An Exploratory Analysis of in Situ Hyperspectral Data for Broadleaf Species Recognition

Timely and accurate identification of tree species by spectral methods is crucial for forest and urban ecological management. It has been proved that traditional methods and data cannot meet such requirements. In this study, a total of 394 reflectance spectra (between 350 and 2500 nm) from foliage branches or canopy of 11 important urban forest broadleaf species were measured in the City of Tampa, Florida, U.S. with a spectrometer. The 11 species include American Elm (Ulmus americana), Bluejack Oak (Q. incana), Crape Myrtle (Lagerstroemia indica), Laurel Oak (Q. laurifolia), Live Oak (Q. virginiana), Southern Magnolia (Magnolia grandiflora), Persimmon (Diospyros virginiana), Red Maple (Acer rubrum), Sand Live Oak (Q. geminata), American Sycamore (Platanus occidentalis), and Turkey Oak (Q. laevis). A total of 46 spectral variables, including normalized spectra, derivative spectra, spectral vegetation indices, spectral position variables, and spectral absorption features were extracted and analyzed from the in situ hyperspectral measurements. Two classification algorithms were used to identify the 11 broadleaf species: a non-linear artificial neural network (ANN) and a linear discriminant analysis (LDA). An ANOVA analysis indicates that the 30 selected spectral variables are effective to differentiate the 11 species. The 30 selected spectral variables account for water absorption features at 970 nm, 1200, and 1750 nm and reflect characteristics of pigments in tree leaves, especially variability of chlorophyll content in leaves. The experimental results indicate that both classification algorithms (ANN and LDA) have produced acceptable accuracies (OAA from 86.3 % to 87.8%, Kappa from 0.83 to 0.87) and have a similar performance for classifying the 11 broadleaf species with input of the 30 selected spectral variables. The preliminary results of identifying the 11 species with the in situ hyperspectral data imply that current remote-sensing techniques are still difficult but possible to identify similar species to such 11 broadleaf species with an acceptable accuracy.