Collaborative Research: Planning: FIRE-PLAN:High-Spatiotemporal-Resolution Sensing and Digital Twin to Advance Wildland Fire Science

近几十年来，美国发生的灾难性野火数量稳步增加，造成人员伤亡、大量财产损失和剧烈的环境变化。然而，消防员和应急小组很难实时准确预测野火的蔓延情况。尽管已经开发了许多火灾蔓延模型，但在其操作使用中面临的最大挑战之一是缺乏高时空分辨率的地面真实火灾数据，而这些数据对于模型的评估和改进是必不可少的。该规划项目的目标是将野火科学研究人员、火灾传感和数据科学专家以及不同利益相关者聚集在一起，制定高时空分辨率野火传感和数字孪生体建设的标准和要求。组委会将由野火科学、工程和利益相关者社区组成，包括火灾生态学和行为建模、污染监测、机器人、网络物理系统（CPS）、野火灭火、土著文化烧伤和规定火灾。将举办一系列物理和远程研讨会，重点讨论野火建模验证的明火数据、规定火灾的数字孪生以及安全有效的野火数据收集等主题。本规划项目的研究任务包括：1)确定关键的高时空分辨率火灾度量和数据表示，以支持火灾模型验证和火灾操作；2)提出火灾传感和灭火机器人和网络物理系统的传感策略和算法，以支持安全有效地收集所需的高分辨率火灾数据；3)利用高分辨率数据开发和评估数据同化和数字孪生构建，以推进火灾行为建模。4)原型和初始火灾数据生态系统演示，包括收集文化燃烧数据和建立基准火灾数据共享和数字孪生网站GeoFireData，该网站可以支持不同的火灾操作类型，如火灾蔓延模型验证和控制燃烧规划。特别关注的是在计算科学、工程、生态学和数据科学的交叉领域对下一代从事野火风险研究的科学家进行跨学科培训。该奖项反映了美国国家科学基金会的法定使命，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 number of catastrophic wildfires in the United States has been steadily increasing in recent decades, which generate casualties, large loss of properties, and dramatic environmental changes. However, it is difficult to make accurate predictions of wildland fire spread in real time for firefighters and emergency response teams. Although many fire spread models have been developed, one of the biggest challenges in their operational use is the lack of ground truth fire data at high spatiotemporal resolutions, which are indispensable for model evaluation and improvements. The objective of this planning project is to bring together wildland fire science researchers, fire sensing and data science experts, and diverse stakeholders to develop standards and requirements for high-spatiotemporal-resolution wildland fire sensing and digital twin construction. An organizing committee will be formed from wildland fire science, engineering, and stake holder communities including fire ecology and behavior modeling, pollution monitoring, robotics, cyber physical systems (CPS), wildfire fighting, indigenous cultural burns, and prescribed fires. A series of physical and remote workshops will be held focusing on themes such as open fire data for wildland fire modeling validation, digital twins for prescribed fires, and safe and efficient wildland fire data collection. Research tasks of this planning project include: 1) identification of key high-spatiotemporal-resolution fire metrics and data representations to support fire model validation and fire operations, 2) proposition of sensing strategies and algorithms for fire sensing and suppression robots and cyber physical systems that can support safe and efficient collection of desired high-resolution fire data, 3) development and evaluation of data assimilation and digital twin construction using high-resolution data to advance fire behavior modeling, coupled fire-atmosphere modeling, and smoke modeling, and 4) prototype and initial fire data ecosystem demonstration including collection of cultural burn data and establishment of GeoFireData, a benchmark fire data sharing and digital twin website, which can support different fire operation types such as fire spread model validation and controlled burn planning. The special attention will be devoted to interdisciplinary training of the next generation of scientists working with wildfire risks at the interface of computational sciences, engineering, ecology, and data sciences.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.