Evaluating Two Optical Methods of Woody-to-Total Area Ratio with Destructive Measurements at Five Larix gmelinii Rupr. Forest Plots in China

Zou, Jie; Leng, Ping; Hou, Wei; Zhong, Peihong; Chen, Ling; Mai, Chunna; Qian, Y. Q.; Zuo, Yong

doi:10.3390/f9120746

Cited by 6 publications

(32 citation statements)

References 44 publications

(108 reference statements)

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“…Therefore, all three Ω e or Ω w algorithms of CC, LX, and CLX were used in this study. Table 1 lists the corrected needle-to-shoot area ratio and Ω e estimation formulae used in this study [23]. In this study, the equations of the three Ω w algorithms of CC, LX, and CLX to estimate the Ω w of leaf-off forest canopies from DHP or MCI are the same as those for estimating the Ω e_CC (θ), Ω e_LX (θ), and Ω e_CLX (θ) of leaf-on forest canopies, respectively (Equations (1)- (3).…”

Section: Corrected Needle-to-shoot Area Ratio and Canopy Element Clummentioning

confidence: 99%

“…The inversion model is a key factor that affects the PAI or LAI estimation from optical methods due to the large differences observed between the PAIs or LAIs obtained using different inversion models [19,23,24,35]. Two fundamental inversion models were proposed previously and are widely used in the ESU LAI estimation of forests, i.e., Beer's Law (Beer) (Equations (8) and (12)) [44] and Miller theorem (Miller) (Equations (9) and (13)) [45].…”

Section: Pai Wai and Laimentioning

confidence: 99%

“…Therefore, several inversion models were proposed with the development of these optical methods [14,19,41,46]. For example, Equation (10) or (14) is the modified Miller integration adopted by the LAI-2200 method (LAI-2200) in estimating the PAI or LAI of forest canopies [23,46]. Similarly, Equation (11) is also the modified Miller integration adopted by the MCI method (MCI_0-85) for estimating the PAI of forest canopies [14,23].…”

Section: Pai Wai and Laimentioning

confidence: 99%

“…For example, Equation (10) or (14) is the modified Miller integration adopted by the LAI-2200 method (LAI-2200) in estimating the PAI or LAI of forest canopies [23,46]. Similarly, Equation (11) is also the modified Miller integration adopted by the MCI method (MCI_0-85) for estimating the PAI of forest canopies [14,23]. Table 2 lists the inversion models used in the present study.…”

Section: Pai Wai and Laimentioning

confidence: 99%

“…Cao et al [22] suggested three solutions to correct the influence of slope on the ESU LAI estimation of forests from DHP. Several studies have attempted to correct the overestimation of woody components on the ESU LAI estimation of forests by determining the woody-to-total area ratio or WAI through MCI [14,23], DHP [24], and terrestrial laser scanner [25,26]. However, few studies have attempted to evaluate whether these optical methods or solutions are effective in obtaining the ESU LAI of forests with estimation errors of <20% or 5% required by GCOS, especially for Larix principis-rupprechtii forests.…”

Section: Introductionmentioning

confidence: 99%

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Performance of Four Optical Methods in Estimating Leaf Area Index at Elementary Sampling Unit of Larix principis-rupprechtii Forests

Zou

Zuo

Zhong

et al. 2019

Forests

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Optical methods are frequently used as a routine method to obtain the elementary sampling unit (ESU) leaf area index (LAI) of forests. However, few studies have attempted to evaluate whether the ESU LAI obtained from optical methods matches the accuracy required by the LAI map product validation community. In this study, four commonly used optical methods, including digital hemispherical photography (DHP), digital cover photography (DCP), tracing radiation of canopy and architecture (TRAC) and multispectral canopy imager (MCI), were adopted to estimate the ESU (25 m × 25 m) LAI of five Larix principis-rupprechtii forests with contrasting structural characteristics. The impacts of three factors, namely, inversion model, canopy element or woody components clumping index ( Ω e or Ω w ) algorithm, and the woody components correction method, on the ESU LAI estimation of the four optical methods were analyzed. Then, the LAI derived from the four optical methods was evaluated using the LAI obtained from litter collection measurements. Results show that the performance of the four optical methods in estimating the ESU LAI of the five forests was largely affected by the three factors. The accuracy of the LAI obtained from the DHP and MCI strongly relied on the inversion model, the Ω e or Ω w algorithm, and the woody components correction method adopted in the estimation. Then the best Ω e or Ω w algorithm, inversion model and woody components correction method to be used to obtain the ESU LAI of L. principis-rupprechtii forests with the smallest root mean square error (RMSE) and mean absolute error (MAE) were identified. Amongst the three typical woody components correction methods evaluated in this study, the woody-to-total area ratio obtained from the destructive measurements is the most effective method for DHP to derive the ESU LAI with the smallest RMSE and MAE. In contrast, using the woody area index obtained from the leaf-off DHP or DCP images as the woody components correction method would result in a large LAI underestimation. TRAC and MCI outperformed DHP and DCP in the ESU LAI estimation of the five forests, with the smallest RMSE and MAE. All the optical methods, except DCP, are qualified to obtain the ESU LAI of L. principis-rupprechtii forests with an MAE of <20% that is required by the global climate observation system. None of the optical methods, except TRAC, show the potential to obtain the ESU LAI of L. principis-rupprechtii forests with an MAE of <5%.

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Section: Corrected Needle-to-shoot Area Ratio and Canopy Element Clummentioning

confidence: 99%