## Materials and Methods
### Red Edge Technique  
The Red Edge (RE) refers to the wavelengths at which vegetation grows fastest from 670 to 760 nm. The RE is closely related to various physical and chemical parameters of vegetation such as chlorophyll content, leaf cell structure and hydric status. The RE is a vitality indicator for surveying vegetation growth status because it is related to plant pigment status and growth health. The reflectance on the left side of RE is mainly related to the chlorophyll content, while the reflectance on the right side of RE is mainly affected by the tissue structure in the leaf and the water content in the plant. [PERSON] [38] proposed that the shift of RE's location in the long wave direction reflected the increase of chlorophyll concentration in plants, which was due to the increased chlorophyll concentration and the enhanced photosynthesis of plants, requiring more photons to be consumed. As a conclusion, RE's peak value, location, and area are the most commonly used factors for hyperspectral remote sensing to monitor the chlorophyll, physiological activities, and biomasses of agricultural and forestry crops [39; 40]. Figure 2: Sub-area of processed Sentinel-2A multispectral image including UAV flight site (located at the red dot). Obviously, there were varying proportions of light and dark pixels from the detailed tree canopy region in the UAV hyperspectral images, which caused missing or wrong spectral information in regard to species recognition. Alternative reasons may be the direction of solar irradiation, the sensor orientation, and obstructing tree components (leaves, stems, etc.). Typical leaf pixels with different illumination proportions from the two tree species were chosen; their spectra are shown in Figure 3.  
The result displayed in Figure 3 shows that the overall shape of a single tree species with different proportions of shadow and illumination is highly similar, and the brighter pixel's spectrum has an observably higher amplitude overall. The difference is very small in the blue or green bands but becomes greater in the red or near-infrared (NIR) bands. Therefore, illumination conditions can be distinguished more easily using red-nearby vegetation index related bands. We selected 80 typical canopy pixels of _Pinus_ and _Larix_ with different proportions of illumination and shadow from the UAV hyperspectral image, whose spectral sampling interval was 4 nm from 450 to 950 nm. The interval was narrow enough, so only the sampled wavelengths of the UAV spectral are calculated, such as 450, 454, 458... 946, 950, every 4 nm of the progression. Therefore, we defined that a differential transformation formula (Equation (1)) was used to calculate the first-order differential of the spectrum, whose maximum value determined the location of the RE:  
\[R(\lambda_{i})^{\prime}=\frac{R(\lambda_{i+1})-R(\lambda_{i})}{\Delta\lambda} \tag{1}\]  
where \(R(\lambda_{i})\) is the reflectance at the wavelength of \(\lambda_{i}\), and \(\Delta\lambda\) is the interval distance between adjacent wavelengths. Other characters such as curve slope and reflectance can be easily obtained when the location is determined. The RE curve slope can be calculated using its former and latter wavelength (in nanometers) and the corresponding reflectance because the spectral curve in RE range is smooth enough. All data of slope and reflectance of RE from Table 2 are strictly calculated with corresponding bands after determination of RE's location using Equation (1). The precise location of RE determined using Equation (1) is required for study only using typical pixels. While for quantitative calculation of the whole image and practical application afterwards. The RE's location should be set at a wavelength with the statistical maximum frequency of the selected canopy pixels. In this study, all original and processed data from 160 typical canopy pixels were calculated using Equation (1) to determine the location of RE. RE's location is calculated at first.