## Abstract

### Background

Digital hemispherical photography (DHP) is widely used to estimate the leaf area index (LAI) of forest plots due to its advantages of high efficiency and low cost. A crucial step in the LAI estimation of forest plots via DHP is choosing a sampling scheme. However, various sampling schemes involving DHP have been used for the LAI estimation of forest plots. To date, the impact of sampling schemes on LAI estimation from DHP has not been comprehensively investigated.

### Methods

In this study, 13 commonly used sampling schemes which belong to five sampling types (i.e. dispersed, square, cross, transect and circle) were adopted in the LAI estimation of five *Larix principis-rupprechtii* plots (25 m × 25 m). An additional sampling scheme (with a sample size of 89) was generated on the basis of all the sample points of the 13 sampling schemes. Three typical inversion models and four canopy element clumping index (*Ω*_{e}) algorithms were involved in the LAI estimation. The impacts of the sampling schemes on four variables, including gap fraction, *Ω*_{e}, effective plant area index (PAI_{e}) and LAI estimation from DHP were analysed. The LAI estimates obtained with different sampling schemes were then compared with those obtained from litter collection measurements.

### Results

Large differences were observed for all four variable estimates (i.e. gap fraction, *Ω*_{e}, PAI_{e} and LAI) under different sampling schemes. The differences in impact of sampling schemes on LAI estimation were not obvious for the three inversion models, if the four *Ω*_{e} algorithms, except for the traditional gap-size analysis algorithm were adopted in the estimation. The accuracy of LAI estimation was not always improved with an increase in sample size. Moreover, results indicated that with the appropriate inversion model, *Ω*_{e} algorithm and sampling scheme, the maximum estimation error of DHP-estimated LAI at elementary sampling unit can be less than 20%, which is required by the global climate observing system, except in forest plots with extremely large LAI values (~ > 6.0). However, obtaining an LAI from DHP with an estimation error lower than 5% is impossible regardless of which combination of inversion model, *Ω*_{e} algorithm and sampling scheme is used.

### Conclusion

The LAI estimation of *L*. *principis-rupprechtii* forests from DHP was largely affected by the sampling schemes adopted in the estimation. Thus, the sampling scheme should be seriously considered in the LAI estimation. One square and two transect sampling schemes (with sample sizes ranging from 3 to 9) were recommended to be used to estimate the LAI of *L*. *principis-rupprechtii* forests with the smallest mean relative error (MRE). By contrast, three cross and one dispersed sampling schemes were identified to provide LAI estimates with relatively large MREs.