In Texas, mesquite and yellow-bluestem invasions are widespread. Identifying and monitoring juvenile and adult plants using high-resolution imagery from airborne sensors while they colonize new areas across the landscape can help land managers prioritize locations for treatment and eradication. In this study, we evaluated how data collection design using an unmanned aerial system (UAS) can affect plant detection and mapping. We used a Phantom 3 Professional unmanned aerial vehicle with a Parrot Sequoia multispectral camera for detecting and mapping native honey mesquite (Prosopis glandulosa) and non-native yellow bluestem (Bothriochloa ischaemum) at a rangeland site in northwest Texas. Flights were conducted seasonally during the period from summer 2017 to fall 2018 to test the seasonal impact of detecting plant species. Flights were conducted at altitudes of 30, 60, and 100 m, and four image classification techniques were tested to determine their viability of detecting distinct plant species. Results suggest that flights at 100-m aircraft altitude during the spring season are more effective (>80% user accuracies) for mapping mesquite canopies based on reflectance values and image segmentation information. Yellow bluestem mapping accuracies were low (< 20% user accuracies). Lower spatial resolution (100-m altitude flights, 12-cm pixel resolution) provided less noise and more generalization capabilities for the image classification methods. Overall, random forests and Support Vector Machine classification algorithms outperformed probability-based image classifiers. Land owners and rangeland ecologists using their own UAS in rangeland management can use this information to plan their data collection campaigns before the application of chemical treatments or manual eradication.

在得克萨斯州，豆科灌木和黄蓝茎入侵很普遍。在机载传感器遍及景观的新区域时，使用来自机载传感器的高分辨率图像来识别和监视未成年植物和成年植物，可以帮助土地管理者确定处理和根除地点的优先级。在这项研究中，我们评估了使用无人机系统（UAS）进行的数据收集设计如何影响植物的检测和制图。我们将Phantom 3 Professional无人机与鹦鹉红杉多光谱相机配合使用，以检测和绘制本地蜂蜜豆科灌木（Prosopis glandulosa）和非本地黄色蓝茎（Bothriochloa ischaemum）在德克萨斯州西北部的牧场上。从2017年夏季到2018年秋季，按季节进行飞行，以测试检测植物物种的季节性影响。飞行在30、60和100 m的高度进行，并测试了四种图像分类技术以确定它们检测不同植物物种的可行性。结果表明，在春季，以100米飞机高度飞行时，基于反射率值和图像分割信息来绘制豆科灌木林机盖更为有效（用户准确度> 80％）。黄色bluestem映射准确度较低（<20％用户准确度）。较低的空间分辨率（100米高空飞行，12厘米像素分辨率）为图像分类方法提供了更少的噪声和更多的归纳能力。总体，随机森林和支持向量机分类算法的性能优于基于概率的图像分类器。在牧场管理中使用他们自己的UAS的土地所有者和牧场生态学家可以在应用化学处理或人工根除之前，使用此信息来计划其数据收集活动。