Fast and automated collection of forest data, such as species composition information, is required to support climate mitigation actions. Recently, there have been significant advances in the use of terrestrial laser scanning (TLS) instruments, which facilitate the capture of detailed forest structure. However, for tree species recognition the structural information from TLS has mainly been used to complement spectral information. TLS-only classification studies have been limited in size and diversity of plot forest types. In this paper, we investigate the potential of TLS for tree species classification. We used quantitative structure models to determine 17 structural tree features. These features were computed for 758 trees of five tree species, including two understory species, of a 1.4 hectare mixed deciduous forest plot. Three classification methods were compared: k-nearest neighbours, multinomial logistic regression and support vector machine. We assessed the potential underlying causes for structural differences with principal component analysis. We obtained classification success rates of approximately 80%, however, with producer accuracies for three of the five species ranging from 0 to 60%. Low producer accuracies were the result of a high intra- and low inter-species variability. These effects were, respectively, caused by a high size-dependency of the structural features and a convergence of structural traits across species as a result of the individual tree position in the forest canopy and shade tolerance. Nevertheless, the producer accuracies could be improved through sensitivity vs. specificity trade-offs, with over 50% for all species being obtainable. The high intra -and low inter-species variability complicate the classification. Furthermore, the classification performance and best classification method greatly depend on its targeted application. In conclusion, this study proves the added value of TLS for tree species classification but also shows that TLS opens up potential for testing and further development of ecological theory.

需要快速，自动收集森林数据，例如物种组成信息，以支持缓解气候变化的行动。最近，在使用陆地激光扫描（TLS）仪器方面取得了重大进展，这有助于捕获详细的森林结构。但是，对于树种识别，来自TLS的结构信息已主要用于补充光谱信息。仅TLS的分类研究在小规模林类型的大小和多样性方面受到限制。在本文中，我们研究了TLS在树种分类中的潜力。我们使用定量结构模型来确定17个结构树特征。这些特征是针对1.4公顷混合落叶林地中的5种树种（包括2种林下树种）的758棵树木计算得出的。比较了三种分类方法：k最近邻，多项式逻辑回归和支持向量机。我们通过主成分分析评估了结构差异的潜在根本原因。我们获得了大约80％的分类成功率，但是，这5个物种中有3个的生产者准确度在0％到60％之间。生产者准确性低是种内和种间变异性高的结果。这些影响分别是由于树冠中各个树的位置和耐荫性造成的，结构特征的高度依赖和物种间结构性状的趋同引起。然而，可以通过敏感性与特异性的权衡取舍来提高生产者的准确性，可以获得所有物种的50％以上。高种内和低种间变异使分类复杂化。此外，分类性能和最佳分类方法在很大程度上取决于其目标应用。总之，本研究证明了TLS在树种分类中的附加价值，但也表明TLS为测试和进一步发展生态学理论开辟了潜力。