Variations of leaf inclination angle distribution with height over the growing season and light exposure for eight broadleaf tree species

Raabe, Kairi; Písek, Jan; Sonnentag, Oliver; Annuk, K.

doi:10.1016/j.agrformet.2015.07.008

Cited by 79 publications

(58 citation statements)

References 32 publications

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“…As in the virtual canopies of this study, field measurements have shown that leaf inclination distributions are usually highly anisotropic (Niinemets, 1998(Niinemets, , 2010Raabe et al, 2015;Bailey and Mahaffee, 2017b). The azimuthal distribution of leaves may be strongly anisotropic within a single plant (Bailey and Mahaffee, 2017b), but for relatively dense canopies, the azimuthal distribution is often fairly isotropic (Ross, 1981).…”

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confidence: 71%

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Evaluating the use of Beer's law for estimating light interception in canopy architectures with varying heterogeneity and anisotropy

León

Bailey

2019

Ecological Modelling

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Light interception in plant canopies is most commonly estimated using a simple one-dimensional turbid medium model (i.e., Beer's law). Inherent in this class of models are assumptions that vegetation is uniformly distributed in space (homogeneous) and in many cases that vegetation orientation is uniformly distributed (isotropic). It is known that these assumptions are violated in a wide range of canopies, as real canopies commonly have heterogeneity at multiple scales and almost always have highly anisotropic leaf angle distributions. However, it is not quantitatively known under what conditions these assumptions become problematic given the difficulty of robustly evaluating model results for a range of canopy architectures. In this study, assumptions of vegetation homogeneity and isotropy were evaluated under clear sky conditions for a range of virtually-generated crop canopies with the aid of a detailed three-dimensional, leaf-resolving radiation model. Results showed that Beer's law consistently over predicted light interception for all canopy configurations. For canopies where the plant spacing was comparable to the plant height, Beer's law performed poorly, and over predicted daily intercepted sunlight by up to ∼115%. For vegetation with a highly anisotropic leaf inclination distribution but a relatively isotropic leaf azimuth distribution, the assumption of canopy isotropy (i.e., G=0.5) resulted in relatively small errors. However, if leaf elevation and azimuth were 1

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confidence: 71%

“…Crop canopies also commonly have a sparse, row-oriented configuration that creates high heterogeneity in light interception. Furthermore, it is rare to find canopies with isotropic leaf angle distributions, as this is typically not the most efficient configuration for light interception (Raabe et al, 2015;Bailey and Mahaffee, 2017b).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the use of Beer's law for estimating light interception in canopy architectures with varying heterogeneity and anisotropy

León

Bailey

2019

Ecological Modelling

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“…Ross 1981) and the clumping index (CI; Chen 1996). While both G(Ω) and CI are measurable quantities, both strongly vary in time and space, making them difficult to quantify remotely (Raabe et al, 2015;Ryu et al, 2010).…”

Section: Measuring F Esc Remotelymentioning

confidence: 99%

A practical approach for estimating the escape ratio of near-infrared solar-induced chlorophyll fluorescence

Zeng¹,

Badgley²,

Dechant³

et al. 2019

Preprint

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Highlights• The escape ratio of near-infrared SIF can be estimated using NIR V and fPAR.• The approach applies broadly, including sparse canopies with bright soil backgrounds.• The approach allows estimation of total emitted SIF from directional SIF data. AbstractSolar-induced chlorophyll fluorescence (SIF) has emerged as a leading approach for remote sensing of gross primary productivity (GPP). While SIF has an intrinsic, underlying relationship with canopy light capture and light use efficiency, these physiological relationships are obscured by the fact that satellites observe a small and variable fraction of total emitted canopy SIF. Upon

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“…The average inclination angles for the top part and lower part were 35.5 • and 30.2 • , respectively. Adjusting the leaf inclination angle distribution to improve the transmission of light through the canopy provides an important way to maximize the light availability throughout the entire vertical profile of the plant canopy [45,46]. It is suggested that to let sunlight into the inside canopy, the leaves in the top part are greatly inclined [46,47].…”

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confidence: 99%

Estimation of Leaf Inclination Angle in Three-Dimensional Plant Images Obtained from Lidar

Itakura

Hosoi

2019

Remote Sensing

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The leaf inclination angle is a fundamental variable for determining the plant profile. In this study, the leaf inclination angle was estimated automatically from voxel-based three-dimensional (3D) images obtained from lidar (light detection and ranging). The distribution of the leaf inclination angle within a tree was then calculated. The 3D images were first converted into voxel coordinates. Then, a plane was fitted to some voxels surrounding the point (voxel) of interest. The inclination angle and azimuth angle were obtained from the normal. The measured leaf inclination angle and its actual value were correlated and indicated a high correlation (R 2 = 0.95). The absolute error of the leaf inclination angle estimation was 2.5 • . Furthermore, the leaf inclination angle can be estimated even when the distance between the lidar and leaves is about 20 m. This suggests that the inclination angle estimation of leaves in a top part is reliable. Then, the leaf inclination angle distribution within a tree was calculated. The difference in the leaf inclination angle distribution between different parts within a tree was observed, and a detailed tree structural analysis was conducted. We found that this method enables accurate and efficient leaf inclination angle distribution.A 3D scanner called lidar (light detection and ranging) provides highly accurate and dense 3D point measurements [15,16]. The lidar is very useful for the retrieval of plant structural parameters. Trunk diameter and plant height can be estimated accurately [17][18][19]. Leaf area and parameters related to leaf area can be calculated from the 3D images of plants obtained by the lidar [20][21][22][23][24]. Other than the parameters above, tree volume and species can be estimated [25][26][27]. The detailed and accurate information from lidar provides a useful tool, for example, in the field of plant phenotyping [28]. Guo et al (2018) developed a high-throughput crop phenotyping platform with lidar that can retrieve various crop structural and physiological relevant parameters efficiently [29]. For more detailed information of the lidar application for plant structural parameters, please refer to the comprehensive reviews in [30,31].Some previous studies on leaf inclination angle estimation with 3D images are available [32][33][34][35][36][37]. In these studies, the points that composed each leaf were fitted to a plane by a least-squares method, and normals to the planes were calculated. Although this method is quite effective for leaf inclination angle estimation, each leaf should be selected manually in the 3D images one by one. Thus, exploring the distribution of the leaf inclination angle is very tedious and time-consuming. Further, owing to the manual operation, the number of leaves that can be examined is limited.To overcome this problem, a previous study [38] converted 3D point cloud images obtained with structure from a motion method into a voxel-based 3D image. In voxel-based 3D models, a geographical space is systematically decomposed into a ...

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Variations of leaf inclination angle distribution with height over the growing season and light exposure for eight broadleaf tree species

Cited by 79 publications

References 32 publications

Evaluating the use of Beer's law for estimating light interception in canopy architectures with varying heterogeneity and anisotropy

Evaluating the use of Beer's law for estimating light interception in canopy architectures with varying heterogeneity and anisotropy

A practical approach for estimating the escape ratio of near-infrared solar-induced chlorophyll fluorescence

Estimation of Leaf Inclination Angle in Three-Dimensional Plant Images Obtained from Lidar

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