Conference Agenda

The intensity, frequency, and duration of wildland fires are growing in Canada and around the world. Timely fire intelligence products from remote sensing platforms can assist fire managers and lead to fewer impacts. New onboard processing techniques using machine learning allow greater levels of analysis and refinement on edge devices like aircraft and satellites, reducing bandwidth and latencies. Our Fire Band Analysis Network approach brings together wildfire science and management experts and academics, an Indigenous owned business that specializes in satellite communication and community outreach, and a Canadian space company with expertise in deploying machine learning models to spacecraft. We show initial results with onboard segmentation models and present a path to prototype this onboard processing model on a cubesat currently in orbit and on drones equipped with infrared sensors, ultimately bringing the derived data products to user communities on the ground.

This presentation will provide an overview and update on the WildFireSat mission and its data product algorithm development. Specifically, we will summarize the 2025 Science and Applications Plan and share updates from the Tier 2 stage of products and algorithms. The Tier 2 products that will be shown include the multi-source fire events, time of arrival outputs, and satellite-derived fire behaviour products (e.g., satellite-observed rate and direction of spread, fireline intensity). Ongoing science-development activities include algorithmic validation, uncertainty characterization, and completion of algorithmic theoretical basis documents.

Built through Canadian and international partnerships, WildFireSat will support fire monitoring and management while enabling major scientific advancements for the global fire monitoring community. The scientific applications of WildFireSat are broad, covering all stages of a fire event’s life cycle. By prioritizing the needs of wildfire managers and a broad range of end-users, the WildFireSat mission is a strong model for future satellite missions to integrate user engagement throughout all phases of the mission timeline.

Normalized Hotspot Indices (NHI)—originally developed for volcanic hotspot detection—has emerged as a powerful, flexible tool for the identification and characterization of high-temperature sources using Sentinel-2 MSI and Landsat-8/9 OLI/OLI-2 observations. By exploiting the combined radiance information from the Near Infrared (NIR) and Short-Wave Infrared (SWIR) spectral bands, the NHI algorithm leverages the multispectral capabilities to identify and characterize hotspots of various origins.

A specific configuration of the NHI algorithm has recently been developed for wildfire mapping. This improved version demonstrated strong performance in complex environments such as California, Hawaii, Canada, Greece, Spain, and Australia, significantly improving the delineation of flame fronts and substantially reducing omission and commission errors.

In this work, we present the results of applying NHI-F to various wildfire events, including the wildfires in Canada in May 2025. Our analysis focuses on two main dimensions essential for modern fire science: (i) the spatial characterization of active flaming fronts and burned-area dynamics at 20–30 m scale and (ii) the quantification of fire intensity through Fire Radiative Power (FRP) and SWIR-based radiance metrics.

In the initial response to wildfires, securing rapid and accurate geographic information is essential. However, helicopter imagery acquired on-site often lacks precise sensor metadata, such as camera pose and internal parameters, making the application of georeferencing difficult. In particular, obliquely captured wildfire imagery presents additional registration challenges due to severe viewpoint changes, scale variations, and low-texture environments. This study proposes an automated georeferencing pipeline capable of operating under these constraints. The proposed method consists of five stages: preprocessing, image retrieval, feature extraction and matching, Exterior Orientation Parameters (EOP) estimation, and orthomosaic generation. An initial Area of Interest (AOI) is defined using inaccurate initial position data, and the Region of Interest (ROI) within the reference map is obtained through a ResNet50-based image retrieval approach. Subsequently, virtual Ground Control Points (GCPs) are generated through deep learning-based feature matching. Elevation data is then assigned using a Digital Elevation Model (DEM), and EOP are estimated via Perspective-n-Point (PnP) and RANSAC algorithms. Intermediate frames are initialized via interpolation and refined through bundle adjustment to produce the final orthomosaic. Experimental results demonstrated that utilizing SuperGlue and LightGlue complementarily increased the number of successfully georeferenced intervals from 5 to 9. Furthermore, a minimum RMSE of 28.30 m was achieved in the most accurate interval. This method proves that by automating the feature-based georeferencing process, practical geographic information can be rapidly provided for initial disaster response, even in sensor-limited environments.

California experiences globally intense wildfire activity with accelerating human casualties and economic losses. Existing research quantifies anthropogenic and climatic contributions to wildland-urban interface (WUI) fires at aggregate levels, yet overlooks heterogeneity arising from differences in ignition locations and dominant spread areas. Using multi-source data from California (2002–2023), we classified WUI fires into four behavioral modes based on ignition site and primary spread zone: I-I (WUI ignition, WUI spread), I-W (WUI ignition, wildland spread), W-I (wildland ignition, WUI spread), and W-W (wildland ignition, wildland spread). We systematically analyzed size characteristics, inter-annual trends, fuel composition, and climate sensitivity across modes. Key findings include: (1) WUI fires accounted for 95.6% of total burned area from large fires, with only 12.2% of burned area within the WUI; both total and mean burned area increased significantly over two decades. (2) Lightning-caused WUI fires showed significantly delayed ignition dates, whereas human-caused fires occurred significantly earlier, with elevated fire frequency observed during Independence Day, Labor Day, and Thanksgiving. (3) I-I fires were predominantly driven by anthropogenic factors with the highest proportion of shrub/grass fuel and the smallest mean size; W-W and I-W fires exhibited significant climate sensitivity, with I-W showing a higher rate of increase than W-W over the study period. These findings reveal differentiated driving mechanisms across WUI fire behavioral types, providing scientific evidence for targeted fire management strategies.