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Object-oriented Land Cover Classification in an Urbanizing Watershed

This study presents an advanced land cover classification tailored for use in ArcHydro pollution models within an urbanizing watershed. Utilizing high-resolution satellite imagery from QuickBird and employing object-oriented classification methods, the research focuses on accurately categorizing impervious surfaces, tree canopies, and wetland areas. The classification leverages spectral, textural, and thematic data to enhance precision. Results show an overall accuracy of 73.9%, making it a valuable resource for urban planning and environmental management.

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Object-oriented Land Cover Classification in an Urbanizing Watershed

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  1. Object-oriented Land Cover Classification in an Urbanizing Watershed Erik Nordman, Lindi Quackenbush, and Lee Herrington SUNY College of Environmental Science and Forestry

  2. 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

  3. Study Area Introduction Objective Study Area Methods Results Discussion Conclusions

  4. 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

  5. Detail of Imagery Upper Lake Introduction Methods Imagery Software Classification Results Discussion Conclusions Carmans River

  6. 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

  7. 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

  8. 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

  9. Level 2 Segmentation Introduction Methods Imagery Software Classification Results Discussion Conclusions

  10. Level 1 Segmentation Introduction Methods Imagery Software Classification Results Discussion Conclusions

  11. Detail: ALIS roads in Level 2 segmentation Introduction Methods Imagery Software Classification Results Discussion Conclusions

  12. 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

  13. 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

  14. Introduction Methods Results Discussion Conclusions

  15. Introduction Methods Results Discussion Conclusions

  16. Accuracy Assessment • Reference data • Digital orthophotos • Acquired April, 2004 • “Leaf-off” • Stratified random sample, 727 points Introduction Methods Results Discussion Conclusions

  17. 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

  18. Discussion • Accuracy comparable to other studies • ALIS road layer successfully used to aid classification Introduction Methods Results Discussion Conclusions

  19. 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

  20. Discussion • Accuracy Assessment • Response unit: 1 pixel in classified image • Response unit should be object, not pixel Introduction Methods Results Discussion Conclusions

  21. Conclusions • QuickBird and eCognition produced a highly detailed classification • Adequate for pollution and economic models • Thematic layers proved useful Introduction Methods Results Discussion Conclusions

  22. Acknowledgements • IAGT • Provided satellite imagery • NYS Department of State Division of Coastal Resources • Provided financial support

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