ABSTRACT Tree species distribution mapping using remotely sensed data has long been an important research area. However, previous studies have rarely established a comprehensive and efficient classification procedure to obtain an accurate result. This study proposes a hierarchical classification procedure with optimized node variables and thresholds to classify tree species based on high spatial resolution satellite imagery. A classification tree structure consisting of parent and leaf nodes was designed based on user experience and visual interpretation. Spectral, textural, and topographic variables were extracted based on pre-segmented images. The random forest algorithm was used to select variables by ranking the impact of all variables. An iterating approach was used to optimize variables and thresholds in each loop by comprehensively considering the test accuracy and selected variables. The threshold range for each selected variable was determined by a statistical method considering the mean and standard deviation for two subnode types at each parent node. Classification of tree species was implemented using the optimized variables and thresholds. The results show that (1) the proposed procedure can accurately map the tree species distribution, with an overall accuracy of over 86% for both training and test stages; (2) critical variables for each class can be identified using this proposed procedure, and optimal variables of most tree plantation nodes are spectra related; (3) the overall forest classification accuracy using the proposed method is more accurate than that using the random forest (RF) and classification and regression tree (CART). The proposed approach provides results with 3.21% and 7.56% higher overall land cover classification accuracy and 4.68% and 10.28% higher overall forest classification accuracy than RF and CART, respectively.

中文翻译：

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使用基于高空间分辨率卫星数据优化节点变量和阈值的分层程序映射多个树种类

摘要 利用遥感数据绘制树种分布图长期以来一直是一个重要的研究领域。然而，以往的研究很少建立一个全面有效的分类程序来获得准确的结果。本研究提出了一种具有优化节点变量和阈值的分层分类程序，以基于高空间分辨率卫星图像对树种进行分类。基于用户体验和视觉解释设计了由父节点和叶节点组成的分类树结构。基于预先分割的图像提取光谱、纹理和地形变量。随机森林算法用于通过对所有变量的影响进行排序来选择变量。通过综合考虑测试精度和所选变量，使用迭代方法优化每个循环中的变量和阈值。每个选定变量的阈值范围是通过统计方法确定的，考虑到每个父节点上两种子节点类型的平均值和标准偏差。使用优化的变量和阈值实现树种分类。结果表明：（1）所提出的程序可以准确地绘制树种分布图，训练和测试阶段的总体准确率均超过 86%；(2) 使用该程序可以识别每个类别的关键变量，并且大多数人工林节点的最佳变量与光谱相关；(3) 使用所提出的方法的整体森林分类精度比使用随机森林（RF）和分类回归树（CART）更准确。与 RF 和 CART 相比，所提出的方法提供的结果总体土地覆盖分类精度分别提高了 3.21% 和 7.56%，森林总体分类精度分别提高了 4.68% 和 10.28%。