CREST Center for Geospatial and Environmental Informatics, Modeling and Simulation
CREST 地理空间和环境信息学、建模和仿真中心
基本信息
- 批准号:2112631
- 负责人:
- 金额:$ 499.54万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2021
- 资助国家:美国
- 起止时间:2021-12-15 至 2026-11-30
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
The Centers of Research Excellence in Science and Technology (CREST) program supports the enhancement of research capabilities of minority-serving institutions through the establishment of centers that effectively integrate education and research. CREST promotes the development of new knowledge, enhancements of the research productivity of individual faculty, and an expanded presence of students historically underrepresented in science, technology, engineering, and mathematics disciplines. With National Science Foundation support, Texas A&M University, Corpus Christi establishes the Center for Geospatial and Environmental Informatics, Modeling and Simulation. Growing population demands, threats from storms, and sea encroachment put coastal communities at the forefront of engineering and scientific efforts to reduce vulnerabilities for their long-term prosperity. Improving coastal resilience is a cost-effective approach to prepare for increasingly uncertain coastal environments. The modeling and simulation capabilities developed by the Center will provide guidance to communities to rebound more quickly from a disaster event and to reduce negative human health, environmental, and economic impacts. The Center will recruit and support underrepresented minority students to pursue degrees in STEM fields with a focus on coastal resilience and expand the university’s education and research capacity. The Center articulates three research subprojects addressing the challenges of coastal resilience by developing new approaches that integrate remote and autonomous sensing with geospatial computing, Artificial Intelligence/deep learning, and big data analytics for comprehensive coastal zone monitoring at different scales. The focus is on improving resiliency to extreme coastal hazards and episodic and persistent events (e.g., hurricane and sea-level rise). Subproject 1 develops new approaches integrating remote and autonomous sensing with geospatial computing and artificial intelligence to improve coastal zone monitoring and resiliency decision-making. It generates detailed and accurate geographic information systems data for characterization of the built and natural environment. Subproject 2 contributes to the understanding of the urban water cycle and the resilience of water infrastructure through integrated characterization, simulation, and assessments. Subproject 3 investigates how emerging data sources and advanced geospatial computing can be applied to evaluate, assist, and improve a coastal community’s physical, behavioral, and social health after disasters.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.
科学和技术卓越研究中心(CREST)方案通过建立有效整合教育和研究的中心,支持加强为少数群体服务的机构的研究能力。 CREST促进新知识的发展,提高个别教师的研究生产力,并扩大历史上在科学,技术,工程和数学学科中代表性不足的学生的存在。 在美国国家科学基金会的支持下,德克萨斯A M大学科珀斯克里斯蒂分校建立了地理空间和环境信息学,建模和模拟中心。 不断增长的人口需求、风暴威胁和海洋侵蚀使沿海社区处于工程和科学努力的前沿,以减少其长期繁荣的脆弱性。 提高沿海地区的复原力是为日益不确定的沿海环境做好准备的一种具有成本效益的方法。 该中心开发的建模和模拟能力将为社区提供指导,以更快地从灾害事件中恢复过来,并减少对人类健康、环境和经济的负面影响。 该中心将招募和支持代表性不足的少数民族学生攻读STEM领域的学位,重点是沿海恢复力,并扩大大学的教育和研究能力。 该中心阐述了三个研究子项目,通过开发将遥感和自主传感与地理空间计算,人工智能/深度学习和大数据分析相结合的新方法,以应对沿海地区恢复力的挑战,以实现不同规模的综合沿海地区监测。 重点是提高对极端沿海灾害以及偶发和持续事件(例如,飓风和海平面上升)。 分项目1开发将遥感和自主遥感与地理空间计算和人工智能相结合的新方法,以改善沿海地区监测和复原力决策。 它生成详细和准确的地理信息系统数据,用于描述建筑和自然环境的特征。 次级项目2通过综合表征、模拟和评估,促进对城市水循环和水基础设施复原力的理解。 子项目3研究新兴数据源和先进的地理空间计算如何应用于评估、帮助和改善沿海社区灾后的身体、行为和社会健康。该奖项反映了NSF的法定使命,并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Integrated coastal subsidence analysis using InSAR, LiDAR, and land cover data
- DOI:10.1016/j.rse.2022.113297
- 发表时间:2022-12
- 期刊:
- 影响因子:13.5
- 作者:W. Zhong;T. Chu;P. Tissot;Zhenming Wu;Jie Chen;Hua Zhang
- 通讯作者:W. Zhong;T. Chu;P. Tissot;Zhenming Wu;Jie Chen;Hua Zhang
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