Transcript 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.

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
Discussion Topics
Introduction to Brownfields and
Redevelopment
Site Identification
Research Objectives and Process
Additional Considerations
Summary
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)
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
Brownfield Redevelopment
Benefits
– Increase tax base
– Use existing infrastructure
– Job growth
– Improve environment
– Conserve open land
Grants available to “eligible entities” for
– Site inventory
– Characterization
– Assessment
– Planning
How do you find
them?
Source: U.S. EPA, 2004a
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
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?
City of Syracuse Land Cover
Thematic Land
Cover Map
Modeling
Analysis
Suitability Studies
No Indication of
Land Use
Need more information
New classification
procedure can help to
address this
Source: Myeong et al., 2001.
Object-Oriented Image Classification
Classify “image objects,” not pixels
Classification based on spatial context
rules
Classify complex ground features
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
Process
Image Segmentation
Data
Rule Development
Land Cover
Classification
Rule Refinement
Structure Group
Assignment
Classification
Export Output
Knowledge
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
What Does a Brownfield Look
Like?
Input Layers
for
Segmentation
 b1  b3 
NDVI  
 * 255 Lillesand et. al.,
 b1  b3 
2004
b1chrom 
b1* 255
b12  b22  b32
Radja, 1994
Image Object Creation (Segmentation)
Scale Parameter = 25
Scale Parameter = 100
Image Objects – Lives of Their Own
Rule Development
Rule Development
Combinations of
functions can be
applied
Working with object
values directly
Transparency
Land Cover
Classification
Level 1
Land Cover
Classification
Level 2
Level 1
Objects
Extracted
from Level 2
Structuring of
Image Objects
Potential
Brownfield
Site
Land cover classes  Land use indicator
Classification
Stability
High (good class
separation)
Low (ambiguous
class assignment)
Membership
Classification Stability
0.86
0.83
Tree Grass
Membership
Classify smaller,
more homogeneous
objects
Refine rules
Create a new class
Live with it
0.89
0.62
Tree Grass
Accuracy Assessment
Output vector layer of potential brownfield parcels
Evaluate classification based on agreement with
reference data
Error Matrix
Reference
Classification
Brownfield
Non Bfield
Row
Total
Brownfield True Positive False Positive TP + FP
Non Bfield
False
Negative
True
Negative
FN + TN
Column
Total
TP + FN
FP + TN
TOTAL
Producer’s
Accuracy
TP / Column
Total
TN / Column
Total
User’s
Accuracy
TP / Row
Total
TN / Row
Total
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
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
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
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.
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.