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Published byLouise York Modified over 9 years ago
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Object-oriented Land Cover Classification in an Urbanizing Watershed Erik Nordman, Lindi Quackenbush, and Lee Herrington SUNY College of Environmental Science and Forestry
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Objectives Create a land cover classification Suitable for ArcHydro pollution model Up-to-date High spatial resolution Emphasis on impervious surface Introduction Objectives Study Area Methods Results Discussion Conclusions
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Study Area Introduction Objective Study Area Methods Results Discussion Conclusions
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Methods: Imagery Satellite: QuickBird (DigitalGlobe) 2.44 m multispectral resolution 4 bands (3 visible + NIR) Created NDVI layer Collected over 2 dates Half on each date May and June 2005 Introduction Methods Imagery Software Classification Results Discussion Conclusions
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Detail of Imagery Introduction Methods Imagery Software Classification Results Discussion Conclusions Upper Lake Carmans River
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eCognition: Object-oriented classification Uses spectral, textural and thematic information Segmentation into homogeneous polygons (objects) Can vary the size (homogeneity) of polygons at different “levels” Introduction Methods Imagery Software Classification Results Discussion Conclusions
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Impervious Cover Critical to analyzing runoff and pollution Challenges High spatial resolution Individual roads, houses Tree canopy covers roads Introduction Methods Imagery Software Classification Results Discussion Conclusions
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Impervious Cover Solution Use road vector layer ALIS data set For public safety NYS GIS Clearinghouse 10 meter buffer Introduction Methods Imagery Software Classification Results Discussion Conclusions
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Level 2 Segmentation Introduction Methods Imagery Software Classification Results Discussion Conclusions
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Level 1 Segmentation Introduction Methods Imagery Software Classification Results Discussion Conclusions
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Detail: ALIS roads in Level 2 segmentation Introduction Methods Imagery Software Classification Results Discussion Conclusions
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Classification Classes based on TR-55 Impervious Includes roads, driveways, roofs Tree, Grass Wetlands 3 classes: woody, emergent, tidal Also from thematic layers Bare, Water Introduction Methods Imagery Software Classification Results Discussion Conclusions
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Classification Attributes used in classification included: Color and Brightness Area Shape Index and Compactness GLMC heterogeneity Proximity to objects in other classes Introduction Methods Imagery Software Classification Results Discussion Conclusions
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Introduction Methods Results Discussion Conclusions
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Introduction Methods Results Discussion Conclusions
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Accuracy Assessment Reference data Digital orthophotos Acquired April, 2004 “Leaf-off” Stratified random sample, 727 points Introduction Methods Results Discussion Conclusions
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Accuracy Assessment Overall: 73.9% User’s accuracy of key classes Impervious: 73.4% Tree : 74.5% Grass: 66.7% Introduction Methods Results Discussion Conclusions
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Discussion Accuracy comparable to other studies ALIS road layer successfully used to aid classification Introduction Methods Results Discussion Conclusions
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Discussion Seasonality Imagery “leaf-on” Orthophotos “leaf-off” Affected agreement between classification and reference data Scrub vegetation Confusion among bare, grass and tree classes Introduction Methods Results Discussion Conclusions
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Discussion Accuracy Assessment Response unit: 1 pixel in classified image Response unit should be object, not pixel Introduction Methods Results Discussion Conclusions
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Conclusions QuickBird and eCognition produced a highly detailed classification Adequate for pollution and economic models Thematic layers proved useful Introduction Methods Results Discussion Conclusions
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Acknowledgements IAGT Provided satellite imagery NYS Department of State Division of Coastal Resources Provided financial support
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