ABSTRACT

Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest aboveground biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering – that is, excluding returns from LiDAR point clouds based on simple height thresholds – is a common practice meant to improve the `signal’ content of LiDAR returns by preventing ground returns from masking useful information about tree size and condition contained within canopy returns. However, ground returns may be helpful for making accurate aboveground biomass predictions in heterogeneous landscapes that include a patchy mosaic of vegetation heights and land cover types. In this paper, we applied several ground noise filtering thresholds while mapping forest AGB across New York State (USA), a heterogenous landscape composed of both contiguously forested and highly fragmented areas with mixed land cover types. We fit random forest models to predictor sets derived from each filtering intensity threshold and compared model accuracies, paying attention to how changes in accuracy correlated with landscape structure. We observed that removing ground noise via any height threshold systematically biases many of the LiDAR-derived variables used in AGB modeling, with mean correlation (Spearman’s $\rho$) between variables increasing from 0.183 to 0.266. We found that that ground noise filtering yields models of forest AGB with lower accuracy than models trained using predictors derived from unfiltered point clouds, with RMSE increasing by up to 2.2 Mg ha-1 statewide. Although we only modeled AGB for forest cover types, models fit to predictors derived from filtered point clouds performed worse as landscape heterogeneity (as measured by patch density and edge density) increased, suggesting ground returns are particularly useful when modeling edge forests. Our results suggest that ground filtering should be a carefully considered decision when mapping forest AGB, particularly when mapping heterogeneous and highly fragmented landscapes, as ground returns are more likely to represent useful `signal’ than extraneous `noise’ in these cases.

摘要

机载 LiDAR 已成为森林地上生物量和碳储量的大规模、高分辨率建模的重要数据源，与仅使用光学图像所能实现的预测相比，其分辨率和准确性要高得多。地面噪声过滤——即基于简单的高度阈值排除来自 LiDAR 点云的回波——是一种常见做法，旨在通过防止地面回波掩盖有关树冠中包含的树木大小和状况的有用信息来改善 LiDAR 回波的“信号”内容返回。然而，地面回波可能有助于在异质景观中进行准确的地上生物量预测，包括植被高度和土地覆盖类型的斑块镶嵌。在本文中，我们在绘制纽约州（美国）的森林 AGB 地图时应用了几个地面噪声过滤阈值，这是一个由连续的森林和高度分散的区域组成的异质景观，具有混合的土地覆盖类型。我们将随机森林模型拟合到从每个过滤强度阈值派生的预测器集，并比较模型精度，注意精度的变化如何与景观结构相关。我们观察到，通过任何高度阈值去除地面噪声会系统地偏向 AGB 建模中使用的许多 LiDAR 衍生变量，具有平均相关性（Spearman's 我们将随机森林模型拟合到从每个过滤强度阈值派生的预测器集，并比较模型精度，注意精度的变化如何与景观结构相关。我们观察到，通过任何高度阈值去除地面噪声会系统地偏向 AGB 建模中使用的许多 LiDAR 衍生变量，具有平均相关性（Spearman's 我们将随机森林模型拟合到从每个过滤强度阈值派生的预测器集，并比较模型精度，注意精度的变化如何与景观结构相关。我们观察到，通过任何高度阈值去除地面噪声会系统地偏向 AGB 建模中使用的许多 LiDAR 衍生变量，具有平均相关性（Spearman's$\rho$) 之间的变量从 0.183 增加到 0.266。我们发现，地面噪声过滤产生的森林 AGB 模型的准确度低于使用源自未过滤点云的预测器训练的模型，全州范围内 RMSE 增加高达 2.2 Mg ha-1。尽管我们只对森林覆盖类型的 AGB 进行了建模，但随着景观异质性（通过斑块密度和边缘密度测量）的增加，拟合来自过滤点云的预测变量的模型表现更差，这表明地面回波在建模边缘森林时特别有用。我们的结果表明，在映射森林 AGB 时，地面过滤应该是一个仔细考虑的决定，特别是在映射异构和高度碎片化的景观时，因为在这些情况下，地面回波更可能代表有用的“信号”而不是无关的“噪声”。