Methods to detect and monitor the spread of invasive grasses are critical to avoid ecosystem transformations and large economic costs. The rapid spread of non‐native buffelgrass(Pennisetum ciliare) has intensified fire risk and is replacing fire intolerant native vegetation in the Sonoran Desert of the southwestern US. Coarse‐resolution satellite imagery has had limited success in detecting small patches of buffelgrass, whereas ground‐based and aerial survey methods are often cost prohibitive. To improve detection, we trained 2 m resolution DigitalGlobe WorldView‐2 satellite imagery with 12 cm resolution unmanned aerial vehicle (UAV) imagery and classified buffelgrass on Google Earth Engine, a cloud computing platform, using Random Forest (RF) models in Saguaro National Park, Arizona, USA. Our classification models had an average overall accuracy of 93% and producer's accuracies of 94–96% for buffelgrass, although user's accuracies were low. We detected a 2.92 km² area of buffelgrass in the eastern Rincon Mountain District (1.07% of the total area) and a 0.46 km² area (0.46% of the total area) in the western Tucson Mountain District of Saguaro National Park. Buffelgrass cover was significantly greater in the Sonoran Paloverde‐Mixed Cacti Desert Scrub vegetation type, on poorly developed Entisols and Inceptisol soils and on south‐facing topographic aspects compared to other areas. Our results demonstrate that high‐resolution imagery improve on previous attempts to detect and classify buffelgrass and indicate potential areas where the invasive grass might spread. The methods demonstrated in this study could be employed by land managers as a low‐cost strategy to identify priority areas for control efforts and continued monitoring.

中文翻译：

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在Google Earth Engine上使用高分辨率卫星和UAV影像进行水牛草入侵检测

检测和监测入侵草蔓延的方法对于避免生态系统转变和巨大的经济成本至关重要。非本地水牛草的快速传播（Pennisetum ciliare）加剧了火灾隐患，并正在取代美国西南部索诺兰沙漠的耐火本地植物。粗分辨率的卫星图像在检测小块水牛草方面取得的成功有限，而地面和空中勘测方法往往成本高昂。为了提高检测效率，我们训练了2 m分辨率的DigitalGlobe WorldView-2卫星图像和12 cm分辨率的无人机图像，并在Saguaro国家公园中使用随机森林（RF）模型在云计算平台Google Earth Engine上对水牛草进行了分类。 ，美国亚利桑那州。我们的分类模型对水牛草的平均总体准确度为93％，生产者的准确度为94–96％，尽管用户的准确度较低。我们检测到2.92公里²仙人掌山东部的野牛草面积（占总面积的1.07％）和萨瓜罗国家公园西部图森山区的野牛草面积为0.46 km ²（占总面积的0.46％）。与其他地区相比，Sonoran Paloverde-Mixed Cacti Desert Scrub植被类型，不发达的Entisols和Inceptisol土壤以及朝南的地形方面的Buffelgrass覆盖明显更大。我们的研究结果表明，高分辨率图像可以改善先前对水牛草进行分类和分类的尝试，并指出入侵草可能扩散的潜在区域。土地管理人员可以采用本研究中证明的方法作为一种低成本战略，以确定控制工作和持续监测的优先领域。