Urban and peri-urban trees in major cities provide a gateway for exotic pests and diseases (hereafter “pests”) to establish and spread into new countries. Consequently, they can be used as sentinels for early detection of exotic pests that could threaten commercial, environmental and amenity forests. Biosecurity surveillance for exotic forest pests relies on monitoring of host trees — or sentinel trees — around high-risk sites, such as airports and seaports. There are few publicly available spatial databases of urban street and park trees, so locating and mapping host trees is conducted via ground surveys. This is time-consuming and resource-intensive, and generally does not provide complete coverage. Advances in remote sensing technologies and machine learning provide an opportunity for semi-automation of tree species mapping to assist in biosecurity surveillance. In this study, we obtained high resolution (≥12 cm), 10-band, multispectral imagery using the ArborCam™ system mounted to a fixed-wing aircraft over Sydney, Australia. We mapped 630 Pinus trees and 439 Platanus trees on-foot, validating their exact location on the airborne imagery using an in-field mapping app. Using a machine learning, convolutional neural network workflow, we were able to classify the two target genera with a high level of accuracy in a complex urban landscape. Overall accuracy was 92.1% for Pinus and 95.2% for Platanus, precision (user’s accuracy) ranged from 61.3% to 77.6%, sensitivity (producer’s accuracy) ranged from 92.7% to 95.2%, and F1-score ranged from 74.6% to 84.4%. Our study validates the potential for using multispectral imagery and machine learning to increase efficiencies in tree biosecurity surveillance. We encourage biosecurity agencies to consider greater use of this technology.

大城市的城市和城郊树木为外来害虫和疾病（以下简称“害虫”）提供了进入新国家并传播到新国家的途径。因此，它们可以用作早期发现可能威胁商业、环境和舒适森林的外来害虫的哨兵。外来森林害虫的生物安全监测依赖于对机场和海港等高风险地点周围寄主树或哨兵树的监测。城市街道和公园树木的公共可用空间数据库很少，因此定位和绘制寄主树木是通过地面调查进行的。这是耗时和资源密集型的，并且通常不提供完整的覆盖。遥感技术和机器学习的进步为树种绘图的半自动化提供了机会，以协助生物安全监测。在这项研究中，我们使用安装在澳大利亚悉尼上空的固定翼飞机上的 ArborCam™ 系统获得了高分辨率（≥12 厘米）、10 波段、多光谱图像。我们映射了 630松树和 439棵悬垂的法国梧桐树，使用现场测绘应用程序验证它们在机载图像上的确切位置。使用机器学习、卷积神经网络工作流程，我们能够在复杂的城市景观中对两个目标属进行高精度分类。Pinus的总体准确度为 92.1% ， Platanus的总体准确度为95.2% ，精度（用户的准确度）范围为 61.3% 至 77.6%，灵敏度（生产者的准确度）范围为 92.7% 至 95.2%，F1 分数范围为 74.6% 至 84.4% . 我们的研究验证了使用多光谱图像和机器学习来提高树木生物安全监测效率的潜力。我们鼓励生物安全机构考虑更多地使用这项技术。