| Summary: | To fully mine information regarding differences among various tree species from remote sensing data and improve the accuracy of tree species recognition, this study utilized the spectral reflection value, wavelength, and time as parameters and employed three algorithms to create an expression for the spectral volume index (SVI). Then, data were obtained by applying RedEdge-MX to four phases, SVI features were extracted, and a mixed feature set of spectral band + texture + digital surface model + SVI was constructed. A random forest algorithm was employed to determine the importance of the SVI features and derive the optimal feature set for tree species classification. The additional objectives were to determine if the SVI features have an active role in tree species classification and which algorithm is more conducive for extracting useful SVI features. The SVI features extracted with volume constraints exhibit better performance in tree species recognition than those extracted without volume constraints. Moreover, the SVI features extracted using a variable-constrained volume were better than those extracted using a constant-constrained volume. The combination of SVI features could improve the accuracy of tree species recognition (the highest overall accuracy was 92.76%), but the improvement effect was limited (the value was 92.16% when SVI features were not combined). These findings show that the SVI obtained using this method could be used to mine the difference information of tree species in images to a certain extent and hence, could be used in tree species identification.
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