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
基本信息
- 批准号:2344174
- 负责人:
- 金额:$ 65万
- 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
- 起止时间:2024-01-15 至 2024-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
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.
该项目将加速美国国家规模的湿地决策支持工具的开发。湿地维持生活质量,并为气候变化的影响和许多其他挑战提供基于自然的解决方案,但在美国和全球范围内损失了50%以上。关于湿地管理,政策,保护和恢复的公平和明智的决策需要准确的地图和科学能力,以考虑湿地在诸如水质,野生动植物栖息地,土著居住食品,供水的水库,减轻干旱或洪水控制,养殖水的养分,娱乐性,seeatient,Seeportion,Seeportation,Seeportation,corbone,以及更多的社会关注方面的作用。美国当前的湿地地图源自早期的科学,并且有限,通常不准确,与其他类型的空间信息无关。该项目将整合湿地科学,计算,遥感和地理空间工具开发中的进步,以预测湿地的位置及其提供的服务。我们的总体目标是创建一个湿地工具包,该工具包可公平地使用最先进的湿地科学;支持有关水,水管理和湿地政策的主动和公平的对话;并提供了知情和环境决策所需的综合信息。在第1阶段,我们将1)收集并合成从湿地工具包不同用户的优先用途和需求的输入,2)确定可用数据和必要的计算资源,3)创建原型,4)制定计划以公平交付和可持续的商业模型。在第2阶段,我们将开发和实施湿地工具包,以在国家规模上进行广泛使用。湿地位置(即地图)构成了工具包的基础,而上层则表征了生态系统服务,这是适应不同用户关注点的。该工具包将产生一个连续的(栅格)数据集,可以在各种空间,时间和光谱分辨率上与其他连续的空间显式数据层分层 更多的。最终的工具包将涵盖分析信息层(即连续的栅格/像素),离散(矢量/多边形),以及报告(PDF和Word Doc)选项和格式,这些选项和格式将使从技术熟练的研究人员到具有有限资源的实践者的用户访问它。该项目还将借鉴新兴技术,例如人工智能和平台设计,这些技术会以改善工具包的产出和模型的方式来激励用户参与。该奖项反映了NSF的法定任务,并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来通过评估来支持的。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Ludmila Moskal其他文献
Ludmila Moskal的其他文献
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{{ truncateString('Ludmila Moskal', 18)}}的其他基金
D-ISN/Collaborative Research: Machine Learning to Improve Detection and Traceability of Forest Products using Stable Isotope Ratio Analysis (SIRA)
D-ISN/合作研究:利用稳定同位素比率分析 (SIRA) 提高林产品检测和可追溯性的机器学习
- 批准号:
2240403 - 财政年份:2023
- 资助金额:
$ 65万 - 项目类别:
Standard Grant
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