AI 中文总结
该研究以博洛尼亚城市区域为案例，对比分水岭算法与区域生长法对机载LiDAR点云的单木分割效果，提取植被指标并构建交互式地图，为城市绿化规划提供可扩展基础。
AI 中文摘要
我们提出了一种基于激光雷达（LiDAR）的流程，用于博洛尼亚城市高大植被的分割与结构表征。从随机森林（Random Forest）模型先前分类为高大植被的机载LiDAR点云出发，我们实现并对比了两种单木分割策略：应用于冠层高度模型（Canopy Height Model）的分水岭算法，以及直接在三维点云上运行的逐点区域生长算法。在覆盖塔莱亚（Talea）区的12块图斑区域中，分水岭方法检测到的树木数量更多（7589株），以高度峰值约5米的矮树为主；而区域生长法检测到的树木数量较少（6432株），但保留了最高的回波信号，能检测到40米以上的个体，这反映出后者缺少平滑步骤。我们进一步将分割结果与市政开放数据目录“维护中的树木（Alberi in manutenzione）”进行对比：在一个样本图斑中，约一半LiDAR检测到的树木在该目录中不存在，多个目录标注的位置没有观测到树木，且大部分高度记录可追溯至约二十年前，这使得该数据集不适合作为真值参考。从分割后的树木中，我们提取高度和冠幅半径，并利用异速生长关系计算初步植被指标，即地上生物量和碳储量，以及通过物种特定花序系数计算年度花粉产量。所有结果被收集在交互式单木元数据地图中。该流程是模块化的，可随着改进的分类、野外调查或互补传感技术的出现而重新校准，为数据驱动的城市绿化规划提供了可扩展的基础。
英文摘要
We present a LiDAR-based pipeline for the segmentation and structural characterisation of tall urban vegetation in Bologna. Starting from airborne LiDAR point clouds previously classified as high vegetation by a Random Forest model, we implement and compare two individual-tree segmentation strategies: a watershed algorithm applied to the Canopy Height Model and a point-wise region growing algorithm operating directly on the three-dimensional cloud. Over an area of 12 tiles covering the Talea district, the watershed method detects a larger number of trees (7589), dominated by short trees with a height peak around 5 meters, whereas region growing yields fewer trees (6432) but retains the highest returns, yielding individuals above 40 meters, reflecting the absence of a smoothing step in the latter. We further confront the segmentation results with the municipal Open Data catalogue Alberi in manutenzione: on a sample tile roughly half of the LiDAR-detected trees turn out to be absent from the catalogue, several catalogued positions correspond to locations where no tree is observed, and the majority of the height records date back about two decades, which makes the dataset unsuitable as a ground truth reference. From the segmented trees we extract height and crown radius and use them to compute preliminary vegetation indicators, namely above-ground biomass and carbon storage through allometric relations and annual pollen production through species-specific inflorescence coefficients. All results are collected in an interactive per-tree metadata map. The pipeline is modular and can be recalibrated as improved classifications, field campaigns, or complementary sensing technologies become available, providing a scalable basis for data-driven urban greenery planning.
Comments26 pages, 21 figures
