pm

15. © 2006 University of Connecticut 15 Overall Project Goals Identify and map coastal wetlands with multi- spectral moderate resolution imagery Identify and map Phragmites australis –Multi-spectral high-res imagery Quickbird satellite imagery 2.5m resolution ADS40 airborne imagery 0.5m resolution –Lidar data Map other tidal marsh plant communities as possible

16. © 2006 University of Connecticut 16 Goals for Mapping Phragmites with Quickbird Explore the use of high-resolution satellite imagery to classify and map P. australis Use multi-temporal images to utilize spectral differences due to phenology and develop mapping protocol Develop maps of untreated P. australis and areas recently treated Create baseline classification of species or communities besides Phragmites

17. © 2006 University of Connecticut 17 The Need to Map and Monitor Where has P. australis been eradicated? How much area has been treated? Where does P. australis still grow? Is P. australis reinvading areas? What other plants can be classified from imagery?

18. © 2006 University of Connecticut 18 High Resolution Imagery DigitalGlobe QuickBird satellite imagery

19. © 2006 University of Connecticut 19 Project Location Lower Connecticut River Drains to Long Island Sound Lord Cove Upper Island

20. © 2006 University of Connecticut 20 Quickbird Image Acquisition 2004 –IAGT Ordered QuickBird imagery every 2 weeks from May to October –Best case would be 13 images –Actually acquired 4 (due to weather conditions and competition) 2005 –Acquisition extended to 2005 summer season –Collected 3 more images 2006 –Acquisition extended to 2006 summer season Take home message: Satellite image acquisition can be unreliable

21. © 2006 University of Connecticut 21 Quickbird Image Acquisition July 8, 2003 May 27, 2004* July 2, 2004 July 20, 2004 Sept. 12, 2004 June 2, 2005 June 17, 2005 July 23, 2005 * Varying quality

22. © 2006 University of Connecticut 22 Quickbird Image Acquisition May 27 2004 June 2 2005 17 2005 July 2 2004 8 2003 20 2004 23 2005 August Sept. 12 2004 BIG HOLE 4 images from 2004 3 images from 2005 Collection continues in 2006 MONTH DAY

23. © 2006 University of Connecticut 23 Measuring Spectral Differences Species change throughout the growing season Each species changes differently Changes are unique and revealing

24. © 2006 University of Connecticut 24 Measuring Spectral Differences A portable spectrometer has been used to measure the energy reflected from a variety of plant species at different times during the growing season. May 27, 2004

25. © 2006 University of Connecticut 25 Measuring Spectral Differences

26. © 2006 University of Connecticut 26 GPS-referenced Field Observations Guide classification efforts Train image analysts Eventual accuracy assessment S. patens S. alterniflora P. australis T. augustifolia GPS Legend

27. © 2006 University of Connecticut 27 Image Data Preparation: Subset Important to isolate only marsh pixels to eliminate confusion Use LIDAR data as first cut Still some confusion Great care was taken to remove non-marsh areas from the image. Previous work required large amounts of time and classification energy went into removing via classification non-marsh classes such as lawn, trees and buildings. -Start with Lidar o Sandy change point data to 1-meter grid file o Reclassify into three classes representing upland (non-marsh), marsh and water. o Go through series of filters, clumps, eliminates, dissolves and hand edits to remove extraneous pixels and create final subset shapefile (yellow below). White = Upland Gray = Wetland Dark Gray = Water

28. © 2006 University of Connecticut 28 Image Data Preparation: Subset Series of filters and edits to identify final study area Subset all imagery to this boundary Great care was taken to remove non-marsh areas from the image. Previous work required large amounts of time and classification energy went into removing via classification non-marsh classes such as lawn, trees and buildings. -Start with Lidar o Sandy change point data to 1-meter grid file o Reclassify into three classes representing upland (non-marsh), marsh and water. o Go through series of filters, clumps, eliminates, dissolves and hand edits to remove extraneous pixels and create final subset shapefile (yellow below).

29. © 2006 University of Connecticut 29 Image Data Preparation Select images dates based on spread across growing season (ignore year) and high image quality –June 2, 2005 –June 17, 2005 –July 20, 2004 –Sept. 12, 2004 Select ratios and calculate for each image date –4/3 (nir/red) ratio (stretch 0-255) –2/1 (green/blue) ratio (stretch 0-255) –4/2 (nir/green) ratio (stretch 0-255) Great care was taken to remove non-marsh areas from the image. Previous work required large amounts of time and classification energy went into removing via classification non-marsh classes such as lawn, trees and buildings. -Start with Lidar o Sandy change point data to 1-meter grid file o Reclassify into three classes representing upland (non-marsh), marsh and water. o Go through series of filters, clumps, eliminates, dissolves and hand edits to remove extraneous pixels and create final subset shapefile (yellow below).

30. © 2006 University of Connecticut 30 Image Classification with eCognition Well suited for high resolution data Groups pixels into objects –Uses spectral and shape properties –Minimizes intra-object differences –Maximizes inter-object differences Creates multi-resolution hierarchy of objects Can include ancillary datasets in the classification process Can use classification rules

31. © 2006 University of Connecticut 31 Land Cover Classification Techniques Image Segmentation and Object-Oriented Classification –1) Segment the image (group similar adjacent pixels) –2) Classify segments Advantages: –Closer to how we interpret imagery –Each segment has spectral and spatial attributes

32. © 2006 University of Connecticut 32 Image Segmentation and Classification Create eCognition project with each ratio for each date along with raw bands from July 20 image Experiment and decide on segment size Write rules based on a priori knowledge and results from field spectrometry

33. © 2006 University of Connecticut 33 Image Segmentation and Classification Ragged Rock Creek Marsh LEVEL 4 LEVEL 3LEVEL 2 LEVEL 1

34. © 2006 University of Connecticut 34 Development of Mapping Protocol For Phrag Use late summer image High 4/3 ratio

35. © 2006 University of Connecticut 35 Feature View: Blue areas indicate Phrag areas used in rule

36. © 2006 University of Connecticut 36 Development of Mapping Protocol For Patens Use mid-July image High 2/1 ratio

37. © 2006 University of Connecticut 37 Feature View: Blue areas indicate Patens areas used in rule

38. © 2006 University of Connecticut 38 Preliminary Results

39. © 2006 University of Connecticut 39 Preliminary Results Typha Phrag Patens

40. © 2006 University of Connecticut 40 Further Work Refine unclassified areas Look more closely into ground data and more refined rules Check accuracy Save hierarchy and apply to other areas in the lower Connecticut River

41. Classification of Tidal Wetland Communities Using Multi-temporal, Single Season Quickbird Imagery Emily Wilson, Sandy Prisloe, James Hurd & Dan Civco Center for Land Use Education and Research University of Connecticut Martha Gilmore Department of Earth and Environmental Sciences Wesleyan University ASPRS 2006 Reno, NV