Tree Species Classification Using Plant Functional Traits and Leaf Spectral Properties along the Vertical Canopy Position

doi:10.3390/rs14246227

	Tree Species Classification Using Plant Functional Traits and Leaf Spectral Properties along the Vertical Canopy Position
	Zhang, Yicen 1; Wang, Junjie 2; Wu, Zhifeng ; Lian, Juyu 3; Ye, Wanhui 3; Yu, Fangyuan
	2022
发表期刊	REMOTE SENSING
卷号	14 期号:24
摘要	Plant functional traits are rarely used in tree species classification, and the impact of vertical canopy positions on collecting samples for classification also remains unclear. We aim to explore the feasibility and effectiveness of leaf traits in classification, as well as to detect the effect of vertical position on classification accuracy. This work will deepen our understanding of the ecological mechanism of natural forest structure and succession from new perspectives. In this study, we collected foliar samples from three canopy layers (upper, middle and lower) and measured their spectra, as well as eight well-known leaf traits. We used a leaf hyperspectral reflectance (LHR) dataset, leaf functional traits (LFT) dataset and LFT + LHR dataset to classify six dominant tree species in a subtropical evergreen broad-leaved forest. Our results showed that the LFT + LHR dataset achieved the highest classification results (overall accuracy (OA) = 77.65% and Kappa = 0.73), followed by the LFT dataset (OA = 74.26% and Kappa = 0.69) and the LHR dataset (OA = 69.06% and Kappa = 0.63). Along the vertical canopy, the OA and Kappa increased from the lower to the upper layers, and the combination data of the three canopy layers achieved the highest accuracy. For the individual tree species, the shade-tolerant species (including Machilus chinensis, Cryptocarya chinensis and Cryptocarya concinna) produced higher accuracies than the light-demanding species (including Schima superba and Castanopsis chinensis). Our results provide an approach for enhancing tree species recognition from the plant physiology and biochemistry perspective and emphasize the importance of vertical direction in forest community research.
关键词	species classification canopy layer leaf hyperspectral data data fusion evergreen broad-leaved forest
学科领域	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
DOI	10.3390/rs14246227
收录类别	SCI
语种	英语
WOS关键词	TROPICAL RAIN-FOREST ; BROAD-LEAVED FOREST ; RADIATIVE-TRANSFER MODELS ; HYPERSPECTRAL DATA ; LIDAR DATA ; BIOMASS ; METRICS ; DISCRIMINATION ; WORLDVIEW-2 ; VARIABILITY
WOS研究方向	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000903435100001
出版者	MDPI
文献子类	Article
出版地	BASEL
EISSN	2072-4292
资助机构	Strategic Priority Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; NSFC-Guangdong Joint Foundation Key Project ; [XDB31030000] ; [41901060] ; [U1901219]
作者邮箱	yfy@gzhu.edu.cn
作品OA属性	gold
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ibcas.ac.cn/handle/2S10CLM1/28707
专题	植被与环境变化国家重点实验室
作者单位	1.Guangzhou Univ, Sch Geog Sci & Remote Sensing, Guangzhou 510006, Peoples R China 2.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 3.Shenzhen Univ, Coll Life Sci & Oceanog, Shenzhen 518060, Peoples R China 4.Chinese Acad Sci, Key Lab Vegetat Restorat & Management Degraded Ec, South China Bot Garden, Guangzhou 510650, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Yicen,Wang, Junjie,Wu, Zhifeng,et al. Tree Species Classification Using Plant Functional Traits and Leaf Spectral Properties along the Vertical Canopy Position[J]. REMOTE SENSING,2022,14(24).
APA	Zhang, Yicen,Wang, Junjie,Wu, Zhifeng,Lian, Juyu,Ye, Wanhui,&Yu, Fangyuan.(2022).Tree Species Classification Using Plant Functional Traits and Leaf Spectral Properties along the Vertical Canopy Position.REMOTE SENSING,14(24).
MLA	Zhang, Yicen,et al."Tree Species Classification Using Plant Functional Traits and Leaf Spectral Properties along the Vertical Canopy Position".REMOTE SENSING 14.24(2022).

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