Evaluating the impact of sampling schemes on leaf area index measurements from digital hemispherical photography in Larix principis-rupprechtii forest plots
Evaluating the impact of sampling schemes on leaf area index measurements from digital hemispherical photography in Larix principis-rupprechtii forest plots
复制标题
评估抽样方案对华北落叶松林地数字半球摄影叶面积指数测量的影响
DOI：
10.1186/s40663-020-00262-z
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发表时间：
2020-08
影响因子：
4.1
通讯作者：
Leng Peng
中科院分区：
文献类型：
--
作者：
Zou Jie;Hou Wei;Chen Ling;Wang Qianfeng;Zhong Peihong;Zuo Yong;Luo Shezhou;Leng Peng
背景
数字半球面摄影(DHP)以其高效、低成本等优点被广泛应用于林地叶面积指数(LAI)的估算。利用DHP方法估算林地叶面积指数的一个关键步骤是选择抽样方案。然而，涉及DHP的各种抽样方案已被用于林地叶面积指数的估计。到目前为止，还没有全面研究抽样方案对DHP叶面积指数估算的影响。
方法
本研究采用了13种常用的抽样方案，对5个25m×25m的华北落叶松样地的叶面积指数进行了估算，它们分别属于分散、正方形、交叉、样带和圆形5种抽样类型。在13个抽样方案的所有抽样点的基础上，生成了一个额外的抽样方案(抽样数量为89个)。在叶面积指数的估算中，采用了3种典型的反演模型和4种冠层元素聚集指数(Ωe)算法。分析了抽样方案对林隙系数、Ωe、有效植物面积指数(PAIE)和叶面积指数(LAI)的影响。然后，将通过不同抽样方案获得的叶面积指数估计值与通过垃圾收集测量获得的叶面积指数进行比较。
结果
在不同的抽样方案下，所有四个变量估计值(即GAP分数、Ωe、PAIE和LAI)都有很大的差异。如果采用除传统的空位分析算法外的四种Ωe算法，则三种反演模型的抽样方案对叶面积指数估计的影响差异不明显。叶面积指数估计的准确性并不总是随着样本量的增加而提高。结果表明，在选择合适的反演模型、Ωe算法和抽样方案的情况下，除LAI值极大(~6.0)的森林样地外，在基本采样单元，DHP估算的叶面积指数的最大估计误差可以小于全球气候观测系统所要求的20%。然而，无论采用哪种反演模型、Ωe算法和采样方案，都不可能得到估计误差小于5%的叶面积指数。
结论
DHP对华北落叶松林分叶面积指数的估计受抽样方案的影响较大。因此，在估算叶面积指数时，应认真考虑抽样方案。在估算华北落叶松林分叶面积指数时，推荐采用1平方和2样带抽样方案(样本量为3~9)，其平均相对误差(MRE)最小。相比之下，确定了三个交叉抽样方案和一个分散抽样方案，以提供相对较大的最大相对误差。
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 (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.
Results
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.
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.
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影响因子：
3.7
作者：
Cutini, A;Matteucci, G;Mugnozza, GS
通讯作者：
Mugnozza, GS
影响因子：
6.2
作者：
Titta Majasalmi;M. Rautiainen;P. Stenberg;H. Rita
通讯作者：
Titta Majasalmi;M. Rautiainen;P. Stenberg;H. Rita
影响因子：
4.4
作者：
Chen, JM;Rich, PM;Plummer, S
通讯作者：
Plummer, S
DOI：
--
发表时间：
1993
期刊：
--
影响因子：
--
作者：
S. Running
通讯作者：
S. Running
影响因子：
6.2
作者：
H. Neumann;G. Hartog;R. Shaw
通讯作者：
H. Neumann;G. Hartog;R. Shaw