Tree Species Identification in Mixed Baltic Forest Using LiDAR and Multispectral Data
Tree Species Identification in Mixed Baltic Forest Using LiDAR and Multispectral Data
复制标题
DOI：
10.1109/jstars.2012.2196978
复制
发表时间：
2012-04-01
影响因子：
5.5
通讯作者：
Sinica-Sinavskis, Juris
中科院分区：
文献类型：
--
作者：
Dinuls, Romans;Erins, Gatis;Sinica-Sinavskis, Juris
这项工作描述了在单个树级清查波罗的海混交林的任务。目标是开发一种实用的方法，用于半自动识别树种。介绍了数据采集设备和预处理软件、勘查森林面积、处理方法、分类结果以及新开发的软件。为了解决树种识别的核心问题，提出了一种用于处理来自树顶附近的多光谱图像数据的分类方法。一个多类分类器的设计从多光谱数据的交互式选择的树木包括在初始设计（训练）集为两个针叶树和三个落叶树种的利益。提出了一种用于稳定分类结果的方法，该方法基于通过选择来自不同位置的树木来提高设计集的代表性，解雇具有重叠树冠和异常的树木，然后计算设计集的光谱相异性参数，并解雇任何物种的树木过于相似的集合。最好的分类结果，获得了使用两个阶段的程序。在第一阶段中，物种聚类是通过添加从整个分析的森林区域随机选择的树木。所有树木的最终分类是通过使用贝叶斯分类器设计的聚类属性的基础上。提出了一种用于增加聚类阶段的鲁棒性的过程，基于执行多个聚类尝试，每个尝试使用用于分类器设计的所选设计集的随机采样子集，并且通过从这些尝试的结果的多数投票来做出关于每个树的类的决定。这种分类算法进行了测试，对一组树木，其信息可从实地工作。结果表明，平均分类误差低于3%，可以实现和最大错误率大大降低，通过应用所提出的方法选择的代表性设计集。
This work describes the task of inventorying Baltic mixed forests at an individual tree level. The development of a practicable methodology for semi-automated identification of tree species was targeted. Data acquisition equipment and preprocessing software, explored forest area, processing approaches, obtained classification results as well as newly developed software are described. To resolve the core problem - tree species identification - a classification approach is proposed for processing multi-spectral imagery data from the vicinity of tree tops. A multi-class classifier is designed from multi-spectral data of interactively selected trees included in initial design (training) sets for two conifer and three deciduous species of interest. An approach for the stabilization of the classification results is proposed, based on improving the representativeness of the design sets by selection of trees from different locations, dismissing trees with overlapping crowns and anomalies, followed by the calculation of a spectral dissimilarity parameter of the design sets and dismissing the sets of trees of any species which are too similar. The best classification results were obtained using a two-stage procedure. In the first stage, species clusters were created by adding randomly selected trees from the whole analyzed forest area. Final classification of all trees was done by using a Bayes classifier designed on the basis of cluster properties. A procedure for increasing robustness of the clustering stage is proposed, based on performing multiple clustering attempts, each using a randomly sampled subset of a chosen design set for the classifier design, and making a decision about the class of each tree by the majority vote from the results of these attempts. This classification algorithm was tested against the set of trees, for which information was available from field work. It is shown that a mean classification error below 3% can be achieved and the maximum error rate was decreased substantially by applying the proposed approach for selection of representative design sets.