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

RII Track-4：NSF：利用 Landsat/Sentinel-2、无人机图像和机器学习监测入侵的黄花苜蓿

• 批准号: 2229746
• 负责人: Ranjeet John
• 金额: $ 10.06万
• 依托单位: University of South Dakota Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2229746&HistoricalAwards=false
• 关键词: RII; Track; NSF; Monitoring; Invasive

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.

入侵性黄花苜蓿 (YSC) 是一种一年生豆科草本开花植物，最初是为了蜜蜂栖息地和土壤侵蚀管理而种植的，YSC 作为干草成分也会导致牲畜出血和中毒。南达科他大学（USD，主场）和美国地质调查局（USGS）地球资源观测和科学中心（EROS，主办方）之间启动长期合作，绘制 YSC 水华图。 PI 和研究生将在东道主科学家的帮助下，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 处理链适配到 USD 的超级计算机上，这将立即完成。提高美国在入侵植物物种绘图方面的计算能力和长期竞争力。这里开发的研究方法将能够制作物种分布图，为土地管理者和政策制定者提供信息，帮助管理 YSC 在南达科他州 (SD) 的快速蔓延。和北方大帝拟议的产品将为本科生研究、硕士论文和博士论文提供一系列主题，从而对美国地质勘探局的 STEM 教育产生长期影响。学生还可以与世界一流的联邦实验室的美国地质调查局科学家合作。 ，从而获得暑期实习机会，并可能获得全职工作机会。这项研究基础设施改进 Track-4 EPSCoR 研究员 (RII Track-4) 提案将为南达科他大学的一名助理教授提供奖学金，并为一名研究生提供培训。 .这项工作将是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，随着降水量增加，南达科他州和北部大平原 (NGP) 的黄草木樨 (Melilotus officinalis; YSC) 出现了超级开花现象。 YSC 有潜力在其两年一次的生命周期中建立大量的生物量，并通过遮荫为本地草种提供竞争。关于其驱动因素、时空范围或临界点，存在重大知识和数据差距。因此，大空间尺度和高分辨率的近实时绘图工具将有助于识别驱动因素并实现 YSC 的有针对性的监测和管理。我们的目标是 1) 开发年度 YSC 覆盖率预测模型。用于 SD 和对 YSC 的现场样本以及特定地点变量（地形、土地覆盖、土壤湿度和土壤因素）和气候进行训练，以优化模型估计；2) 模型参数将应用于 Sentinel-2 (HLS) ） 时间系列，以制作年度 YSC 覆盖率和生物量图的时间序列。 PI 将调整和评估 USGS EROS 流程，以开发高分辨率植被绘图功能，其中包括三种未来情景：湿润和凉爽、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。 。开发的方法可以作为绘制其他入侵植物物种以及牧场植被的结构和功能属性的原型，反映出新的机遇和创新。该奖项授予 NSF 的法定使命，并通过使用基金会的智力优势和评估进行评估，被认为值得支持。更广泛的影响审查标准。