Prediction of Wildfires Based on the Spatio-Temporal Variability of Fire Danger Factors

Geography, Environment, Sustainability. 2022. Vol. 15. No. 2. P. 31–37.

Gizatullin A., Alekseenko N.

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

Keywords: прогнозирование, дистанционное зондирование, prediction, Remote Sensing, danger, Wildfire, fire, природных, пожаров, пожароопасность