Modelling vegetation understory cover using LiDAR metrics
Modelling vegetation understory cover using LiDAR metrics
复制标题
使用 LiDAR 指标对植被林下覆盖进行建模
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Xianli Wang
中科院分区：
文献类型：
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作者：
L. Venier;Tom Swystun;M. Mazerolle;D. Kreutzweiser;K. Wainio;Ken A. McIlwrick;M. Woods;Xianli Wang
森林林下植被是野生动物栖息地的重要特征之一。预测和绘制林下植被是森林管理和保护规划的关键需求，但事实证明这很困难。激光雷达有潜力产生遥感森林下层结构数据，但这种潜力必须得到充分验证。我们的目标是检验激光雷达点云数据预测森林下层植被覆盖的能力。我们利用混合效应和随机森林模型模拟了三个垂直地层（0.5 m至< 1.5 m， 1.5 m至< 2.5 m， 2.5 m至< 3.5 m）的林下结构地面观测数据，并将其作为各种LiDAR指标的函数。我们比较了四种用于控制采样密度空间异质性的林下激光雷达指标。这四个指标是高度相关的，它们都产生了高方差值，在混合效应模型中得到了解释。排名最高的模型使用基于体素的林下植被度量和垂直地层（Akaike权重=1，解释方差= 87%，SMAPE=15.6%）。我们在最底层发现了激光雷达脉冲遮挡的证据，但没有证据表明遮挡影响了林下结构的可预测性。随机森林模型的结果与混合效应模型的结果一致，因为所有四种林下植被激光雷达指标都被确定为重要指标，以及垂直地层。随机森林模型解释了74.4%的方差，但交叉验证误差较低，为12.9%。基于这些结果，我们得出结论，预测林下结构的最佳方法是使用混合效应模型和基于体素的林下激光雷达指标以及垂直地层，但其他林下激光雷达指标（分数覆盖度、归一化覆盖度和叶面积密度）在混合效应和随机森林建模方法中仍然有效。
Forest understory vegetation is an important feature of wildlife habitat among other things. Predicting and mapping understory is a critical need for forest management and conservation planning, but it has proved difficult. LiDAR has the potential to generate remotely sensed forest understory structure data, yet this potential has to be fully validated. Our objective was to examine the capacity of LiDAR point cloud data to predict forest understory cover. We modeled ground-based observations of understory structure in three vertical strata (0.5 m to < 1.5 m, 1.5 m to < 2.5 m, 2.5 m to < 3.5 m) as a function of a variety of LiDAR metrics using both mixed-effects and Random Forest models. We compared four understory LiDAR metrics designed to control for the spatial heterogeneity of sampling density. The four metrics were highly correlated and they all produced high values of variance explained in mixed-effects models. The top-ranked model used a voxel-based understory metric along with vertical stratum (Akaike weight = 1, explained variance = 87%, SMAPE=15.6%). We found evidence of occlusion of LiDAR pulses in the lowest stratum but no evidence that the occlusion influenced the predictability of understory structure. The Random Forest model results were consistent with those of the mixed-effects models, in that all four understory LiDAR metrics were identified as important, along with vertical stratum. The Random Forest model explained 74.4% of the variance, but had a lower cross-validation error of 12.9%. Based on these results, we conclude that the best approach to predict understory structure is using the mixed-effects model with the voxel-based understory LiDAR metric along with vertical stratum, but that other understory LiDAR metrics (fractional cover, normalized cover and leaf area density) would still be effective in mixed-effects and Random Forest modelling approaches.
影响因子：
6.6
作者：
Nakagawa, Shinichi;Schielzeth, Holger
通讯作者：
Schielzeth, Holger