Multiscale mapping of plant functional groups and plant traits in the High Arctic using field spectroscopy, UAV imagery and Sentinel-2A data
Multiscale mapping of plant functional groups and plant traits in the High Arctic using field spectroscopy, UAV imagery and Sentinel-2A data
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使用野外光谱、无人机图像和 Sentinel-2A 数据对高纬度北极地区的植物功能群和植物性状进行多尺度测绘
DOI：
10.1088/1748-9326/abf464
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发表时间：
2021
影响因子：
6.7
通讯作者：
Klanderud, Kari
中科院分区：
文献类型：
--
作者：
Thomson, Eleanor R;Spiegel, Marcus P;Althuizen, Inge H;Bass, Polly;Chen, Shuli;Chmurzynski, Adam;Halbritter, Aud H;Henn, Jonathan J;Jónsdóttir, Ingibjörg S;Klanderud, Kari
北极的变暖速度是地球其他地区的两倍，导致物种组成和植物功能性状的快速变化。为了扩大空间有限的样地研究，克服与最易接近的研究区域相关的抽样偏差，并建立监测环境变化的基线，需要植被组成和性状分布的景观级地图。无人驾驶飞行器（uav）已经成为一种低成本的方法来生成高分辨率图像，并弥合了精细实地研究与低分辨率卫星分析之间的差距。利用400-2500 nm的野外光谱数据和无人机多光谱图像，对斯瓦尔巴群岛朗伊尔城附近植物物种鉴定和植物水分化学成分检索的光谱方法进行了试验。利用野外光谱数据和随机森林分析，我们能够以74%的准确率区分8种常见的高北极植物冻土带物种。利用偏最小二乘回归（PLSR），我们能够预测相应的水、氮、磷和C: N值（r 2= 0.61-0.88, RMSEmean= 12%-64%）。我们利用无人机图像（五个波段：蓝、绿、红、红边和近红外）开发了类似的模型，并在位于海鸟群落下方的450米长的营养梯度上扩大了结果。在无人机水平上，我们能够以72%的准确率绘制出三个植物功能群（苔藓、禾本科植物和矮灌木），并生成植物化学图谱。我们的地图显示了一个清晰的由地貌介导的海洋肥力梯度。我们利用无人机的结果探索了两种方法，利用Sentinel-2A图像将植物含水量提升到更广泛的景观。我们的研究结果与北极的高分辨率、低成本测绘有关。
The Arctic is warming twice as fast as the rest of the planet, leading to rapid changes in species composition and plant functional trait variation. Landscape-level maps of vegetation composition and trait distributions are required to expand spatially-limited plot studies, overcome sampling biases associated with the most accessible research areas, and create baselines from which to monitor environmental change. Unmanned aerial vehicles (UAVs) have emerged as a low-cost method to generate high-resolution imagery and bridge the gap between fine-scale field studies and lower resolution satellite analyses. Here we used field spectroscopy data (400–2500 nm) and UAV multispectral imagery to test spectral methods of species identification and plant water and chemistry retrieval near Longyearbyen, Svalbard. Using the field spectroscopy data and Random Forest analysis, we were able to distinguish eight common High Arctic plant tundra species with 74% accuracy. Using partial least squares regression (PLSR), we were able to predict corresponding water, nitrogen, phosphorus and C: N values (r 2= 0.61–0.88, RMSEmean= 12%–64%). We developed analogous models using UAV imagery (five bands: Blue, Green, Red, Red Edge and Near-Infrared) and scaled up the results across a 450 m long nutrient gradient located underneath a seabird colony. At the UAV level, we were able to map three plant functional groups (mosses, graminoids and dwarf shrubs) at 72% accuracy and generate maps of plant chemistry. Our maps show a clear marine-derived fertility gradient, mediated by geomorphology. We used the UAV results to explore two methods of upscaling plant water content to the wider landscape using Sentinel-2A imagery. Our results are pertinent for high resolution, low-cost mapping of the Arctic.
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南施皮茨卑尔根州地质植物学研究，1960 年；
影响因子：
5.7
作者：
T. T. Elkington;W. Hofmann;J. Budel;A. Wirthmann
通讯作者：
A. Wirthmann
影响因子：
3.7
作者：
Pattison, Robert R.;Jorgenson, Janet C.;Welker, Jeffery M.
通讯作者：
Welker, Jeffery M.
DOI：
10.1073/pnas.0504929102
发表时间：
2005-08
影响因子：
11.1
作者：
S. Porder;G. Asner;P. Vitousek
通讯作者：
S. Porder;G. Asner;P. Vitousek
南斯皮茨卑尔根地质植物学研究 1960
影响因子：
5.5
作者：
G. Halliday;W. Hofmann
通讯作者：
W. Hofmann
北极陆地生态系统对环境变化响应的时空变化
影响因子：
1.9
作者：
T. Callaghan;M. Press;John A. Lee;D. Robinson;Clive W. Anderson
通讯作者：
Clive W. Anderson