Project period: 8 14 2023-8 13 24PROJECT title: lidar-derived fuels estimatesterrestrial lidar scanning (TLS) is becoming a popular tool for fast and objective sampling in fuels monitoring

However, its capabilities need to be further tested for describing fuels in various fuel types, vegetation density, and fire management scenarios. Propose to test the utility of TLS in estimating specific fuels variables including tons per acre of fuel load, percent cover of understory vegetation, and vertical profile of plant area and density. Such variables are key to accurately estimating and predicting wildfire spread and behavior. Will develop methods to estimate these variables from TLS data combined with other remote sensing data sources. Will assess the accuracies of the resulting estimates across a gradient of fuel types, vegetation cover types and density, and fire management actions. To develop and validate the fuels estimates from TLS data, will establish field plots, where will measure vegetation cover, density, and fuel types. These measurements will then be compared to the TLS-derived estimates. Both field-based and TLS-based measurements will take place over summer growing seasons of 2023-2024. The project will support one PHD student for 2 years. The deliverables will include:TLS measurements of field plots across varying vegetation cover types, density, and fuel typesfield-based measurements of vegetation cover, density, and fuel typescomparison of these products and validation of the TLS-based estimates of vegetation cover, density, and fuel loadstatistical relationship between TLS-based estimates combined with other remote sensing data sources versus field-based measurements of vegetation cover, density, and fuels