Estimation of Leaf Area Index of Moso Bamboo Canopies

ABSTRACT

All ground-based estimations of leaf area index (LAI) of Moso bamboo canopies are currently conducted based on indirect remote sensing methods. However, the relatively small values of LAI estimated by previous studies conflict with the expected values of such extremely dense canopies of Moso bamboo. This is the first attempt to accurately estimate the LAI of Moso bamboo canopies using an allometric model based on destructive measurements. The results indicate that (1) LAI of Moso bamboo canopies range was 6.7–30.6 m²·m⁻², which is clearly higher than the range 2.2–6.5 m²·m⁻² estimated by previous studies; (2) there is a strong linear relationship between LAI and crown density (R² = 0.947, RMSE = 1.343); (3) LAI is largely underestimated using the digital hemispherical photography (DHP) because of the overestimation of clumping index; and (4) there is a strong exponential relationship between LAI and effective leaf area (L_e) estimated using DHP (R² = 0.734, RMSE = 3.011). Based on the results, three methods are recommended for LAI estimations of Moso bamboo canopies using the allometric relationship, the empirical relationship with crown density, and the empirical relationship with L_e.

KEYWORDS:

• Moso bamboo
• leaf area index
• destructive sampling
• allometric model
• empirical model

Acknowledgments

This work was supported in part by the Zhejiang Provincial Natural Science Foundation of China under Grant no. LY18D010003; the National Science Foundation of China under Grant no. 41631180/D0106, and the Talent Innovation Foundation of Zhejiang A&F University under Grant no. 2034020072.

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article. Raw data from DHP images supporting the findings of this study are available from the corresponding authors on request.

Additional information

Funding

This work was supported by the Zhejiang Provincial Natural Science Foundation of China [No. LY18D010003]; The Talent Innovation Foundation of Zhejiang A&F University [2034020072]; The National Science Foundation of China [41631180/D0106].