Identifying Santa Barbara's urban tree species from AVIRIS imagery using canonical discriminant analysis

Abstract

In this research, we classify 15 common urban trees in downtown Santa Barbara, California, using crown-level canonical discriminant analysis (CDA) on airborne visible/infrared imaging spectrometer (AVIRIS) imagery. We compare the CDA classification accuracy against results obtained from stepwise discriminant analysis. We also examine the impact of various crown-level aggregation techniques and training sample size on classification results. An overall classification accuracy of 86% was achieved using CDA. Species-specific results were highest for dense crowns with high normalized difference vegetation index values. Bands chosen using forward feature selection spanned AVIRIS full spectral range illustrating a need for retaining a full complement of spectral information. Nevertheless, there is some indication that bands along the green edge, green peak and yellow edge are particularly valuable for discriminating structurally similar urban trees.

Acknowledgements

The authors thank the Naval Postgraduate School (Award # N00244-11-1-0028) for funding this research and Seth Peterson for general guidance. AVIRIS radiance imagery was supplied by the Jet Propulsion Laboratory.