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

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

Study Area Introduction Objective Study Area Methods Results Discussion Conclusions

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

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

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

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

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

Level 2 Segmentation Introduction Methods Imagery Software Classification Results Discussion Conclusions

Level 1 Segmentation Introduction Methods Imagery Software Classification Results Discussion Conclusions

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

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

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

Introduction Methods Results Discussion Conclusions

Introduction Methods Results Discussion Conclusions

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

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

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

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

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

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

Acknowledgements  IAGT  Provided satellite imagery  NYS Department of State Division of Coastal Resources  Provided financial support