Most methods in the field of wildfire prevention are based on expert assessment of fire danger factors. However, their weights are usually assumed constant for the entire application area despite the geographical and seasonal changes of factors. This study aimed to develop a wildfire prevention method based on partial and general fire danger ratings taking into account their spatio-temporal variability. The study was conducted for Krasnoyarsk territory, Orenburg region and the Meschera lowland as the most forest, steppe and peat fire dangerous regions of Russia respectively. Surface temperature, moisture, vegetation structure, anthropogenic load, topography and their variation over subzones and in time were used as fire danger factors. They were evaluated by measuring parameters such as radiobrightness temperature, Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), distance to settlements and roads, elevation, slope and aspect. Materials from the Terra/Aqua, Sentinel-3, Landsat-8, Sentinel-2 satellites, ASTER Global Digital Elevation Model and Open Street Maps vector layers were used in the study. Correlation between these parameters and the actual fires in 2016-2018 was analyzed. Linear relationships were established, and correlation coefficients, equations of partial ratings and prevention 90%-threshold values were identified. On their basis, the parameter weights were computed to integrate them into the general fire danger rating. The developed method was validated using data over 2019. The results showed 67% confidence and 61% reliability of fire prevention along with the spatio-temporal patterns of fire danger factors. The method is recommended for preventing wildfires within the study areas and can be extend to similar regions.

This article is devoted to the development of an algorithm for the preventive assessment of the fire danger of natural areas using remote sensing data (the preventive natural fire danger assessment algorithm). The problems of the current state of the remote sensing materials use for fires researches as a justification for the need of the algorithm are considered. A review of existing methods and algorithms of natural fire danger assessment is done. The algorithm development includes description of the general structure and the content filling process of different algorithm components. The algorithm is a stages sequence of remote sensing data processing and analysis in terms of fire danger. As a result of algorithm, the fire danger assessment of the observed territory is formed. A special feature of the algorithm is its preventiveness, universality (applicability for any territory), practical automatability (the ability to represent in the form of a program code for the processing of RSD) and flexibility (the ability to add and branch the sequence). In the end, general conclusions and recommendations on the use of the algorithm are given.

A methodology for surface water flood modelling is proposed, based on the use of small-scale mapping tools together with hydrological observation data. To reproduce the flooding surface during extreme floods, information on historical maximum water levels was selected, and a digital elevation model (DEM) was used as the cartographic basis. The proposed methodology is universal and provides possibility to determine the boundaries of the potential flooding area in river sections during extreme water levels, to identify objects in the potential flooding area, as well as to make operational decisions to prevent disastrous situations arising from floods and minimize adverse consequences. The results of application of the proposed method for certain river sections of the North Caucasus, a region characterized by a high degree of flood hazard, are presented.