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

Abstract

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.

Spatial Modeling of Forest and Land Fire Susceptibility Using the Information Value Method in Kotawaringin Barat Regency, Indonesia

“…Extra care must be taken when allocating weights because expert subjectivity and consistency could skew the results [18]. This adds to the qualitative data based on expert opinion and reduces the subjectivity of the researcher, since the same value is typically used for all parameters, applications, and areas [19].…”

Unveiling Urban Complexity: Research note on integrating OpenStreetMap to enhance representation of fine-scale landscape heterogeneity

“…These data are generated by community members and supplemented with data from governmental and non-governmental agencies around the globe. OSM has been previously used in ecological research for extracting settlements and roads (Bide et al, 2023; Gizatullin & Alekseenko, 2022), extracting elevation (Shaykevich et al, 2022), distance to water sources (de la Torre et al, 2021), and to study the fractal dimension of cities (Malishevsky, 2022). However, a standardized framework to extract all attributes characterizing urban features and develop accurate landcover data layers for ecological analysis and urban planning, has yet to be proposed.…”

Применение Методов Машинного Обучения Для Классификации Склонов По Космическим Снимкам На Примере Некоторых Районов Северного Приохотья

“…Кроме того, использование ML-методов позволяет улучшить прогнозирование и мониторинг различных процессов, как погодных (Voosen, 2020), так и геоморфологически опасных, например обвалов или по-жаров (Gizatullin, Alekseenko, 2022). Модели алгоритмов могут анализировать временны ́е данные и выявлять различия , что может быть полезным для управления экологическими ресурсами и оценки рисков их использования.…”