手持激光雷达传感器可以精确测量地上生物量

IF 2.9 3区 环境科学与生态学 Q2 ECOLOGY
Ecosphere Pub Date : 2025-06-03 DOI:10.1002/ecs2.70232
David H. Atkins, Ryan C. Blackburn, Daniel C. Laughlin, Margaret M. Moore, Andrew J. Sánchez Meador
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引用次数: 0

摘要

最近的许多研究探索了遥感方法来促进地上生物量(AGB)的非破坏性采样。激光雷达平台(例如,iPhone和iPad PRO模型)最近使遥感技术广泛可用,并提供了一种替代传统方法来估计AGB。激光雷达方法可以在许多模拟方法所需的一小部分时间内完成。然而,目前尚不清楚手持传感器是否能够准确预测AGB,或者不同的建模技术如何影响预测精度。在这里,我们收集了美国亚利桑那州Flagstaff周围沿海拔梯度的三个站点的0.25 m2样地(N = 45)的AGB。在植物修剪、干燥和称重之前,用移动激光扫描仪(MLS)和iPad扫描每个地块。我们比较了iPad和MLS传感器通过最小化模型归一化均方根误差(NRMSE)估计AGB的能力。这一过程是在描述结构、光谱和基于现场的特征的预测子集上进行的,这些特征跨越了一套建模方法,包括简单的线性、逐步、套索和随机森林回归。我们发现,无论考虑的变量子集如何,基于MLS和iPad数据开发的模型预测AGB的能力都是一样的(NRMSE分别为26.6%和29.3%)。我们还发现,有规律的逐步回归导致NRMSE最低。在每种建模方法中都一致地选择了结构变量,而很少包括光谱变量。基于场的变量在线性回归模型中很重要,但在随机森林模型中变量选择后不包括。这些发现支持了这样一种观点,即遥感技术提供了一种有效的替代基于模拟现场的数据收集方法。总之,我们的研究结果表明,使用更广泛可用的平台收集的数据将与使用更昂贵的选项执行类似,并概述了单独使用遥感系统建模AGB的工作流程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Handheld lidar sensors can accurately measure aboveground biomass

Handheld lidar sensors can accurately measure aboveground biomass

Many recent studies have explored remote sensing approaches to facilitate non-destructive sampling of aboveground biomass (AGB). Lidar platforms (e.g., iPhone and iPad PRO models) have recently made remote sensing technologies widely available and present an alternative to traditional approaches for estimating AGB. Lidar approaches can be completed within a fraction of the time required by many analog methods. However, it is unknown if handheld sensors are capable of accurately predicting AGB or how different modeling techniques affect prediction accuracy. Here, we collected AGB from 0.25-m2 plots (N = 45) from three sites along an elevational gradient within rangelands surrounding Flagstaff, Arizona, USA. Each plot was scanned with a mobile laser scanner (MLS) and iPad before plants were clipped, dried, and weighed. We compared the capability of iPad and MLS sensors to estimate AGB via minimization of model normalized root mean square error (NRMSE). This process was performed on predictor subsets describing structural, spectral, and field-based characteristics across a suite of modeling approaches including simple linear, stepwise, lasso, and random forest regression. We found that models developed from MLS and iPad data were equally capable of predicting AGB (NRMSE 26.6% and 29.3%, respectively) regardless of the variable subsets considered. We also found that stepwise regression regularly resulted in the lowest NRMSE. Structural variables were consistently selected during each modeling approach, while spectral variables were rarely included. Field-based variables were important in linear regression models but were not included after variable selection within random forest models. These findings support the notion that remote sensing techniques offer a valid alternative to analog field-based data collection methods. Together, our results demonstrate that data collected using a more widely available platform will perform similarly to a more costly option and outline a workflow for modeling AGB using remote sensing systems alone.

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来源期刊
Ecosphere
Ecosphere ECOLOGY-
CiteScore
4.70
自引率
3.70%
发文量
378
审稿时长
15 weeks
期刊介绍: The scope of Ecosphere is as broad as the science of ecology itself. The journal welcomes submissions from all sub-disciplines of ecological science, as well as interdisciplinary studies relating to ecology. The journal''s goal is to provide a rapid-publication, online-only, open-access alternative to ESA''s other journals, while maintaining the rigorous standards of peer review for which ESA publications are renowned.
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