Use of UAV images for precision agriculture and environmental applications

对于若干环境应用，机载/卫星图像不如无人驾驶航空器图像合适。无人机可以在低得多的高度运行，从而产生高空间分辨率的图像。它们更加移动的、快速、适应性强，并且更易于使用。此外，它们允许以重复和更便宜的方式进行调查。在NSERC-CRD的资助下，我们正在开发一种用于作物管理的新型多光谱无人机相机。申请的发现补助金将建立在NSERC-CRD补助金和几个正在进行的项目的基础上。首先，我们将改进我们开发的自动化方法，从无人机图像中计算苹果果园树木，以检测受损树木。我们还将测试其他类型的果园的方法。第二，我们将测试使用无人机图像监测鳗草床恢复，结合加拿大环境部的一项成功的赠款支持领域和无人机数据采集。第三，无人机图像将用于监测最近恢复的盐沼的自然植被退化。对于这三个项目，将开发使用光谱和纹理图像信息的新方法。鉴于图像的高空间分辨率，除了标准的基于像素的分类器之外，还将测试高级分类器。无人机图像处理可能具有挑战性，因为无人机更容易受到有风条件的影响，这可能会影响观察和照明几何形状。此外，低的UAV飞行高度需要更多的图像来覆盖该区域，这可能导致传感器温度的增加和可变天气条件的概率的增加。对于鳗草项目，另一个挑战是考虑水柱对图像采集和处理的影响。同样，对于这两种沿海应用，图像采集应考虑潜在的太阳闪烁效应。这项研究对于那些需要负担得起且灵活的监测工具来提供高空间图像的人来说非常重要。种植者将使用受损树木测绘方法来评估其果园的状况，作物保险机构将使用这种方法来确定对种植者的赔偿。渔业和海洋部以及其他从事鳗草恢复项目的非政府组织将使用鳗草绘图方法。加拿大鸭子无限公司将使用盐沼测绘方法来监测盐沼的恢复。除了这三个应用之外，该研究还将产生可用于其他应用的无人机图像处理新方法。拟议的研究将有助于加拿大蓬勃发展的商用无人机行业的发展，预计未来五年全球销售额将增长四倍以上。

For several environmental applications, airborne /satellite images are less suitable than unmanned aerial vehicles (UAV) images. UAVs can operate at much low altitudes that results in high spatial resolution images. They are more mobile, fast, adaptable, and easier to use. Furthermore, they allow surveys in a repetitive and much more cheaply way. Under a NSERC-CRD grant, we are developing a new multispectral UAV camera for crop management. The requested Discovery Grant will built upon this NSERC-CRD grant and several on-going projects. First, we will improve the automated method we developed to count apple orchard trees from UAV images to detect damaged trees. We will also test the method to other types of orchards. Second, we will test the use of UAV images for monitoring eelgrass bed restoration, in conjunction to a successful Environment Canada grant supporting field and UAV data acquisition. Third, UAV images will be used to monitor natural vegetation recolonization on recently restored salt marshes. For the three projects, novel methods that use spectral and textural image information will be developed. Given the high spatial resolution of the images, advanced classifiers will be tested in addition to standard pixel-based classifiers. UAV image processing can be challenging because UAV are more susceptible to windy conditions, which can affect the viewing and illumination geometries. Also, the low UAV flying altitude requires more images to cover the area, which can lead to increasing of the sensor temperature and of the probability of variable weather conditions. For the eelgrass project, an additional challenge is to consider the effect of the water column on the image acquisition and processing. Also for both coastal applications, image acquisition should take into account potential sun glint effects. The research is important to those who need an affordable and flexible monitoring tool that provides high spatial images. The damaged tree mapping method will be used by growers to assess the status of their orchards and by crop insurance agencies to determine compensations to growers. The eelgrass mapping method will be used by the Department of Fisheries and Oceans and other NGO groups working on eelgrass restoration projects. The salt marsh mapping method will be used by Ducks Unlimited Canada to monitor salt marsh restoration. Beyond these three applications, the research will also produce novel methods for UAV image processing that can be used in other applications. The proposed research will contribute to the development in Canada of a booming commercial UAV sector that is projected to more than quadruple in sales worldwide over the next five years.