## 4 Discussion  
### Discrimination of Tree Species With Hyperspectral Data  
In tree canopies the amount of radiation that is reflected in the different wavelength regions is related to plant chemical properties of the tissue, leaf morphology, canopy structure, and tree size compared to neighboring trees [1]. Hence, the low spectral resolution of the very-high-resolution (VHR) image cannot allow a detailed tree species classification, even if many studies exploit VHR data for forest classification [8; 57; 58]. According to chemical plant components, fine spectral resolution hyperspectral sensors can differentiate tree species [4; 52]. As an example, [PERSON] et al. [59] and [PERSON] et al. [60] used the dense sampling and narrow band measures of the tree spectral signature to relate each portion of the spectrum to specific characteristics of the plants, which can be exploited for classification purposes, as well as the identification of some plant diseases or attribute estimation. In this study, we used two classification methods to classify tree species with hyperspectral images. The results showed that both methods have good performance but there are different accuracies for different tree species that can be explained by different spectral reflectance [51]. In some cases, an individual species was misclassified as other classes, as was demonstrated by the white birch class commonly being incorrectly classified as Amur linden. As discussed by [PERSON] et al. [61], misclassification within the same tree types (conifers and broadleaf) occur at a higher rate than they do between tree types. Some broadleaf tree classes (aspen, white birch, Manchurian walnut, Amur linden) were misclassified as other broadleaf species, likely as a result of similar spectral signatures among the broadleaf species. Figure 7 shows the boxplots of the values of the 32 hyperspectral bands versus the tree species; it can be seen that the spectral reflectivity (SWIR region) of broadleaf trees is significantly higher than that of coniferous trees. It is also clear that the spectral response of the analyzed species was quite similar in the visible range, while for the infrared channels there were significantly different responses considering coniferous and broadleaf species. It is worth noting that, in general, the spectral value range was quite broad across all classes. Even species of broadleaf trees with similar spectral reflectance such as Manchurian walnut and aspen can be well distinguished. These differences in levels of reflectance are the main drivers to discriminate species in the VIS-SWIR region. Figure 7: Boxplots of the reflectance of the 32 hyperspectral bands selected for the seven tree species analyzed. The midline of the box plot represents the median, and the hinges (end of the boxes) represent the 25 th and 75 th quartiles. The lines are drawn from each hinge to 1.5 times the spread (75 th–25 th quartile) or to the most extreme value (if smaller). Any point outside these values is represented as a circular point. In order to prove the performance of OHS-1 hyperspectral data, we compared the classification accuracy in the same area or adjacent area. Based on HJ-1A hyperspectral images, [PERSON] [62] applied the classification method of tree species spectral feature knowledge to classify the tree species in the Wangqing forest region of Jilin province. They classified three tree species (Mongolian oak, white birch, and Dahurian larch) and overall classification accuracy was 75%. Although the classification accuracy of a single tree species can reach 87.5%, other tree species in this region are not separated. In this study, we apply all hyperspectral bands to classify tree species. Compared with HJ-1A images (100 m), the spatial resolution of an OHS-1 image is 10 m, which reduces the influence of mixed pixel effect and greatly improves the classification accuracy.