J Integr Plant Biol. ›› 2023, Vol. 65 ›› Issue (12): 2604-2618.DOI: 10.1111/jipb.13572

• Global Change Biology • Previous Articles     Next Articles

Predicting community traits along an alpine grassland transect using field imaging spectroscopy

Feng Zhang1,2,3*, Wenjuan Wu1,3, Lang Li1,3, Xiaodi Liu1,3, Guangsheng Zhou4* and Zhenzhu Xu1,2*   

  1. 1. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, China;
    2. China National Botanical Garden, Beijing 100093, China;
    3. University of Chinese Academy of Sciences, Beijing 100049, China;
    4. Chinese Academy of Meteorological Sciences, Beijing 100081, China
    *Correspondences:Guangsheng Zhou(zhougs@cma.gov.cn);Zhenzhu Xu(xuzz@ibcas.ac.cn);Feng Zhang(zhangfeng@ibcas.ac.cn, Dr. Zhang is fully responsible for the distributions of all materials associated with this article)
  • Received:2023-06-05 Accepted:2023-10-12 Online:2023-10-13 Published:2023-12-01

Abstract: Assessing plant community traits is important for understanding how terrestrial ecosystems respond and adapt to global climate change. Field hyperspectral remote sensing is effective for quantitatively estimating vegetation properties in most terrestrial ecosystems, although it remains to be tested in areas with dwarf and sparse vegetation, such as the Tibetan Plateau. We measured canopy reflectance in the Tibetan Plateau using a handheld imaging spectrometer and conducted plant community investigations along an alpine grassland transect. We estimated community structural and functional traits, as well as community function based on a field survey and laboratory analysis using 14 spectral vegetation indices (VIs) derived from hyperspectral images. We quantified the contributions of environmental drivers, VIs, and community traits to community function by structural equation modelling (SEM). Univariate linear regression analysis showed that plant community traits are best predicted by the normalized difference vegetation index, enhanced vegetation index, and simple ratio. Structural equation modelling showed that VIs and community traits positively affected community function, whereas environmental drivers and specific leaf area had the opposite effect. Additionally, VIs integrated with environmental drivers were indirectly linked to community function by characterizing the variations in community structural and functional traits. This study demonstrates that community-level spectral reflectance will help scale plant trait information measured at the leaf level to larger-scale ecological processes. Field imaging spectroscopy represents a promising tool to predict the responses of alpine grassland communities to climate change.

Key words: aboveground net primary productivity, canopy chlorophyll content, canopy leaf nitrogen concentration, fractional vegetation cover, hyperspectral remote sensing, Tibetan Plateau

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