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Using remote sensing data assimilation into wildfire model to evaluate fire front position

S.A. Khvostikov^{1, 2} , S.А. Bartalev^{1, 2}

¹ Space Research Institute RAS, Moscow, Russia
² Center for Forest Ecology and Productivity RAS, Moscow, Russia

Accepted: 30.08.2022
DOI: 10.21046/2070-7401-2022-19-5-9-18

Fire front position and dynamics is of critical importance for evaluation of potential fire response measures. Low resolution remote sensing data used to detect burning fires cannot enable determining fire front position with sufficient accuracy. This article presents a method to evaluate fire front position using remote sensing data assimilation into a wildfire model. Unlike previous works on data assimilation, the proposed method was tested on multiple big multi-day wildfires. The proposed method uses probabilistic wildfire spread model, MODIS hotspots and data assimilation approach based on optimization of fire front position that accounts for uncertainties of both model and remote sensing data. The method uses a set of normals to fire front and estimates predicted and remote-sensing-derived fire spread along them, evaluating the real fire front position by minimizing its deviation from the two estimates. This method was applied to 230 fires on the territory of Russia. The evaluated fire front position showed considerable improvement over the original MODIS data.

Keywords: wildfires, modelling, MODIS, Landsat, data assimilation

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