The classification of individual tree species (ITS) is beneficial to forest management and protection. Previous studies in ITS classification that are primarily based on airborne LiDAR and aerial photographs have achieved the highest classification accuracies. However, because of the complex and high cost of data acquisition, it is difficult to apply ITS classification in the classification of large-area forests. High-resolution, satellite remote sensing data have abundant sources and significant application potential in ITS classification. Based on Worldview-3 and Google Earth images, convolutional neural network (CNN) models were employed to improve the classification accuracy of ITS by fully utilizing the feature information contained in different seasonal images. Among the three CNN models, DenseNet yielded better performances than ResNet and GoogLeNet. It offered an OA of 75.1% for seven tree species using only the WorldView-3 image and an OA of 78.1% using the combinations of WorldView-3 and autumn Google Earth images. The results indicated that Google Earth images with suitable temporal detail could be employed as auxiliary data to improve the classification accuracy.

With the wide availability of remotely sensed data from various sensors, fusion-based tree species classification approaches have emerged as a prominent and ongoing research topic. However, most recent studies primarily focused on combining multisource data at the feature level, while few systematically examined their positive or negative contributions to tree species classification. This study aimed to investigate fusion approaches at the feature and decision levels deployed with support vector machine and random forest algorithms to classify five dominant tree species: Norway maple, honey locust, Austrian pine, white spruce, and blue spruce in individual crowns. Spectral, textural, and structural features derived from multispectral imagery (MSI), a very high-resolution panchromatic image (PAN), and LiDAR data were systematically exploited to assess their contributions to accurate classifications. Among the various classification schemes that were explored, both feature- and decision-level fusion approaches demonstrated significant improvements in tree species classification compared with the utilization of MSI (0.7), PAN (0.74), or LiDAR (0.8) in isolation. Notably, the decision-level fusion approach achieved the highest overall accuracies (0.86 for SVM and 0.84 for RF) and kappa coefficients (0.82 for SVM and 0.79 for RF). The misclassification analysis of fusion approaches highlighted the potential and flexibility of decision-level fusion in tree species classification.