Abstract

Little attention has been paid to the impact of vertical canopy position on the leaf spectral properties of tall trees, and few studies have explored the ability of leaf spectra to characterize the variation of leaf traits across different canopy positions. Using a tower crane, we collected leaf samples from three canopy layers (lower, middle, and upper) and measured eight leaf traits (equivalent water thickness, specific leaf area, leaf carbon content, leaf nitrogen content, leaf phosphorus content, leaf chlorophyll content, flavonoid, and nitrogen balance index) in a subtropical evergreen broadleaved forest. We evaluated the variability of leaf traits and leaf spectral properties, as well as the ability of leaf spectra to track the variation of leaf traits among three canopy layers for six species within the entire reflectance spectrum. The results showed that the eight leaf traits that were moderately or highly correlated with each other showed significant differences along the vertical canopy profile. The three canopy layers of leaf spectra showed contrasting patterns for light-demanding (Castanopsis chinensis, Castanopsis fissa, Schima superba, and Machilus chinensis) and shade-tolerant species (Cryptocarya chinensis and Cryptocarya concinna) along the vertical canopy profile. The spectra at the lower and upper canopy layers were more sensitive than the middle layer for tracking the variation of leaf chlorophyll and flavonoid content. Our results revealed that it is important to choose an appropriate canopy layer for the field sampling of tall trees, and we suggest that flavonoid is an important leaf trait that can be used for mapping and monitoring plant growth with hyperspectral remote sensing.

Highlights

  • Introduction distributed under the terms andTree species information provides essential data for biodiversity monitoring and sustainable forest management, and it is a key parameter of evaluation models of forest growth, health, and yield [1]

  • The analysis of variance (ANOVA) test showed that almost all the leaf traits of Castch had significant differences (p < 0.05) across the vertical canopy profile

  • Our results showed that most leaf traits were moderately or highly correlated with the others, and SLA had the strongest correlation with the other traits at the upper layer, while CHLarea had the strongest correlation with the other traits at the lower layer

Read more

Summary

Introduction

Introduction distributed under the terms andTree species information provides essential data for biodiversity monitoring and sustainable forest management, and it is a key parameter of evaluation models of forest growth, health, and yield [1]. The dense forest stands and extraordinary species richness in subtropical evergreen broadleaved forests make it difficult to collect leaf samples from the upper canopies of tall trees that match remote sensing data. When using remote sensing to map and monitor leaf traits, it must be understood that tree species have a unique canopy structure, spectral features, and phenotypic traits [6,7]. The complex factors (e.g., the phenology, region, plant diseases, and remote sensor) could work together, marking different tree species with similar spectral features. Such inconsistencies between different species could be decreased or avoided by considering both their reflectance spectra and their functional traits related to remote sensing data [8]

Objectives
Methods
Results
Discussion
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call