This study contains a spatiotemporal analysis of remote monitoring data MODIS on individual vegetation fires from 2013 to 2022, and open OSM data in the Russian Far East. An algorithm for determining the anthropogenic vegetation fire hazard during spring and fall periods in the forest district blocks located near roads and settlements was proposed. The study identifies settlements and road section for arranging forest protection measures in the Jewish Autonomous Region.
KEYWORDS: Vegetation, Satellites, Satellite imaging, Earth observing sensors, Data modeling, Near infrared, Meteorological satellites, MODIS, Temperature metrology, Lead
In this study, the processing of MODIS Terra and Aqua satellite imagery data was carried out for the assessment and forecast of fire danger according to weather conditions. The time periods and threshold values of the NDVI vegetation index were determined, at which the greatest number of fires occur during the fire hazard period in the territory of the Russian Far East from 2003 to 2016. A significant correlation of the NDVI vegetation index with the distribution of the number and rate of individual vegetation fires was revealed using the fire hazard season of 2016 as an example in the southern part of the Russian Far East. Statistical analysis of the time series of the vegetation index made it possible to obtain a regression equation for the change in daily air temperature and NDVI vegetation index values for predicting the spread of grass fires based on the MacArthur method in the Jewish Autonomous Region.
In this study, data processing of MODIS satellite images was carried out to identify areas with a high fire hazard for 2016. The characteristics of vegetation in 2016–2017 were considered. An algorithm was proposed for the spatiotemporal analysis of the distribution of vegetation fires to assess the biodiversity of the territory and damage from vegetation fires. This algorithm considers the influence of vegetation fires on the transformation of the vegetation territory of the Russian Far East.
The purpose of this study is to develop air patrol schemes depending on the location of fire-prone vegetation areas based on the author's deterministic-probabilistic model for predicting the occurrence of vegetation fires based on remote Earth monitoring data. Verification of air patrol routes is performed on the example of the Jewish Autonomous Region territory.
This study describes the development of the system of short-term fire weather hazard forecast, which takes into account pyrologic data of the quarter network of fire hazardous locations, weather stations and vegetation fires over multi-year period. A deterministic and probabilistic vegetation fire occurrence model was proposed to carry operational territorial units forecasting and verified with example of 2016 fire hazardous season in the federal constituent entities of the Russian Far East.
The purpose of this study is to develop an algorithm for a 10-day fire weather forecast on the basis of the Global Forecast System, a global weather forecast model, and implement it in the proprietary-design information system for forecasting vegetation fire hazard by natural and anthropogenic conditions through the example of the Russian Far East. A relational weather database was designed to store and access the data of the National Centers for Environmental Prediction and the system metadata were integrated to the database.
The purpose of this study is the use of remote sensing data on vegetative index NDVI (Normalized Difference Vegetation Index) for predicting the spread of grassfires in the example of the Jewish Autonomous Region (JAR). Calculation of specific daily indicators of climatic-caused fire hazard is carried out according to the method of V.G. Nesterov. To calculate the spread speed of the grassfire, the MacArthur method for meadow areas was used. Pictures of the MOD09GQK product in the red and near infrared channels were used to calculate the vegetative index NDVI, which is a quantitative index of photosynthetically active biomass.
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