在机载LiDAR(Light Detection and Ranging)数据和高空间分辨率航空影像的支持下，以城市为实验区，实现了单木树冠提取。首先通过LiDAR数据获取高差模型，将其作为包含林木的感兴趣区，再通过掩膜方式提取高分影像上的相同区域，然后采用标记分水岭分割算法分别对两幅感兴趣区影像进行树冠提取，最后以人工勾绘树冠结果为参考评价分割精度，比较了两种数据源提取树冠的优缺点。结果显示，利用LiDAR数据获取的高差模型中包含85.28%的林木信息，林木区域提取的效果显著；基于高分影像得到的分割结果较好，F值为57.14%，基于高度差值模型影像的分割结果较差，F值为42.47%。表明分水岭算法方便可行，且高分影像提供的二维信息更适用于树冠提取。
Abstract：With the support of airborne Light Detection and Ranging (LiDAR) data and high spatial resolution aerial imagery，this paper presents an individual tree extraction method that takes the region of urban as the study area.The elevation difference model originated from LiDAR data was used to extract regions of interest including trees. Then，masking was applied to the high spatial resolution aerial imagery to get the same regions. Besides，image segmentations，based on the marked watershed algorithm，were processed on the high spatial resolution aerial imagery and the elevation difference model separately to extract individual tree crowns. Finally，we took a visual interpretation to delineate tree crowns manually and this result was regarded as the reference crowns map. The extraction accuracies were assessed by comparing the spatial relationships of the reference crowns and the automated delineated tree crowns based on the elevation difference model and the high resolution imagery. The results show that the LiDAR data is developed to improve the efficiency of obtaining forest region that the canopy height model include 85.25% forest information. In addition，the tree crowns extraction accuracy based on the high resolution aerial imagery is 57.14%，while another extraction accuracy based on the elevation difference model is 42.47%，which indicated that the marked watershed algorithm proposed in this paper is effective and the high resolution imagery is better than the elevation difference model to extract tree crowns.
卜帆，石玉立. 机载LiDAR高差和高分影像的城市树冠提取比较[J]. 遥感技术与应用, 2017, 32(5): 875-882.
Bu Fan，Shi Yuli. The Comparison of Urban Tree Crown Extraction based on Airborne LiDARElevation Difference and High Resolution Imagery. Remote Sensing Technology and Application, 2017, 32(5): 875-882.