Percolation model to control the distribution of forest infections on images from space vehicles
Рубрика:
| 1Artiushenko, MV, 1Tomchenko, OV 1State institution «Scientific Centre for Aerospace Research of the Earth of the Institute of Geological Sciences of the National Academy of Sciences of Ukraine», Kyiv, Ukraine |
| Space Sci. & Technol. 2020, 25 ;(4):45-56 |
| https://doi.org/10.15407/knit2020.04.045 |
| Язык публикации: Ukrainian |
Аннотация: For Ukraine and many European countries, it is relevant to protect pine forests from catastrophic drying and the death, the danger, which has emerged in recent years as a result of their infection with stem pests, bark beetles (Ips acuminatus, Ips sexdentatus). Research results of various levels are used to find the proper solution, including remote sensing from a spacecraft. Traditional methods of control from the space images allow us to describe the intensity of infection in a single way, namely, to estimate the area of infection clusters from remote sensing data with a sufficiently high spatial resolution. The result of measuring the infected areas significantly depends on the shooting scale, because the infection field is not smooth. The high cost of obtaining the high-precision data hinders significantly the application of space information technology to control the spread of infection. In the case of a scale-invariant distribution of infected clusters, statistical methods and methods of percolation theory can give much more information about the nature of the infection.
The list of tasks discussed in this article includes the use of scale-invariant indicators. Such indicators make it possible to determine the intensity of infection from satellite images of medium spatial resolution, which makes a significant contribution to the increase in the economic efficiency of the application of space information technology. The methods of control and analysis of forest infection are considered. They are based on the physical and mathematical theory of percolation, which deals with the distribution of fluid (pests) in heterogeneous environments. The methods of processing and interpretation of space information proposed in the article allow us to draw some important conclusions and, on their basis, to obtain sound recommendations aimed at improving the effectiveness of forest pest control. In conclusion, this approach is illustrated by numerical experiments with real images and validation results of a percolation model of the spread of forest infections.
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| Ключевые слова: forest infections, infection clusters, percolation theory, power distributions, satellite imagery, scaling indicators of infection |
References:
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2. Bondar V. N. (2019). Reasons and consequences of worsening of forests vitality and forest ecosystems. Proceedings of International Scientific and Practical Conference «Pine forests: current status, existing challenges and ways forward» (12—13 June 2019), Kyiv, 8—17 [in Ukrainian, English].
3. Tkach V. P., Meshkova V. L. (2019). Modern problems of formation and reproduction of biologically stable pine forests of Ukraine in conditions of climate change. Proceedings of International Scientific and Practical Conference «Pine forests: current status, existing challenges and ways forward» (12—13 June 2019), Kyiv, 70—78 [in Ukrainian, English]
4. Artiushenko M. V. (2018). Statistical analysis of the unsmooth geophysical fields by remote sensing data. J. Automation and Inform. Sci., 50 (6), 14—27.
5. Artiushenko M. V. (2018). Identification and Interpretation of Power-Law Distributions by Spectral Data of Remote Sensing.J. Automation and Inform. Sci., 50 (12), 17—33.
6. Bak P. (1996). How Nature Works: The Science of Self-Organized Criticality. New York: Copernicus. 7. Feder J. (1988). Fractals. New York: Plenum.
8. Newman M. E. J. (2005). Power laws, Pareto distributions and Zipf’s law. J. Contemporary Phys., 46, 323—351.
9. Richards J. A., Jia X. (2006). Remote Sensing Digital Image Analysis. (4th ed.). Berlin, Heidelberg: Springer. 439 p.
10. Stauffer D., Aharony A. (1994). Introduction to Percolation Theory (2nd ed.). London: Taylor & Francis.