Medusahead is an aggressive, winter annual that is of dire concern for the health and sustainability of western rangelands in the United States. Medusahead reduces plant diversity, alters ecosystem function, and reduces carrying capacities for both livestock and wildlife. The species has competitive advantages over cheatgrass and native grasses that causes an increased amount of fine fuels deposited on western rangelands. The Channeled Scablands of eastern Washington in the United States represent a typical example of a region being challenged by the expansion of this weed. The costs of the invasion are high and financial constraints can limit successful management. Managers need the ability to identify medusahead across entire landscapes, so they can work towards effective and efficient management approaches. Remote sensing offers the ability to measure vegetation cover at large spatial scales, which can lead to a better understanding of the invasive characteristics of problematic species like medusahead. For instance, research has been successful in creating large-scale distribution maps of cheatgrass over western rangelands. Many applications rely on the phenological characteristics of a target plant which can present problems in separating two species with similar phenologies (i.e. cheatgrass & medusahead). A medusahead-specific map gives managers the flexibility to prioritize and direct management needs when attempting to control the spread of medusahead into non-invaded areas. This study integrated GPS acquired field locations from three study sites (Sites S, C, & N) and imagery from two remote sensing platforms (1-m aerial imagery & 30-m Landsat), to model and predict fractional cover of medusahead over 37,000+ ha of rangelands in the Channeled Scabland region of eastern Washington. Using a multi-scaled approach, this research showed that regression tree algorithms can model the complex spectral response of senesced medusahead using late summer Landsat scenes. The predictive performances resulted in a R² of 0.80 near the model's training site (Site S) and an average R² of 0.68 away from the training site (Sites C & N). This research provides a non-phenological approach to produce accurate large-scale, distribution maps of medusahead which can aid land managers who are challenged by its invasion.

Medusahead是一个富于挑战性的冬季年度，对美国西部牧场的健康和可持续发展极为关切。Medusahead减少了植物的多样性，改变了生态系统的功能，并降低了牲畜和野生动物的携带能力。该物种比草皮和本地草具有竞争优势，这导致沉积在西部牧场上的精细燃料数量增加。美国东部华盛顿的海沟Sc礁是该杂草的扩张所挑战的地区的典型例子。入侵的成本很高，财务上的限制会限制成功的管理。管理人员需要具有在整个景观中识别美杜莎黑素的能力，因此他们可以朝着有效的管理方法努力。遥感提供了在较大的空间尺度上测量植被覆盖的能力，这可以使人们更好地了解有问题物种（如美杜莎黑头）的入侵特征。例如，研究已经成功地创建了西部牧场上白茅草的大规模分布图。许多应用都依赖于目标植物的物候特征，这可能会在分离具有相似物候特征的两个物种（即白草和美杜莎）时带来问题。在尝试控制美杜莎角向非侵入区域的扩散时，针对美杜莎角的地图使管理人员可以灵活地确定优先级并直接管理需求。这项研究整合了来自三个研究地点（站点S，C和N）的GPS采集的野外位置以及来自两个遥感平台（1-m航空影像和 Landsat 30米），以建模和预测华盛顿东部海峡Scabland地区37,000多公顷公顷的牧场中的美杜莎海角覆盖度。使用多尺度方法，该研究表明，回归树算法可以使用夏末Landsat场景对衰老的美杜莎海豹的复杂光谱响应进行建模。预测性能导致R² 0.80模型的训练站点（站点S）和R平均近² 0.68从训练站点（站点C＆N）的距离。这项研究提供了一种非物候学方法，可以生成准确的大规模美杜莎黑德分布图，这可以帮助受到入侵的土地管理者。