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级湿地决策支持工具的开发。湿地维持着生活质量,并为应对气候变化影响和许多其他挑战提供基于自然的解决方案,但美国和全球超过 50% 的湿地已经消失。关于湿地管理、政策、保护和恢复的公平和知情决策需要准确的地图和科学能力,以考虑湿地在广泛的社会问题中的作用,例如水质、野生动物栖息地、本土第一食品、水储存等。缓解干旱或防洪、农场供水、娱乐、沉积物清除、碳封存等。美国当前的湿地地图源自早期的科学成果,其局限性很大,而且往往不准确,并且与其他类型的空间信息的关联性较差。该项目将整合湿地科学、计算、遥感和地理空间工具开发方面的进步,以预测湿地的位置及其提供的服务。我们的首要目标是创建一个湿地工具包,让人们能够公平地获得最先进的湿地科学知识;支持有关水、水管理和湿地政策的积极和公平的对话;并提供明智且环境公正的决策所需的综合信息。在第一阶段,我们将 1) 收集和综合湿地工具包不同用户对优先用途和需求的意见,2) 确定可用数据和必要的计算资源,3) 创建原型,4) 制定公平交付计划和可持续的商业模式。在第二阶段,我们将开发和实施湿地工具包,以便在全国范围内广泛使用。湿地位置(即地图)构成了工具包的基础,而上层则描述了生态系统服务的特征,可适应不同用户的关注点。该工具包将生成一个连续(栅格)数据集,该数据集可以与各种空间、时间和光谱分辨率的其他连续空间明确数据层分层,例如湿地的水文重建、碳储量核算、栖息地特征、水储存、土著第一食品恢复优先顺序、保护和监管优先顺序、植被物候重建、长期监测等。最终的工具包将包含分析信息层(即连续栅格/像素)、离散(矢量/多边形)和报告(pdf 和 word doc)选项和格式,使从技术熟练的研究人员到具有相关知识的从业人员等用户都可以使用它。资源有限。该项目还将利用人工智能和平台设计等新兴技术,激励用户参与,随着时间的推移改进工具包的输出和模型。该奖项反映了 NSF 的法定使命,并通过使用基金会的知识进行评估,被认为值得支持。优点和更广泛的影响审查标准。
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)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Ludmila Moskal其他文献
Ludmila Moskal的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ 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
相似国自然基金
多飞行器给定任务时间的指定时刻收敛控制研究
- 批准号:62303256
- 批准年份:2023
- 资助金额:30 万元
- 项目类别:青年科学基金项目
熵格子玻尔兹曼方法的边界处理及收敛性分析研究
- 批准号:12301520
- 批准年份:2023
- 资助金额:30 万元
- 项目类别:青年科学基金项目
通算协同的高准确、快收敛无线分布式学习优化研究
- 批准号:62301222
- 批准年份:2023
- 资助金额:30 万元
- 项目类别:青年科学基金项目
Stokes方程的高阶元有限体积法的稳定性和收敛性
- 批准号:12301506
- 批准年份:2023
- 资助金额:30 万元
- 项目类别:青年科学基金项目
数据驱动的降维优化流场加速收敛方法研究
- 批准号:12372290
- 批准年份:2023
- 资助金额:53 万元
- 项目类别:面上项目
相似海外基金
NSF Convergence Accelerator Track L: HEADLINE - HEAlth Diagnostic eLectronIc NosE
NSF 融合加速器轨道 L:标题 - 健康诊断电子 NosE
- 批准号:
2343806 - 财政年份:2024
- 资助金额:
$ 65万 - 项目类别:
Standard Grant
NSF Convergence Accelerator track L: Translating insect olfaction principles into practical and robust chemical sensing platforms
NSF 融合加速器轨道 L:将昆虫嗅觉原理转化为实用且强大的化学传感平台
- 批准号:
2344284 - 财政年份:2024
- 资助金额:
$ 65万 - 项目类别:
Standard Grant
NSF Convergence Accelerator Track K: Unraveling the Benefits, Costs, and Equity of Tree Coverage in Desert Cities
NSF 融合加速器轨道 K:揭示沙漠城市树木覆盖的效益、成本和公平性
- 批准号:
2344472 - 财政年份:2024
- 资助金额:
$ 65万 - 项目类别:
Standard Grant
NSF Convergence Accelerator Track L: Smartphone Time-Resolved Luminescence Imaging and Detection (STRIDE) for Point-of-Care Diagnostics
NSF 融合加速器轨道 L:用于即时诊断的智能手机时间分辨发光成像和检测 (STRIDE)
- 批准号:
2344476 - 财政年份:2024
- 资助金额:
$ 65万 - 项目类别:
Standard Grant
NSF Convergence Accelerator Track L: Intelligent Nature-inspired Olfactory Sensors Engineered to Sniff (iNOSES)
NSF 融合加速器轨道 L:受自然启发的智能嗅觉传感器,专为嗅探而设计 (iNOSES)
- 批准号:
2344256 - 财政年份:2024
- 资助金额:
$ 65万 - 项目类别:
Standard Grant