Models for predicting the spread of landscape grass-roots fires in steppe zones

The article presents an analytical review of modern mathematical models for predicting the spread of landscape grass fires in steppe zones, where such fires are characterized by high dynamics and destructive force due to the peculiarities of the grassy cover of open landscapes. The main classes of models are considered: semi-empirical, based on a combination of physical principles and empirical data; simulation, implementing a space-time simulation of the fire front.; as well as machine learning-based approaches, including regression algorithms and convolutional neural networks for analyzing large amounts of data (weather parameters, satellite images, historical events). The methodological foundations, advantages, limitations and areas of application of models are analyzed, taking into account the specifics of steppe (grass) fires — a high rate of spread, strong dependence on wind, the degree of drying of grass and the topography of open landscapes (slope, exposure of slopes). Special attention is paid to the integration of global digital SRTM terrain models to accurately account for slope slope and exposure, which corrects the fire propagation rate in the models, as well as data from unmanned aerial systems for rapid assessment of vegetation indices (NDVI, biomass, fuel moisture) with high resolution, which improves the accuracy of fuel maps and forecasting. The trends of pyrological activity growth in the steppe regions of Russia under the influence of climatic changes have been revealed. The prospects of combined use of models for operational forecasting and extinguishing management in the conditions of the Russian Ministry of Emergency Situations, including hybrid approaches with integration of SRTM and UAS to minimize risks, are described.