Research Initiation Award: Remote Sensing for Flood Modeling and Management

研究启动奖：洪水建模和管理遥感

• 批准号: 1800768
• 负责人: Leila Hashemi Beni
• 金额: $ 29.98万
• 依托单位: North Carolina Agricultural & Technical State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1800768&HistoricalAwards=false
• 关键词: Research; Initiation; Award; Remote; Sensing

Research Initiation Awards provide support for junior and mid-career faculty at Historically Black Colleges and Universities who are building new research programs or redirecting and rebuilding existing research programs. It is expected that the award helps to further the faculty member's research capability and effectiveness, improves research and teaching at the home institution, and involves undergraduate students in research experiences. The award to North Carolina A&T State University has potential broader impacts in a number of areas. The goal of the project is to gain fundamental understanding of unarmed aerial vehicle (UAV) data processing and to develop a research program on remote sensing data processing for environmental management at the university. Undergraduate students and high school students will gain research experiences and the research will be integrated in a number of undergraduate and graduate courses.Remote sensing data have increasingly been used to develop flood modeling; however, traditional satellite-based techniques are challenging due to the presence of cloud cover, satellite revisit time, viewing angle limitations, and the complexity of urban landscapes. The recent development of UAVs has created a new tool for collecting data to use in geospatial research. Applying UAVs to collect data with an appropriate flight mode and optimized sensors offers the possibility of overcoming issues associated with using satellite-based sensor data. This project aims to investigate and evaluate the application of UAV data for flood modeling and management. More generally, this will improve GIS and remote sensing capabilities for UAV-based simulation and modeling of 3D dynamic phenomena such as floods, forest fires, and air pollution. The proposed project will contribute to the scientific literature by developing a novel method for 3D water surface reconstruction and delineation of flood boundary and water level measurements using UAV data. These measurements can be integrated with flood models to identify areas vulnerable to future flooding.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.

研究启动奖为历史上黑人学院和大学的初级和中级职业教师提供支持，他们正在建立新的研究项目或重新定向和重建现有的研究项目。预计该奖项将有助于进一步提高教师的研究能力和效率，改善家庭机构的研究和教学，并使本科生参与研究经验。授予北卡罗来纳州AT州立大学的奖项在许多领域都有潜在的更广泛的影响。&该项目的目标是获得对非武装飞行器（UAV）数据处理的基本了解，并在该大学制定一个用于环境管理的遥感数据处理研究计划。本科生和高中生将获得研究经验，研究将被整合到一些本科和研究生课程中。遥感数据越来越多地用于开发洪水建模;然而，传统的基于卫星的技术具有挑战性，因为存在云层覆盖，卫星重访时间，视角限制和城市景观的复杂性。最近无人机的发展创造了一个新的工具，用于收集数据，用于地理空间研究。应用无人机以适当的飞行模式和优化的传感器收集数据，可以克服与使用卫星传感器数据相关的问题。该项目旨在调查和评估无人机数据在洪水建模和管理中的应用。更广泛地说，这将提高GIS和遥感能力，用于基于无人机的洪水、森林火灾和空气污染等3D动态现象的模拟和建模。拟议的项目将通过开发一种新的三维水面重建方法和使用无人机数据划定洪水边界和水位测量来贡献科学文献。该奖项反映了NSF的法定使命，并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，被认为值得支持。

项目成果

DEVELOPING GEOSPATIAL SCIENTISTS – USING STUDENTS AS PARTNERS IN DRONE RESEARCH

培养地理空间科学家 — 让学生作为无人机研究的合作伙伴

• DOI: 10.5194/isprs-archives-xliv-m-2-2020-69-2020
• 发表时间: 2020
• 期刊: Remote Sensing and Spatial Information Sciences
• 作者: McKee, T.;Hashemi-Beni, L.
• 通讯作者: Hashemi-Beni, L.

UAV Remote Sensing Assessment of Crop Growth

• DOI: 10.14358/pers.21-00060r2
• 发表时间: 2021-12-01
• 期刊: PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING
• 影响因子: 1.3
• 作者: Dorbu, Freda Elikem;Hashemi-Beni, Leila;Shahbazi, Abolghasem
• 通讯作者: Shahbazi, Abolghasem

Inundated Vegetation Mapping Using SAR Data: A Comparison of Polarization Configurations of UAVSAR L-Band and Sentinel C-Band

• DOI: 10.3390/rs14246374
• 发表时间: 2022-12
• 期刊: Remote. Sens.
• 作者: A. Salem;L. Beni

Deep Convolutional Neural Networks for Weeds and Crops Discrimination From UAS Imagery

• DOI: 10.3389/frsen.2022.755939
• 发表时间: 2022-02-11
• 期刊: FRONTIERS IN REMOTE SENSING
• 作者: Hashemi-Beni, Leila;Gebrehiwot, Asmamaw;Dorbu, Freda
• 通讯作者: Dorbu, Freda

Combining Multiple Geospatial Data for Estimating Aboveground Biomass in North Carolina Forests

• DOI: 10.3390/rs13142731
• 发表时间: 2021-07
• 期刊: Remote. Sens.
• 作者: L. Beni;L. Kurkalova;T. Mulrooney;Chinazor S. Azubike