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

RAPID：与 BB-FLUX 合作利用机载激光雷达和高光谱观测支持野火受影响地区的生态特征描述

• 批准号: 1842139
• 负责人: Tristan Goulden
• 金额: $ 18.35万
• 依托单位: Battelle Memorial Institute
• 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: 2018-09-15 至 2020-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1842139&HistoricalAwards=false
• 关键词: RAPID; Airborne; LiDAR; Hyperspectral; Observations

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.

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（微量气体和气溶胶的生物质燃烧通量测量），该项目专注于测量野火排放通量。 传感器将安装在国家生态观测站（NEON）的机载观测平台（AOP）上，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加深人们对野火排放对人类和生态系统健康造成的风险的了解。 这项工作的目标是： (1) 通过 NEON AOP 收集野火燃烧后的数据，以支持野火排放和生态系统关系的研究； (2) 将收集到的 NEON AOP 数据处理为标准的 NEON 数据产品集，为估计燃烧面积和燃烧的燃料总量提供基础； (3) 通过融合下视激光测高 (LiDAR) 和光学高光谱 (HS) 机载测量，使用与捕获的生态系统（北温带森林）密切匹配的现有公开 NEON 数据，增强地上生物量 (AGB) 估计算法，为 BB-FLUX 排放模型提供高精度 AGB（燃料燃烧）估计。北温带森林中现有的 NEON 采集将被用作候选地点，以开发一种增强算法，利用 LiDAR 和 HS 观测之间的融合方法来预测生物量。NEON AOP 和 BB-FLUX 活动的合作代表了以前不同的观测系统之间的新型协同作用，引入了一种跨学科方法，将野火排放特征与当地生态系统联系起来。 改进的 AGB 估计的总体进步对生态科学、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智力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

A Comparison of Multitemporal Airborne Laser Scanning Data and the Fuel Characteristics Classification System for Estimating Fuel Load and Consumption

多时相机载激光扫描数据与用于估算燃油负荷和消耗的燃油特性分类系统的比较

• DOI: 10.1029/2021jg006733
• 发表时间: 2022
• 期刊: Journal of Geophysical Research: Biogeosciences
• 作者: McCarley, T. Ryan;Hudak, Andrew T.;Restaino, Joseph C.;Billmire, Michael;French, Nancy H. F.;Ottmar, Roger D.;Hass, Bridget;Zarzana, Kyle;Goulden, Tristan;Volkamer, Rainer
• 通讯作者: Volkamer, Rainer