Object-Oriented Image Object-Oriented Image Classification of Brownfields in Classification of Brownfields in

Syracuse, NYSyracuse, NYGreg BaconGreg Bacon

Master of Science Degree CandidateMaster of Science Degree CandidateEnvironmental Resources and Forest EngineeringEnvironmental Resources and Forest Engineering

SUNY College of Environmental Science and ForestrySUNY College of Environmental Science and Forestry

April 5, 2006April 5, 2006

Discussion TopicsDiscussion Topics

Introduction to Brownfields and Introduction to Brownfields and RedevelopmentRedevelopment

Site IdentificationSite Identification

Research Objectives and ProcessResearch Objectives and Process

Additional Considerations Additional Considerations

SummarySummary

IntroductionIntroduction

Brownfield DefinitionBrownfield Definition

“…“…real property, the expansion, redevelopment, or real property, the expansion, redevelopment, or reuse of which may be complicated by the reuse of which may be complicated by the presence or potential presence of a hazardous presence or potential presence of a hazardous substance, pollutant, or contaminant.” substance, pollutant, or contaminant.”

Section 211(a) of the Small Business Liability Section 211(a) of the Small Business Liability Relief and Brownfields Revitalization Act of 2002 Relief and Brownfields Revitalization Act of 2002 (Pub.L. 107-118) (Pub.L. 107-118)

Current StatusCurrent StatusEPA estimates there are 500K – 1M U.S. EPA estimates there are 500K – 1M U.S. brownfield sites brownfield sites

85-90% of these not evaluated or cleaned 85-90% of these not evaluated or cleaned upup

Brownfields Revitalization Act expected to Brownfields Revitalization Act expected to expand number of sites assessed for expand number of sites assessed for cleanup/redevelopmentcleanup/redevelopment– Liability protectionLiability protection– Grant fundingGrant funding

Source: U.S. EPA, 2004b

Brownfield RedevelopmentBrownfield RedevelopmentBenefitsBenefits

Grants available to “eligible entities” forGrants available to “eligible entities” for– Site inventorySite inventory– CharacterizationCharacterization– Assessment Assessment – PlanningPlanning

– Increase tax base

– Job growth

– Conserve open land

– Use existing infrastructure

– Improve environment

How do you find them?

Source: U.S. EPA, 2004a

Brownfield Site IdentificationBrownfield Site Identification

Traditional Site IdentificationTraditional Site Identification

Government derived information: tax/ Government derived information: tax/ ownership records, state environmental ownership records, state environmental datadata– Currency, completeness, costCurrency, completeness, cost

Site visitsSite visits– Site access, practicality, costSite access, practicality, cost

City of Syracuse site inventory used EPA City of Syracuse site inventory used EPA grant grant – Reference data for accuracy assessment Reference data for accuracy assessment

Research ObjectivesResearch Objectives

Apply a brownfield site identification Apply a brownfield site identification method to produce a GIS-ready productmethod to produce a GIS-ready product– More efficient resource useMore efficient resource use– Visual supplement to other site inventory Visual supplement to other site inventory

methods methods

Evaluate accuracy of classificationEvaluate accuracy of classification– Could this be a useful tool in other places?Could this be a useful tool in other places?

Source: Myeong et al., 2001.

City of Syracuse Land Cover Thematic Land Cover Map

ModelingModeling

AnalysisAnalysis

Suitability StudiesSuitability Studies

No Indication of Land Use

Need more informationNeed more information

New classification New classification procedure can help to procedure can help to address thisaddress this

Classify “image objects,” not pixelsClassify “image objects,” not pixels

Classification based on spatial context Classification based on spatial context rulesrules

Classify complex ground features Classify complex ground features

Object-Oriented Image ClassificationObject-Oriented Image Classification

Example ApplicationsExample Applications

Built-Up LandBuilt-Up Land– Johnsson, 1994Johnsson, 1994

Undeclared Nuclear FacilitiesUndeclared Nuclear Facilities– Niemeyer and Canty, 2001Niemeyer and Canty, 2001

Forest Cut BlocksForest Cut Blocks– Flanders Flanders et. alet. al., 2003., 2003

BrownfieldsBrownfields– Banzhaf and Netzband, 2004Banzhaf and Netzband, 2004

ProcessProcess

Land Cover Classification

Structure GroupAssignment

Classification

Export Output

Rule Refinement

Data

Knowledge

Image Segmentation

Rule Development

Project Data Needs Project Data Needs

Syracuse streets (vector shapefile) Syracuse streets (vector shapefile)

Tax parcels (vector shapefile)Tax parcels (vector shapefile)

Brownfield addresses (Excel spreadsheet)Brownfield addresses (Excel spreadsheet)

Emerge Imagery Emerge Imagery – NIR, red, green bandsNIR, red, green bands– 0.61 m (2 ft) ground sample distance0.61 m (2 ft) ground sample distance– 8-bit radiometry 8-bit radiometry – Collected 13 July 1999Collected 13 July 1999

What Does a Brownfield Look Like?What Does a Brownfield Look Like?

Radja, 1994

Lillesand et. al., 2004

Input LayersInput Layersfor for

SegmentationSegmentation

255*31

31

bb

bbNDVI

222 321

255*11

bbb

bb chrom

Image Object Creation (Segmentation)Image Object Creation (Segmentation)

Scale Parameter = 25

Scale Parameter = 100

Image Objects – Lives of Their OwnImage Objects – Lives of Their Own

Rule DevelopmentRule Development

Rule DevelopmentRule Development

Combinations of Combinations of functions can be functions can be appliedapplied

Working with object Working with object values directlyvalues directly

TransparencyTransparency

Land Cover Land Cover Classification Classification

Level 1Level 1

Land Cover Land Cover Classification Classification

Level 2Level 2

Level 1 Objects

Extracted from Level 2

Structuring of Structuring of Image ObjectsImage Objects

Potential Brownfield Site

Land cover classes Land use indicator

Classification Classification StabilityStability

Low (ambiguous class assignment)

High (good class separation)

Classification StabilityClassification Stability

Classify smaller, Classify smaller, more homogeneous more homogeneous objectsobjects

Refine rulesRefine rules

Create a new classCreate a new class

Live with itLive with it

Tree Grass

0.860.83

Mem

bers

hip

Mem

bers

hip

Tree Grass

0.89

0.62

Accuracy AssessmentAccuracy AssessmentOutput vector layer of potential brownfield parcelsOutput vector layer of potential brownfield parcels

Evaluate classification based on agreement with Evaluate classification based on agreement with reference datareference data

Error Matrix

Additional ConsiderationsAdditional ConsiderationsBrownfield definition Brownfield definition – What qualifies as a brownfield is debatableWhat qualifies as a brownfield is debatable– Characteristics not described by legal definitionCharacteristics not described by legal definition– Remote sensing alone cannot fully examine site Remote sensing alone cannot fully examine site

functionfunction, only , only formform

Accuracy IssuesAccuracy Issues– Quality of land cover classification directly affects land Quality of land cover classification directly affects land

use indicatoruse indicator– Completeness and quality of reference dataCompleteness and quality of reference data– Temporal difference between image and reference Temporal difference between image and reference

data collectiondata collection

SummarySummaryBrownfields represented by group of Brownfields represented by group of collocated cover typescollocated cover types– Accuracy is affected by strength of this Accuracy is affected by strength of this

assumptionassumption

Object-oriented classification Object-oriented classification – Attempt to imitate human pattern recognitionAttempt to imitate human pattern recognition– Membership functions classify objects on a Membership functions classify objects on a

sliding scalesliding scale

Transition from land cover to land use Transition from land cover to land use

AcknowledgementsAcknowledgements

Dr. Lindi Quackenbush – SUNY ESF Faculty of Dr. Lindi Quackenbush – SUNY ESF Faculty of Environmental Resources & Forest EngineeringEnvironmental Resources & Forest Engineering

Dr. Stephen Stehman – SUNY ESF Faculty of Dr. Stephen Stehman – SUNY ESF Faculty of Forest & Natural Resources ManagementForest & Natural Resources Management

Mr. Mike Haggerty – (formerly) City of Syracuse Mr. Mike Haggerty – (formerly) City of Syracuse Department of Economic DevelopmentDepartment of Economic Development

Ms. Amy Santos – Environmental Finance Center, Ms. Amy Santos – Environmental Finance Center, Maxwell School of Citizenship and Public AffairsMaxwell School of Citizenship and Public Affairs

ReferencesReferencesBanzhaf, 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)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.