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Monte Carlo Method Based Radiative Transfer Simulation of Stochastic Open Forest Generated by Circle Packing Application

机译:基于蒙特卡罗方法的圆堆积应用产生的随机阔叶林辐射传递模拟。

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Monte Carlo Ray Tracing (MCRT) method is a versatile application for simulating radiative transfer regime of the Solar - Atmosphere - Landscape system. Moreover, it can be used to compute the radiation distribution over a complex landscape configuration, as an example like a forest area. Due to its robustness to the complexity of the 3-D scene altering, MCRT method is also employed for simulating canopy radiative transfer regime as the validation source of other radiative transfer models. In MCRT modeling within vegetation, one basic step is the canopy scene set up. 3-D scanning application was used for representing canopy structure as accurately as possible, but it is time consuming. Botanical growth function can be used to model the single tree growth, but cannot be used to express the impaction among trees. L-System is also a functional controlled tree growth simulation model, but it costs large computing memory. Additionally, it only models the current tree patterns rather than tree growth during we simulate the radiative transfer regime. Therefore, it is much more constructive to use regular solid pattern like ellipsoidal, cone, cylinder etc. to indicate single canopy. Considering the allelopathy phenomenon in some open forest optical images, each tree in its own 'domain' repels other trees. According to this assumption a stochastic circle packing algorithm is developed to generate the 3-D canopy scene in this study. The canopy coverage (%) and the tree amount (N) of the 3-D scene are declared at first, similar to the random open forest image. Accordingly, we randomly generate each canopy radius (r_c). Then we set the circle central coordinate on XY-plane as well as to keep circles separate from each other by the circle packing algorithm. To model the individual tree, we employ the Ishikawa's tree growth regressive model to set the tree parameters including DBH (d_t), tree height (H). However, the relationship between canopy height (H_c) and trunk height (H_t) is unclear to us. We assume the proportion between H_c and H_t as a random number in the interval from 2.0 to 3.0. De Wit's sphere leaf angle distribution function was used within the canopy for acceleration. Finally, we simulate the open forest albedo using MCRT method. The MCRT algorithm of this study is summarized as follows (1) Initialize the photon with a position (r_0), source direction (Ω_0) and intensity (I_0), respectively. (2) Simulate the free path (s) of a photon under the condition of (r', Ω, I') in the canopy. (3) Calculate the new position of the photon r = r'+sΩ'. (4) Determine the new scattering direction (Ω) after collision at r, and then calculate the new intensity I = γ_L(Ω_L, Ω'→Ω)I'.(5) Accumulate the intensity Ⅰ of a photon escaping from the top boundary of the 3-D Scene, otherwise redo from step (2), until I is smaller than a threshold. (6) Repeat from step (1), for each photon. We testify the model on four different simulated open forests and the effectiveness of the model is demonstrated in details.
机译:蒙特卡洛射线追踪(MCRT)方法是一种用于模拟太阳-大气-景观系统的辐射传输状态的通用应用程序。此外,它可用于计算复杂景观配置上的辐射分布,例如林区。由于其对3D场景更改的复杂性的鲁棒性,MCRT方法还被用于模拟冠层辐射传输方式,作为其他辐射传输模型的验证来源。在植被内的MCRT建模中,基本步骤之一是建立树冠场景。 3-D扫描应用程序被用来尽可能准确地表示树冠结构,但这很费时。植物生长函数可用于模拟单棵树的生长,但不能用于表达树木之间的影响。 L-System还是功能受控的树生长模拟模型,但它会占用大量计算内存。另外,在我们模拟辐射传输过程中,它仅对当前的树木模式进行建模,而不对树木的生长进行建模。因此,使用规则的实心图案(例如椭圆形,圆锥形,圆柱形等)来表示单个树冠更具建设性。考虑到一些开放森林光学图像中的化感病现象,每棵树在其自己的“域”中会排斥其他树。根据此假设,在本研究中,开发了一种随机圆堆积算法来生成3-D顶篷场景。首先声明3-D场景的树冠覆盖率(%)和树数量(N),类似于随机开阔的森林图像。因此,我们随机生成每个树冠半径(r_c)。然后,我们在XY平面上设置圆的中心坐标,并通过圆填充算法使圆彼此分开。为了建模单个树,我们使用Ishikawa的树生长回归模型来设置树参数,包括DBH(d_t),树高(H)。但是,我们不清楚树冠高度(H_c)和躯干高度(H_t)之间的关系。我们假设H_c和H_t之间的比例为2.0到3.0之间的一个随机数。在机盖内使用De Wit的球面叶片角度分布函数进行加速。最后,我们使用MCRT方法模拟了开放森林反照率。本研究的MCRT算法总结如下:(1)分别用位置(r_0),源方向(Ω_0)和强度(I_0)初始化光子。 (2)在冠层中的(r',Ω,I')条件下模拟光子的自由路径。 (3)计算光子的新位置r = r'+sΩ'。 (4)确定在r处碰撞后新的散射方向(Ω),然后计算新的强度I =γ_L(Ω_L,Ω'→Ω)I'。(5)累积从顶部逸出的光子的强度Ⅰ。 3D场景的边界,否则从步骤(2)重做,直到I小于阈值。 (6)对每个光子重复步骤(1)。我们在四个不同的模拟开放森林上对该模型进行了验证,并详细证明了该模型的有效性。

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