Tree Species Discrimination Based on Leaf-Level Hyperspectral Characteristic Analysis

The emergence of hyperspectral remote sensing technology will provide chance for solving problems of identifying forest tree species precisely. For discrimination of tree species with hyperspectral remote sensing technology, extraction and selection of the spectral characteristics is a very important process. Compared with multispectral data, hyperspectral data have the characteristics of more bands, larger amount of data and larger redundancy degree. The method of derivative reflectance was used to deal with the original spectral data, analyze and compare curves of the original spectrum, the first derivative reflectance and second derivative reflectance of the different tree species, and the bands with bigger difference were selected to identify the different tree species. Then the Euclidean distance method was used to test the selective bands identifying different tree species, and the results showed that the selective bands could identify different tree species effectively. The bands for identifying different tree species were most near-infrared bands, and the bands with maximum difference derived from the three methods are 1 6571 666, 1 868-1 877 and 1 868-1 877 nm respectively.