Scientists from Tomsk Polytechnic University are developing a system for predicting the likelihood of forest fires. Using Gilbirinsky Forestry in the basin of Baikal Lake they created a map of the territory and identified forest areas where the likelihood of fire emergency is the highest due to the vegetative conditions of the territory itself. This data will underpin a geographic information system (GIS) for predicting wildfires. The study results were published in the journal Mathematical Problems in Engineering.
Nikolay Baranovsky, Project leader, Associate Professor from the TPU Butakov Research Center says:
‘What are the ways to fight forest fires? Either predict them to prevent or localize to smallest areas, or to extinguish the fires covering larger areas. We believe that predicting is the most optimal way. It is less expensive and safer for people involved in firefighting.
Speaking of predicting fire in forests we should take into account some important aspects. First, the appearance and spread of fire are affected by the peculiarities of territory itself: what species of trees, herbs and so on are there. Second, meteorological conditions, including heat, wind, thunderstorm activity in a territory. Third: human activities such as enterprise operation, trivial bonfires in the forest also affect the situation.’
All of these factors can be predicted. Scientists from Tomsk Polytechnic University and their colleagues from different Russian institutions are working on such a predicting geographic information system which will be able to take into account all the factors. They have already developed one of them, anthropogenic load. They designed a model to estimate the anthropogenic impact on a forest area. They created a mathematical model for evaluating the effect of human activities on a forest territory. The scientists consider three types of objects: point, linear, and area ones. For example, campgrounds, settlements refer to point objects, whereas roads are linear ones. Anthropogenic load in such areas varies and affects differently the likelihood of a fire. Everything depends on the size of an object: the larger the object, the higher a potential load on a territory.
The next factor is the disposition of territory itself toward to fire occurrence, which the scientists studied based on Gilbirinskiy Forestry. The area, located between Lake Baikal and Ulan-Ude in a conservation area, is about 27- km2 and.
Associate Professor Baranovsky says:
‘Baikal’s significance for the entire planet is tremendous, it is an amazing natural area. Every summer the basin of Lake Baikal suffers from wildfires. It is meaningless to focus on fire statistics of the previous season. Since there can be, for example, abrupt weather changes, and such weather condition could not happen last year and the statistics does not take them into account. Therefore, we propose to underpin other data.’
Images from the Landsat-8 satellite helped the scientist estimate the disposition of forestry toward fires. Homogeneous areas were identified and classified: it was determined the location of water bodies, meadows, swamps, deciduous forests, and old coniferous forests.
‘At first, we ruled out roads, there is not forest fuel there, then swamps, water bodies, young forests. The rest was old dry forests. This is the most dangerous areas in terms of risks. And this is coniferous forests not deciduous. It is related to the physical characteristics of forest fuel,’ explains the researcher.
As a result, the most dangerous areas were mapped as red, the less dangerous as blue.
‘We can already estimate the disposition of territory towards fire occurrence and likelihood related to the human impact. Now our colleagues are estimating the likelihood associated with thunderstorm activity.
When these factors are combined in a system, it will be possible to make a short-term forecast on fire danger. That is, where exactly a wildfire is most likely to occur under given weather conditions, given thunderstorm activity, and given anthropogenic situation. It is about just a short-term forecast as it is the most accurate. This can be a basis for certain measures pay more attention to certain areas,’ says the scientist.
Russian Foundation for Basic Research supported the project. The scientists expect to complete the study by 2021. The next stage will be a practical implementation of the geographic information system.