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BackgroundPrescribed fire is vital for fuel reduction and ecological restoration, but the effectiveness and fine-scale interactions are poorly understood. AimsWe developed methods for processing uncrewed aircraft systems (UAS) imagery into spatially explicit pyrometrics, including measurements of fuel consumption, rate of spread, and residence time to quantitatively measure three prescribed fires. MethodsWe collected infrared (IR) imagery continuously (0.2 Hz) over prescribed burns and one experimental calibration burn, capturing fire progression and combustion for multiple hours. Key resultsPyrometrics were successfully extracted from UAS-IR imagery with sufficient spatiotemporal resolution to effectively measure and differentiate between fires. UAS-IR fuel consumption correlated with weight-based measurements of 10 1-m2 experimental burn plots, validating our approach to estimating consumption with a cost-effective UAS-IR sensor (R2 = 0.99; RMSE = 0.38 kg m-2). ConclusionsOur findings demonstrate UAS-IR pyrometrics are an accurate approach to monitoring fire behaviour and effects, such as measurements of consumption. Prescribed fire is a fine-scale process; a ground sampling distance of <2.3 m2 is recommended. Additional research is needed to validate other derived measurements. ImplicationsRefined fire monitoring coupled with refined objectives will be pivotal in informing fire management of best practices, justifying the use of prescribed fire and providing quantitative feedback in an uncertain environment. 

FLAME 3 is the third dataset in the FLAME series of aerial UAV-collected side-by-side multi-spectral wildlands fire imagery (see FLAME 1 and FLAME 2). This set contains a single-burn subset of the larger FLAME 3 dataset focusing specifically on Computer Vision tasks such as fire detection and segmentation. Included are 622 image quartets labeled Fire and 116 image quartets labeled No Fire. The No Fire images are of the surrounding forestry of the prescribed burn plot. Each image quartet is composed of four images - a raw RGB image, a raw thermal image, a corrected FOV RGB image, and a thermal TIFF. Each of the four data types are detailed in the below Table 1. More information on data collection methods, data processing procedures, and data labeling can be found in https://arxiv.org/abs/2412.02831. This dataset also contains a NADIR Thermal Fire set, providing georeferenced overhead thermal imagery, captured by UAV every 3-5 seconds, focusing on monitoring fire progression and burn behaviors over time. This data, when processed, enables centimeter-grade measurements of fire spread and energy release over time. Pre, post, and during burn imagery are included, along with ground control point (GCP) data.  This dataset is based on the research conducted in the paper: FLAME 3 Dataset: Unleashing the Power of Radiometric Thermal UAV Imagery for Wildfire Management. It provides detailed insights and analysis related to forest fire monitoring and modeling.If you use this dataset in your research or projects, please cite the original paper as follows: APA: Hopkins, B., ONeill, L., Marinaccio, M., Rowell, E., Parsons, R., Flanary, S., Nazim I, Seielstad C, Afghah, F. (2024). FLAME 3 Dataset: Unleashing the Power of Radiometric Thermal UAV Imagery for Wildfire Management. arXiv preprint arXiv:2412.02831.BibTeX: @misc{hopkins2024flame3datasetunleashing, title={FLAME 3 Dataset: Unleashing the Power of Radiometric Thermal UAV Imagery for Wildfire Management}, author={Bryce Hopkins and Leo ONeill and Michael Marinaccio and Eric Rowell and Russell Parsons and Sarah Flanary and Irtija Nazim and Carl Seielstad and Fatemeh Afghah}, year={2024}, eprint={2412.02831}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2412.02831}, } 

In this study, we focus on the effects of fuel bed representation and fire heat and smoke distribution in a coupled fire-atmosphere simulation platform for two landscape-scale fires: the 2018 Camp Fire and the 2021 Caldor Fire. The fuel bed representation in the coupled fire-atmosphere simulation platform WRF-Fire currently includes only surface fuels. Thus, we enhance the model by adding canopy fuel characteristics and heat release, for which a method to calculate the heat generated from canopy fuel consumption is developed and implemented in WRF-Fire. Furthermore, the current WRF-Fire heat and smoke distribution in the atmosphere is replaced with a heat-conserving Truncated Gaussian (TG) function and its effects are evaluated. The simulated fire perimeters of case studies are validated against semi-continuous, high-resolution fire perimeters derived from NEXRAD radar observations. Furthermore, simulated plumes of the two fire cases are compared to NEXRAD radar reflectivity observations, followed by buoyancy analysis using simulated temperature and vertical velocity fields. The results show that while the improved fuel bed and the TG heat release scheme have small effects on the simulated fire perimeters of the wind-driven Camp Fire, they affect the propagation direction of the plume-driven Caldor Fire, leading to better-matching fire perimeters with the observations. However, the improved fuel bed representation, together with the TG heat smoke release scheme, leads to a more realistic plume structure in comparison to the observations in both fires. The buoyancy analysis also depicts more realistic fire-induced temperature anomalies and atmospheric circulation when the fuel bed is improved.