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

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 (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.

Estimating canopy fuel load with hemispherical photographs: A rapid method for opportunistic fuel documentation with smartphones

“…The area covered by a given HP will depend on lens height, tree height, and tree distance to the lens (Leblanc et al 2005). It is possible for HPs to oversample the canopy inside the plot or include canopy regions outside of the plot (Leblanc et al 2005, Zou et al 2020). Due to the homogeneity of the forest canopy in our study area, we expect these factors to have a negligible impact on our results; however, in more heterogenous forest ecosystems, the plot layout and procedures used in this study may not be suitable.…”

Estimating Needle and Shoot Inclination Angle Distributions and Projection Functions in Five Larix principis-rupprechtii Plots via Leveled Digital Camera Photography

“…The images were taken from three height classes (i.e., top, middle, and bottom) in the five plots, except in plots 1 and 2. In plots 1 and 2, images were taken from the top and middle height classes because the majority of the lower canopy branches were harvested during forest management activities prior to the field experiment [34]. The images were taken between 17-18 September 2017 and 9-10 August 2018 at the maximum plant area index (PAI) period of the five plots.…”

A reinterpretation of the gap fraction of tree crowns from the perspectives of computer graphics and porous media theory

“…Partial interception and gap identification from light detection and ranging (LiDAR) data at a fixed emission angle are related to the horizontal and vertical distributions of the crown mass considering the stem base position at a given angle ( Milenković et al., 2017 ); thus, a statistically significant sample of trees is needed, and it is difficult to quantify full tree crowns with asymmetrical phenotypic characteristics and allometric growth properties. In addition, some statistical methods ( Zou et al., 2020 ) are applicable only to large-scale forestlands, and they require large amounts of data analysis ( Kong et al., 2022 ) or standardization of the distance and orientation of sample data to compensate for the deviations of individual specimens; therefore, these methods are not suitable for single trees without guidance from prior analyses of comparatively larger forest plots with large populations of the same species.…”