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.

入侵黄三叶(YSC)是一种一年生豆科草本开花植物，不是草，最初种植是为了蜜蜂栖息地和土壤侵蚀管理。YSC还可以作为干草成分导致牲畜出血和中毒。这项奖学金的目标是启动南达科他大学(总部)和美国地质调查局(USGS)地球资源观测和科学中心(EROS，东道主)之间的长期合作，绘制YSC水华地图。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处理链适应市政总署的超级计算机，这将立即提高市政总署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)和北部大平原的快速传播。拟议的产品将通过为本科生研究、硕士论文和博士学位论文提供一系列主题，对美国大学的STEM教育产生长期影响。学生们还可以在世界级的联邦实验室与美国地质调查局的科学家合作，从而获得暑期实习机会，并可能获得全职工作机会。这项研究基础设施改进Track-4 EPSCoR研究学者(RII Track-4)提案将为南达科他州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，随着降雨量的增加，黄三叶在SD和北部大平原(NGP)出现了超级开花现象，数量急剧增加。YSC有可能在其两年一次的生命周期中建立大量的生物量，并通过遮荫对当地草种构成竞争。关于YSC水华的驱动因素、时空范围或临界点，存在着重大的知识和数据差距。因此，宽广空间尺度和高分辨率的近乎实时的绘图工具将有助于查明驱动因素，并能够有针对性地监测和管理夜总会。我们的目标是1)为SD开发一个年度YSC%覆盖率预测模型，并根据YSC的野外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)和气候来优化模型估计；2)模型参数将被应用于Sentinel-2(HLS)时间序列，以产生年度YSC%覆盖率和生物量地图的时间序列。PI将调整和评估USGS EROS流程，以开发高分辨率植被地图功能，其中包括来自短期、中期和长期天气预报的三种未来情景：更潮湿、更凉爽、更正常、更炎热和更干燥。这些地图可以用来检测YSC的年际变化，并使物种分布图能够为土地管理人员和政策制定者提供信息，以帮助管理YSC在SD和NGP之间的快速传播。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，从而带来新的机会和创新。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Biophysical drivers for predicting the distribution and abundance of invasive yellow sweetclover in the Northern Great Plains

• DOI: 10.1007/s10980-023-01613-1
• 发表时间: 2023-03
• 期刊: Landscape Ecology
• 影响因子: 5.2
• 作者: S. Saraf;R. John;Reza Goljani Amirkhiz;V. Kolluru;K. Jain;M. Rigge;Vincenzo Giannico;S. Boyte;Jiquan Chen;G. Henebry;M. Jarchow;R. Lafortezza