Evaluating the impact of sampling schemes on leaf area index measurements from digital hemispherical photography in Larix principis-rupprechtii forest plots

• 作者: Jie Zou Wei Hou Ling Chen Qianfeng Wang Peihong Zhong Yong Zuo Shezhou Luo Peng Leng
• 来源期刊: 森林生态系统（英文版）
• 年，卷（期）: 2020,（4）
• 页码范围: 686-703
• 页数: 18页
• 语种: 英文

摘要

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 x 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 (PAle) 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, PAle 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.