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

Area of Science:

• Ecology
• Remote Sensing
• Plant Science

Background:

• Assessing plant community traits is crucial for understanding ecosystem responses to climate change.
• Hyperspectral remote sensing is a valuable tool for estimating vegetation properties, but its application in sparse, dwarf vegetation areas like the Tibetan Plateau requires further investigation.

Purpose of the Study:

• To evaluate the effectiveness of field hyperspectral remote sensing for estimating plant community traits and function in Tibetan alpine grasslands.
• To quantify the influence of environmental drivers, spectral vegetation indices (VIs), and community traits on ecosystem function using structural equation modelling (SEM).

Main Methods:

• Collected canopy reflectance data using a handheld imaging spectrometer on the Tibetan Plateau.
• Conducted plant community investigations and laboratory analyses to estimate community structural and functional traits.
• Utilized 14 spectral vegetation indices (VIs) and SEM to analyze relationships between environmental drivers, VIs, community traits, and community function.

Main Results:

• Plant community traits were best predicted by the normalized difference vegetation index, enhanced vegetation index, and simple ratio.
• VIs and community traits positively influenced community function, while environmental drivers and specific leaf area showed negative effects.
• VIs, integrated with environmental drivers, indirectly affected community function by characterizing variations in community traits.

Conclusions:

• Community-level spectral reflectance can scale leaf-level plant trait information to larger ecological processes.
• Field imaging spectroscopy is a promising method for predicting the responses of alpine grassland communities to climate change.