Abstract
QUIC-Fire is a modern fire simulation tool that can simulate the progression of three-dimensional fuel consumption over a landscape, modeling the interaction of a wildfire with weather such as wind conditions around the wildfire. The resulting simulation gives a detailed progression of the consumed three-dimensional fuel that can be eloquently mapped to an image of a burn area in the landscape as the wildfire progresses over time. Although an image of burned vegetation over a landscape gives detailed information of the activity and coverage area of a wildfire, a numerical characterization of the boundary of the burn area can be used for a variety of computations. The boundary of the burn area, also labeled as the wildfire perimeter, can be parametrized with a closed polygon. The set of ordered vertices of the closed polygon provide a compact numerical representation of the location of the wildfire and can be used for computations related to fire coverage area and modern wildfire assimilation techniques to improve the prediction of wildfire progression. Designing a robust algorithm to create a wildfire perimeter in the form of a set of ordered vertices of a closed polygon around the image of consumed vegetation in a landscape is not a trivial task. This paper discusses the properties of two such algorithms: the iterative minimum distance algorithm (IMDA) and quadriculation algorithm (QA) to obtain a closed polygon for a wildfire perimeter. To illustrate the effectiveness, these two algorithms are applied to multiple image (raster) data of a burn area in the landscape of a wildfire created by QUIC-Fire simulations. It is shown that both algorithms are robust in computing wildfire perimeters, and computational time are less than one second for each image created by QUIC-Fire. As such, this work contributes to the development of computational methods to automate the process of characterizing the closed polygon of a wildfire perimeter based on burn area images.
Work is supported by WIFIRE Commons and funded by NSF 2040676 and NSF 2134904 under the Convergence Accelerator program.
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Tan, L., de Callafon, R.A., Altıntaş, I. (2022). Characterizing Wildfire Perimeter Polygons from QUIC-Fire. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13350. Springer, Cham. https://doi.org/10.1007/978-3-031-08751-6_44
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