4.7 Article

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

期刊

REMOTE SENSING
卷 13, 期 15, 页码 -

出版社

MDPI
DOI: 10.3390/rs13153034

关键词

grassland; biodiversity; remote sensing; functional trait; spectral diversity; imaging spectroscopy

资金

  1. Strategic Priority Research Program of the Chinese Academy of Sciences [XDA23080303]
  2. National Natural Science Foundation of China [41801230]

向作者/读者索取更多资源

Mapping biodiversity in global terrestrial ecosystems is crucial for conservation and ecosystem services. Grassland plant diversity has been less studied compared to forest biodiversity mapping, but advances in UAV-based spectroscopy provide new ways to assess grassland plant diversity. This study found that different grassland plant species have unique functional traits, leading to diverse spectral patterns. As species richness increased, functional traits and spectral diversity also increased nonlinearly, eventually saturating. Hyperspectral data and cluster algorithms accurately predicted grassland plant species diversity, aiding in grassland biodiversity conservation and understanding ecosystem functions.
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.

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