Publication Date
10-24-2025
Document Type
Article
Publication Title
Remote Sensing
Volume
17
Issue
21
DOI
10.3390/rs17213525
Abstract
Highlights: What are the main findings? This article presents an image processing method to semi-automatically track wildfire progression. The algorithm was successfully applied to aerial infrared imagery acquired during tactical fire management operations. What are the implications of the main finding? These results illustrate how tactical data can be used in fire behavior studies. The proposed method may facilitate real-time analysis of tactical information during wildfire emergencies. Remote sensing of wildland fires has become an integral part of fire science. Airborne sensors provide high spatial resolution and can provide high temporal resolution, enabling fire behavior monitoring at fine scales. Fire agencies frequently use airborne long-wave infrared (LWIR) imagery for fire monitoring and to aid in operational decision-making. While tactical remote sensing systems may differ from scientific instruments, our objective is to illustrate that operational support data has the capacity to aid scientific fire behavior studies and to facilitate the data analysis. We present an image processing algorithm that automatically delineates active fire edges in tactical LWIR orthomosaics. Several thresholding and edge detection methodologies were investigated and combined into a new algorithm. Our proposed method was tested on tactical LWIR imagery acquired during several fires in California in 2020 and compared to manually annotated mosaics. Jaccard index values ranged from 0.725 to 0.928. The semi-automated algorithm successfully extracted active fire edges over a wide range of image complexity. These results contribute to the integration of infrared fire observations captured during firefighting operations into scientific studies of fire spread and support landscape-scale fire behavior modeling efforts.
Funding Number
2053619
Funding Sponsor
National Science Foundation
Keywords
fire behavior, fire monitoring, image processing, infrared imagery, remote sensing, wildland fire
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Meteorology and Climate Science
Recommended Citation
Christopher C. Giesige, Eric Goldbeck-Dimon, Andrew Klofas, and Mario Miguel Valero. "Semi-Automated Extraction of Active Fire Edges from Tactical Infrared Observations of Wildfires" Remote Sensing (2025). https://doi.org/10.3390/rs17213525
