Drone imagery protocols to map vegetation are transferable between dryland sites across an elevational gradient
Drone imagery protocols to map vegetation are transferable between dryland sites across an elevational gradient
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DOI：
10.1002/ecs2.4330
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发表时间：
2022-12
期刊：
影响因子：
2.7
通讯作者：
A. Roser;Josh Enterkine;J. M. Requena-Mullor;N. Glenn;Alex R. Boehm;M. de Graaff;P. Clark;F. Pierson;T. T. Caughlin-T.
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文献类型：
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作者：
A. Roser;Josh Enterkine;J. M. Requena-Mullor;N. Glenn;Alex R. Boehm;M. de Graaff;P. Clark;F. Pierson;T. T. Caughlin-T.
从微型站点到景观，旱地植物群落的结构和组成在不同尺度上都是高度不同的。对旱地植物进行精细的空间分辨率实地调查对于揭示气候变化的影响至关重要；然而，考虑到采样工作和成本，传统的实地数据收集具有挑战性。无人值守空中系统(UAS)可以通过提供高分辨率的植物群落属性的标准化测量来缓解这一挑战。然而，鉴于旱地植物群落的广泛异质性，特别是跨环境梯度，UAS图像协议的可转移性尚不清楚。植物功能类型(PFT)是一种集合了植物结构和功能多样性的分类方案。我们使用相同的UAS图像协议对三个旱地群落的PFT和部分光合作用覆盖率进行了映射和建模，通过海拔和降水量的横向梯度进行了区分。我们比较了三个旱地站点的UAS产品的准确性。在植被密度较大的高海拔地区(2241 m)，PFT分类和光合作用盖度模型的精度最高。最低处(1101 m)，裸地较多，与野外数据的一致性最低。值得注意的是，灌木覆盖在海拔和降水量(约230-1100 mm/年)的梯度上被很好地预测。UAS调查捕捉到了不同地点植物覆盖的异质性，并提出了在景观水平上测量叶片水平组成和结构的选项。我们的结果表明，一些PFT(即灌木)可以很容易地使用相同的UAS图像协议跨站点进行检测，而其他一些(即草类)可能需要特定于站点的飞行协议才能达到最佳精度。随着无人机越来越多地被用于监测旱地植被，开发最大限度地提高信息和效率的协议是一项研究和管理优先事项。
The structure and composition of plant communities in drylands are highly variable across scales, from microsites to landscapes. Fine spatial resolution field surveys of dryland plants are essential to unravel the impact of climate change; however, traditional field data collection is challenging considering sampling efforts and costs. Unoccupied aerial systems (UAS) can alleviate this challenge by providing standardized measurements of plant community attributes with high resolution. However, given widespread heterogeneity in plant communities in drylands, and especially across environmental gradients, the transferability of UAS imagery protocols is unclear. Plant functional types (PFTs) are a classification scheme that aggregates the diversity of plant structure and function. We mapped and modeled PFTs and fractional photosynthetic cover using the same UAS imagery protocol across three dryland communities, differentiated by a landscape‐scale gradient of elevation and precipitation. We compared the accuracy of the UAS products between the three dryland sites. PFT classifications and modeled photosynthetic cover had highest accuracies at higher elevations (2241 m) with denser vegetation. The lowest site (1101 m), with more bare ground, had the least agreement with the field data. Notably, shrub cover was well predicted across the gradient of elevation and precipitation (~230–1100 mm/year). UAS surveys captured the heterogeneity of plant cover across sites and presented options to measure leaf‐level composition and structure at landscape levels. Our results demonstrate that some PFTs (i.e., shrubs) can readily be detected across sites using the same UAS imagery protocols, while others (i.e., grasses) may require site‐specific flight protocols for best accuracy. As UAS are increasingly used to monitor dryland vegetation, developing protocols that maximize information and efficiency is a research and management priority.