Vegetation maps play a key role in an estimation of forest fire danger. To estimate forest fire danger, a vegetation type map of Gilbirinsky forestry situated in the Lake Baikal basin was created on basis of both the remote sensing data and field study. A Sentinel-2A satellite image was classified by the maximum likelihood method. Zones with different levels of forest fire danger have been identified: coniferous forests–extremely dangerous level, mixed forests – high level, and deciduous forests–moderate level of forest fire danger. Normalized Difference Water Index has been calculated and moisture content in vegetation has been evaluated.
Forest fires are a significant factor that affects the natural heritage of our planet – the lake Baikal basin. Moreover, often this impact is negative and causes damage to the forest areas of the region. The main condition for effective forest management and fire protection is the availability of reliable and comprehensive static and dynamic information on the state of the forest fund. The report provides a description of the methodology for preliminary analysis of the structure of the forest and assessing forest fire danger. LANDSAT images are used as primary information. One of the territories of the Baikal region is considered. LANDSAT images were used for 2015.
The northern part of the Tomsk region is a typical area of the boreal forest zone. Large forest resources occupy this territory. The forest fund is situated on the 90.5% of its entire area. There are 17 million hectares of forested area, including 9.9 million hectares covered by coniferous. 20-30% of forest fires in the Tomsk region caused by the lightning activity. Analysis of the thunderstorm activity in the context of fires is possible using Tomsk region as sample region. World Wide Lightning Location Network (WWLLN) data for 2010-2015 years are used in paper. This network registers lightning discharges throughout the year. The majority of thunderstorms occur over Tomsk region during the summer time. Data on lightning discharges during the period from May to September was selected for investigation. Data on spherics recorded by WWLLN contain the points: date, time, latitude, longitude, error and number of stations in which the electromagnetic pulse was recorded. Lightning discharge density maps were performed for cells 10×10 km.
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.
The aim of the work is to develop a comprehensive method for assessing thunderstorm activity using WWLLN and RS data. It is necessary to group lightning discharges to solve practical problems of lightning protection and lightningcaused forest fire danger, as well as climatology problems using information on the spatial and temporal characteristics of thunderstorms. For grouping lightning discharges, it is proposed to use clustering algorithms. The region covering Timiryazevskiy forestry (Tomsk region, borders (55.93 - 56.86)x(83.94 - 85.07)) was selected for the computational experiment. We used the data on lightning discharges registered by the WWLLN network in this region on July 23, 2014. 273 lightning discharges were sampling. A relatively small number of discharges allowed us a visual analysis of solutions obtained during clustering.
Present work is devoted to the description of information and algorithmic support for creation of a program complex for an assessment of forest fire danger. The assessment of forest fire danger is made on the basis of algorithm for classification of the forest territory by vegetation conditions and the modified Nesterov's index. Meteorological data (air temperature and cloudiness) and also the data on thermal anomalies received from satellite measurements by MODIS spectroradiometer for the territory of the Timiryazevskiy forestry of the Tomsk region are used as information on the Environment state.
KEYWORDS: Vegetation, Remote sensing, MODIS, Meteorology, Geographic information systems, Data modeling, Decision support systems, Combustion, Satellites, Internet
There are many different statistical and empirical methods of forest fire danger use at present time. All systems have not physical basis. Last decade deterministic-probabilistic method is rapidly developed in Tomsk Polytechnic University. Forest sites classification is one way to estimate forest fire danger. We used this method in present work. Forest fire danger estimation depends on forest vegetation condition, forest fire retrospective, precipitation and air temperature. In fact, we use modified Nesterov Criterion. Lightning activity is under consideration as a high temperature source in present work. We use Web-GIS platform for program realization of this method. The program realization of the fire danger assessment system is the Web-oriented geoinformation system developed by the Django platform in the programming language Python. The GeoDjango framework was used for realization of cartographic functions. We suggest using of Terra/Aqua MODIS products for hot spot monitoring. Typical territory for forest fire danger estimation is Proletarskoe forestry of Kherson region (Ukraine).
The present article describes a new concept of lightning-caused forest fire danger using a probabilistic criterion. The assessment of forest fire danger is made on the basis of the algorithm that classifies the forest territory by vegetation conditions. Lightning activity is processed by the MODIS spectroradiometer according to the World Wide Lightning Location Network data and remote sensing data for the Timiryazevskiy forestry in the Tomsk Region. The cluster analysis of the WWLLN and MOD06_L2 product data are used in the present paper.
Timiryazevskiy forestry of Tomsk region (Siberia, Russia) is a study area elaborated in current research. Forest fire danger assessment is based on unique technology using probabilistic criterion, statistical data on forest fires, meteorological conditions, forest sites classification and remote sensing data. MODIS products are used for estimating some meteorological conditions and current forest fire situation. Geonformation technologies are used for geospatial analysis of forest fire danger situation on controlled forested territories. GIS-engine provides opportunities to construct electronic maps with different levels of forest fire probability and support raster layer for satellite remote sensing data on current forest fires. Web-interface is used for data loading on specific web-site and for forest fire danger data representation via World Wide Web. Special web-forms provide interface for choosing of relevant input data in order to process the forest fire danger data and assess the forest fire probability.
KEYWORDS: Oxidation, Solar radiation, Mathematical modeling, Solar radiation models, Lead, Diffusion, Glasses, Data modeling, Process modeling, Carbon monoxide
Forest fuel layer ignition conditions analysis by focused flow of sunlight is lead. Scenarios of simulation corresponds to occurrence of forest fire as result of focused flux of sunlight influence on forest fuel layer. Scenarios calculations taking into account various intensity of radiation are lead. Recommendations on the further development of this component of determined model are submitted.
This article reviews the project of subsystem that reflects the Earth remote sensing data from the space in order to monitor the forest fire danger, caused by the focused solar radiation effect. This subsystem is based on the use of sensing data from the MODIS instrument aboard the Terra satellite. We consider the Timiryazevsky Forestry of Tomsk region to be a typical territory of the boreal forest zone. To estimate the forest fire danger level, we use an original method to classify the forest areas according to their characteristics (the ground mensuration data) and the main meteorological parameters, namely, the cloud cover on this territory, obtained from the MODIS satellite data.
Estimation of forest fire danger has traditionally been based on historical fire weather climatology. This
presentation describes a new concept for an improved estimation of forest fire danger, which takes into account the
possibility of forest fuel ignition as a result of focused sun’s light. For example, glass containers, their splinters and large
drops of coniferous trees pitch can be a fire hazard due to their potential for focusing the sun’s rays (under favorable
conditions) and, consequently for setting forest fuel ablaze. Our analysis of numerous observational reports suggests that
the forest fuel ignition process can be described by system of the non-stationary nonlinear equations of heat conductivity
and diffusion with corresponding initial and boundary conditions. To solve these equations, we apply well-established
numerical methods. This presentation includes model results and their comparison with available observational
constrains together with suggestions for using remote sensing data.
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