基于无人机多源遥感数据的亚热带森林树种分类

树种多样性是生态学研究的重要内容,树木的种类和空间分布信息可有效服务于可持续森林管理。但在复杂林分条件下,获取高精度分类结果的难度大。而无人机遥感可获取局域超精细数据,为树种分类精度的提高提供了可能。基于可见光、高光谱、激光雷达等多源无人机遥感数据,探究其在亚热带林分条件下的树种分类潜力。研究发现:(1)随机森林分类器总体精度和各树种的F1分数最高,适合亚热带多树种的分类制图,其区分13种类别(8乔木,4草本)的总体精度为95.63%,Kappa系数为0.948;(2)多源数据的使用可以显著提高分类精度,全特征模型精度最高,且高光谱和激光雷达数据显著影响全特征模型分类精度,可见光纹理数据作用较小;(3)分类特征重要性从大到小排序为结构信息,植被指数,纹理信息,最小噪声变换分量。

The classification of subtropical forest tree species based on UAV multi-source remote sensing data

The diversity of tree species is an important content of ecological research. The information on tree species and spatial distribution can effectively serve sustainable forest management. However, it is difficult to obtain detailed information on the spatial distribution of tree species in traditional field-based forest inventory. Tree species classification based on remote sensing costs less and has high spatial accuracy, which has become an effective method. Although satellite remote sensing data has been successfully applied in the study of tree species distribution, it is difficult to obtain high-precision classification results limited by its spatial resolution and spectral resolution, especially in complex stand conditions. The UAV remote sensing can obtain local ultra-fine data, which provides the possibility to improve the classification accuracy of tree species. Therefore, this research based on the method of machine learning and the concept of feature fusion to explore the potential for tree species classification under subtropical forest conditions. The multi-source data used in this study includes visible light, hyperspectral, Light Detection, respectively based on which texture features, Minimum Noise Fraction Rotation (MNF), as well as vegetation indexes, And Ranging (LiDAR) structural parameters are extracted and calculated. The impact of classification processes and methods such as classifiers, different data sources, and different classification features on classification accuracy are studied in this research to provide experience and examples for high-precision classification and mapping of subtropical forests. The study found that: (1) The random forests classifier has the highest overall accuracy and F1 score of each tree species, which is more suitable for subtropical multi-tree species classification and mapping. The overall accuracy is 95.63%, and the Kappa coefficient is 0.948 when distinguishing 13 categories (8 trees, 4 herbs). (2) As far as the single data source is concerned, the order from high to low of the model accuracy is hyperspectral, LiDAR and visible light data. Multi-source data can significantly improve the classification accuracy. The full-feature model has the highest accuracy, and hyperspectral and LiDAR data significantly affect the classification accuracy of the full-feature model. The visible light texture data has less effect. (3) The importance of classification features is sorted from largest to smallest into structural information, vegetation index, texture information, and minimum noise transformation component. In addition, texture and MNF characteristics cannot effectively distinguish tree species in subtropical forests. And the data after MNF dimensionality reduction will lose lots of information so that original band information is more important when hyperspectral data are used.