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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2026, V. 23, No. 2, pp. 111-125

Comparison of the results of applying structural analysis to remote sensing data and values of normalized burn ratio

A.V. Lapko 1, 2 , V.A. Lapko 1, 2 , S.T. Im 1, 3 , Yu.P. Yuronen 1 
1 Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia
2 Institute of Computational Modelling SB RAS, Krasnoyarsk, Russia
3 Sukachev Institute of Forest SB RAS, Krasnoyarsk, Russia
Accepted: 17.02.2026
DOI: 10.21046/2070-7401-2026-23-2-111-125
Methods of decomposition of spectral remote sensing data are the basis for the creation of automated information processing systems. A new technique for structural analysis of remote sensing data is proposed that uses the components of correlation coefficient estimation. These components are normalized values of spectral features. Their product forms the components of correlation coefficient estimate. Based on the values of the components of correlation coefficient estimate, a decisive rule is formed that defines four classes of spectral feature values. The classes are characterized by positive, negative, and alternating values of the components of correlation coefficient estimate. Using the decisive rule, an algorithm is formed for assessing whether control situations belong to certain classes. The results of applying the proposed approach are considered using data from a test plot of forest vegetation damaged by Siberian silkmoth in the spectral feature space of NIR (near infrared) and SWIR-2 (shortwave infrared, range-2) channels of the Landsat-8/OLI instrument. The NIR and SWIR-2 spectral feature pair under consideration is used to calculate the normalized burn ratio (NBR) value. The results of applying the structural analysis method to remote sensing data and the NBR spectral index are compared. The comparison uses kernel probability density estimates for the random variables being analyzed. The results are illustrated with maps, graphs of kernel probability density estimates, and tables of key decipherment indicators for the forest test plot. The proposed method of structural data analysis is universal. Its application does not require the assignment of threshold values, unlike the NBR index, and can be adapted for studying technical, biochemical, biomedical, and environmental systems.
Keywords: structural data analysis, automatic classification, components of correlation coefficient, kernel probability density estimation, NBR index, remote sensing data, spectral features, forest area, Siberian silkmoth
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References:

  1. Babiy I. A., Vodnevskaya V. S., Zhdanovskiy M. N. et al., Comparison of NDVI, GNDVI, BNDVI, NDII and COASTAL NDVI vegetation indices for assessing forests damaged by the Siberian silkmoth, Agrofizika, 2025, No. 4, pp. 60–70 (in Russian), DOI: 10.25695/AGRPH.2025.04.09.
  2. Vasil’ev V. I., Ehsh S. N., Features of self-learning algorithms and clustering, Control Systems and Computers, 2011, No. 3, pp. 3–9 (in Russian).
  3. Dorofeyuk A. A., Methodology of expert classification analysis in the management and processing of complex data (history and prospects of development), Control Sciences, 2009, No. 3.1, pp. 19–28 (in Russian).
  4. Epanechnikov V. A., Non-parametric estimation of a multivariate probability density, Theory of Probability and Its Applications, 1969, V. 14, No. 1, pp. 153–158, DOI: 10.1137/1114019.
  5. Zhurbin I. V., Shaura A. S., Zlobina A. G., Bazhenova A. I., Detection of areas of the anthropogenically transformed natural environment based on a comprehensive analysis of multiseasonal multispectral survey, Optoelectronics, Instrumentation and Data Processing, 2024, V. 60, No. 1, pp. 106–115, DOI: 10.3103/ s8756699024700080.
  6. Zagoruiko N. G., Kognitivnyi analiz dannykh (Cognitive data analysis), Novosibirsk: GEO, 2013, 183 p. (in Russian).
  7. Lapko A. V., Lapko V. A., Yadernye otsenki plotnosti veroyatnosti i ikh primenenie (Kernel probability density estimates and their applications), Krasnoyarsk: Reshetnev Siberian State University of Science and Technology, 2021, 308 p. (in Russian).
  8. Lapko A. V., Lapko V. A., Procedure for decomposing the values of two-dimensional spectral features of remote sensing based on the analysis of correlation coefficient components, Measurement Techniques, 2024, V. 67, No. 6, pp. 427–432, DOI: 10.1007/s11018-024-02362-6.
  9. Lapko A. V., Lapko V. A., Im S. T., Methodology for decomposition of spectral data from remote sensing of forest fires, Informatika i sistemy upravleniya, 2024, V. 81, No. 3, pp. 112–120 (in Russian), DOI: 10.22250/1 8142400_2024_81_3_112.
  10. Lapko A. V., Lapko V. A., Im S. T. (2025a), Decomposition of spectral features of remote sensing based on the correlation coefficient components, Optoelectronics, Instrumentation and Data Processing, 2025, V. 61, No. 3, pp. 310–317, DOI: 10.3103/S8756699025700360.
  11. Lapko A. V., Lapko V. A., Im S. T., Yuronen Yu. P. (2025b), Modified method of structural analysis of remote sensing data, Izmeritel’naya Tekhnika, 2025, V. 74, No. 6, pp. 4–12 (in Russian), DOI: 10.32446/0368-1025it.2025-6-4-12.
  12. Lapko A. V., Lapko V. A., Sharueva A. V. (2025c), A nonparametric pattern recognition algorithm in the problems of analyzing the data of remote sensing over anthropogenic territories, Optoelectronics, Instrumentation and Data Processing, 2025, V. 61, No. 1, pp. 75–84, DOI: 10.3103/S8756699025700116.
  13. Ponomarev E. I., Yakimov N. D., Tretyakov P. D., Sultson S. M., Estimation of defoliation features in dark coniferous tree stands after the impact of Siberian silk moth according to remote data, Cosmic Research, 2024, V. 62, No. S1, pp. S73–S80, DOI: 10.1134/S0010952524601208.
  14. Sinyutkina A. A., Gashkova L. P., Assessment of post-fire vegetation dynamics in a raised bog (Western Siberia) based on Landsat satellite data, Regional Geosystems, 2025, V. 49, No. 1, pp. 112–127 (in Russian), DOI: 10.52575/2712-7443-2025-49-1-112-127.
  15. Sultson S. M., Goroshko A. A., Demidko D. A. et al., Analysis of the spatial distribution of the Siberian silkmoth outbreak area based on terrain features in the Siberian mountain southern taiga forests, Siberian J. Life Sciences and Agriculture, 2025, V. 17, No. 1, pp. 282–307 (in Russian), DOI: 10.12731/2658-6649-2025-17-1-1054.
  16. Tuboltsev V. P., Lapko A. V., Lapko V. A., Modified nonparametric algorithm for automatic classification of largevolume statistical data and its application, Scientific and Technical Information Processing, 2024, V. 51, No. 6, pp. 592–597, DOI: 10.3103/S0147688224700552.
  17. Sharueva A. V., Lapko A. V., Lapko V. A., Neparametricheskie metody proverki gipotez o raspredeleniyakh sluchainykh velichin pri analize dannykh distantsionnogo zondirovaniya (Nonparametric methods for hypotheses testing about distributions of random variables in the analysis of remote sensing data), Novosibirsk: SB RAS, 2024, 189 p. (in Russian), DOI: 10.53954/9785604990094.
  18. Avetisyan D., Stankova N., Dimitrov Z., Assessment of spectral vegetation indices performance for post-fire monitoring of different forest environments, Fire, 2023, V. 6, No. 8, Article 290, DOI: 10.3390/ fire6080290.
  19. Duin R. P. W., On the choice of smoothing parameters for Parzen estimators of probability density functions, IEEE Trans. Computers, 1976, V. C-25, No. 11, pp. 1175–1179, DOI: 10.1109/TC.1976.1674577.
  20. Gitelson A. A., Kaufman Y. J., Merzlyak M. N., Use of a green channel in remote sensing of global vegetation from EOS-MODIS, Remote Sensing of Environment, 1996, V. 58, pp. 289–298, DOI: 10.1016/ S0034-4094257(96)00072-7.
  21. Hardisky M., Klemas V., Smart R., The influences of soil salinity, growth form, and leaf moisture on the spectral reflectance of Spartina alterniflora canopies, Photogrammetric Engineering and Remote Sensing, 1983, V. 49, pp. 77–83.
  22. Härdle W. K., Lu H. H. S., Shen X., Handbook of big data analytics, Berlin; Heidelberg: Springer, 2018, 538 p.
  23. Huang S., Tang L., Hupy J. P. et al., A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing, J. Forestry Research, 2021, V. 32, pp. 1–6, DOI: 10.1007/s11676-020-01155-1.
  24. Hunt E. R., Jr., Hively W. D., Fujikawa S. J. et al., Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring, Remote Sensing, 2010, V. 2, No. 1, pp. 290–305, DOI: 10.3390/ rs2010290.
  25. Key C. H., Ecological and sampling constraints on defining landscape fire severity, Fire Ecology, 2006, V. 2, No. 2, pp. 34–59.
  26. Kharuk V. I., Im S. T., Soldatov V. V., Siberian silkmoth outbreaks surpassed geoclimatic barrier in Siberian mountains, J. Mountain Science, 2020, V. 17, No. 8, pp. 1891–1900, DOI: 10.1007/s11629-020-5989-3.
  27. Kim Y., Jeong M.-H., Youm M. et al., Recovery of forest vegetation in a burnt area in the Republic of Korea: A perspective based on Sentinel 2 data, Applied Sciences, 2021, V. 11, Article 2570, DOI: 10.3390/ app11062570.
  28. Lopes L. F., Dias F. S., Fernandes P. M., Acácio V., A remote sensing assessment of oak forest recovery after postfire restoration, European J. Forest Research, 2024, V. 143, pp. 1001–1014, DOI: 10.1007/ s10342-024-01667-z.
  29. Meneses-Tovar C. L., El índice normalizado diferencial de la vegetación como indicador de la degradación del bosque, Unasylva, 2011, V. 62, No. 238, pp. 39–46.
  30. Miller J. D., Yool S. R., Mapping forest post-fire canopy consumption in several overstory types using multi-temporal Landsat TM and ETM data, Remote Sensing of Environment, 2002, V. 82, pp. 481–496, DOI: 10.1016/S0034-4257(02)00071-8.
  31. Pan T., Wang H., Chen J. et al., Escalating landscape fire exposure and its deteriorating global equity: Role of associated population changes, socioeconomic levels, and climate changes, Science of the Total Environment, 2026, V. 1013, Article 181228, DOI: 10.1016/j .scitotenv.2025.181228.
  32. Potter C., Landscape patterns of vegetation canopy regrowth following wildfires in the Sierra Nevada mountains of California, Open J. Forestry, 2015, V. 5, pp. 723–732, DOI: 10.4236/ojf.2015.57064.
  33. Riano D., Chuvieco E., Salas J., Aguado I., Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types (2003), IEEE Trans. Geoscience and Remote Sensing, 2003, V. 41, pp. 1056–1061, DOI: 10.1109/TGRS.2003.811693.
  34. Rudemo M., Empirical choice of histogram and kernel density estimators, Scandinavian J. Statistics, 1982, No. 9, pp. 65–78.
  35. Slinkina O. A., Mikhaylov P. V., Sultson S. M. et al., Mapping tree mortality caused by Siberian silkmoth outbreak using Sentinel 2 remote sensing data, Forests, 2023, V. 14, No. 12, Article 2436, DOI: 10.3390/ f14122436.
  36. Sriwongsitanon N., Jandang W., Williams J. et al., Using normalised difference infrared index patterns to constrain semi-distributed rainfall–runoff models in tropical nested catchments, Hydrology and Earth System Sciences, 2023, V. 27, No. 11, pp. 2149–2171, DOI: 10.5194/hess-27-2149-2023.
  37. Stephens C. W., Ives A. R., Radeloff V. C., Substantial increases in burned area in circumboreal forests from 1983 to 2020 captured by the AVHRR record and a new autoregressive burned area detection algorithm, Remote Sensing of Environment, 2025, V. 325, Article 114789, DOI: 10.1016/j .rse.2025.114789.
  38. Vermote E., Justice C., Claverie M., Franch B., Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product, Remote Sensing of Environment, 2016, V. 185, pp. 46–56, DOI: 10.1016/j .rse.2016.04.008.
  39. Zhao G., Xu E., Yi X. et al., Comparison of forest restorations with different burning severities using various restoration methods at Tuqiang Forestry Bureau of Greater Hinggan Mountains, Remote Sensing, 2023, V. 15, Article 2683, DOI: 10.3390/rs15102683.
  40. Zeng Y., Deng F., Yang W. et al., Fire has emerged as a critical disturbance agent in the boreal forest ecosystems of Northeast Asia, Global Ecology and Conservation, 2026, V. 65, Article e04041 DOI: 10.1016/j . gecco.2025.e04041.