## 4 Discussion
### Application of Deep Learning in Tree Species Identification  
Many studies have been conducted on tree species classification using remote sensing datasets through traditional machine-learning techniques, and so far, very few of them have used deep learning models. Contrary to the popular object-based approach to tree species classification, deep learning eliminates the manual feature extraction step by examining the local spatial arrangement and structural patterns characterized by the low-level feature. In the specific classification process, compared with 2D convolution, the hierarchical feature generation process of the Conv1D-based model provides a flexible way to formulate and identify complex sequential patterns in hyperspectral data. In this study, we used 10,269 pixels as training samples from hyperspectral images. For one-dimensional convolutional neural network (Conv1D) classifiers, in addition to adding all spectral features, we also added texture features of various bands as a low-level feature but did not add vegetation indices. In the convolution process of a one-dimensional convolutional neural network, the front and rear nodes have strong correlation and continuity, the single vegetation index feature cannot improve accuracy and has certain interference [35; 41]. Based on the same training samples, we also adopted random forest (RF) classifiers as a comparison. The results show that the Conv1D classifiers had 5% higher overall accuracy than the RF classifier. This is similar to a 7% increase in overall accuracy observed for 1-D CNN over RF in a study focused on land-cover classification with multi-seasonal hyperspectral imagery in Northern California, USA [35]. The Chinese pine class with the lowest number of test samples was misclassified into several different types, and the classification accuracy was the lowest. Although the spectral response of Chinese pine in the near-infrared region was significantly different from that of other tree species (Figure 7), it has been found in our specific field survey that Chinese pine always appeared as a single plant and had many broadleaf tree species around it. Because it is difficult to divide the sample in this situation as Chinese pine, we obtained very few Chinese pine sample data. Thus, a possible reason for lower classification accuracy of Chinese pine in this research could be the complexity and heterogeneity of forests and insufficient training samples. [PERSON] et al. [6] gave the same explanation when applying support vector machine (SVM) and random forest (RF) classifier to classify tree species. Similarly, [PERSON] et al. [63] also experienced similar issues with their classification, highlighting that limited testing samples can result in inaccurate classifications. Although using the same training samples, we found that by comparing the producer's accuracy of the two classifiers there were significant differences in classification accuracy for some tree species, such as Manchurian walnut and aspen. For instance, the producer's accuracy of Manchurian walnut with Conv1D-based classifier was 89.10%, while that of RF classifier was 71.77%. We believe that the algorithm of two kinds of classifiers is the main reason that the classification accuracy of the same tree species differs greatly. For deep convolutional neural networks, each neuron is no longer connected with all neurons in the upper layer but only some of them [23; 40; 64]. Furthermore, the use of an activation function increases the nonlinearity of the neural network [24]. Compared with other functions [65], the ReLU function can effectively alleviate the problem of overfitting and increase the accuracy of tree species classification [66], so the convolutional neural network can achieve better learning by keeping as many important parameters as possible and removing a large number of unimportant parameters [64; 67]. Although random forest has strong generalization ability, it is easy to overfit the training data with large noise [39; 54; 68].