1. Object-Oriented Image Classification of Brownfields in Syracuse, NY Greg Bacon Master of Science Degree Candidate Environmental Resources and Forest Engineering SUNY College of Environmental Science and Forestry April 5, 2006

2. Discussion Topics Introduction to Brownfields and Redevelopment Site Identification Research Objectives and Process Additional Considerations Summary

3. Introduction Brownfield Definition “…real property, the expansion, redevelopment, or reuse of which may be complicated by the presence or potential presence of a hazardous substance, pollutant, or contaminant.” Section 211(a) of the Small Business Liability Relief and Brownfields Revitalization Act of 2002 (Pub.L. 107-118)

4. Current Status EPA estimates there are 500K – 1M U.S. brownfield sites 85-90% of these not evaluated or cleaned up Brownfields Revitalization Act expected to expand number of sites assessed for cleanup/redevelopment –Liability protection –Grant funding Source: U.S. EPA, 2004b

5. Brownfield Redevelopment Benefits Grants available to “eligible entities” for –Site inventory –Characterization –Assessment –Planning – Increase tax base – Job growth – Conserve open land – Use existing infrastructure – Improve environment How do you find them? Source: U.S. EPA, 2004a

6. Brownfield Site Identification Traditional Site Identification Government derived information: tax/ ownership records, state environmental data –Currency, completeness, cost Site visits –Site access, practicality, cost City of Syracuse site inventory used EPA grant –Reference data for accuracy assessment

7. Research Objectives Apply a brownfield site identification method to produce a GIS-ready product –More efficient resource use –Visual supplement to other site inventory methods Evaluate accuracy of classification –Could this be a useful tool in other places?

8. Source: Myeong et al., 2001. City of Syracuse Land Cover Thematic Land Cover MapModelingAnalysis Suitability Studies No Indication of Land Use Need more information New classification procedure can help to address this

9. Classify “image objects,” not pixels Classification based on spatial context rules Classify complex ground features Object-Oriented Image Classification

10. Example Applications Built-Up Land –Johnsson, 1994 Undeclared Nuclear Facilities –Niemeyer and Canty, 2001 Forest Cut Blocks –Flanders et. al., 2003 Brownfields –Banzhaf and Netzband, 2004

11. Process Land Cover Classification Structure Group Assignment Classification Export Output Rule Refinement Data Knowledge Image Segmentation Rule Development

12. Project Data Needs Syracuse streets (vector shapefile) Tax parcels (vector shapefile) Brownfield addresses (Excel spreadsheet) Emerge Imagery –NIR, red, green bands –0.61 m (2 ft) ground sample distance –8-bit radiometry –Collected 13 July 1999

13. What Does a Brownfield Look Like?

14. Radja, 1994 Lillesand et. al., 2004 Input Layers for Segmentation

15. Image Object Creation (Segmentation) Scale Parameter = 25 Scale Parameter = 100

16. Image Objects – Lives of Their Own

17. Rule Development

18. Combinations of functions can be applied Working with object values directly Transparency

19. Land Cover Classification Level 1

20. Land Cover Classification Level 2

21. Level 1 Objects Extracted from Level 2

22. Structuring of Image Objects Potential Brownfield Site Land cover classes  Land use indicator

23. Classification Stability Low (ambiguous class assignment) High (good class separation)

24. Classification Stability Classify smaller, more homogeneous objects Refine rules Create a new class Live with it TreeGrass 0.86 0.83 Membership TreeGrass 0.89 0.62

25. Accuracy Assessment Output vector layer of potential brownfield parcels Evaluate classification based on agreement with reference data Error Matrix

26. Additional Considerations Brownfield definition –What qualifies as a brownfield is debatable –Characteristics not described by legal definition –Remote sensing alone cannot fully examine site function, only form Accuracy Issues –Quality of land cover classification directly affects land use indicator –Completeness and quality of reference data –Temporal difference between image and reference data collection

27. Summary Brownfields represented by group of collocated cover types –Accuracy is affected by strength of this assumption Object-oriented classification –Attempt to imitate human pattern recognition –Membership functions classify objects on a sliding scale Transition from land cover to land use

28. Acknowledgements Dr. Lindi Quackenbush – SUNY ESF Faculty of Environmental Resources & Forest Engineering Dr. Stephen Stehman – SUNY ESF Faculty of Forest & Natural Resources Management Mr. Mike Haggerty – (formerly) City of Syracuse Department of Economic Development Ms. Amy Santos – Environmental Finance Center, Maxwell School of Citizenship and Public Affairs

29. References Banzhaf, E. and M. Netzband, 2004. Detecting Urban Brownfields by Means of High Resolution Satellite Imagery. International Society for Photogrammetry and Remote Sensing (ISPRS) Conference Proceedings, July 2004, Istanbul, Turkey. Flanders, D., M. Hall-Beyer, and J. Pereverzoff, 2003. Preliminary Evaluation of eCognition Object-Based Software for Cut Block Delineation and Feature Extraction. Canadian Journal of Remote Sensing. 29(4), 441-452. Johnsson, K., 1994. Segment-Based Land-Use Classification from SPOT Satellite Data. Photogrammetric Engineering and Remote Sensing. 60(1), 47-53. Lillesand, T.M., R.W. Kiefer, and J.W. Chipman, 2004. Remote Sensing and Image Interpretation, Fifth Edition, John Wiley & Sons, Inc., New York, 763 p. Myeong, S., D. Nowak, P. Hopkins, and R. Brock, 2001. Urban Cover Mapping Using Digital, High-Spatial Resolution Aerial Imagery. Urban Ecosystems. 5, 243-256.

30. References (cont’d) Niemeyer, I. and M.J. Canty, 2001. Knowledge-Based Interpretation of Satellite Data by Object-Based and Multi-Scale Image Analysis in the Context of Nuclear Verification. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), July 2001, Sydney, Australia,. 7, 2982-2984. URL: http://www.niemeyer.de/publications/igarss01nie.pdf. Radja, P.G., 1994. Green: Segmentation of an Aerial Video Recording for Tree Counting, M.S. Thesis, University of Illinois at Urbana-Champaign, 104 p. U.S. Environmental Protection Agency 2004a. Brownfields Assessment Grants: Interested in Applying for Funding? EPA560-F-04-254, URL: http://www.epa.gov/brownfields/facts/fy05assessment_factsheet.pdf. ----- 2004b. Cleaning Up the Nation’s Waste Sites: Markets and Technology Trends, 2004 Edition, EPA542-R-04-015.