Quasi-Monte Carlo simulation of the light environment of plants

The distribution of light in the canopy is a major factor regulating the growth and development of a plant. The main variables of interest are the amount of photosynthetically active radiation (PAR) reaching different elements of the plant canopy, and the quality (spectral composition) of light reaching these elements. A light environment model based on Monte Carlo (MC) path tracing of photons, capable of computing both PAR and the spectral composition of light, was developed by Mech (1997), and can be conveniently interfaced with virtual plants expressed using the open L-system formalism. To improve the efficiency of the light distribution calculations provided by Mech's MonteCarlo program, we have implemented a similar program QuasiMC, which supports a more efficient randomised quasi-Monte Carlo sampling method (RQMC). We have validated QuasiMC by comparing it with MonteCarlo and with the radiosity-based CARIBU software (Chelle et al. 2004), and we show that these two programs produce consistent results. Wealso assessed the performance of the RQMC path tracing algorithm by comparing it with Monte Carlo path tracing and confirmed that RQMC offers a speed and/or accuracy improvement over MC.

冠层内的光照分布是调节植物生长发育的一个主要因素。主要关注的变量是到达植物冠层不同部分的光合有效辐射（PAR）量，以及到达这些部分的光的质量（光谱组成）。梅赫（Mech，1997）开发了一种基于光子的蒙特卡罗（MC）路径追踪的光环境模型，该模型能够计算光合有效辐射和光的光谱组成，并且可以方便地与用开放式L - 系统形式表达的虚拟植物相结合。为了提高梅赫的蒙特卡罗程序所提供的光分布计算效率，我们实现了一个类似的程序准蒙特卡罗（QuasiMC），它支持一种更高效的随机化准蒙特卡罗采样方法（RQMC）。我们通过将准蒙特卡罗程序与蒙特卡罗程序以及基于辐射度的CARIBU软件（谢勒等人，2004）进行比较，验证了准蒙特卡罗程序，并且表明这两个程序产生的结果是一致的。我们还通过将RQMC路径追踪算法与蒙特卡罗路径追踪算法进行比较，评估了RQMC路径追踪算法的性能，并证实RQMC在速度和/或精度上比蒙特卡罗有所提高。