Classification of Vegetation to Estimate Forest Fire Danger Using Landsat 8 Images: Case Study / K. S. Yankovich, E. P. Yankovich, N. V. Baranovskiy

Уровень набора: Mathematical Problems in Engineering, open access journal = 1995-Основной Автор-лицо: Yankovich, K. S., Kseniya StanislavovnaАльтернативный автор-лицо: Yankovich, E. P., Geologist, Senior Lecturer of Tomsk Polytechnic University, 1967-, Elena Petrovna;Baranovskiy, N. V., specialist in electrical engineering, Associate Professor of Tomsk Polytechnic University, Candidate of physical and mathematical sciences, 1978-, Nikolay ViktorovichКоллективный автор (вторичный): Национальный исследовательский Томский политехнический университет, Инженерная школа природных ресурсов, Отделение геологии;Национальный исследовательский Томский политехнический университет, Инженерная школа энергетики, Научно-образовательный центр И. Н. Бутакова (НОЦ И. Н. Бутакова)Язык: английский.Страна: .Резюме или реферат: 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..Примечания о наличии в документе библиографии/указателя: [References: 62 tit.].Тематика: электронный ресурс | труды учёных ТПУ | растительность | лесные пожары | опасность Ресурсы он-лайн:Щелкните здесь для доступа в онлайн | Щелкните здесь для доступа в онлайн
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[References: 62 tit.]

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.

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