采用地面激光扫描获取树木的光探测和测距数据,并将其作为遥感数据源,选取水杉、棕榈、无患子、竹子和橡胶树为研究对象,提出了三类有效特征:树木相对聚类特征、点云分布特征和树木表观特征,列举了68个特征参数。采用支持向量机在交叉验证中对训练数据集进行检验计算,确定最优的特征参数组,最终在测试数据集中进行树种分类。研究结果表明:基于树木相对聚类特征的最优特征参数组进行树种分类的平均分类精度较低(45%);基于点云分布特征的最优特征参数组进行树种分类的平均分类精度有所增加(58.8%);基于树木表观特征的最优特征参数组进行树种分类的平均分类精度较高(63.8%);基于三类特征的13个最优特征参数进行树种分类的平均分类精度最高(87.5%)。此外,由于水杉与其他树种形态差异较为明显,在分类中表现突出,错判率最低(6.5%)。所提方法具有较高的可行性,为获得更准确的森林树种分布提供了强有力的工具。

Herein, light detection and ranging data were collected as remoting data sources by terrestrial laser scanning (TLS). Metasequoia, palm, sapindus, bamboo, and rubber trees were selected as research objects. Three effective features are proposed, which are relative clustering features of trees, features of point cloud distribution of trees, and apparent features of trees. 68 feature parameters are listed. A support vector machine (SVM) classifier was then used to verify and calculate the training dataset and to determine the optimal feature parameters in cross-validation. Finally, the tree species is classified in the test dataset. The research results show that the average classification accuracy of tree classification based on the optimal parameters of relative clustering features of trees is low (45%), that based on the optimal feature parameters of point cloud distribution slightly increases (58.8%), that based on the optimal parameters of tree appearance features is relatively high (63.8%), and that based on the 13 optimal parameters of three types of features is the highest (87.5%). In addition, due to the difference between metasequoia and other tree species is obvious, the metasequoia is outstanding in classification and its misjudgement rate is the lowest (6.5%). The proposed method has high feasibility and provides a powerful tool for obtaining a more accurate distribution of forest species.