TitleNS53A-08 - Quantifying surface fuels using drone-based Lidar
Session Start2022-12-16 11:00:00 CST
Session End2022-12-16 12:30:00 CST
LocationMcCormick Place, S104b
AuthorShu
Presentation TypeOral
AbstractThe simulation of wildland fire behavior, the prediction of wildfire hazards, and the decision on fuel treatment strategies all require accurate, high-precision, and up-to-date fuel maps. Existing maps of vegetation or fuel generally show coarse categories such as floristic composition and limited fuel types, but not vertical fuel structures and amounts. Although airborne Lidar data can provide reliable estimates of canopy traits, they are far away from the ground, so that laser light will be attenuated or unable to penetrate dense leaf layers, making it hard to assess the fuel beneath the forest canopy. Drone-based Lidar, can fly at low altitudes through the forest and deliver dense point cloud data, is ideal for collecting forest fuel structures and the amount of fuel in vertical layers. To quantify the small spatial scale changes in surface fuels, we used the Zenmuse L1 Lidar onboard the DJI Matrice 300 to observe the fuel structure and loading at the Blodgett Forest Research Station in the foothills of the California Sierra Nevada mountains. Two 5m x 5m square sample areas with different fuel compositions and loads were selected and scanned using drone-based Lidar at a height of 30�40m before and after a prescribed fire. Site 1 has lower fuel loads and compositions than site 2. Then, we filtered surface fuels from point cloud data using height measurement as a proxy. Fuel loads were calculated using random forest models and adjusted by field survey records. Comparing the fuel data before and after the prescribed burn, the results indicate that at site 1, the majority of the surface fuel was converted to debris and ash, while at site 2, the near-surface shrubs decreased and the vegetation structure became simpler. This method accurately captures the changes in fuel structure and fuel loads, and it demonstrates how drone-based Lidar can be used to fill gaps in existing fuel maps, improve their accuracy, and build comprehensive forest fuel libraries. These data products will facilitate future improvements to the performance of fire behavior or predictive models and support fuel treatment decisions.