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

Battelle Memorial Institute, Richland OH

Investigators

Abstract

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.