Influence Of Aerial Wildfire Long-Term Fire-Retardant Drops on Environmental Transfer

Aerial application of fire retardant is an essential tool for controlling wildland fires. Retardant drops are carefully planned to optimize fireline effectiveness, enhance firefighter safety, protect valuable resources and assets, and reduce environmental impact. However, factors such as topography, wind, vegetation structure, and aircraft orientation can create differences between the planned drop points and the actual area covered by the retardant. Accurate information on the exact placement and extent of deposited retardant can assist wildland fire managers in (1) evaluating how well the retardant slows or stops fire spread, (2) adaptively managing resources during the event, and (3) documenting placement in relation to ecologically sensitive areas. Specifically, precise footprint mapping improves drop placement assessment and supports more effective wildfire suppression and asset protection. This study employs UAV multispectral imagery and UAV LiDAR to test an automated method for detecting and mapping retardant footprints at very high spatial resolution. Drone data are processed using Agisoft Metashape software to generate georeferenced orthomosaic models, which are then used to develop predictors for classification. We apply supervised machine learning trained on labeled reference polygons to distinguish retardant deposits from surrounding land cover conditions (e.g., vegetation, bare soil, and burned surfaces) in Single-class and Multi-class machine learning tests. The resulting maps outline the full extent of retardant coverage and provide a detailed footprint rather than simplified linear drop traces. This approach enables a standardized, reproducible workflow to evaluate retardant placement and enhances documentation of drop locations relative to sensitive environments, while allowing for a more objective assessment of whether the drop contributed to slowing fire spread and protecting valued resources.