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Tree species classification using plant functional traits from LiDAR and hyperspectral data
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2018-06-30 , DOI: 10.1016/j.jag.2018.06.018
Yifang Shi , Andrew K. Skidmore , Tiejun Wang , Stefanie Holzwarth , Uta Heiden , Nicole Pinnel , Xi Zhu , Marco Heurich

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.



中文翻译:

利用来自LiDAR和高光谱数据的植物功能性状对树木进行分类

在经典植物分类学中,根据植物之间的变异性,植物功能性状已被广泛用于描述,排名和区分物种。然而,尚未明确建立植物功能性状从天然林中通过遥感数据对树种进行分类的用途。在这项研究中,我们将从高光谱数据中检索到的三个选定的植物功能性状(即等效水厚(Cw),每单位面积叶质量(Cm)和叶绿素(Cab))与高光谱派生的光谱特征和机载LiDAR派生的指标进行了映射德国天然森林中有五个树种。我们的结果表明,将植物功能性状与光谱特征和LiDAR指标结合使用时,总体准确度为83.7%,与仅使用LiDAR(65.1%)或高光谱(69.3%)数据相比,该数据在统计学上显着更高。我们的研究结果表明,使用辐射转移模型从高光谱数据中检索到的植物功能性状可以与高光谱特征和LiDAR指标结合使用,以进一步改善混合温带森林中的单个树种分类。

更新日期:2018-06-30
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