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

入侵黄甜三叶草（YSC）是一种一年生豆科草本开花植物，除草外，最初种植用于蜜蜂栖息地和土壤侵蚀管理。YSC作为干草成分也能引起牲畜出血和中毒。该奖学金的目标是启动南达科他州大学（USD, home）和美国地质调查局（USGS）地球资源观测与科学中心（EROS, host）之间的长期合作，以绘制YSC花朵。PI和研究生将在Host科学家的帮助下，共同开发使用高性能计算（HPC）的机器学习预测模型。知识转移将涉及将EROS处理链适应于USD的超级计算机，这将立即提高USD在入侵植物物种制图中的计算能力和长期竞争力。这里开发的研究方法将使物种分布图的生产为土地管理者和政策制定者提供信息，以帮助管理YSC在南达科他州（SD）和北部大平原的快速传播。该产品将为本科研究、硕士论文和博士论文提供一系列主题，对美元的STEM教育产生长期影响。学生还可以在世界一流的联邦实验室与美国地质勘探局的科学家合作，获得暑期实习机会，甚至可能获得全职工作机会。这项研究基础设施改进轨道4 EPSCoR研究研究员（RII Track-4）提案将为南达科他州大学的助理教授提供奖学金，并为研究生提供培训。这项工作将与美国地质调查局地球资源观测和科学中心的研究人员合作进行。近年来，随着降水的增加，SD和北部大平原地区的黄甜三叶草（Melilotus officinalis； YSC）的超级花数量急剧增加。YSC具有在其两年生周期中建立显著生物量的潜力，并通过遮阳与本地草物种竞争。关于YSC华的驱动因素、时空范围或临界点，存在重大的知识和数据缺口。因此，宽空间尺度和高分辨率的近实时制图工具将有助于确定驱动因素并实现有针对性的监测和管理YSC。我们的目标是1)为SD开发一个年度YSC %覆盖预测模型，并对YSC的现场样本以及特定地点的变量（地形、土地覆盖、土壤湿度和土壤因子）和气候进行训练，以优化模型估计；2)将模型参数应用于Sentinel-2 （HLS）时间序列，生成年度YSC %覆盖率和生物量图的时间序列。PI将适应和评估USGS EROS过程流程，以开发高分辨率植被测绘能力，包括三种未来情景：潮湿和凉爽、正常、炎热和干燥，从短期、中期和长期天气预报。这些地图可用于检测YSC的年度变化，并使物种分布图为土地管理者和政策制定者提供信息，以帮助管理YSC在SD和NGP中的快速传播。所建立的方法可以为其他入侵植物物种的绘制以及草地植被的结构和功能属性提供原型，从而带来新的机遇和创新。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.