SCC-IRG Track 1: Smart and Safe Prescribed Burning for Rangeland and Wildland Urban Interface Communities

SCC-IRG 第 1 轨道：牧场和荒地城市界面社区的智能、安全规定燃烧

• 批准号: 2306603
• 负责人: Xiaolin Hu
• 金额: $ 200万
• 依托单位: Georgia State University Research Foundation, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2306603&HistoricalAwards=false
• 关键词: SCC; IRG; Track; Smart; Safe; SCC; IRG; Track; Smart; Safe

Prescribed fires have long been used by ranchers and farmers in the Great Plains as a land management tool. They help farming and grazing by replenishing the soil, increasing forage production, and protecting prairies from invasive overgrowth. They are also used by rural and Wildland Urban Interface (WUI) communities to remove built-up fuels for reducing risks of wildfires. Despite the many benefits of prescribed fires, there are safety and environmental concerns for prescribed burn events. On the safety aspect, an escaped fire or a fire reignited from smoldering fuels can become uncontrolled and result in severe property damages and injuries to people. On the environmental aspect, smoke from prescribed fires causes air pollution for local communities and communities downwind. To manage and minimize these concerns, optimal planning and execution of prescribed fires are crucial. The objective of this project is to develop a community sensing, planning, and learning infrastructure to support smart and safe prescribed burning for communities that use prescribed fires for rangeland and wildfire risk management. The developed infrastructure will be integrated into a cloud-based platform to support landowners to optimally plan and operate prescribed burns, collect and share data about burning, and train fire operators to learn the most effective ways of burning. The project will also promote technology awareness for building smart communities in rural areas, by increasing partnerships among academia, rural communities, and local governments. The integrated research of this project includes: 1) technical research on multi-scale sensing and data fusion, data-driven burn condition modeling, grassland fuel mapping & hotspot detection, and fire behavior modeling and simulation; 2) social science research that addresses the knowledge gap on how communities engage with and coordinate burn practices through the use of technology; and 3) community engagement that develops tools, data repositories, and activities to support communities’ smart and safe prescribed burning. The multiscale sensing and data fusion integrate data from heterogeneous sources including satellite remote sensing, unmanned aircraft systems, and crowdsourced reports. We will work with two communities in Kansas to evaluate and demonstrate the developed research: 1) The Gyp Hills community represents a rangeland community where an average prescribed fire covers over hundreds of acres for grasslands primarily used for grazing; 2) the Eastern Kansas community represents a suburban WUI community where prescribed fires are employed at a smaller scale.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.

长期以来，大平原的牧场主和农民一直使用开处方的大火作为土地管理工具。它们通过补充土壤，增加草料生产并保护草原免受侵入性过度生长来帮助耕种和磨碎。它们还被粗糙和野生世界的城市界面（WUI）社区使用，以消除降低野火风险的积累燃料。尽管处方大火带来了许多好处，但规定的烧伤事件仍存在安全性和环境问题。在安全方面，逃脱的大火或因闷烧燃料而终止的火可能会变得不受控制，并对人们造成严重的财产损失和伤害。在环境方面，处方大火的烟雾会给当地社区和下风引起空气污染。为了管理和最大程度地减少这些关注点，开处方的火灾的最佳计划和执行至关重要。该项目的目的是开发社区感应，计划和学习基础设施，以支持智能且安全的开处方燃烧的燃烧，这些社区使用开处方的大火进行牧场和野火风险管理。开发的基础设施将集成到一个基于云的平台中，以支持土地所有者，以最佳计划和操作规定的烧伤，收集和共享有关燃烧的数据，并培训消防人员，以学习最有效的燃烧方式。该项目还将通过增加学术界，农村社区和地方政府之间的伙伴关系来提高在农村地区建立智能社区的技术意识。该项目的综合研究包括：1）关于多尺度灵敏度和数据融合的技术研究，数据驱动的烧伤状况建模，草地燃料映射和热点检测以及火灾行为建模和仿真； 2）社会科学研究解决了有关通过技术使用和协调烧伤实践的知识差距； 3）开发工具，数据存储库和活动的社区参与，以支持社区的智能和安全规定的燃烧。来自异质来源的多尺度感测和数据融合了数据，包括卫星遥感，无人飞机系统和众包报告。我们将与堪萨斯州的两个社区合作评估和证明已发达的研究：1）吉普山社区代表了一个牧场社区，普通处方的火灾覆盖了数百英亩用于用于磨碎的草地； 2）堪萨斯州东部社区代表了一个郊区的WUI社区，处方大火以较小的规模雇用。该奖项反映了NSF的法定任务，并通过使用基金会的知识分子优点和更广泛的影响评估标准来评估，被认为是珍贵的支持。