Examining Landscape-Scale Fuel and Terrain Controls of Wildfire Spread Rates Using Repetitive Airborne Thermal Infrared (ATIR) Imagery

Current forest monitoring technologies including satellite remote sensing, manned/piloted aircraft, and observation towers leave uncertainties about a wildfire's extent, behavior, and conditions in the fire's near environment, particularly during its early growth. Rapid mapping and real-time fire monitoring can inform in-time intervention or management solutions to maximize beneficial fire outcomes. Drone systems' unique features of 3D mobility, low flight altitude, and fast and easy deployment make them a valuable tool for early detection and assessment of wildland fires, especially in remote forests that are not easily accessible by ground vehicles. In addition, the lack of abundant, well-annotated aerial datasets -in part due to unmanned aerial vehicles' (UAVs') flight restrictions during prescribed burns and wildfires -has limited research advances in reliable data-driven fire detection and modeling techniques. While existing wildland fire datasets often include either color or thermal fire images, here we present (1) a multimodal UAV-collected dataset of dual-feed side-by-side videos including both RGB and thermal images of a prescribed fire in an open canopy pine forest in Northern Arizona and (2) a deep learning-based methodology for detecting fire and smoke pixels at accuracy much higher than the usual single-channel video feeds. The collected images are labeled to "fire" or "no-fire" frames by two human experts using side-by-side RGB and thermal images to determine the label. To provide context to the main dataset's aerial imagery, the included supplementary dataset provides a georeferenced pre-burn point cloud, an RGB orthomosaic, weather information, a burn plan, and other burn information. By using and expanding on this guide dataset, research can develop new data-driven fire detection, fire segmentation, and fire modeling techniques.INDEX TERMS Data-driven fire detection, prescribed fire, fire modeling, fire data, unmanned aerial vehicle (UAV), deep learning.

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“…Thus, such datasets (e.g. [64], [65], [66]) allow better models to test better representation of transient aspects of fire growth (e.g. [67]).…”

Section: Data-driven Fire Spread Modelingmentioning

confidence: 99%

Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset

et al. 2022

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“…The ROS is calculated by the distance and time between the fire points on the two adjacent infrared images [52]. The angle between the camera and the combustion bed causes rectangles in space to become irregular shapes in the image.…”

Section: Calculating the Rosmentioning

confidence: 99%

Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

Zhang

et al. 2022

Fire

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The simulation of forest fire spread is a key problem for the management of fire, and Cellular Automata (CA) has been used to simulate the complex mechanism of the fire spread for a long time. The simulation of CA is driven by the rate of fire spread (ROS), which is hard to estimate, because some input parameters of the current ROS model cannot be provided with a high precision, so the CA approach has not been well applied yet in the forest fire management system to date. The forest fire spread simulation model LSTM-CA using CA with LSTM is proposed in this paper. Based on the interaction between wind and fire, S-LSTM is proposed, which takes full advantage of the time dependency of the ROS. The ROS estimated by the S-LSTM is satisfactory, even though the input parameters are not perfect. Fifteen kinds of ROS models with the same structure are trained for different cases of slope direction and wind direction, and the model with the closest case is selected to drive the transmission between the adjacent cells. In order to simulate the actual spread of forest fire, the LSTM-based models are trained based on the data captured, and three correction rules are added to the CA model. Finally, the prediction accuracy of forest fire spread is verified though the KAPPA coefficient, Hausdorff distance, and horizontal comparison experiments based on remote sensing images of wildfires. The LSTM-CA model has good practicality in simulating the spread of forest fires.

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“…Fire detection ('hotspots') and progression metrics (i.e. ROS) are predominately acquired from satellite-based thermal-infrared sensors [35]. The most popular being the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) -Active Fire and Burned Area products [36,37].…”

Section: Fire Simulation Frameworkmentioning

confidence: 99%

Fire behavior modeling for operational decision-making

Cardíl

Monedero²,

Schag³

et al. 2021

Current Opinion in Environmental Science & Health

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Examining Landscape-Scale Fuel and Terrain Controls of Wildfire Spread Rates Using Repetitive Airborne Thermal Infrared (ATIR) Imagery

Cited by 9 publications

References 79 publications

Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset

Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset

Simulating Forest Fire Spread with Cellular Automation Driven by a LSTM Based Speed Model

Fire behavior modeling for operational decision-making

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