Urban trees provide a range of vital social and environmental services. Currently, inventories of individual urban trees are conducted in-situ by professional arborists. Such an approach to urban tree inventories means they are challenging to maintain and only capture information describing trees on accessible land. Whilst remote sensing approaches have shown the potential to derive individual tree attributes, these studies rarely make use of existing inventory information. In this study, we present a method to parameterise an algorithm for individual tree detection and delineation from airborne remote sensing data. The approach uses existing inventory data as training information firstly for the detection of the canopy area and secondly to parameterise a marker-based watershed segmentation algorithm. In this parameterisation, crown segmentation shape, as well as features derived from the remote sensing data, are used to determine if a segment contains one or more trees. If a segment contains more than one tree, it is split with the number of markers increased until each segment includes only one tree. The approach was evaluated within three distinct urban areas: the central business district, an urban park and a residential area, to be determined. Commission and omission errors ranged between 11% and 27% across the three regions, with commission typically caused by land covers on private land that were unaccounted for in the training processes. In all areas the overall tree count was within two per cent of that defined by reference information. The accurate tree count produced by this approach suggests it has the potential to be adopted by government agencies for routine tree inventory maintenance.

城市树木提供了一系列重要的社会和环境服务。目前，城市树木的清单是由专业树木学家现场进行的。这种城市树木清单的处理方法意味着它们在维护方面极具挑战性，只能捕获描述可及土地上树木的信息。尽管遥感方法显示出了推导单个树属性的潜力，但这些研究很少利用现有的清单信息。在这项研究中，我们提出了一种参数化算法的方法，该算法用于从空中遥感数据中对单个树进行检测和描绘。该方法首先将现有的清单数据用作训练信息，以检测树冠区域，其次将参数化基于标记的分水岭分割算法参数化。在此参数化中 树冠分割形状，以及从遥感数据中得出的特征，用于确定某段是否包含一棵或多棵树。如果一个段包含一棵以上的树，则将其拆分，并增加标记的数量，直到每个段仅包含一棵树。在三个不同的城市区域中对该方法进行了评估：待确定的中央商务区，城市公园和居民区。在这三个区域中，佣金和遗漏误差在11％到27％之间，佣金通常是由培训过程中未说明的私人土地上的土地覆盖引起的。在所有领域中，总的树木数量都在参考信息定义的树木数量的百分之二以内。