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Light exchanges in discrete directions as an alternative to raytracing and radiosity



Introduction Light modelling at the scale of organs is essential to account accurately for the complex interactions between biophysical processes such as photosynthesis, stomatal conductance and energy balance. Yet, the calculation of radiative exchanges at fine scales is computationally-intensive and it remains a hindrance to a widespread use of FSPMs despite advances in light modelling using either radiosity (Chelle and Andrieu, 1998) or raytracing (Bailey, 2018). This study shows that simplifications based on the discretization of radiative fluxes allow processing radiative exchanges in a natural environment while maintaining good accuracy on the simulation of biophysical processes such as carbon assimilation. Material and Methods The present study is based on biophysical simulations performed using the ARCHIMED model. Incident radiation is depicted as a set of specular fluxes (i.e. parallel rays) in discrete directions using the sun direction for direct radiation and predefined “turtle” directions for the diffuse radiation. The “turtle” directions are obtained by splitting the sky hemisphere into sectors of equal solid angle (Dauzat et al, 2001). Optionally, direct radiation can be distributed in neighboring "turtle" sectors (turtle only). For each direction, the scene is projected on an image plane and the interception of incident light is deduced from rasterized pixel projections. Additionally, Z-Buffering gives the overlay of scene objects and, in this regard, pixels can be viewed as rays traced from outside down to the ground level. Light scattering can thus be processed similarly to raytracing. In the case of Lambertian objects, we further assume that all rays scattered by an object carry the same energy whatever the “turtle” direction. Net assimilation (An) is calculated with Farquhar’s model (Farquhar et al. 1980), stomatal conductance with Medlyn’s model (Medlyn et al. 2011) and the leaf temperature is found by solving the energy balance of the system. Simulations are run on a dense three-dimensional scene including two palms (Elaeis guineensis) with the following configuration: latitude= 15°, Day of year 71, time steps of 30mn, clearness index Kt= 0.5. A “toricity” option is used to generate a virtually infinite canopy. The number of “turtle” directions is set to 6, 16, 46 or 136. The sun position is either integrated into the turtle or separately computed. The pixel density ranges from 341 to 6821 pixels m-2. The reference outputs are obtained with the highest number of directions and pixels. * Scene metrics: plot= 15.9m*9.2m, meshes= 24 863, triangles= 571 934, LAI= 3.2, leaflets= 24 493 Results and Discussions Figure 1 (left) illustrates the effect of the number of discrete light directions on the estimation of biophysical processes in comparison with the reference of 136 directions. Sampling the sun direction provides best results since direct radiation largely contributes to the PAR irradiance, the energy load of leaflets and, finally, their assimilation. Bias remain low when the sun direction is not sampled except when the number of “turtle” directions is decreased to six. The dispersion of residuals remains quite limited for 46 directions, meaning that reliable values can be obtained at leaflet scale for such configuration. Figure 1 (right) shows that a low pixel density (682 pixels m-2, i.e. 50 000 pixels) is sufficient to get a relatively unbiased estimation of carbon assimilation at plot level, but a higher density is necessary to get reliable estimation at leaflet scale. The reference configuration in the left pane of Fig. 1 generates 68.5M rays for each time step and, since several hits are recorded per ray (6 on average) this generates about 5 sub-rays that are used for the calculation of light scattering. Running the complete simulation with the reference configuration from the right pane of Fig. 1 lasts ~3.4 min for each time step (23M rays). This time can be decreased to only 2 seconds per step by storing partial scene illumination for each direction, but this preliminary step can be time-consuming, mainly during the multiple scattering for the PAR and NIR ranges. A considerable shortening is expected by treating light exchanges using directional form factors between pairs of objects instead of propagating scattered light by individual rays. Conclusion Using discrete ordinates allows performing accurate and unbiased simulations of light interception. Biases arise when decreasing the number of directions but with limited consequences on carbon assimilation. Larger biases occur when pixel density is too low to sample correctly individual leaflets. A configuration with 46 turtle directions for depicting both direct and diffuse radiation and a pixel density of 682 pixels m-2 allows fast computations while providing sufficient information to get precise light budget at fine scales. References Bailey, 2018, Ecological Modelling. 368:233-245, doi: 10.1016/j.ecolmodel.2017.11.022. Chelle and Andrieu, 1998, Ecological Modelling 111:75-91, doi: 10.1016/S0304-3800(98)00100-8 Dauzat et al., 2001, Agric. & Forest Met. 109(2)143-160, doi: 10.1016/S0168-1923(01)00236-2 Farquhar et al., 1980, Planta. 149:78-90, doi: 10.1007/BF00386231 Medlyn et al., 2011, Global Change Biology. 17:2134-2144, doi: 10.1111/j.1365-2486.2010.02375.x
Light exchanges in discrete directions as an alternative to raytracing and radiosity
Light budget in 3D plant stands under natural conditions
Irradiance in PAR and NIR at scale of individual scene items (e.g. leaves , soil patches…)
Calculation of temperature, carbon assimilation and transpiration for each leaf
Shortens computations in comparison to ray-tracing or radiosity
Does not require high computer memory
This allows repetitive simulations for monitoring evolution of eco-
physiological variables over time or for performing response curves over
physical and physiological variables
Rémi Vezy1, Raphaël Perez2, François Grand1, Jean Dauzat1
Strength of the approach
Running a single simulation with 46 directions and 1 million of pixels
per direction lasts about 3.4 min with the sole use of CPU
This duration can be decreased to only 2 seconds for subsequent
simulations after storing the scene illumination for each direction
Very small items are poorly sampled compared to big ones
Aim and
For a given direction
Rasterized projection of items
Each pixel equivalent to a ray
Several hits along a ray obtained with Z-
Interception of incident light by uppermost
Scattering calculated between pairs of items
proportional to
number of hits
Principle of discretization
Light interception and scattering in
discrete “turtle” directions
Direct radiation
interception calculated either in sun
direction or distributed in turtle sectors
Diffuse light split into directional fluxes
compliantly with “turtle model”
Incident solar radiation
Two palms (Elaeis guineensis)
Toricity option
24 863 meshes, 571 934
triangles, 24 493 leaflets, LAI=
Simulation runs
Infinite canopy with “toricity” option
Rays pathway are stored and used as
long the scene is not modified
The “toricity” option generates an
infinite canopy by virtually duplicating
the scene
Tips and tricks
1) CIRAD, UMR AMAP, F‐34398 Montpellier, France.AMAP, Univ Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France
2) CIRAD, UMR AGAP, F-34398 Montpellier, France. AGAP, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France
Sum intercepted energy for all mesh
facets of the item
Distribute scattered energy to rays
Transfer energy to other items
along the rays pathway
Light scattered by a Lambertian item
is proportional to its apparent area
which is approximated by the
number of hits
Energy transfer
Example of “turtle” with
46 directions
Sun course Direct and
diffuse radiation
Error (%) for An, PAR and Energy relatively for experiments A and B. The reference is 136 directions + sun for A and 1 million pixels for B
Experiment A
Best results are obtained with the option
turtle only= false (blue). In this case, the
turtle directions are only used for
calculating diffuse and scattered light
When using the option turtle only= true,
(red) the % error increases for small
numbers of turtle directions (6 or 16)
and the biases becomes important
Exp.A (0.5 M pixels in all cases)
6, 16, 46 or 136 turtle directions
plus sun direction when option
turtle only” = false
Ex. B (46 directions)
50 000 pixels to 1 million pixels
Java; ARCHIMED software
Experiment B
The % error increases when
decreasing the number of
Biases remain small as long
as the number of pixels is >
500 000 (i.e. 342 pixels per
6 cores, Intel Xeon W2133
3.60 GHz, RAM 32Go
FSPM2020, 5-9 October 2020
Bailey, 2018, Ecological Modelling. 368:233-245, doi: 10.1016/j.ecolmodel.2017.11.022.
Chelle and Andrieu, 1998, Ecological Modelling 111:75-91, doi: 10.1016/S0304-3800(98)00100-8
Dauzat et al., 2001, Agric. & Forest Met. 109(2)143-160, doi: 10.1016/S0168-1923(01)00236-2
Yin et al, Remote Sensing of Environment 135 (2013) 213223
... The ARHIMED model has a different approach in modeling the photosynthesis process: it is controlled together with transpiration by stomatal conductance parameter which can limit photosynthesis by reducing carbon dioxide influx and indirectly modulates leaf temperature through evaporation. Light exchange has been modeled by Vezy et al. [80] as a discretized model along the directions, alternative to ray tracing and radiosity models, while the internal behavior of the leaves is governed by the water potential calculated according to a sap flow model. Perez et al. [121] used ARHIMED to study architectural plasticity in response to planting density of oil palm. ...
Full-text available
Computer-Generated Imagery (CGI) has received increasing interest in both research and the entertainment industry. Recent advancements in computer graphics allowed researchers and companies to create large-scale virtual environments with growing resolution and complexity. Among the different applications, the generation of biological assets is a relevant task that implies challenges due to the extreme complexity associated with natural structures. An example is represented by trees, whose composition made by thousands of leaves, branches, branchlets, and stems with oriented directions is hard to be modeled. Realistic 3D models of trees can be exploited for a wide range of applications including decision-making support, visualization of ecosystem changes over time, and for simple visualization purposes. In this review, we give an overview of the most common approaches used to generate 3D tree models, discussing both methodologies and available commercial software. We focus on strategies for modeling and rendering of plants, highlighting their accordance or not with botanical knowledge and biological models. We also present a proof of concept to link biological models and 3D rendering engines through Ordinary Differential Equations.
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