Evaluating the sensitivity of forest structural diversity characterization to LiDAR point density
Evaluating the sensitivity of forest structural diversity characterization to LiDAR point density
复制标题
评估森林结构多样性表征对 LiDAR 点密度的敏感性
DOI：
10.1002/ecs2.4209
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发表时间：
2022
期刊：
影响因子：
2.7
通讯作者：
LaRue, EA
中科院分区：
文献类型：
--
作者：
LaRue, EA
最近数据共享的扩大为探索生态系统在空间和时间尺度上的结构-功能联系创造了前所未有的机会。然而，同一数据产品的特征，如分辨率，可能会随着时间或空间位置的变化而变化，因为协议会适应新的技术或条件，这可能会影响数据在解决最终用户科学问题方面的潜在效用和准确性。国家生态观测网络（氖）为来自81个站点的用户提供数据产品，并计划在30年的时间框架内，包括来自机载观测平台的离散返回光探测和测距（LiDAR）。LiDAR是一种成熟且日益可用的遥感技术，用于测量生态系统和景观结构的三维特征，包括森林结构多样性。氖提供的LiDAR产品的点密度可以从2到25+ pt/m2不等，具体取决于仪器和采集日期。我们使用来自五个森林地点的氖激光雷达来（1）确定最小点密度，在该密度下可以对美国不同生态气候区的森林地点的结构多样性指标进行稳健估计，以及（2）测试可变点密度的影响森林地点内和森林地点之间的一套结构多样性指标和多元结构复杂性类型的估计。16个结构多样性指标中的12个对5个氖森林覆盖点中的至少一个的激光雷达点密度敏感。可靠估计指标的最小点密度范围从2.0到7.5 pt/m2，但我们的研究结果表明，点密度高于7-8 pt/m2应该提供强大的测量森林结构多样性的时间或空间比较。从一套16个结构多样性指标的多变量结构复杂性类型的划定是强大的网站内和整个森林类型的激光雷达点密度为4 pt/m2及以上。这项研究表明，不同的结构多样性指标对LiDAR数据分辨率的敏感性可能会有所不同，这些开源数据产品的用户应该考虑其数据的点密度，并在从这些数据集进行空间或时间比较时谨慎选择指标。
Recent expansion in data sharing has created unprecedented opportunities to explore structure–function linkages in ecosystems across spatial and temporal scales. However, characteristics of the same data product, such as resolution, can change over time or spatial locations, as protocols are adapted to new technology or conditions, which may impact the data's potential utility and accuracy for addressing end user scientific questions. The National Ecological Observatory Network (NEON) provides data products for users from 81 sites and over a planned 30‐year time frame, including discrete‐return light detection and ranging (LiDAR) from an airborne observation platform. LiDAR is a well‐established and increasingly available remote sensing technology for measuring three‐dimensional characteristics of ecosystem and landscape structure, including forest structural diversity. The LiDAR product that NEON provides can vary in point density from 2 to 25+ pt/m2depending on the instrument and acquisition date. We used NEON LiDAR from five forested sites to (1) identify the minimum point density at which structural diversity metrics can be robustly estimated across forested sites from different ecoclimatic zones in the United States and (2) to test the effects of variable point density on the estimation of a suite of structural diversity metrics and multivariate structural complexity types within and across forested sites. Twelve of 16 structural diversity metrics were sensitive to LiDAR point density in at least one of the five NEON forested sites. The minimum point density to reliably estimate the metrics ranged from 2.0 to 7.5 pt/m2, but our results indicate that point densities above 7–8 pt/m2should provide robust measurements of structural diversity in forests for temporal or spatial comparisons. The delineation of multivariate structural complexity types from a suite of 16 structural diversity metrics was robust within sites and across forest types for a LiDAR point density of 4 pt/m2and above. This study shows that different metrics of structural diversity can vary in their sensitivity to the resolution of LiDAR data and that users of these open‐source data products should consider the point density of their data and use caution in metric selection when making spatial or temporal comparisons from these datasets.