Forest inventories rely on field plots, the measurement of which is costly and time consuming by manual means. Thus, there is a need to automate plot-level field data collection. Mobile laser scanning has yet to be demonstrated for deriving stem curve and volume from standing trees with sufficient accuracy for supporting forest inventory needs. We tested a new approach based on pulse-based backpack mobile laser scanner (Riegl VUX-1HA) combined with in-house developed SLAM (Simultaneous Localization and Mapping), and a novel post-processing algorithm chain that allows one to extract stem curves from scan-line arcs corresponding to individual standing trees. The post-processing step included, among others, an algorithm for scan-line arc extraction, a stem inclination angle correction and an arc matching algorithm correcting for the drifts that are still present in the stem points after applying the SLAM algorithm. By using the stem curves defined by the detected arcs and tree heights provided by the pulse-based scanner, stem volume estimates for standing trees in easy (n = 40) and medium (n = 37) difficult boreal forest were calculated. In the easy and medium plots, 100% of pine and birch stems were correctly detected. The total RMSE of the extracted stem curves was 1.2 cm (5.1%) and 1.7 cm (6.7%) for the easy and medium plots, respectively. The RMSE were 1.8 m (8.7%) and 1.1 m (4.9%) for the estimated tree heights, and 9.7% and 10.9% for the stem volumes for the easy and medium plots, correspondingly. Thus, our processing chain provided stem volume estimates with a better accuracy than previous methods based on mobile laser scanning data. Importantly, the accuracy of stem volume estimation was comparable to that provided by terrestrial laser scanning approaches in similar forest conditions. To further demonstrate the performance of the proposed method, we compared our results against stem volumes calculated using the standard Finnish allometric volume model, and found that our method provided more accurate volume estimates for the two test sites. The findings are important steps towards future individual-tree-based airborne laser scanning inventories which currently lack cost-efficient and accurate field reference data collection techniques. The tree geometry defined by the stem curve is also an important input parameter for deriving quality-related information from trees. Forest management decision making will benefit from improvements to the efficiency and quality of individual tree reference information.

森林资源清查依靠田间地块，而这种田间地块的测量成本高昂，而且需要人工来耗时。因此，需要自动化绘图级现场数据收集。移动激光扫描尚未得到证实，它可以以足够的精度从站立的树木中得出茎曲线和体积，以支持森林资源需求。我们测试了一种基于脉冲的背包式移动激光扫描仪（Riegl VUX-1HA）与内部开发的SLAM（同时定位和制图）相结合的新方法，以及一种新颖的后处理算法链，该算法可以从中提取主曲线对应于单个站立树木的扫描线弧。后处理步骤尤其包括用于扫描线电弧提取的算法，应用SLAM算法后，杆倾斜角校正和弧匹配算法可校正杆点中仍然存在的漂移。通过使用由基于脉冲的扫描仪提供的检测到的弧线和树木高度定义的茎曲线，可以计算出在易（n = 40）和中等（n = 37）困难的北方森林中站立的树木的茎量估计值。在简单和中等的地块中，正确检测到100％的松树和桦木茎。对于简单图和中等图，提取的茎曲线的总RMSE分别为1.2 cm（5.1％）和1.7 cm（6.7％）。估计的树高的RMSE分别为1.8 m（8.7％）和1.1 m（4.9％），而对于简单和中等地块，其树干体积的RMSE分别为9.7％和10.9％。从而，我们的处理链提供了比以前基于移动激光扫描数据的方法更好的准确性。重要的是，在相似的森林条件下，茎体积估计的准确性可与陆地激光扫描方法提供的准确性相媲美。为了进一步证明所提出方法的性能，我们将我们的结果与使用标准芬兰异形体积模型计算出的茎体积进行了比较，发现我们的方法为两个测试位置提供了更准确的体积估计。这些发现是迈向未来基于单树的机载激光扫描清单的重要步骤，目前这些清单缺乏成本有效且准确的现场参考数据收集技术。茎曲线定义的树木几何形状也是从树木中获取质量相关信息的重要输入参数。森林管理决策将受益于单个树木参考信息的效率和质量的提高。