Spokes: MEDIUM: SOUTH: Collaborative: Enhanced 3-D Mapping for Habitat, Biodiversity, and Flood Hazard Assessments of Coastal and Wetland Areas of the Southern US

由于海平面上升，严重风暴和河流排放，美国南部各州沿海人口和基础设施面临的洪水风险将增加。该项目的愿景是通过基于知识、保护和明智使用敏感土地的规划，以可持续的方式保护和发展美国南部低洼沿海地区的社区。来自南佛罗里达大学海洋科学学院和德克萨斯州农工大学科珀斯克里斯蒂地球科学学院的研究人员以及谷歌地球引擎正在通过该项目与南方大数据中心合作，开发更准确，超高分辨率的地形，土地覆盖和城市环境地理空间产品。该项目详细研究了2017年直接受到飓风哈维和伊尔玛影响的地区，并确定了整个地区的洪水易发地区。3D地图显示了这些重要生态系统的保护和发展计划所需的生境多样性。该项目将开发从德克萨斯州到佛罗里达海岸50公里范围内（面积220，000平方公里）的南部各州的改进地形和土地覆盖图。这些地图将使用大数据方法构建，使用从飞机上收集的详细历史机载LiDAR（光探测和测距）数据与高空间分辨率（2百万像素）多光谱商业卫星图像合并。该项目还将包括使用运动结构（SfM）方法对城市地区进行详细的3D测绘研究;特别是该项目将使用风筝摄影和轻型飞机绘制德克萨斯州休斯顿/科珀斯克里斯蒂和佛罗里达的坦帕/圣彼得堡的部分地图。制作土地覆盖图和数字高程模型需要融合大量不同的数据和高效的自动化技术。该项目将制定战略，利用谷歌地球引擎和高性能计算集群将这些数据汇总成有用的产品。该项目将通过NOAA的数字海岸门户网站公开分发所有产品。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。

The risk to coastal populations and infrastructure from flooding due to sea level rise, severe storms, and river discharge will increase for U.S. southern states. The vision of this project is that communities occupying low-lying coastal areas of the southern US will be protected and develop in a sustainable manner through planning based on knowledge, conservation, and wise use of sensitive lands. Researchers from the University of South Florida's College of Marine Science and the School of Geosciences, Texas A&M University Corpus Christi, and Google Earth Engine are collaborating with the South Big Data Hub through this project to develop more accurate, ultra-high resolution topographic, land cover, and urban environment geospatial products. The project examines in detail areas that were directly impacted by Hurricanes Harvey and Irma in 2017, and identifies flood-prone areas across the region. The 3D maps show habitat diversity, needed to plan for conservation and development in these important ecosystems.This project will develop the improved topographic and land cover maps of the south States within 50 Km of the coast from Texas to Florida (an area 220,000 square Km). The maps will be constructed using a Big Data approach, using detailed historical airborne LiDAR (Light Detection and Ranging) data collected from airplanes merged with high spatial resolution (2 m pixel) multispectral commercial satellite imagery. The project will also include research into detailed 3D mapping of urban areas using Structure-from-Motion (SfM) methods; specifically the project will map portions of Houston/Corpus Christi in Texas, and Tampa/Saint Petersburg in Florida, using Kite Photography and light aircraft. The production of land cover maps and digital elevation models requires the fusion of very large amounts of disparate data and efficient, automated techniques. The project will develop the strategies to aggregate these data into useful products using Google Earth Engine and a High Performance Computing cluster. The project will distribute all products openly via NOAA's Digital Coastal portal.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.