Modelling vegetation understory cover using LiDAR metrics

Forest understory vegetation is an important characteristic of the forest. Predicting and mapping understory is a critical need for forest management and conservation planning, but it has proved difficult with available methods to date. LiDAR has the potential to generate remotely sensed forest understory structure data, but this potential has yet 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%, cross-validation error = 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%. 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, because it yielded the highest explained variance with the fewest number of variables. However, results show 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.

林下植被是森林的重要特征之一。对林下植被进行预测与空间制图是森林经营与保护规划的核心需求，但现有方法迄今仍难以满足这一需求。激光雷达（LiDAR）具备获取遥感森林林下结构数据的潜力，然而该潜力尚未得到充分验证。本研究旨在探究激光雷达点云数据预测森林林下覆盖度的能力。我们针对三个垂直分层（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，结合混合效应模型与随机森林（Random Forest）模型，以多种激光雷达指标作为预测因子构建回归模型。本研究对比了四种旨在控制采样密度空间异质性的林下激光雷达指标。这四类指标相关性极强，且在混合效应模型中均能解释较高比例的响应变量方差。表现最优的模型采用了基于体素的林下激光雷达指标与垂直分层作为预测变量（赤池权重=1，解释方差=87%，交叉验证误差=15.6%）。研究发现，最下层植被存在激光雷达脉冲被遮挡的现象，但未发现该遮挡对林下结构预测精度产生影响的证据。随机森林模型的结果与混合效应模型一致：四类林下激光雷达指标与垂直分层均被识别为重要预测变量。随机森林模型解释了74.4%的响应变量方差，但其交叉验证误差更低，为12.9%。综上，预测林下结构的最佳方案为采用结合基于体素的林下激光雷达指标与垂直分层的混合效应模型，该方案在使用变量数量最少的前提下实现了最高的解释方差。不过研究结果表明，其余三类林下激光雷达指标（分数盖度、归一化盖度与叶面积密度）在混合效应模型与随机森林建模中同样具备有效性。