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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Fine-scale three-dimensional modeling of boreal forest plots to improve forest characterization with remote sensing
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Fine-scale three-dimensional modeling of boreal forest plots to improve forest characterization with remote sensing

机译:北方森林地块的精细三维建模,提高遥感林特征

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Improving the quality of information that can be obtained from forest inventories can enhance planning for the best use of forest resources. In this study, we demonstrate the capability to improve the characterization of forest inventory attributes using terrestrial laser scanner (TLS) data, a fine-scale architectural model (L-Architect), and airborne laser scanner (ALS) data. Terrestrial laser scanning provides detailed and accurate three-dimensional data and has the potential to characterize forest plots with comprehensive structural information. We use TLS data and in situ measurements as input to L-Architect to create reference plots. The use of L-Architect for modeling was validated by comparing selected attributes of the reference plots with validation plots produced using simulated TLS data, with normalized root-mean square error (NMRSE) values below 17%. Surrogate plots were then created using a library of tree models where individual trees were selected according to three attributes tree height, diameter at breast height, and crown projected area either measured from in situ plots or derived from ALS data. The accuracy of the surrogate plots was assessed by comparing several key forest attributes from the reference plots, including branching structure (e.g., number of whorls, knot surface), crown shape and size (e.g., base height, asymmetry), heterogeneity (e.g., lacunarity, fractal dimension), tree volume, and the spatial distribution of material (e.g., Weibull fit, leaf area index). Overall, the surrogate plots reproduced the attributes of the reference plots with NRMSE mean value of 17% (R-2 = 0.68) using in situ ground measurements and 24% (R-2 = 0.51) using inputs estimated with ALS. Some attributes, such as leaf area index, knot surface, and fractal dimension, were well predicted (R-2 0.80), whereas others, like crown asymmetry and lacunarity, had weak correspondence (R-2 0.16). The ability to create surrogate forest plots with L-Architect m
机译:提高可以从森林清单获得的信息质量可以增强森林资源的最佳规划。在这项研究中,我们展示了使用地面激光扫描仪(TLS)数据,微尺寸架构模型(L-Architect)和空中激光扫描仪(ALS)数据来改善森林库存属性表征的能力。陆地激光扫描提供了详细和准确的三维数据,具有综合结构信息的森林地块的潜力。我们使用TLS数据和原位测量作为L-Architect的输入来创建参考图。通过使用模拟TLS数据产生的验证图来进行验证的参考图的选定属性,验证了L-Architect用于建模的建模的使用,归一化的根均线误差(NMRSE)值低于17%。然后使用由三个属性树高度,乳房高度的直径选择单个树的树模型,以及从原位图测量或衍生自ALS数据的冠部预计区域来创建替代图。通过比较来自参考图的若干关键林属性来评估代理地块的准确性,包括分支结构(例如,螺纹,结表面),冠状和尺寸(例如,基础高度,不对称),异质性(例如,宽度,分形尺寸),树木体积和材料的空间分布(例如,Weibull Fit,叶面积指数)。总的来说,代理地块使用使用ALS估计的输入,使用原位接地测量和24%(R-2 = 0.51)来使用NRMSE平均值为17%(R-2 = 0.68)的参考图的属性。一些属性,例如叶面积指数,结表面和分形尺寸,预测(R-2& 0.80),而其他属性(R-2&gt为0.80),而其他属性相同的冠长不对称性和脉乏程度,则具有弱对应关系(R-2 <0.16)。使用L-Architect M创建代理林地块的能力

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