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Method and algorithms of forecasting the seasonal characteristics of anthropogenic impact areas using long-term remote sensing data

Journal of Computer and Systems Sciences International Aims and scope

Abstract

A method for predicting the characteristics of areas subject to the anthropogenic impact according to the Earth’s remote sensing data is proposed. The developed method is based on the identification of patterns using long-term periodic observations. These patterns are applied to the seasonal observations of the current year. The method is implemented in a set of algorithms and predictive models. An example of the use of the method of the agricultural yield forecasting is given. The training and validation of models for the prediction of crop yields based on the long-term spatial data on the state of vegetation are described. Different regions of the Russian Federation including the Arctic regions are considered.

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References

  1. A. Murynin, K. Gorokhovskiy, and V. Ignatiev, “Analysis of large long-term remote sensing image sequence for agricultural yield forecasting. Image mining. Theory and applications,” in Proceedings of the 4th International Workshop on Image Mining (Barcelona, Spain, 2013), pp. 48–55.

    Google Scholar 

  2. V. G. Bondur, K. Yu. Gorokhovskiy, V. Yu. Ignatiev, A. B. Murynin, and E. V. Gaponova, “Yield forecasting method according to space-based observations of the dynamics of vegetation,” Izv. Vyssh. Uchebn. Zaved., Geodez. Aerofotos’emka, No. 6, 61–68 (2013).

    Google Scholar 

  3. A. B. Murynin, V. G. Bondur, V. Yu. Ignatiev, and K. Yu. Gorokhovskiy, “Yield forecasting on the basis of long-term space-based observations of the dynamics of vegetation,” Sovrem. Problemy Distants. Zondir. Zemli Kosmosa 10(4), 245–256 (2013).

    Google Scholar 

  4. A. Murynin, K. Gorokhovskiy, and V. Ignatiev, “Trainable method for predicting characteristics of land surface objects,” in Proceedings of the IADIS International Conference on Computer Graphics, Visualization, Computer Vision and Image Processing 2013, Prague, Czech Republic (2013), pp. 119–125.

    Google Scholar 

  5. V. G. Bondur, K. Yu. Gorokhovskiy, V. Yu. Ignatiev, and A. B. Murynin, “Yield forecasting on the basis of long-term space-based observations of the dynamics of vegetation,” in Proceedings of the International Scientific Extramural Conference on Technical Sciences in Russian and Abroad II (Buki-Vedi, Moscow, 2012), pp. 1–8.

    Google Scholar 

  6. A. Murynin, A. Rihter, and V. Ignatiev, “Detection of the soil degradation areas on multispectral images by measuring the response of vegetation to salinity,” in Proceedings of the 11th International Conference on Pattern Recognition and Image Analysis: New Information Technologies (PRIA-11-2013) (Samara, 2013), Vol. 2, pp. 678–681.

    Google Scholar 

  7. V. G. Bondur and V. F. Krapivin, Space Monitoring of Tropical Cyclones (Nauchnyi mir, Moscow, 2014) [in Russian].

    Google Scholar 

  8. V. G. Bondur, “Airspace monitoring of oil-gas territories and objects,” Issled. Zemli Kosmosa, No. 6, 3–17 (2010).

    Google Scholar 

  9. V. G. Bondur, Airspace Monitoring of Oil-Gas Complex Objects (Nauchnyi mir, Moscow, 2012) [in Russian].

    Google Scholar 

  10. V. G. Bondur, V. F. Krapivin, and V. P. Savinykh, Monitoring and Forecasting of Natural Disasters (Nauchnyi mir, Moscow, 2009) [in Russian].

    Google Scholar 

  11. V. G. Bondur, “Modern approaches to processing large hyperspectral and multispectral aerospace data flows,” Izv., Atmos. Ocean. Phys. 50, 840–852 (2014).

    Article  Google Scholar 

  12. Yu. G. Simonov, Problems of Regional Geographic Forecasting: State, Theory and Methods (Nauka, Moscow, 1982) [in Russian].

    Google Scholar 

  13. L. Phillips, A. Hansen, and C. Flather, “Evaluating the species energy relationship with the newest measures of ecosystem energy: NDVI versus MODIS primary production,” Remote Sens. Environ. 112(9), 3538–3549 (2008).

    Article  Google Scholar 

  14. R. Fischer, D. Byerlee, and G. Edmeades, “Can technology deliver on the yield challenge to 2050?,” in Proceedings of the Expert Meeting on How to Feed the World, Food and Agriculture Organization of the United Nations (Rome, Italy, 2009), pp. 8–12.

    Google Scholar 

  15. K. Gorokhovskyi, V. Ignatiev, and A. Murynin, “Efficiency of crop yield forecasting depending on the moment of prediction based on large remote sensing data set,” in Proceedings of the International Conference on Data Mining, Las Vegas Nevada, USA (CSREA Press USA, 2013), pp. 36–41.

    Google Scholar 

  16. ftp://e4ftl01.cr.usgs.gov/MODIS_Composites/

  17. A. A. Mironov and V. I. Tsurkov, “Transportation problems with a minimax criterion,” Dokl. Math. 53, 119 (1996).

    MATH  Google Scholar 

  18. A. A. Mironov and V. I. Tsurkov, “Transportation and network problems with a minimax criterion,” Zh. Vych. Mat. Mat. Fiz. 35, 148–158 (1995).

    MathSciNet  Google Scholar 

  19. A. P. Tizik and V. I. Tsurkov, “Iterative Functional Modification Method for Solving a Transportation Problem,” Autom. Remote Control 73, 134 (2012).

    Article  MATH  MathSciNet  Google Scholar 

  20. A. A. Mironov and V. I. Tsurkov, “Transport-type problems with a minimax criterion,” Autom. Remote Control 56, 1752 (1995).

    MATH  MathSciNet  Google Scholar 

  21. V. G. Bondur, A. B. Murynin, I. A. Matveev, A. N. Trekin, and I. A. Yudin, “A method of computational optimization for matching vector and raster remote sensing data,” Sovrem. Probl. Distants. Zondir. Zemli Kosmosa 10(4), 98–106 (2013).

    Google Scholar 

  22. V. I. Tsurkov, “An Analytical Model of Edge Protection under Noise Suppression by Anisotropic Diffusion,” J. Comput. Syst. Sci. Int. 39, 437 (2000).

    Google Scholar 

  23. V. I. Tsurkov and D. V. Kovkov, “Method for removing noise on an image,” RF Patent No. 2316816 (2005).

    Google Scholar 

  24. O. V. Dzhosan and A. B. Murynin, “Method for edge enhancement on an image,” in Dynamics of Nonlinear Systems, Tr. ISA RAN, Vol. 29 (Inst. Sistem. Analiza RAN, 2007), pp. 211–218 [in Russian].

    Google Scholar 

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Correspondence to A. B. Murynin.

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Original Russian Text © V.Yu. Ignatiev, A.B. Murynin, 2015, published in Izvestiya Akademii Nauk. Teoriya i Sistemy Upravleniya, 2015, No. 3, pp. 79–87.

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Ignatiev, V.Y., Murynin, A.B. Method and algorithms of forecasting the seasonal characteristics of anthropogenic impact areas using long-term remote sensing data. J. Comput. Syst. Sci. Int. 54, 406–414 (2015). https://doi.org/10.1134/S1064230715030119

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