Tree species classification using plant functional traits from LiDAR and hyperspectral data
Tree species classification using plant functional traits from LiDAR and hyperspectral data
复制标题
DOI：
10.1016/j.jag.2018.06.018
复制
发表时间：
2018-06
期刊：
影响因子：
--
通讯作者：
Yifang Shi;A. Skidmore;Tiejun Wang;Stefanie Holzwarth;U. Heiden;N. Pinnel;Xi Zhu;M. Heurich
中科院分区：
文献类型：
--
作者：
Yifang Shi;A. Skidmore;Tiejun Wang;Stefanie Holzwarth;U. Heiden;N. Pinnel;Xi Zhu;M. Heurich
植物功能性状在经典植物分类学中被广泛应用于物种的描述、分类和鉴别。然而，利用植物功能性状进行树种分类的遥感数据在天然林还没有明确的建立。在这项研究中，我们集成了三个选定的植物功能性状（即等效水厚度（Cw），叶质量单位面积（Cm）和叶叶绿素（Cab））检索高光谱数据与高光谱衍生的光谱特征和机载激光雷达衍生的度量映射在德国的天然森林中的五个树种。我们的研究结果表明，当植物功能性状与光谱特征和LiDAR指标相结合时，获得了83.7%的总体准确率，这在统计学上显著高于单独使用LiDAR（65.1%）或高光谱（69.3%）数据。我们的研究结果表明，从高光谱数据中检索的植物功能性状，使用辐射传输模型，可用于与高光谱特征和激光雷达指标，以进一步提高在混合温带森林中的个别树种分类。
Plant functional traits have been extensively used to describe, rank and discriminate species according to their variability between species in classical plant taxonomy. However, the utility of plant functional traits for tree species classification from remote sensing data in natural forests has not been clearly established. In this study, we integrated three selected plant functional traits (i.e. equivalent water thickness (Cw), leaf mass per area (Cm) and leaf chlorophyll (Cab)) retrieved from hyperspectral data with hyperspectral derived spectral features and airborne LiDAR derived metrics for mapping five tree species in a natural forest in Germany. Our results showed that when plant functional traits were combined with spectral features and LiDAR metrics, an overall accuracy of 83.7% was obtained, which was statistically significantly higher than using LiDAR (65.1%) or hyperspectral (69.3%) data alone. The results of our study demonstrate that plant functional traits retrieved from hyperspectral data using radiative transfer models can be used in conjunction with hyperspectral features and LiDAR metrics to further improve individual tree species classification in a mixed temperate forest.