RII Track-4:NSF: The Monitoring of Invasive Yellow Sweet Clover Using Landsat/Sentinel-2, UAV Imagery and Machine Learning

入侵性黄甜三叶草（YSC）是一种一年生豆科草本开花植物，最初种植用于蜜蜂栖息地和土壤侵蚀治理。YSC作为干草成分也可导致牲畜中毒。该奖学金的目标是启动南达科他州大学（USD，家）和美国地质调查局（USGS）地球资源观测和科学中心（EROS，主机）之间的长期合作，以绘制YSC水华。PI和研究生在Host科学家的帮助下，将使用高性能计算（HPC）联合开发机器学习预测模型。知识转让将涉及调整地球资源观测系统的处理链，使之适应美国农业部的超级计算机，这将立即提高美国农业部在入侵植物物种测绘方面的计算能力和长期竞争力。这里开发的研究方法将使生产的物种分布图，通知土地管理者和政策制定者，以帮助管理整个南达科他州（SD）和北方大平原YSC的快速蔓延。拟议的产品将通过为本科生研究，硕士论文和博士学位提供一系列主题，对USD的STEM教育产生长期影响。学位论文学生还可以在世界级的联邦实验室与美国地质勘探局的科学家合作，从而获得暑期实习机会，并可能获得全职工作机会。该研究基础设施改善轨道-4 EPSCoR研究员（RII轨道-4）提案将为南达科他州大学的助理教授和研究生培训提供奖学金。这项工作将与美国地质勘探局地球资源观测和科学中心的研究人员合作进行。近年来，随着降水量的增加，在SD和北方大平原（NGP），黄甜三叶草（Melilotus officinalis; YSC）的数量急剧增加。YSC有潜力建立显着的生物量在其两年的生命周期，并提供竞争，以本地草种通过遮荫。在YSC大量繁殖的驱动因素、时空范围或临界点方面存在着重大的知识和数据缺口。因此，大空间尺度和高分辨率的近实时绘图工具将有助于确定驱动因素，并能够有针对性地监测和管理YSC。我们的目标是1）开发一个年度YSC %覆盖预测模型SD和培训的YSC沿着与现场特定的变量（地形，土地覆盖，土壤水分和土壤因素）和气候，以优化模型估计; 2）模型参数将被应用到哨兵2（HLS）的时间序列，以产生一个时间序列的年度YSC %覆盖和生物量地图。PI将调整和评估USGS EROS流程，以开发高分辨率植被测绘能力，包括三种未来情景：从短期，中期和长期天气预报中获得的潮湿，凉爽，正常，炎热和干燥。这些地图可用于检测YSC的逐年变化，并使物种分布图能够告知土地管理者和政策制定者，以帮助管理YSC在SD和NGP中的快速传播。开发的方法可以作为原型来绘制其他入侵植物物种以及牧场植被的结构和功能属性，从而带来新的机会和创新。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

Invasive yellow sweet clover (YSC) is an annual legume herbaceous flowering plant, other than grass, planted initially for bee habitat and soil erosion management. YSC can also cause hemorrhaging and poisoning in livestock as a hay component. The goal of this fellowship is to initiate a long-term collaboration between the University of South Dakota (USD, home) and the United States Geological Survey (USGS) Earth Resources Observation and Science Center (EROS, host) for the mapping of YSC blooms. The PI and graduate student, with the help of Host scientists, will jointly develop machine learning predictive models using High-Performance Computing (HPC). The knowledge transfer will involve the adaptation of the EROS processing chain to the supercomputer at USD, which would immediately improve computational ability and the long-term competitiveness of USD in invasive plant species mapping. The research methods developed here will enable the production of species distribution maps to inform land managers and policymakers to help manage the rapid spread of YSC across South Dakota (SD) and the Northern Great Plains. The proposed product would have long-term impacts on STEM education at USD by providing a series of topics for undergraduate research, master’s theses, and Ph.D. dissertations. Students could also collaborate with USGS scientists at a world-class Federal Lab, leading to summer internships and possibly full-time job offers. This Research Infrastructure Improvement Track-4 EPSCoR Research Fellows (RII Track-4) proposal would provide a fellowship to an Assistant Professor and training for a graduate student at the University of South Dakota. This work would be conducted in collaboration with researchers at the USGS Earth Resources Observation and Science Center. There has been a dramatic increase of Yellow Sweet Clover (Melilotus officinalis; YSC) with super blooms in SD and the Northern Great Plains (NGP) following higher precipitation in recent years. YSC has the potential for establishing significant biomass in its biennial lifecycle and provides competition to native grass species through shading. There are major knowledge and data gaps regarding the drivers, spatiotemporal extent, or tipping points of YSC blooms. Hence, near-real-time mapping tools, at a broad spatial scale and high resolution would be helpful in identifying drivers and enabling targeted monitoring and management of YSC. Our objectives are 1) to develop an annual YSC % cover predictive model for SD and train on field samples of YSC along with site-specific variables (topography, land cover, soil moisture, and edaphic factors) and climate to optimize model estimates; 2) The model parameters will be applied to the Sentinel-2 (HLS) time series to produce a time series of annual YSC % cover and biomass maps. The PI will adapt and evaluate USGS EROS process flows to develop high-resolution vegetation mapping capabilities that include three future scenarios: wetter & cooler, normal, hotter and drier, from short, mid, and long-term weather forecasts. These maps could be used to detect year-to-year changes in YSC and enable species distribution maps to inform land managers and policymakers to help manage the rapid spread of YSC across SD and the NGP. Methods developed could serve as prototypes to map other invasive plant species as well as structure and function attributes of rangeland vegetation leading to new opportunities and innovations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.