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

文献类型：期刊论文

作者Zhang, Yicen1; Wang, Junjie4; Wu, Zhifeng; Lian, Juyu3; Ye, Wanhui3; Yu, Fangyuan
刊名REMOTE SENSING
出版日期2022
卷号14期号:24
关键词species classification canopy layer leaf hyperspectral data data fusion evergreen broad-leaved forest
DOI10.3390/rs14246227
文献子类Article
英文摘要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.
学科主题Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
电子版国际标准刊号2072-4292
出版地BASEL
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)
语种英语
出版者MDPI
WOS记录号WOS:000903435100001
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]
源URL[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, Key Lab Vegetat Restorat & Management Degraded Ec, South China Bot Garden, Guangzhou 510650, Peoples R China
3.Shenzhen Univ, Coll Life Sci & Oceanog, Shenzhen 518060, Peoples R China
4.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, 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).

入库方式： OAI收割

来源：植物研究所

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