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. 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 (Ωe) algorithms were involved in the LAI estimation. The impacts of the sampling schemes on four variables, including gap fraction, Ωe, effective plant area index (PAIe) 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. Large differences were observed for all four variable estimates (i.e. gap fraction, Ωe, PAIe 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 Ωe 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, Ωe 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, Ωe algorithm and sampling scheme is used. 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.

中文翻译：

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在华北落叶松林地数字半球摄影中评估采样方案对叶面积指数测量的影响

数字半球摄影（DHP）由于其高效和低成本的优点而被广泛用于估算森林地的叶面积指数（LAI）。通过DHP对林地进行LAI评估的关键步骤是选择抽样方案。但是，涉及DHP的各种采样方案已用于林地的LAI估算。迄今为止，尚未全面研究抽样方案对DHP评估LAI的影响。在这项研究中，采用了13种常用的采样方案，分别属于五个采样类型（即分散，正方形，交叉，横断面和圆形），用于五个华北落叶松（25 m×25 m）样地的LAI估计。根据13个采样方案的所有采样点，生成了另一个采样方案（样本大小为89）。LAI估计涉及三种典型的反演模型和四种树冠元素丛集指数（Ωe）算法。分析了抽样方案对缺口变量，Ωe，有效植物面积指数（PAIe）和DHP估计的LAI四个变量的影响。然后将不同采样方案获得的LAI估计值与垃圾收集测量获得的LAI估计值进行比较。在不同的采样方案下，所有四个变量估计值（即缺口分数，Ωe，PAIe和LAI）均存在较大差异。如果在估计中采用传统的间隙大小分析算法以外的四个Ωe算法，则对于三个反演模型，采样方案对LAI估计的影响差异并不明显。随着样本量的增加，LAI估计的准确性并不一定总是得到提高。此外，结果表明，通过适当的反演模型，Ωe算法和采样方案，基本采样单元中DHP估计的LAI的最大估计误差可以小于20％，这是全球气候观测系统所要求的，除了在森林中LAI值非常大（〜> 6.0）的曲线。但是，无论使用反演模型，Ωe算法和采样方案的哪种组合，都无法从DHP获得估计误差低于5％的LAI。来自DHP的L. principis-rupprechtii森林的LAI估算在很大程度上受到估算中采用的抽样方案的影响。因此，在LAI估计中应认真考虑采样方案。建议使用一平方和两个样点抽样方案（样本大小在3到9之间）来估计平均相对误差（MRE）最小的华氏乳杆菌森林的LAI。相比之下，确定了三个交叉和一个分散的采样方案，以提供具有相对较大MRE的LAI估计值。