The development of an unsupervised hierarchical clustering analysis of dual-polarization weather surveillance radar observations to assess nocturnal insect abundance and diversity.
The development of an unsupervised hierarchical clustering analysis of dual-polarization weather surveillance radar observations to assess nocturnal insect abundance and diversity.
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DOI：
10.1002/rse2.270
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发表时间：
2022-10
影响因子：
5.5
通讯作者：
Neely, Ryan R., III
中科院分区：
文献类型：
--
作者：
Lukach, Maryna;Dally, Thomas;Evans, William;Hassall, Christopher;Duncan, Elizabeth J.;Bennett, Lindsay;Addison, Freya I.;Kunin, William E.;Chapman, Jason W.;Neely, Ryan R., III
当代对昆虫种群趋势的分析在很大程度上是基于代表有限空间域的大量异质和短期的昼夜物种数据集。这使得监测昆虫生物量和生物多样性的变化变得困难。现在需要的是一种监测方法，这种方法能够在白天和晚上对大面积的昆虫种群提供一致的、高分辨率的图像。在这里，我们探索使用X波段天气监测雷达(WSR)来研究当地昆虫种群，使用高质量的夜间飞蛾光诱捕数据的多周时间序列。具体地说，我们测试了这样的假设：(I)非监督数据驱动的分类算法可以区分气象和生物现象，(Ii)生物散射器类别的多样性与地面测量的昆虫多样性定量相关，(Iii)可以基于双极化多普勒WSR变量对地面测量的昆虫丰度进行定量预测。将准垂直剖面分析方法和数据聚类技术应用于水流星的分析，我们的生物散射器分类算法成功地在大空间尺度和高时间分辨率上区分了生物散射器和水流星。此外，我们的结果还显示了生物散射体和气象散射体之间的明确关系，以及雷达生物散射体集群的丰富度和多样性与夜间航空昆虫的丰富度和多样性之间的联系。因此，我们展示了这种方法在景观尺度生物多样性监测中的潜在用途。在这里，我们探索使用X波段天气雷达来研究当地昆虫种群，使用高质量的夜间诱蛾灯光数周时间序列数据。具体地说，我们测试了以下假设：(I)分类算法可以区分气象和生物现象，(Ii)生物散射器类别的多样性与地面测量的昆虫多样性定量相关，以及(Iii)可以基于双极化多普勒天气雷达变量对地面测量的昆虫丰度进行定量预测。将准垂直剖面(QVP)分析方法和数据聚类技术应用于水流星的分析，我们的生物散射器分类算法成功地在大空间尺度和高时间分辨率上区分了生物散射器和水流星。
Contemporary analyses of insect population trends are based, for the most part, on a large body of heterogeneous and short‐term datasets of diurnal species that are representative of limited spatial domains. This makes monitoring changes in insect biomass and biodiversity difficult. What is needed is a method for monitoring that provides a consistent, high‐resolution picture of insect populations through time over large areas during day and night. Here, we explore the use of X‐band weather surveillance radar (WSR) for the study of local insect populations using a high‐quality, multi‐week time series of nocturnal moth light trapping data. Specifically, we test the hypotheses that (i) unsupervised data‐driven classification algorithms can differentiate meteorological and biological phenomena, (ii) the diversity of the classes of bioscatterers are quantitatively related to the diversity of insects as measured on the ground and (iii) insect abundance measured at ground level can be predicted quantitatively based on dual‐polarization Doppler WSR variables. Adapting the quasi‐vertical profile analysis method and data clustering techniques developed for the analysis of hydrometeors, we demonstrate that our bioscatterer classification algorithm successfully differentiates bioscatterers from hydrometeors over a large spatial scale and at high temporal resolutions. Furthermore, our results also show a clear relationship between biological and meteorological scatterers and a link between the abundance and diversity of radar‐based bioscatterer clusters and that of nocturnal aerial insects. Thus, we demonstrate the potential utility of this approach for landscape scale monitoring of biodiversity. Here, we explore the use of X‐band weather radar for the study of local insect populations using a high‐quality, multi‐week time series of nocturnal moth light trapping data. Specifically, we test the hypotheses that (i) classification algorithms can differentiate meteorological and biological phenomena, (ii) the diversity of the classes of bioscatterers are quantitatively related to the diversity of insects as measured on the ground, and (iii) insect abundance measured at ground level can be predicted quantitatively based on dual‐polarisation Doppler weather radar variables. Adapting the quasi‐vertical profile (QVP) analysis method and data clustering techniques developed for the analysis of hydrometeors, we demonstrate that our bioscatterer classification algorithm successfully differentiates bioscatterers from hydrometeors over a large spatial scale and at high temporal resolutions.
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影响因子：
6.2
作者：
Boulanger, Yan;Fabry, Frederic;Saint-Amant, Remi
通讯作者：
Saint-Amant, Remi
影响因子：
3.5
作者：
Bell, James R.;Blumgart, Dan;Shortall, Chris R.
通讯作者：
Shortall, Chris R.
影响因子：
3.5
作者：
Didham, Raphael K.;Basset, Yves;Hassall, Christopher
通讯作者：
Hassall, Christopher
影响因子：
2.2
作者：
Chapman, JW;Reynolds, DR;Woiwod, IP
通讯作者：
Woiwod, IP
影响因子：
2.2
作者：
Bachmann, Svetlana;Zrnic, Dusan
通讯作者：
Zrnic, Dusan