Robust retrieval of forest canopy structural attributes using multi‐platform airborne LiDAR

IF 3.9 2区环境科学与生态学 Q1 ECOLOGY

Remote Sensing in Ecology and Conservation Pub Date : 2024-05-17 DOI:10.1002/rse2.398

Beibei Zhang, Fabian J. Fischer, Suzanne M. Prober, Paul B. Yeoh, Carl R. Gosper, Katherine Zdunic, Tommaso Jucker

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引用次数: 0

Abstract

LiDAR data acquired from airplanes and helicopters – known as airborne laser scanning (ALS) – are widely regarded as the gold standard for characterizing the 3D structure of forests at scale. But in the last decade, advances in unoccupied aerial vehicle (UAV) technologies have led to a rapid rise in the use of UAV laser scanning (ULS) for mapping forest structure. As both ALS and ULS data become increasingly available, they are being used to derive an ever‐growing number of metrics designed to measure different facets of canopy structure. However, which metrics can be robustly retrieved from both ALS and ULS platforms remains unclear. To address this question, we acquired coincident, high‐density ALS and ULS scans covering 115 plots (4‐ha in size) in an open‐canopy temperate ecosystem in Western Australia. Using this unique dataset, we quantified 32 canopy structural metrics related to canopy height, openness and heterogeneity, including metrics calculated directly from the point clouds and ones measured from derived canopy height models (CHM). Overall, we found that ALS and ULS‐derived metrics were strongly correlated (r2 = 0.90). However, this high degree of correlation masked considerable systematic differences between platforms. Specifically, point cloud metrics were less strongly (r2 = 0.87) correlated and had higher bias (10.7%) compared to CHM‐derived ones (r2 = 0.98; bias = 2.5%). Similarly, metrics of canopy openness and heterogeneity were less strongly correlated (r2 = 0.84 and 0.87) and exhibited greater bias (14.4 and 7.9%) than ones relating to canopy height (r2 = 0.96; bias = 3.8%). Our results indicate that only a small subset of the 32 metrics we tested were directly comparable between ALS and ULS platforms. Consequently, future efforts to combine laser scanning data across platforms and instruments should think carefully about which metrics are most appropriate, especially when working with point cloud data.

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利用多平台机载激光雷达鲁棒检索林冠结构属性

从飞机和直升机上获取的激光雷达数据--即机载激光扫描（ALS）--被广泛认为是描述森林三维结构的黄金标准。但在过去的十年中，无人飞行器（UAV）技术的进步使用于绘制森林结构图的无人飞行器激光扫描（ULS）技术迅速崛起。随着 ALS 和 ULS 数据越来越多地可用，它们被用来推导出越来越多的指标，这些指标旨在测量冠层结构的不同方面。然而，哪些指标可以同时从 ALS 和 ULS 平台上稳健地检索到仍不清楚。为了解决这个问题，我们在西澳大利亚的一个开阔树冠温带生态系统中获取了重合、高密度的 ALS 和 ULS 扫描，覆盖了 115 个地块（面积为 4 公顷）。利用这一独特的数据集，我们量化了与冠层高度、开阔度和异质性有关的 32 个冠层结构指标，包括直接从点云计算得出的指标和从衍生冠层高度模型（CHM）测量得出的指标。总体而言，我们发现 ALS 和 ULS 得出的指标具有很强的相关性（r2 = 0.90）。然而，这种高度相关性掩盖了不同平台之间存在的相当大的系统性差异。具体而言，点云指标的相关性较弱（r2 = 0.87），与 CHM 衍生指标（r2 = 0.98；偏差 = 2.5%）相比，偏差更高（10.7%）。同样，与冠层高度相关指标（r2 = 0.96；偏差 = 3.8%）相比，冠层开阔度和异质性指标的相关性较弱（r2 = 0.84 和 0.87），偏差较大（14.4 和 7.9%）。我们的结果表明，在我们测试的 32 项指标中，只有一小部分在 ALS 和 ULS 平台之间具有直接可比性。因此，未来在跨平台和仪器组合激光扫描数据时，应仔细考虑哪些指标是最合适的，尤其是在处理点云数据时。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Remote Sensing in Ecology and Conservation Earth and Planetary Sciences-Computers in Earth Sciences

CiteScore

9.80

自引率

5.50%

发文量

审稿时长

18 weeks

期刊介绍： emote Sensing in Ecology and Conservation provides a forum for rapid, peer-reviewed publication of novel, multidisciplinary research at the interface between remote sensing science and ecology and conservation. The journal prioritizes findings that advance the scientific basis of ecology and conservation, promoting the development of remote-sensing based methods relevant to the management of land use and biological systems at all levels, from populations and species to ecosystems and biomes. The journal defines remote sensing in its broadest sense, including data acquisition by hand-held and fixed ground-based sensors, such as camera traps and acoustic recorders, and sensors on airplanes and satellites. The intended journal’s audience includes ecologists, conservation scientists, policy makers, managers of terrestrial and aquatic systems, remote sensing scientists, and students. Remote Sensing in Ecology and Conservation is a fully open access journal from Wiley and the Zoological Society of London. Remote sensing has enormous potential as to provide information on the state of, and pressures on, biological diversity and ecosystem services, at multiple spatial and temporal scales. This new publication provides a forum for multidisciplinary research in remote sensing science, ecological research and conservation science.