Recovery of Forest Canopy Parameters by Inversion of Multispectral LiDAR Data
Recovery of Forest Canopy Parameters by Inversion of Multispectral LiDAR Data
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DOI：
10.3390/rs4020509
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发表时间：
2012-02
期刊：
影响因子：
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通讯作者：
Andrew M. Wallace;C. Nichol;Iain H. Woodhouse
中科院分区：
文献类型：
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作者：
Andrew M. Wallace;C. Nichol;Iain H. Woodhouse
我们描述了使用贝叶斯推理技术，特别是马尔可夫链蒙特卡罗（MCMC）和可逆跳MCMC（RJMCMC）方法，恢复森林结构和生物化学参数的多光谱激光雷达（光探测和测距）数据。我们使用一个变维，多层模型来代表森林冠层或树木，并讨论恢复的结构和深度剖面，涉及到光化学性质。我们首先演示了如何简单的植被指数，如归一化植被指数（NDVI），涉及到冠层生物量和光吸收，和光化学反射指数（PRI），这是一个衡量植被光利用效率，可以从多光谱数据测量。我们进一步描述和证明我们的分层方法上的单波长真实的数据，并模拟多光谱数据来自真实的，而不是模拟，数据集。此评估显示成功恢复的一个子集的参数，作为完整的恢复问题是不适定的可用数据。我们的结论是，该方法有希望，并建议未来的发展，以解决目前的困难，参数反演。
We describe the use of Bayesian inference techniques, notably Markov chain Monte Carlo (MCMC) and reversible jump MCMC (RJMCMC) methods, to recover forest structural and biochemical parameters from multispectral LiDAR (Light Detection and Ranging) data. We use a variable dimension, multi-layered model to represent a forest canopy or tree, and discuss the recovery of structure and depth profiles that relate to photochemical properties. We first demonstrate how simple vegetation indices such as the Normalized Differential Vegetation Index (NDVI), which relates to canopy biomass and light absorption, and Photochemical Reflectance Index (PRI) which is a measure of vegetation light use efficiency, can be measured from multispectral data. We further describe and demonstrate our layered approach on single wavelength real data, and on simulated multispectral data derived from real, rather than simulated, data sets. This evaluation shows successful recovery of a subset of parameters, as the complete recovery problem is ill-posed with the available data. We conclude that the approach has promise, and suggest future developments to address the current difficulties in parameter inversion.