目的 通过无人机获取沙糖橘果园的遥感图像，快速提取果树分布位置，为果树的长势监测和产量预估提供参考。方法 以无人机拍摄的可见光遥感图像为研究对象，计算超红指数、超绿指数、超蓝指数、可见光波段差异植被指数、红绿比指数和蓝绿比指数6种可见光植被指数，使用双峰阈值法选取阈值进行果树的提取。在使用光谱指数进行识别的基础上，结合数字表面模型作为识别模型的输入变量，进行对比试验。结果 相比使用单一光谱指数，结合数字表面模型提高了果树和非果树像元的提取精度，6次波段融合后的总体精度均大于97%。超红指数与数字表面模型结合后的总体精度最高，为98.77%，Kappa系数为0.956 7，植被信息提取精度优于其他5种可见光植被指数与数字表面模型结合后的提取精度。结论 数字表面模型结合可见光植被指数的提取方法能够更深层次地挖掘遥感数据蕴含的信息量，为影像中色调相似地物的提取提供参考。
Objective To obtain remote sensing image of sand sugar tangerine orchard by UAV, rapidly extract the distribution position of fruit trees, and provide references for growth monitoring and yield prediction of fruit trees.Method The visible light remote sensing images taken by drones were used as the research object. Six visible light vegetation indexes of excess red index, excess green index, excess blue index, visible band differential vegetation index, red-green ratio index and blue-green ratio index were calculated. We used the double peak threshold method to select the threshold for fruit tree extraction. Based on the spectral index identification, digital surface model was added as input variable of the identification model, and the comparative test was conducted.Result Compared with using a single spectral index, the addition of digital surface model improved the extraction accuracies of fruit tree and non fruit tree pixels. The total accuracies of six band fusions were all greater than 97%. The total accuracy of excess red index combined with digital surface model was the highest (98.77%) with Kappa coefficient of 0.956 7, and the vegetation extraction accuracies were superior to those of other five combinations of visible light vegetation indexes with digital surface model.Conclusion The combination of digital surface model with visible light vegetation index can excavate more deeply the information contained in the remote sensing data, and provide a reference for the extraction of similar tonal features in the image.