## 3 Results and Discussion
### Production of Decision Tree  
The study area comprised two types of terrain: mountains and plains. Therefore, the first layer of the decision tree was based on the DEM data to separate the mountains and plains, and the area with DEM greater than 300 was recognized as a mountainous area by combining the high-resolution image, and the area with DEM lower than 300 was recognized as a plain. The sensitivity of mountains and plains to different vegetation indices is different, so, according to the sensitivity analysis of the vegetation indices, the EVI and NDVI, which were the most effective in distinguishing mountains and plains, were selected as the second layer of the decision tree. A value of 2.5 was selected as the threshold value of the EVI, and the range of fruit trees was defined as less than 2.5. Meanwhile, 0.45 was selected as the threshold value of the NDVI, the range of fruit trees was defined as less than 0.45, and the threshold value of the range greater than 0.45 was defined as the range of mountain vegetation. Cropland was mostly distributed in the plain area, and the third layer of the decision tree first distinguished cropland. To ensure that the threshold value could cover all cropland, the determination condition of cropland \(C\_D>3\), the threshold value was greater than 3 for the recognition of cropland, and the value was less than 3 for the recognition of vegetation containing fruit trees. Influenced by the topography of the southern region, there were hills with lower elevations in the plain area, with the planting of some non-cash-crop species, so the fourth layer of the decision tree used the elevation to retain only the height range suitable for lithi fruit tree planting, elevations (ELE) to 150 m as the boundary to remove the mountainous vegetation, and those less than 150 for the existence of fruit trees. The last layer of the decision tree was used to distinguish the fruit trees and other forest and vegetation, based on the two identification parameters of \(Hv\) and \(Tb\). We set the identification conditions as \(Hv>1\) and \(Tb>1\) to extract the lithi fruit trees. The values of the two identification parameters, \(Cr\) and \(Lo\), were set to \(Cr>1.5\) and \(Lo>1\) to extract citrus and longan, respectively (as shown in Figure 6).  
The validity of the decision tree model for the extraction of fruit trees was confirmed through field survey data and area statistics. However, SAR data can be influenced by topographic factors, and the backscattering coefficients of various features may be relatively similar due to the varying surface properties of lowlands and highlands and the disparate reflection and absorption characteristics of electromagnetic waves [47]. Identifying different features in complex terrain areas using SAR data alone is difficult. The integration of SAR and optical data was necessary to highlight unique features and extract target features successfully. The study area comprised complex mountainous and hilly terrain. The analysis of S-2 visible data using the NDVI and EVI indices effectively distinguished vegetation in different terrain conditions [55]. The backscattering coefficients of the S-1 data were influenced by factors such as the leaf density, planting structure, and water content. These factors made it difficult to determine reasonable vegetation information and a segmentation threshold for vegetation information [56]. The microwave data's backward scattering coefficient showed marked differences during crop harvesting and sowing compared to fruit trees and other vegetation, with relatively smaller differences.