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Terrestrial laser scanning (TLS) was introduced for basic forest measurements, such as tree height and diameter, in the early 2000s. Recent advances in sensor and algorithm development have allowed us to assess in situ 3D forest structure explicitly and revolutionised the way we monitor and quantify ecosystem structure and function. Here, we provide an interdisciplinary focus to explore current developments in TLS to measure and monitor forest structure. We argue that TLS data will play a critical role in understanding fundamental ecological questions about tree size and shape, allometric scaling, metabolic function and plasticity of form. Furthermore, these new developments enable new applications such as radiative transfer modelling with realistic virtual forests, monitoring of urban forests and larger scale ecosystem monitoring through long-range scanning. Finally, we discuss upscaling of TLS data through data fusion with unmanned aerial vehicles, airborne and spaceborne data, as well as the essential role of TLS in validation of spaceborne missions that monitor ecosystem structure.

International audience; The 3D distribution of plant material is a key parameter to describe vegetation structure, which influences several processes such as radiation interception and ecosystem functioning. Vegetation covers are often described using Leaf Area Index (LAI) or Plant Area Index (PAI) for monitoring or modeling purposes. Characterizing vegetation 3D structure at fine scale is increasingly required, notably in order to be able to apply radiative transfer simulations at scales consistent with the spatial resolution of recent remote sensing sensors. To assess 3D PAI of a vegetation plot, this paper evaluates the potential of a voxelization method using Terrestrial Laser Scanning (TLS) data, based on the Beer-Lambert transmittance computation law. The theoretical validation was performed using a simulation framework based on a radiative transfer model (DART). The framework allowed simulating TLS acquisition on a theoretical distribution of leaves and a realistic representation (single tree), for which all characteristics are well known. Hence, a sensitivity analysis was performed to study the influence of instrument parameters (i.e. single- or multi-echo, beam divergence), scanning configuration (scan angle step), vegetation characteristics (leaf size and density, leaf angle distribution), and voxel parameters (cubic versus spherical geometry, at different resolutions, with and without occlusion) on the estimation of PAI. For a theoretical distribution of leaves, results showed good accuracy of the voxelization method (R2 = 0.91 and RMSE = 20% for a mean case, at voxel level) with a high resolution multi-echo TLS scan, cubic voxels over 0.5-m resolution, low inter-voxel occlusion, small leaves, and up to a surface density of 2 m2.m −3. Error increased with a larger scan angle resolution, single echo TLS systems, and vegetation density. Also, without clumping, error increased with smaller voxels or larger leaves. Best results were obtained with multi-echo TLS scans (angular resolution of 0.05°), cubic voxels at 1-m resolution when occlusion is low (voxel sampling higher than 50% of maximum sampling at 15 m) and small leaves (e.g. 10 cm2), which provided very good agreement (RMSD = 7.6%, R2 = 0.98, p = 0.99). On a realistic isolated tree, PAI was correctly assessed with cubic voxels at 0.25 m resolution. A method to merge voxelized scans was proposed to deal with inter-voxel occlusion effects.

Equipe MEA; International audience; In a tree, the distribution of leaf inclination angles plays an important role in photosynthesis and water interception. We investigate here the effect of mechanical deformations of leaves due to wind or their own weight on this distribution. First, the specific role of the geometry of the tree is identified and shown to be weak, using models of idealized tree and tools of statistical mechanics. Then the deformation of individual leaves under gravity or wind is quantified experimentally. New dimensionless parameters are proposed, and used in simple models of these deformations. By combining models of tree geometry and models of leaf deformation, we explore the role of all mechanical parameters on the Leaf Inclination Angle Distributions. These are found to have a significant influence, which is exemplified finally in computations of direct light interception by idealized trees. (C) 2013 Elsevier Ltd. All rights reserved.

International audience; This study describes a technique to estimate green area index (GAI) of row crops from gap fraction measurements at 57.5° perpendicular to the row using downward looking digital photos. This particular directional configuration makes the gap fraction independent from leaf angle distribution and minimizes leaf clumping when plants overlap within the row and when rows overlap from this particular direction which is the case for several crops including wheat, maize, sorghum, sunflower and soybean. This was demonstrated from generic row crop canopy architecture models. Additional simulations over realistic 3D scenes of wheat crop allowed to calibrate the following equation relating the gap fraction (Po(57.5°)) to GAI for this particular directional configuration: Po(57.5°) = e−0.824·GAI. This relationship appears very robust across development stages, cultivars and variations due to environmental conditions. When comparing with the situation where leaves are randomly distributed, performances degrade significantly, demonstrating that some residual clumping (Ω = 0.89) has to be accounted for. Field experiments were conducted over wheat crops using colour digital photos taken at 57.5° zenith angle from above in a compass direction perpendicular to the rows. The corresponding gap fraction was computed after image segmentation based on the three colours. The equation derived from wheat architecture model simulations was then used to estimate GAI. Comparison with destructive GAI field measurements shows very good performances with a relative RMSE of 12%. GAI values estimated with this technique were also showing a good consistency with LAI2000 PAI (plant area index) estimates. However, systematic biases between the two estimates were observed, due to canopy elements at the bottom of the canopy not sampled by the instrument because of the height of the LAI2000 sensor, as well as accounting for the residual clumping in the proposed method. These results suggest that this GAI estimation method is very efficient over wheat crops from emergence up to flowering independently from possible architecture variation due to genotype or environmental condition differences. Possible extension to other crops is discussed

International audience; Sun-shade models proved to be simple, fast and reliable tools for estimating the fraction of absorbed PAR (fAPAR) and the photosynthesis of low and simple canopies (e.g. wheat). Applications on tall canopies, or non-ideal canopies (e.g. row-planting, azimuthal heterogeneity, clumping, non-spherical leaf angle distribution, large proportion of non-green elements) were limited so far. We attempted to apply the Sun-shade model proposed by de Pury and Farquhar [de Pury, D.G.G., Farquhar, G.D., 1997. Simple scaling of photosynthesis from leaves to canopies without the errors of big-leaf models. Plant Cell Environ. 20, 537–557] at the scale of the non-ideal canopy of adult coconut palms. The gap fractions of the whole cover, fraction of intercepted PAR (fIPAR), clumping index and leaf orientation derived from LAI-2000 (PCA) were closely matching the simulations of a reference 3D architectural model (3DM) used for cross-validation. Moreover, 3DM supplied a relationship for down-scaling the gap-fractions from the whole cover to green elements of the canopy. The derived Sun-shade simulations of fAPAR by green leaves agreed within 5% with 3DM, on a half-hourly time-step and for 1 year, confirming combined PCA and Sun-shade methods as a fast and reliable tool, even for tall or complex canopies. fIPAR and plant area index (PAI) were compared with the coconut literature and an empirical model was proposed for estimating fIPAR from age and planting density. The coefficient of extinction, K, was adjusted to 0.33 for the regular range of planting density.

Among many indirect approaches to retrieve effective leaf area index (LAI), hemispherical photography is now widely used by the scientific community in forestry applications. A recent software (CAN_EYE) is used to estimate effective and true LAI from unidirectional gap fractions measured in crops. The effective LAI is computed with the Poisson law whereas the true LAI is estimated introducing a clumping index in the Poisson law. The clumping index estimation is based on the Lang and Xiang averaging method. CAN_EYE includes an automatic image classification and allows the processing of series of photographs which is mandatory to sample the spatial variability of the canopy. The objective of this study is to determine if the use of the clumping index in the gap fraction formulation improves seasonal LAI estimates of crops. Hemispherical photographs were taken throughout two growing seasons over wheat, sunflower and maize canopies. CAN_EYE LAI estimates were then compared to destructive LAI. The conditions under which photographs were acquired and processed are discussed. For the three crops studied here, the minimum distance required between camera and canopy is 1 m. When feasible, there is a clear advantage in acquiring the images from above canopies and on overcast days to facilitate the image classification. For wheat and sunflower, the best LAI estimates are assessed with effective LAI (RMSE of 0.15, y = 0.9540x for wheat and RMSE of 0.38, y = 0.8427x for sunflower). For maize, the best LAI estimates are obtained using the clumping index (RMSE of 0.39 and y = 0.9010x). Despite good fits between CAN_EYE and destructive LAI estimates, compensation effects between leaf area index and leaf angle distribution may occur during the inversion procedure. Moreover, values of clumping index given by CAN_EYE are in certain cases correlated with the size of the cells used to divide photographs. The Lang and Xiang averaging method introduced into CAN-EYE should be improved.

Several studies have considered maize canopy as a homogeneous medium with a random leaf orientation distribution. Recent studies, however, have detected that maize leaf orientation in the horizontal plane (i.e., leaf azimuth distribution) can react filling empty spaces (e.g., intra-row or inter-row) due to plant spatial arrangement. “Rigid” genotypes present a random distribution of leaf azimuth, independent of planting pattern. “Plastic” genotypes have the ability to modify leaf orientation. They generally present a random leaf azimuth distribution under square planting patterns, but tend to align their leaves perpendicularly to the row direction under rectangular planting patterns. We used three-dimensional models to reconstruct canopy architecture, mimicking these two behaviors. We simulated mid-day (maximum sun elevation 79.4° and 65.3°) and daily light interception of fully developed canopies at various plant densities (3, 9 and 12 plants m−2) and row spacings (0.35 and 0.70 m), and compared the results of these simulations with data from a field experiment. Simulations and field measurements showed that canopy behavior (plastic or rigid) has a significant (P

This paper extends the turbid medium approach used for modeling bidirectional reflectance from horizontal plant canopies to sloping terrain with a vertically oriented plant stand. Previous treatments have accounted for terrain slope by simple adaptation to an inclined plane of models for horizontal surfaces. However, such treatments implicitly assume that plants grow perpendicularly to the surface, despite the fact that plant stems continue to grow vertically on slopes. The authors investigate the differences between their new "vertical growth" model and the more usual "perpendicular to the surface growth" model in terms of the effect on canopy albedo and bidirectional reflectance factors. Although the effect of leaf angle distribution on the albedo is different for both the vertical-growth and perpendicular-growth models, it appears to be a much smaller effect than that due to terrain slope. For the bidirectional reflectance factors (BRFs), the magnitude and sign of the differences between the two models varies with the direction of observation, the slope, and the leaf angle distribution, and can exceed 10% for a planophile canopy. A comparison between modeled and measured data shows that model predictions under the vertical growth assumption are consistent with measurements, whereas the assumption of perpendicular growth can lead to large errors.

A 3D model of tree architecture is developed to simulate the photosynthesis of peach trees. The model uses a simple graphical procedure to compute the light interception and the corresponding photosynthesis. It allows to consider complex canopy arrangements, and is used to simulate the photosynthesis of laterals, trees and orchards. Results showed that the photosynthesis varied from 5 to 15 μmol m −2 s −1 between laterals of different architectures. The photosynthesis per unit leaf area was sensitive to the orientation of the lateral, its size (number of leaves, length, number of shoots), to the angle between the stem and the shoots, the distance between the leaves, and the angle between the leaf and the shoot. When the isolated lateral is placed in the context of a single tree, its photosynthesis per unit leaf area decreases by a factor of two due to the shadowing. For orchards, the photosynthesis per unit leaf area decreases as a function of the tree density. The photosynthesis per unit area of sunlit leaves at the lateral, tree or orchard levels, was found to be almost constant, because the leaf angle distribution of the illuminated leaves do not vary much. Therefore, one important result of this study is that the variation of photosynthesis per unit leaf area, is mainly determined by the fraction of sunlit leaf area.

The aim of this study is to develop a 3D model of maize (Zea mays) canopy structure for accurate reflectance simulations. We focus on fully developed maize plants without paying attention to the reproductive organs. Several experiments are used to describe the dimension, shape, position and orientation of the leaves and stems. They correspond to a wide range of cultural practices. Empirical models are proposed to derive the position, size, and shape of the leaves and stems as a function of three main variables: the final number of leaves, the final height of the canopy and the cumulated leaf area per plant that is considered as a vigor index. Leaf orientation is described through the curvature of the main rib and the cross section used to represent the undulation of the lamina. The statistical distributions of the parameters of each structure sub-models are investigated. It allows to generate realistic computerized plant and canopy 3D architecture representation. The canopy structure is then used to compare the SAIL reflectance model to the reflectance simulated with PARCINOPY which is a monte-carlo ray tracing model. The combined use of detailed canopy architecture models and quasi exact radiative transfer models such as PARCINOPY appears to be a very convenient tool to evaluate more simple reflectance models applied to specific vegetation types. Results show a general good agreement between the two models, except for the exponential hot-spot function used in the SAIL model, and the directionality of the multiple scattering.

Modelling crop architecture and light interception by maize usually assumes a uniform distribution of leaf azimuth. A developmental study of the spatial display of the shoot structure was carried out during the vegetative growth through a field experiment with four treatments: two initial plant orientations (one treatment with the first leaves of all plants initially perpendicular to the row direction and another treatment with the first leaves of all plants initially parallel to the row direction) and two densities (about 10 plants/m 2 and 20 plants/m 2 ). The position of each leaf was characterized over time using three parameters: leaf azimuth, leaf base inclination, and leaf insertion height. A circular data analysis was necessary to study leaf angle (azimuth, inclination). The first leaves grow in the same vertical plane until leaf 8 or 9. For these leaves, there was a low dispersion of leaf azimuths around the initial plant azimuth. For upper leaves however, the dispersion of leaf azimuths increased, tending to a uniform distribution for leaves 12 to 14. We observed no effect of initial plant orientation on final leaf mean azimuths. The only important effect of initial plant azimuthal clumping was to increase the trend toward azimuthal dispersion. For an individual leaf, azimuthal movements occurred mainly during the growing period, but some movements could occur between ligulation and silking too. These movements were caused at least in part by a twisting of the internodes, and may also result from the twisting of the sheaths and the nodes. These results, especially for the upper leaves, are in contrast with those of previous workers with comparable experimental design, but in growth chamber conditions, in which leaves tended to be orientated more and more perpendicular to the row, due to internode torsion. The effects of initial plant orientation on leaf inclination and height were less spectacular. Initial plant orientation and density had a small effect on leaf base inclinations. An effect of density was observed only on leaf insertion heights, but no intercalation of leaves between adjacent plants was noticed.

Leaf azimuth is not used in the estimation of light interception by a crop canopy, implying a random distribution of leaf azimuth. For a maize ( Zea mays L.) crop, random leaf azimuth implies that plant orientation is random and all the leaves are in the same plane. We have tested this assumption by recording the leaf azimuths of plants grown according to different canopy patterns: plants are oriented 1) parallel to the row (± 22° 30'), 2) perpendicular to the row (± 22° 30') and 3) random. Experiments were carried out in a growth room to avoid the impact of solar path and of wind effects on leaf azimuth. Independently of the plant orientation of the young plant, less than 15 per cent of the leaves were oriented row wise, from the 9-leaf stage, which was significantly different from the initial orientation. In the crop where plants were randomly oriented, 25 per cent of them were perpendicular to the row at the 2-leaf stage and this proportion rose to 35, 45 and 70 per cent at the 7, 9 and 14-leaf stages, respectively. At the plant density used in this experiment (11 plants per m 2 ), there was an azimuth shift from the bottom to the top of the plant, with a strong tendency to a leaf orientation perpendicular to the row for the upper leaves. When the young leaves were emerging from the whorl, the azimuth of the leaf tip changed. The final leaf azimuth was fixed for 91 per cent of the plants at 2 days before complete expansion of the leaf. At early stages, the leaf azimuth shift corresponded to a twisting of leaf sheaths, and later to a twisting of internodes. The non-random nature of leaf azimuth should be taken into consideration in future models of light interception.

The radiation regime of the plant canopies is largely influenced by the geometrical structure, i.e. the angular and spatial distributions of the leaf area. In this paper, two methods for investigating the structure of a maize row crop are compared. The first is a new method which consists of a three-dimensional (3D) digitizing of the foliage using a sonic 3D digitizer. The second one is the plant profile method in which a picture of the plant is taken and then digitized in two dimensions. The latter assumes that the leaves of a plant are in a single vertical plane called the ‘azimuthal plane’. The 3D digitizer has the ability to locate a given point with an accuracy of within ±1 cm: the representation and the repeatability of a leaf description are satisfactory and not largely influenced by the digitizing density. The azimuthal plane assumption of the plant profile method was then tested using the results of 3D digitizing as the reference. Regarding the individual plant description, the leaf azimuth function is bimodal as expected for the maize phyllotaxy. The theoretical azimuthal plane was computed by a principal component analysis. The plant leaf area projection onto this plane represents about 96% of the 3D spatial distribution. Moreover, this plane is close to the azimuthal plane estimated by eye. That is why the spatial distributions of the leaf area in the azimuthal plane are quite similar for the two methods. However, the leaf area with an azimuth within ±15° around the plant azimuth is less than 40% of the total area and the foliage spreads out in a slice 40 cm thick, around the azimuthal plane. Regarding the canopy structure description, the two methods gave similar results for both the leaf inclination and the vertical leaf area density function. In contrast, the distributions in the horizontal plane were different: the plant profile method overestimates the row effect, increasing the foliage clumping around the stem because of the plant azimuth plane assumption. Such discrepancies have repercussions on the modelled radiative balance of a row crop, so the plant profile method should not be used in estimating the spatial distribution of the leaf area in the horizontal plane.