RAPID: Airborne LiDAR and Hyperspectral Observations to Support Ecological Characterization of Wildfire Affected Areas in Partnership with BB-FLUX

该项目将评估2018年火灾季节美国西部特定野火期间燃烧的地上生物量。 这项工作是为了支持另一个NSF支持的项目，BB-FLUX（痕量气体和气溶胶的生物质燃烧通量测量），该项目的重点是测量野火排放通量。 传感器将在国家生态观测站（氖）的空中观测平台（AOP）上飞行，以量化野火期间燃烧的面积和地上生物量。该项目的结果将有助于提高对与野火排放有关的人类和生态系统健康风险的认识。 这项工作的目标是：（1）用氖AOP收集野火后燃烧数据，以支持野火排放和生态系统关系的研究;（2）将收集的氖AOP数据处理成标准的氖数据产品集，为估计燃烧面积和燃烧的总燃料提供基础;以及（3）通过融合下视激光测高（LiDAR）和光学超光谱（HS）机载测量来增强地上生物量（AGB）估计算法，使用现有的可获得的氖数据，这些数据与捕获的生态系统（北方温带森林）密切匹配，提供BB-FLUX排放模型，对AGB（燃料燃烧）进行高精度估计。现有的氖收集在北方温带森林将被用作候选网站，以开发一个增强的算法，预测生物量使用激光雷达和HS observations.The伙伴关系之间的融合方法氖AOP和BB-FLUX运动代表了一种新的协同作用，以前不同的观测系统，介绍了一个跨学科的方法有关野火排放特性的当地生态系统。 改进AGB估算的普遍进步对生态科学、林业、农业和环境管理具有广泛的影响。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。

This project will assess the above ground biomass burned during specific wildfires in the Western US during the 2018 fire season. This effort is in support of another NSF-supported project, BB-FLUX (Biomass Burning Flux Measurements of Trace Gases and Aerosols), that is focused on measuring wildfire emission fluxes. Sensors will be flown on the Airborne Observation Platform (AOP) of the National Ecological Observatory (NEON) to quantify the area and above ground biomass burned during wildfires. The project results will help improve understanding of the risk to human and ecosystem health associated with emissions from wildfires. The objectives of this effort are to: (1) Collect post-wildfire burn data with the NEON AOP to support research on wildfire emission and ecosystem relationships; (2) Process the collected NEON AOP data through to the standard set of NEON data products that will provide the basis for estimating the area burned and total fuel burned; and (3) Enhance above ground biomass (AGB) estimation algorithms through fusion of downward-looking laser altimetry (LiDAR) and optical hyperspectral (HS) airborne measurements, using existing publically available NEON data that closely matches the ecosystems captured (northern temperate forests), providing BB-FLUX emission models with high accuracy estimates of AGB (fuel burned). Existing NEON collections in northern temperate forests will be used as candidate sites to develop an enhanced algorithm for predicting biomass using fusion approaches between LiDAR and HS observations.The partnership of the NEON AOP and the BB-FLUX campaign represents a novel synergy between previously disparate observation systems that introduces an inter-disciplinary approach for relating wildfire emission characteristics to the local ecosystems. The general advancement of improved AGB estimates have broad implications for ecological sciences, forestry, agriculture and environmental management.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.