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The Potential of Mapping Grassland Plant Diversity with the Links among Spectral Diversity, Functional Trait Diversity, and Species Diversity

文献类型：期刊论文

作者Zhao, Yujin; Sun, Yihan4; Chen, Wenhe; Zhao, Yanping; Liu, Xiaoliang2; Bai, Yongfei1
刊名REMOTE SENSING
出版日期2021
卷号13期号:15
关键词grassland biodiversity remote sensing functional trait spectral diversity imaging spectroscopy
DOI10.3390/rs13153034
文献子类Article
英文摘要Mapping biodiversity is essential for assessing conservation and ecosystem services in global terrestrial ecosystems. Compared with remotely sensed mapping of forest biodiversity, that of grassland plant diversity has been less studied, because of the small size of individual grass species and the inherent difficulty in identifying these species. The technological advances in unmanned aerial vehicle (UAV)-based or proximal imaging spectroscopy with high spatial resolution provide new approaches for mapping and assessing grassland plant diversity based on spectral diversity and functional trait diversity. However, relatively few studies have explored the relationships among spectral diversity, remote-sensing-estimated functional trait diversity, and species diversity in grassland ecosystems. In this study, we examined the links among spectral diversity, functional trait diversity, and species diversity in a semi-arid grassland monoculture experimental site. The results showed that (1) different grassland plant species harbored different functional traits or trait combinations (functional trait diversity), leading to different spectral patterns (spectral diversity). (2) The spectral diversity of grassland plant species increased gradually from the visible (VIR, 400-700 nm) to the near-infrared (NIR, 700-1100 nm) region, and to the short-wave infrared (SWIR, 1100-2400 nm) region. (3) As the species richness increased, the functional traits and spectral diversity increased in a nonlinear manner, finally tending to saturate. (4) Grassland plant species diversity could be accurately predicted using hyperspectral data (R-2 = 0.73, p < 0.001) and remotely sensed functional traits (R-2 = 0.66, p < 0.001) using cluster algorithms. This will enhance our understanding of the effect of biodiversity on ecosystem functions and support regional grassland biodiversity conservation.
学科主题Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
出版地BASEL
电子版国际标准刊号2072-4292
WOS关键词IMAGING SPECTROSCOPY ; TIME-SERIES ; BIODIVERSITY ; CLASSIFICATION ; PRODUCTIVITY ; VARIABILITY ; FOREST ; DISCRIMINATION ; CONSEQUENCES ; INFORMATION
WOS研究方向Science Citation Index Expanded (SCI-EXPANDED)
语种英语
WOS记录号WOS:000682205300001
出版者MDPI
资助机构Strategic Priority Research Program of the Chinese Academy of Sciences [XDA23080303] ; National Natural Science Foundation of China [41801230]
源URL[http://ir.ibcas.ac.cn/handle/2S10CLM1/26532]  
专题植被与环境变化国家重点实验室
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100049, Peoples R China
3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, 20 Nanxincun, Beijing 100093, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Yujin,Sun, Yihan,Chen, Wenhe,et al. The Potential of Mapping Grassland Plant Diversity with the Links among Spectral Diversity, Functional Trait Diversity, and Species Diversity[J]. REMOTE SENSING,2021,13(15).
APA Zhao, Yujin,Sun, Yihan,Chen, Wenhe,Zhao, Yanping,Liu, Xiaoliang,&Bai, Yongfei.(2021).The Potential of Mapping Grassland Plant Diversity with the Links among Spectral Diversity, Functional Trait Diversity, and Species Diversity.REMOTE SENSING,13(15).
MLA Zhao, Yujin,et al."The Potential of Mapping Grassland Plant Diversity with the Links among Spectral Diversity, Functional Trait Diversity, and Species Diversity".REMOTE SENSING 13.15(2021).

入库方式： OAI收割

来源：植物研究所

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