NEON-SD:从 NEON 离散回归激光雷达点云中提取的 30 米结构多样性产品。

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Jianmin Wang, Dennis H Choi, Elizabeth LaRue, Jeff W Atkins, Jane R Foster, Jaclyn H Matthes, Robert T Fahey, Songlin Fei, Brady S Hardiman
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引用次数: 0

摘要

结构多样性(SD)描述了生态系统中生物成分的数量和物理排列,它们控制着生态系统的关键功能和过程。激光雷达数据可提供成分的详细三维空间位置信息,已被广泛用于计算结构多样性。然而,对于缺乏高性能计算资源的研究人员来说,从大量 LiDAR 数据集中密集计算 SD 指标既耗时又具有挑战性。此外,对激光雷达数据和算法缺乏了解也会导致标度指标不一致。在此,我们利用 NEON 空中观测平台的离散回归激光雷达点云开发了 SD 产品。该产品以30米的空间分辨率提供了2013年至2022年45个陆地NEON站点211个站点年的SD指标,包括高度、密度、开阔度和复杂性,并与大地遥感卫星网格对齐。为了适应具有不同林下高度的各种生态系统,它包括三种不同的截断高度(0.5 米、2 米和 5 米)。该结构多样性产品可用于生态系统生产力估算和干扰监测等多种应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
NEON-SD: A 30-m Structural Diversity Product Derived from the NEON Discrete-Return LiDAR Point Cloud.

Structural diversity (SD) characterizes the volume and physical arrangement of biotic components in an ecosystem which control critical ecosystem functions and processes. LiDAR data provides detailed 3-D spatial position information of components and has been widely used to calculate SD. However, the intensive computation of SD metrics from extensive LiDAR datasets is time-consuming and challenging for researchers who lack access to high-performance computing resources. Moreover, a lack of understanding of LiDAR data and algorithms could lead to inconsistent SD metrics. Here, we developed a SD product using the Discrete-Return LiDAR Point Cloud from the NEON Aerial Observation Platform. This product provides SD metrics detailing height, density, openness, and complexity at a spatial resolution of 30 m, aligned to the Landsat grids, for 211 site-years for 45 Terrestrial NEON sites from 2013 to 2022. To accommodate various ecosystems with different understory heights, it includes three different cut-off heights (0.5 m, 2 m, and 5 m). This structural diversity product can enable various applications such as ecosystem productivity estimation and disturbance monitoring.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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