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

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.