Abstract
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.
TopBackground
The ability to determine the place where a fire is probably to occur is a prerequisite for planning fire management. The vegetation cover is the most important factor in forest fires, because it reflects the presence of forest fuels (Roy, 2003). The use of remote sensing data in the classification and mapping of vegetation becomes the main method of fuel assessment. Existing methodologies for determining the type of vegetation include the traditional classification with and without training (Hansen et al., 2000 ; Churches et al., 2014), the method based on phenology (Yan et al., 2015), and the object classification (Mitchell et al., 2016).
Vegetation type maps are necessary for the spatial calculation of fire danger and for estimation of fire risks by using them in models that simulate the growth and intensity of a fire in a landscape. Forest fuel maps are used in various widespread systems for the forest fire danger forecasting. For example, such models are used in the North American models National Fire Danger Rating System (NFDRS), Fire behavior (BEHAVE), Fire Area Simulator (FARSITE), National Fire Management Analysis System (NFMAS) (Deeming et al., 1978 ; Finney, 1998 ; Lundgren et al., 1995 ; Arroyo et al., 2008). The McArthur Forest Fire Danger Rating System and the McArthur Grassland Fire Danger Rating System (McArthur, 1967) are widely used in Australia. The Canadian Forest Fire Danger Rating System is used in Canada, which consists of two main subsystems: the Fire Weather Index (FWI) and Fire Behavior Prediction System (Arroyo et al., 2008; van Wagner, 1987). The Forest Fire Satellite Monitoring Information System of Russian Federal Forestry Agency (SMIS-Rosleshoz) is used in the Russian Federation and is based on the Nesterov index. The ground-based observations, aerial photography, modeling and remote sensing data (Arroyo et al., 2008; Lasaponara and Lanorte, 2007; Keane et al., 2001; Wang et al., 2004; Ustin et al., 2004) are used for mapping. In general, data on the greenness of vegetation, meteorological data, data on wetting the surface and moisture content of forest fuel are necessary to forecast and to monitor forest fires (Kononov and Ka, 2008).
Key Terms in this Chapter
Image Classification: A process of grouping pixels into several classes of land use/land cover (LULC) based on the application of statistical decision rules in the multispectral domain or logical decision rules in the spatial domain
Supervised Classification: Uses the spectral signatures obtained from training samples to classify an image.
Landsat 7: The seventh satellite of the Landsat program. Launched on April 15, 1999, Landsat 7's primary goal is to refresh the global archive of satellite photos, providing up-to-date and cloud-free images.
Python: An interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.
Unsupervised Classification: Finds spectral classes (or clusters) in a multiband image without the analyst’s intervention.
Remote Sensing: The collecting of information about the earth using aircraft and satellites Geographic information systems: a system designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data.