Dual-Polarisation Weather Radar for Advanced Monitoring of Aerial Biodiversity (BioDAR)

用于空中生物多样性高级监测的双偏振天气雷达 (BioDAR)

• 批准号: NE/S001298/1
• 负责人: Christopher Hassall
• 金额: $ 77.36万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FS001298%2F1
• 关键词: Dual; Polarisation; Weather; Radar; Advanced

The BioDAR Project will revolutionise the way in which we record the abundance and diversity of animals that live in the air, by harnessing the power of next generation weather radar. Weather radar scan the entirety of the UK every 5 minutes, and similar types of radar are used around the world for the same purpose. These radar routinely detect bees and other insects, but since animals are not of interest to meteorologists, they are discarded as unwanted "noise". That "noise" is a veritable treasure trove of information on insect diversity and abundance, but what is required is a way to link what a radar sees to the insects that we wish to monitor. The BioDAR Project brings together leading ecologists and radar scientists to collaborate on a programme of work that will produce, test, and disseminate computer algorithms to turn radar noise into high quality biological data with the potential to produce a step change in the way in which we monitor the environment.In the first phase of the project, we will use computer scanning techniques that can image objects 1/10th the width of a human hair to produce high resolution 3D models of a range of 60 different insects of different shapes. Using software techniques from physics, we can simulate what the radar might see when each of those animals passes through the radar beam. The results of those simulations will be used to produce algorithms that can classify results from the radar data into different kinds of insects based on their shape, as well as quantifying the diversity and number of insects passing through the beam.In the second phase of the project, we will then test the classification algorithms by comparing our radar predictions against three different datasets. First, we will look at three existing datasets that have used (i) special radar called "vertical looking radar" to scan small areas of sky, (ii) a network of 18 suction traps that capture insects every day, and (iii) a network of 83 light traps that catch nocturnal moths. Next, we will conduct our own insect sampling using nets at a range of heights from 12m to 1km attached to balloons. Finally, we will attempt to produce our own insect assemblage in the radar beam using lab-reared bluebottle flies to saturate the air in different locations around the radar. These three tests will help us to understand how our algorithms perform in the field.In the third phase of the project, we will combine the lessons learned about our classification algorithms in the first and second phases to produce a national map of aerial insect biodiversity and abundance. This map will be used to investigate a pressing issue in conservation: the effect of human modification of the landscape on insects. We will examine this issue in three ways, by looking at the impacts of light pollution, urbanisation, and agri-environment schemes (which are designed to help nature on farmland). We would expect lower insect biodiversity and abundance near areas with high nocturnal light pollution, higher intensity of urbanisation, and in the absence of agri-environment schemes.The final part of the project will take everything that we have learned (the classification algorithms from phase 1, the validation studies in phase 2, and the national mapping data from phase 3, and make them available to all researchers and the general public. We will make all of our data and analysis transparent so that any researcher can replicate the work, which we hope will enable other countries to make use of our findings to apply the BioDAR approach to their own weather radar networks. The data will also be turned into an online portal which can be accessed by the general public to see insect biodiversity and abundance in an interface similar to a weather forecast. The final datasets will be of great interest to a range of end users, including local and national governments, farmers, and conservation groups.

生物雷达项目将通过利用下一代气象雷达的力量，彻底改变我们记录生活在空气中的动物的丰富性和多样性的方式。气象雷达每5分钟扫描一次整个英国，世界各地都在使用类似的雷达来达到同样的目的。这些雷达经常探测蜜蜂和其他昆虫，但由于气象学家对动物不感兴趣，它们就被当作不必要的“噪音”而丢弃了。这种“噪音”是关于昆虫多样性和丰度的名副其实的信息宝库，但我们需要的是一种将雷达所看到的与我们希望监测的昆虫联系起来的方法。生物雷达项目将领先的生态学家和雷达科学家聚集在一起，共同开展一项工作计划，该计划将生产、测试和传播计算机算法，将雷达噪声转化为高质量的生物数据，并有可能在我们监测环境的方式上产生重大变化。在项目的第一阶段，我们将使用计算机扫描技术，可以对人类头发宽度的十分之一的物体进行成像，以产生60种不同形状的不同昆虫的高分辨率3D模型。利用物理学的软件技术，我们可以模拟出当这些动物通过雷达波束时，雷达可能会看到什么。这些模拟的结果将用于生成算法，该算法可以根据雷达数据的结果将不同种类的昆虫根据它们的形状进行分类，并量化通过光束的昆虫的多样性和数量。在项目的第二阶段，我们将通过比较我们的雷达预测与三个不同的数据集来测试分类算法。首先，我们将查看三个现有的数据集，这些数据集使用了(i)称为“垂直观测雷达”的特殊雷达扫描天空的小区域，（ii）由18个每天捕捉昆虫的吸盘陷阱组成的网络，以及（iii）由83个捕捉夜间飞蛾的光陷阱组成的网络。接下来，我们将使用绑在气球上的网在12米到1公里的高度范围内进行我们自己的昆虫取样。最后，我们将尝试在雷达波束中产生我们自己的昆虫组合，使用实验室饲养的蓝瓶蝇来饱和雷达周围不同位置的空气。这三个测试将帮助我们了解我们的算法在该领域的表现。在项目的第三阶段，我们将结合第一阶段和第二阶段的分类算法的经验教训，制作一份全国航空昆虫生物多样性和丰度地图。这张地图将用于研究保护中的一个紧迫问题：人类对景观的改造对昆虫的影响。我们将从三个方面来研究这个问题，即光污染、城市化和农业环境计划（旨在帮助农田恢复自然）的影响。我们预计，在夜间光污染严重、城市化程度较高、缺乏农业环境规划的地区，昆虫的生物多样性和丰度会较低。项目的最后一部分将采用我们所学到的一切（第一阶段的分类算法，第二阶段的验证研究，以及第三阶段的国家地图数据），并将它们提供给所有研究人员和公众。我们将使我们所有的数据和分析透明，以便任何研究人员都可以复制这项工作，我们希望这将使其他国家能够利用我们的发现，将BioDAR方法应用到他们自己的气象雷达网络中。这些数据还将被转化为一个在线门户网站，公众可以通过类似天气预报的界面查看昆虫的生物多样性和丰度。最终的数据集将引起一系列终端用户的极大兴趣，包括地方和国家政府、农民和保护组织。

项目成果

Simulation of the Radar Cross Section of a Noctuid Moth

• DOI: 10.3390/rs14061494
• 发表时间: 2022-03-01
• 期刊: REMOTE SENSING
• 影响因子: 5
• 作者: Addison，Freya;Dally，Thomas;Neely，Ryan R.
• 通讯作者: Neely，Ryan R.

The development of an unsupervised hierarchical clustering analysis of dual-polarization weather surveillance radar observations to assess nocturnal insect abundance and diversity.

• DOI: 10.1002/rse2.270
• 发表时间: 2022-10
• 期刊: REMOTE SENSING IN ECOLOGY AND CONSERVATION
• 影响因子: 5.5
• 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
• 通讯作者: Neely, Ryan R., III

Hydrometeor classification of quasi-vertical profiles of polarimetric radar measurements using a top-down iterative hierarchical clustering method

使用自上而下迭代层次聚类方法对极化雷达测量的准垂直剖面进行水凝物分类

• DOI: 10.5194/amt-14-1075-2021
• 发表时间: 2021
• 期刊: Atmospheric Measurement Techniques
• 影响因子: 3.8
• 作者: Lukach M