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Mapping and modeling fuels and fire at the Sycan Marsh, Oregon

The research being performed through multidisciplinary collaboration efforts at TNC’s Sycan Marsh Preserve in Oregon is providing much needed data for fuel mapping efforts by linking surface fuel datasets with TLS and UAS data pre, during, and post-fire. It also provides essential data for fire mapping and behavioral understanding of forest and grass fires through the UAS fire mapping metrics, as well as for 3D fuels modeling and fuel treatment analysis with STANDFIRE.

Larger and more severe wildfires as the result of fire exclusion policies, heavy fuel accumulations, and climate change related drought, insect, and pathogen outbreaks are threatening our forests and communities. These factors along with increasing wildland urban interface in fire-adapted ecosystems mean that proactive management needs to be applied to ensure the health and safety of our forests and communities, and tools and models need to be developed to keep up with the demands from management. Prescribed fires often provide the best way to manage our forests but continuing research is needed to help plan, carry out, and evaluate prescribed fire effects and fuel treatments. With funding from the Department of Defense’s Strategic Environmental Research and Development Program (SERDP), a collaborative effort among a diverse group of research groups is under way to develop integrated and reliable fuels, weather, and fire datasets for evaluation of different fire models as well as test and develop new methods for fuel mapping, characterizing weather, capturing fire behavior, data integration, and the development of a streamlined research footprint.

The goal of this project is to advance capabilities for managers to plan and evaluate prescribed fires and fuel treatments as proactive strategies to successfully protect communities, enhance firefighter safety, and restore ecosystems.

  • Beginning in 2017, prescribed fires have been implanted in forest and grassland units on TNC’s Sycan Marsh Preserve, Oregon. Pre-fire, ground control plots (GCPs) in the form of metal plates, visible in the UAS imagery, were distributed and mapped using a survey grade GPS. Surface fuels are measured and mapped using modified Photoload methods, UAS imagery flights, and TLS scans. Wind anemometers were set up outside of the burn unit in a design thought to best capture the landscape patterns and conditions about to be burned.
  • During the burn, UAS NIR and thermal IR as well as ground level thermal IR and standard imagery was recorded.
  • Post-burn UAS imagery flights are performed, along with surface fuel evaluations using adapted Photoload protocol measures.

The research approach has two primary focus areas.First, mapping fuels and tracking fire behavior will result in new approaches and quantitative methods for fuel mapping. This may result in new methods that make it easier for managers to quickly characterize fuels in their project areas.

The second research approach focuses on the development of new capabilities in fuel and fire modeling. Modeling provides a way to assess how different factors, such as higher winds or drier fuels, might affect the effectiveness of a given fuel treatment. Development of these systems will give managers more robust guidance for fuel treatment decisions.

This project is ongoing, and the methodology is still adjusting to ensure the most pertinent data collection. Preliminary work using the data collected has resulted in one publication, included below. UAS data used to develop metrics of fire behavior – A recent publication described the use of drone (UAS) imagery to derive metrics quantifying fire behavior using thermal imagery (Moran et al 2019). This paper demonstrates the value of UAS data collection on fires, and lays groundwork for future developments in this project.

Modified: Mar 03, 2020

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Moran, C.J., Seielstad, C.A., Cunningham, M.R., Hoff, V., Parsons, R.A., Queen, L., Sauerbrey, K. and Wallace, T., 2019. Deriving Fire Behavior Metrics from UAS Imagery. Fire, 2(2), p.36.