Search for: All records

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}, } 

Wildfires propagate through interactions between wind, fuel, and terrain, resulting in complex behaviors that challenge accurate predictions. This study investigates the interaction between wind velocity topology and wildfire dynamics, aiming to enhance our understanding of wildfire spread patterns through a simplified nonlinear convection–diffusion–reaction wildfire model, adopting a fundamental reactive flow dynamics perspective. We revisited the non-dimensionalizion of the governing combustion model by incorporating three distinct time scales. This approach revealed two new non-dimensional numbers, contrasting with the conventional non-dimensionalization that considers only a single time scale. Through scaling analysis, we analytically identified the critical determinants of transient wildfire behavior and established a state-neutral curve, indicating where initial wildfires extinguish for specific combinations of the identified non-dimensional numbers. Subsequently, a wildfire transport solver was developed, integrating upwind compact schemes and implicit–explicit Runge–Kutta methods. We explored the influence of stable and unstable manifolds in wind topology on the transport of wildfire under steady wind conditions defined using a saddle-type fixed point flow, emphasizing the role of the non-dimensional numbers. Additionally, we considered the benchmark unsteady double-gyre flow, examined the effect of unsteady wind topology on wildfire propagation, and quantified the wildfire response to varying wind oscillation frequencies and amplitudes using a transfer function approach. The results were compared to Lagrangian coherent structures (LCS) used to characterize the correspondence of manifolds with wildfire propagation. The approach of utilizing the wind flow manifolds provides valuable insight into wildfire dynamics across diverse wind scenarios, offering a potential tool for improved predictive modeling and management strategies.