Selection of sensitive bands for classification of tree species based on pigment content and hyperspectral data

Abstract

Hyperspectral techniques have made it possible the recognition and classification of tree species. However, it is unknown which bands are the most suitable to distinguish tree species. This study aimed at obtaining sensitive bands that can be used for tree species classification based on correlation analysis between the hyperspectral data and pigment contents of trees. In this study, a total of 264 spectral data sets were collected with a band range of 400 nm to 925 nm at fixed points and times form March 2010 to February 2011, including 171 and 93 pieces of branches and leaves for Cunninghamia Lanceolata and Pinus massoniana Lamb, respectively. The in situ hyperspectral reflectance data of these two tree species were collected at canopy using ASD (Analytical Spectral Devices) FieldSpec HandHeld spectrradiometer, a new product of ASD America Inc. At the same time, chlorophyll a&b, chlorophyll-a, chlorophyll-b, carotenoids and xanthophyll of branches and leaves for these species were measured in lab. First, the correlation between the hyperspectral data and the pigment contents was analyzed, and the spectral intervals of these two conifers with higher coefficients of correlation were obtained. The data of spectral intervals selected were used for classification of these tree species using five classification algorithms including Support Vector Machine-Radial Basis Function (SVM-RBF), BP neural network, Mahalanobis Distance, Bayes, and Spectral Angle Mapping (SAM). The results showed that the use of chlorophyll led to better performance of classification than other pigments. The sensitive spectral intervals for these two conifers based on chlorophyll ranged from 401 nm to 504 nm and from 659 nm to 686 nm. Among these five methods, it was found that SVM-RBF and BP neural network classifications resulted in better performance. Second, in order to test the dependence and adaptability for imaging spectral data, the obtained spectral intervals were combined using Gaussian curve instead of spectral response function (SRF) and the combined data was classified using the above five methods. The accuracy of classification obviously reduced around 15% to 20%. But, the performance of chlorophyll a&b could be still better than other pigments. Third, In order to test the applicability of the spectral intervals, we added 46 spectral data sets of Cinnamomum camphora, collected from 2004 to 2006, to do the classification. The performance of chlorophyll was also better than other pigments. This implied that the obtained spectral intervals 401 nm to 504 nm and 659 nm to 686 nm based on chlorophyll were consistent and could be applied to classification of other tree species. But, the spectral intervals were not narrow enough and there is a need of further study and examination.