The vegetation cover of the Earth plays an important role in the life of mankind, whether it is natural forest or agricultural crop. The study of the variability of the vegetation cover, as well as observation of its condition, allows timely actions to make a forecast and monitor and estimate the forest fire condition. The objectives of the research were (i) to process the satellite image of the Gilbirinskiy forestry located in the basin of Lake Baikal; (ii) to select homogeneous areas of forest vegetation on the basis of their spectral characteristics; (iii) to estimate the level of forest fire danger of the area by vegetation types. The paper presents an approach for estimation of forest fire danger depending on vegetation type and radiant heat flux influence using geographic information systems (GIS) and remote sensing data. The Environment for Visualizing Images (ENVI) and the Geographic Resources Analysis Support System (GRASS) software were used to process satellite images. The area’s forest fire danger estimation and visual presentation of the results were carried out in ArcGIS Desktop software. Information on the vegetation was obtained using the analysis of the Landsat 8 Operational Land Imager (OLI) images for a typical forestry of the Lake Baikal natural area. The maps (schemes) of the Gilbirinskiy forestry were also used in the present article. The unsupervised k‐means classification was used. Principal component analysis (PCA) was applied to increase the accuracy of decoding. The classification of forest areas according to the level of fire danger caused by the typical ignition source was carried out using the developed method. The final information product was the map displaying vector polygonal feature class, containing the type of vegetation and the level of fire danger for each forest quarter in the attribute table. The fire danger estimation method developed by the authors was applied to each separate quarter and showed realistic results. The method used may be applicable for other areas with prevailing forest vegetation.
The secondary geochemical field structure was modelled on the basis of the lithogeochemical dispersion trains of the Providenskaya Area of the Chukotka Peninsula. The factor and cluster analysis were applied to interpret the nature of geochemical anomalies. It was proved that a range of anomalies were prospective for gold-silver, polymetallic, tin, and tungsten deposit allocation.
One of the most important criterion of forest fire occurrence probability is the ratio of coniferous vegetation area to total area of the forestry. The work develops method of automated analysis of dynamic of coniferous forest area on the basis of NDVI index received from the temporal ranges of Landsat images, as well as tests it on the example of a forestry in Baikal Region. Maps of summary results of carried out spatio-temporal analysis of the forestry area are shown. Developed method and algorithm of processing Landsat images allow assessing condition and dynamics of spatiotemporal changes in vegetation (forest) cover in the area of study.
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.
In recent years, the vegetation cover in urban agglomerations has been changing very rapidly due to technogenic influence. Satellite images play a huge role in studying the dynamics of forest vegetation. Special programs are used to process satellite images. The purpose of the study is to analyze forest vegetation within the territory of the Tomsk agglomeration based on Landsat remote sensing data for the period from 1990 to 2022. The novelty of the study is explained by the development of a unique program code for the analysis of Landsat satellite data on the previously unexplored territory of the Tomsk agglomeration with the prospect of moving to the scale of the entire state in the future. In this study, the authors present an algorithm implemented in Python to quantify the change in the area of vegetation in an urban agglomeration using Landsat multispectral data. The tool allows you to read space images, calculate spectral indices (NDVI, UI, NDWI), and perform statistical processing of interpretation results. The created tool was applied to study the dynamics of vegetation within the Tomsk urban agglomeration during the period 1990–2022. Key findings and conclusions: (1) The non-forest areas increased from 1990 to 1999 and from 2013 to 2022. It is very likely that this is due to the deterioration of the standard of living in the country during these periods. The first time interval corresponds to the post-Soviet period and the devastation in the economy in the 1990s. The second period corresponds to the implementation and strengthening of sanctions pressure on the Russian Federation. (2) The area of territories inhabited by people has been steadily falling since 1990. This is due to the destruction of collective agriculture in the Russian Federation and the outflow of the population from the surrounding rural settlements to Tomsk and Seversk.
This paper describes geoinformation system that includes toolset for analysis of forest areas taxation aimed at quantitative evaluation of forest fire danger. The system takes into consideration such factors as anthropogenic load, storm activity and influence focused sunlight. Conceptual basis of GIS system is physically and mathematically proved methodic of forest fire danger assessment. Computational formulae of probability of forest fire initiation are derived from basic statement of probability theory. The system is implemented in specialized software ArcGIS. The system uses standard user interface with additional functionality for assessment of probability of fire incidents in the forest quarters area due to action of caused sunlight. Received information is displayed on the map. For extra capabilities in evaluating forest fire danger unique instruments were developed in built-in Python programming language. The system is capable of evaluating probability and classification of fire danger and can be used for early detection and prediction of disasters of natural and technogenic origin.
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.
The vegetation cover is the most important factor in forest fires, because it reflects the presence of forest fuels. The study of the variability of the vegetation cover, as well as observation of its condition, allows estimating the level of fire danger of the forest quarter. The work presents a geo-information system containing a set of tools to determine the level of fire danger of the forest quarter. The system is able to predict (determine the probability) and classify forest quarters according to the level of fire danger. The assessment of forest fire danger of Tomsk forestry of Tomsk region has been carried out. Fire probability maps of forest quarters were created based on remote sensing data and ArcGIS software.
Regional security aspects of economic activities are of great importance for legal regulation in environmental management. This has become a critical issue due to climate change, especially in regions where severe climate conditions have a great impact on almost all types of natural resource uses. A detailed analysis of climate and hydrological situation in Tomsk Oblast considering water use risks was carried out. Based on developed author's techniques an informational and analytical database was created using ArcGIS software platform, which combines statistical (quantitative) and spatial characteristics of natural hazards and socio-economic factors. This system was employed to perform areal zoning according to the degree of water use risks involved.
