This document summarizes a presentation on remote sensing approaches for ecosystem assessment and restoration in the Great Lakes region. It discusses using remote sensing technologies like satellite imagery, aerial photography, LiDAR, and radar to map wetlands, identify invasive species, monitor bird nesting sites, and detect oil spills. Remote sensing allows assessment of large areas to determine habitat extent and condition, enabling prioritized and targeted restoration efforts. Regular monitoring with technologies like radar is important for assessing wetland changes and preventing future, more costly restoration projects.

1. Great Lakes Restoration Initiative Remote Sensing Approaches for Ecosystem Assessment and Restoration February 18, 2011 WLIA Madison, WI Brian Huberty FWS Remote Sensing Lead brian_huberty@fws.gov

3. TOP 5 REASONS TO ATTEND THE 2011 ASPRS MILWAUKEE CONFERENCE 5. AGS Map Library 4. Discovery World 3. Harley-Davidson Motorcycles 2. << Lake Michigan 1. BEER! http://www.asprs.org

5. 1980-82 B.S. U of MN–Natural Resource Inv.

6. 1982-84 MN DNR Forest Inventory

7. Air Photo Interpretation/Forest Inventory

8. 1984-86 USGS EROS

9. Landsat, AVHRR, etc

10. 1986-89 M.S. U of MN – Remote Sensing/GIS

11. 1989-91 USFS Remote Sensing App. Center

12. NASA ER-2 Flights

13. 1991-1993 Aerial Image Technology

14. Air Photo/Glasses

15. 1993-2002 USDA NRCS

16. GIS Madison, WI

17. 2002-2010 U.S. Fish & Wildlife Service

19. Let’s Play WHERE IS IT?

27. SeaWIFS April 24, 1999

28. Radarsat2 February 2009

30. The Mission of the U.S. Fish & Wildlife Service:working with others to conserve, protect and enhance fish, wildlife, and plants and their habitats for the continuing benefit of the American people.

31. Great Lakes Restoration Initiative 20% of the World’s available freshwater!

32. Habitat Space and Time REMOTE SENSING >>> NGO, Local, Tribal, State, Federal, International Datasets Physical Chemical Biological WEB Sharedgeo MTRI radar DU GL NWI UMN geospatial SMU WI NWI

33. Photo Interpretation Process Spring Update at 1:10,000 (1/2 acre MMU) Summer

34. Wisconsin Wetland InventoryDigital Conversion Scanning Orthorectification

35. Classification based on CASI Imagery and LiDAR data

36. Bird Nesting Site Identification Integrates Spectral and Visual Analysis Cormorant nesting areas are roughly delineated on photo A level slice of band 1 was performed to identify the cormorant spectral signature The cormorant spectral signature is then converted to polygons…. And the polygons to points.

37. Overview: This technology can deliver 2" on ground pixel size resolutions and in color infrared. Camera Collection System Spectra-View 12W-M 24 2/18/11 The Stewardship Network webcast

38. Head to Head Comparison 2’’ Resolution 12’’ Resolution 6’’ Resolution Image Resolution 2/18/11 The Stewardship Network webcast

39. SPOT 2010 Image Mosaic

40. Bathymetric LIDAR

41. SONAR Mapping _ National Park Service

42. Mapping Invasive Phragmites and Wetland Extent in the Coastal Great Lakes Laura L. Bourgeau-Chavez, Richard Powell, Liza Jenkins, Colin Brooks, Tyler Erickson Michigan Technological University Michigan Tech Research Institute (MTRI) Ann Arbor, MI January 19, 2010

44. within 10km coastline

45. Coastal region emergent wetlands most vulnerable to Phragmites invasion

46. water level changes

47. Typha-dominated

49. 459 validation, 316 training

50. Phragmites observed at 29% of sites. (228 of the 775).

51. 14% Validation sites

52. 15% training sites

53. Only NWI "Palustrine Emergent" polygons used to generate random points for validation sites of these, only 53% were documented as emergent in the field observationsLake Huron Mapping

54. from radar … … to Phragmites Image: MTRI

55. NWI-blue=water, cyan=emergent, green=forested wetland, Dark green=shrubby wetland, purple=floating aquatic. Great Lakes Forested Wetland Inundation Mapping Multi-temporal JERS L-band Composite NWI with SAR-derived Inundation Overlaid N NASDA 1992-4 Extent of Inundation

56. CBP Coastal Ortho Imagery

57. Geospatial image streaming evaluation Evaluate for multiple features: ease of data integration, outputs formats, performance, ability to scale to multi-terabyte archives Publish document, assess technologies for meeting USFWS needs to share imagery Examples of 2008 DHS Border Imagery

58. GLRI Research SummaryJoe Knight and teamJ. Corcoran, L. Rampi, B. Tolcser, M. Voth Remote Sensing and Geospatial Analysis Lab

59. LiDAR Topography

60. Radar: Processing Freeman-Durden

61. Decision Trees, cont

62. Ground Radar Remote Sensing for Bird and Bad Tracking - Wind Power Impacts.

64. “Due, in part, to their limited capacity for adaptation, wetlands are considered to be among the ecosystems most vulnerable to climate change.” Climate Change and Water IPCC June 2008

65. So why is Radar so important? Daily coverage in 5 years regardless of clouds We could map wetlands over all of North America in a week! Radar sees of water containing features Wetlands and vegetation structure Map water elevation change in wetlands

67. Water Elevation Change Via InSAR

68. Oil Spills

69. ENVISAT 5/2/2010

70. REAL-TIME ORTHO DELIVERY

71. Optical Multi-spectral Tunalble Imagery

72. 2010 July 26 Michigan Oil SpillOne Million Gallons – 20,000 Barrels

73. 51 http://www.sharedgeo.org/ HABITAT ATLAS Social Networking Visualization = Decision Support – timely for both local and national Prioritization Accountability Michigan Tech Collaborative Tools EPA Ducks Unlimited Unknown SharedGeo U of M St. Mary’s U S & L USFWS

74. CONCLUSION: One needs ASSESSMENT Before targeted restoration in order to PREVENT future and more expensive restorations.

75. GOOGLE OMB Place-based

76. ACTION ITEM Contact your local, state, tribal and federal government leaders to support and maintain geospatial assessment! Why? To PREVENT or minimize cleanup (restoration) of larger future disasters = lower taxes!

77. Questions?

78. Remote Sensing Technologies Overview Brian Huberty, FWS NWI Midwest Region Brian_huberty@fws.gov(612) 713-5332 Acknowledgements: Brian Brisco, CCRS Robb Macleod, DU GLARO Laura Chavez, MTRI Steve Apfelbaum, AES Dave Fuhr, Airborne Data Systems Megan Lang, USDA Kurt Kowalski, USGS Dr. William Welsch, EMU Dr. Joe Knight, U of MN Steve Kloiber, MN DNR Mike Hoppus, MN DNR Richard Powell, MTRI Chet Wilberg, CAP Jim Klassen, U of MN Roger Gauthier, GLC Dr. Marvin Bauer, U of MN Dr. Chris Wright, SDSU