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Articles

Comparison of four methods for estimating leaf area index based on terrestrial three-dimensional laser scanning

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Pages 244-261 | Published online: 19 Oct 2018
 

ABSTRACT

The leaf area index (LAI) of 16 sample plots was estimated based on terrestrial three-dimensional laser scanning. The point-cloud data of stand canopy were first scaled and projected onto a hemisphere according to Lambert azimuthal equal-area projection or stereographic projection, and the resulting hemispherical point-cloud images were used to extract the canopy porosity coefficients. Then, single-angle inversion and Miller formula inversion methods were used, respectively, to calculate the effective leaf area indices with canopy porosity coefficients. Results showed that the effective LAIs estimated by single-angle inversion method with Lambert projection and stereographic projection were within the range of 2.14~5.36 and 1.83~4.67, respectively. The effective LAIs obtained by Miller formula inversion method with Lambert projection and stereographic projection were within the range of 1.84~4.67 and 1.68~4.34, respectively. As a comparison, the LAI measured with a fish-eye camera ranged from 1.55 to 3.87. The LAI values estimated with four different calculation methods were linearly correlated with those measured by a fish-eye camera. The highest coefficient of determination (R2) 90.28% was obtained by the Miller formula inversion method combined with stereographic projection, and Duncan’s new multiple range test also further showed that this method had a relatively higher precision compared to other three methods.

Acknowledgments

The authors would like to acknowledge the support of the Fundamental Research Funds for the Central Universities (Grant Number: 2572016CB10), the National Natural Science Foundation of China (Grant Number: 31500587), and Heilongjiang Province Postdoctoral Foundation (Grant Number: LBH-Z15007).

Additional information

Funding

This work was supported by the Fundamental Research Funds for the Central Universities [Grant Number: 2572016CB10];Heilongjiang Province Postdoctoral Foundation [Grant Number: LBH-Z15007];National Natural Science Foundation of China [Grant Number: 31500587];

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