## 3 Materials and Methods
### Data Acquisition and Processing  
In this study, the experimental data were obtained from UAV-based remote sensing imagery. A DJI Phantom 4 Pro V2.0 quadcopter served as the UAV platform, with a total weight of 1.375 kg (including battery and propellers), a maximum horizontal flight speed of 72 km/s, and a maximum flight altitude of 6000 m. The payload was a MicaSense RedEdge-M multispectral camera developed by Micasense (USA). This camera simultaneously captures data across five bands, covering visible, near-infrared, and red-edge wavelengths, as detailed in Table 1. Data acquisition was conducted under favorable lighting conditions and wind speeds below Force 3. Before and after each flight, a calibration reflectance panel was placed on the ground and oriented as vertically as possible to the camera in order to facilitate relative radiometric calibration, thus ensuring accurate reflectance data.  
To acquire stable imagery, the flight route was meticulously planned in advance, enabling the UAV to follow a predetermined flight path that covered the entire study area. The flight altitude was set to 60 m, with a cruising speed of 7 m/s; the imagery had a spatial resolution of approximately 4 cm, and the side overlap and forward overlap were 80% and 70%, respectively. Following data collection, the images were mosaicked using the Pix4D Mapper software V4.5.6 and then subjected to geometric correction, radiometric calibration, and clipping as part of the preprocessing workflow [28].  
In the field of quantitative remote sensing research, a series of vegetation indices have been proposed for the extraction of tree vegetation. To investigate whether these vegetation indices could be integrated with deep learning models to achieve better segmentation results, this study selected three relevant vegetation indices based on the characteristics of the research area: the ratio vegetation index (RVI) [29], the difference vegetation index (DVI) [30], and the modified soil-adjusted vegetation index (MSAVI) [31]. These indices were calculated separately and incorporated as new spectral features of the training data input into the model. The RVI enhances the radiation difference between vegetation and soil, enabling it to characterize biomass information across varying vegetation coverage. The DVI is highly sensitive to changes in the soil background, making it more effective in distinguishing vegetation from water bodies. The MSAVI reflects information on ground soil and vegetation coverage under the influence of soil background factors, allowing for the accurate identification of areas with low vegetation coverage. The calculation formulas for these three vegetation indices are presented in Table 2. To ensure the separability of spectral features, this study performed normalization when fusing the original data with the vegetation index data.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Waveband**} & **Central** & **Spectral** & **Panel** \\  & **Wavelength/nm** & **Bandwidth/nm** & **Reflectivity** \\ \hline Blue & 475 & 20 & 0.49 \\ Green & 560 & 20 & 0.49 \\ Red & 668 & 10 & 0.49 \\ Near-infrared & 840 & 40 & 0.49 \\ Red edge & 717 & 10 & 0.49 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of MicaSense RedEdge-M multispectral.