Classification of Vegetation to Estimate Forest Fire Danger Using Landsat 8 Images: Case Study

Abstract

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.

Mathematical Simulation of Forest Fuel Pyrolysis in One-Dimensional Statement Taking into Account Soot Formation

“…The mathematical model implementation was performed in the high-level programming language Delphi in the RAD Studio software [74]. In the future, it will be possible to integrate the developed model with geo-information systems [75][76][77] to visualize the predicted values of the volume fraction of soot particles formed during active forest fires. ArcGIS [78] and QGIS [79] can be used as host systems.…”

Rating Wildfire Danger by Big Weather Data and Machine Learning: A Case Study in Eastern China

“…Multispectral UAV images had been used to classify burn severity after a wildland fire, which is then processed into a mosaic reflectance image [22]. Ksenia et al [23] presented an approach for estimation of wildland fire danger depending on vegetation type and radiant heat flux influence using geographic information systems (GIS) and remote sensing data.…”

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“…References Land Surface Temperature (LST) [17], [18] Fuel Moisture Content (FMC) [19], [20] Normalized Differential Vegetation Index (NDVI) [19], [21], [22], [23] Enhanced Vegetation Index (EVI)…”