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NSF Convergence Accelerator, Track K: Mapping the nation's wetlands for equitable water quality, monitoring, conservation, and policy development

NSF Convergence Accelerator, Track K: Mapping the nation's wetlands for equitable water quality, monitoring, conservation, and policy development
NSF 融合加速器,K 轨道:绘制全国湿地地图,以实现公平的水质、监测、保护和政策制定
批准号:
2344174
负责人:
Ludmila Moskal
金额:
$65.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2024
资助国家:
美国
项目状态:
已结题
起止时间:
2024-01-15 至 2024-12-31

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中文摘要
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英文摘要
This project will accelerate development of the a national-scale wetlands decision support tool for the United States. Wetlands sustain quality of life and provide nature-based solutions to climate change impacts and many other challenges, yet more than 50% have been lost in the United States and globally. Equitable and informed decisions about wetlands management, policy, conservation, and restoration require accurate maps and scientific capacity to consider the role of wetlands in relation to a wide range of societal concerns such as water quality, wildlife habitat, indigenous First Foods, water storage for drought mitigation or flood control, farm water provisioning, recreation, sediment removal, carbon sequestration, and more. Current maps of wetlands in the United States derive from an earlier generation of science and are limited, often inaccurate, and poorly linked to other kinds of spatial information. This project will integrate advances in wetland science, computing, remote sensing, and geospatial tool development to predict where wetlands are and the services they provide. Our overarching goal is to create a Wetland Toolkit that provides equitable access to state-of-the-art wetlands science; supports proactive and equitable conversations about water, water management, and wetlands policy; and provides the integrated information necessary for informed and environmentally just decision-making. In Phase 1 we will 1) gather and synthesize input on priority uses and needs from diverse users of the Wetland Toolkit, 2) identify available data and necessary computing resources, 3) create a prototype, and 4) develop plans for equitable delivery and a sustainable business model. In Phase 2 we will develop and implement the Wetland Toolkit for broad use at a national scale. Wetland locations (i.e. maps) form the foundation of the Toolkit, while upper layers characterize ecosystem services, adaptable to different user concerns. The Toolkit will generate a continuous (raster) dataset that can be layered with other continuous spatially explicit data layers at various spatial, temporal, and spectral resolutions, such as hydrologic reconstructions of wetlands, carbon stock accounting, habitat characterization, water storage, indigenous First Foods restoration prioritization, conservation and regulatory prioritization, vegetation phenological reconstruction, long-term monitoring, and more. The final toolkit will encompass both analytical information layers (i.e. continuous rasters/pixels), discrete (vectors/polygons), and reporting (pdf and word doc) options and formats that will make it accessible for users ranging from technically skilled researchers to practitioners with limited resources. This project also will draw upon emerging technologies such as artificial intelligence and platform designs that incentivize user participation in ways that improve the Toolkit outputs and models over time.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.
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D-ISN/Collaborative Research: Machine Learning to Improve Detection and Traceability of Forest Products using Stable Isotope Ratio Analysis (SIRA)
  • 批准号:
    2240403
  • 项目类别:
    Standard Grant
  • 资助金额:
    $37.41万
  • 财政年份:
    2023
  • 负责人:
    Ludmila Moskal
  • 依托单位:
海外基金