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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. 4, pp. 70-88

Application of the VSS videospectral system for mapping vegetation types based on the results of space imagery

B.I. Belyaev 1 , M.Yu. Belyaev 2 , Yu.S. Davidovich 1 , L.V. Katkovsky 1 , S.V. Konev 2 , V.М. Kuznetzov 2 , G.S. Litvinovich 1 , A.O. Martinov 1 , L.A. Smolentseva 2 
1 A.N. Sevchenko Institute of Applied Physical Problems of Belarusian State University, Minsk, Belarus
2 S.P. Korolev Rocket and Space Сorporation “Energia”, Korolev, Moscow Region, Russia
Accepted: 03.06.2026
DOI: 10.21046/2070-7401-2026-23-4-70-88
The article presents the results of processing individual images from the International Space Station using the VSS video spectral equipment as part of the Uragan space experiment aimed at monitoring natural and man-made disasters. The VSS hardware complex, developed by the A. N. Sevchenko Institute of Applied Physical Problems of Belarusian State University, combines synchronous high-resolution video recording (visible and near infrared range) with hyperspectral measurements (400–950 nm), which ensures increased accuracy of visual and spectral data analysis. As part of the experiment, in 2024, a survey of the territory of Belarus was carried out with the participation of the first Belarusian female cosmonaut M. Vasilevskaya. The purpose of this work is to classify the spectral radiance data and assess the significance of the spectral channels. The principal component analysis (PCA) was used for data processing, which allowed us to reduce the dimensionality of the original spectra (700 channels) to 2 components explaining 95.6 % of the variance. Hierarchical clustering in the reduced space identified five classes of natural objects: water surfaces, winter crops at the tillering stage, coniferous forests, peat-bog and sod-podzolic soils. A method for assessing the significance of spectral channels based on the PCA loading matrix is proposed, which revealed the significance of the entire spectral range of the VSS equipment (400–950 nm, except for the O2 and H2O absorption bands) for the analysis of surfaces with a predominance of vegetation. The results confirmed the correlation of biophysical parameters of the objects and their optical characteristics and also demonstrated the reproducibility of the method due to the preservation of the PCA parameters for processing new data. The practical significance of the work lies in the creation of a universal tool for operational analysis of hyperspectral remote sensing data, adapted for the tasks of monitoring land covers and natural disasters.
Keywords: classification, principal component analysis, spectral density of radiance, spectrum, spectral channels, Random Forest
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References:

  1. Balabko P. N., Khusnetdinova T. I., Metechko L. B., Sorokin A. E., Aehrokosmicheskii i pochvenno-ehkologicheskii monitoring pedosfery Zemli (na primere Rossiiskoi Federatsii i nekotorykh sopredel’nykh territorii): monografiya (Aerospace and soil-ecological monitoring of the Earth’s pedosphere (on the example of the Russian Federation and some adjacent territories): monograph), Moscow: Izdatelsko-torgovaya kompaniya “Dashkov i K”, 2024, 276 p. (in Russian).
  2. Bambalov N. N., Rakovich V. A., Spectral reflectivity of peat, Vestnik Belorusskogo gosudarstvennogo universiteta imeni V. I. Lenina. Ser. 2. Khimiya. Biologiya. Geografiya, 1992, No. 1, pp. 68–71 (in Russian).
  3. Belyaev M. Yu., Scientific equipment and Earth studies techniques in space experiment Uragan on board the International Space Station, Sovremennye problemy distantsionnogo zondirovaniya zemli iz kosmosa, 2021, V. 18, No. 3, pp. 92–107 (in Russian), DOI: 10.21046/2070-7401-2021-18-3-92-107.
  4. Belyaev B. I., Kurikina T. M., Lisitsa V. D., Sergeenko V. T., Spektral’nye otrazhatel’nye svoistva pochv: analiticheskii obzor (Spectral reflectivity properties of soils: analytical review), B. I. Belyaev (ed.), Minsk: Belorusskii filial of VNIITEI agroprom, 1991, 58 p. (in Russian).
  5. Belyaev B. I., Belyaev M. Yu., Gusev V. F., Ivanov V. A., Krot Yu. A., Ryazantsev V. V., Sosenko V. A., Chumakov A. V. (2014a), Videospectral system for monitoring the Earth’s surface from the ISS, 6-i Belorusskii kosmicheskii kongress: materialy kongressa (Proc. 6th Belarusian Space Congress), Minsk, 2014, V. 1, pp. 211–214 (in Russian).
  6. Belyaev B. I., Belyaev M. Yu., Desinov L. V. et al. (2014b), Spectral and images processing from the photospectral system in the space experiment “Hurricane” on the ISS, Issledovanie Zemli iz kosmosa, 2014, No. 6, pp. 54–65 (in Russian), DOI: 10.7868/S0205961414060025.
  7. Belyaev M. Yu., Desinov L. V., Karavaev D. Yu., Legostaev V. P., Ryazantsev V. V., Yurina O. A. (2015a), Features of imaging the Earth surface and using the results of the imaging made by the ISS Russian segment crews, Space engineering and technologies, 2015, No. 1 (8), pp. 17–30 (in Russian).
  8. Belyaev M. Yu., Desinov L. V., Karavaev D. Yu., Sarmin E. E., Yurina O. A. (2015b), The study of catastrophic events that may lead to environmental challenges under the program “Hurricane” onboard the Russian segment of the International Space Station, Cosmonautics and rocket engineering, 2015, No. 1 (80), pp. 71–79 (in Russian).
  9. Belyaev B. I., Belyaev M. Yu., Sarmin E. E., Gusev V. F., Desinov L. V., Ivanov V. A., Krot Yu. A., Martinov A. O., Ryazantsev V. V., Sosenko V. A., Design and flight tests of the scientific hardware Video-spectral System on board the Russian segment of the ISS, Space engineering and technologies, 2016, No. 2 (13), pp. 70–79 (in Russian).
  10. Belyaev M. Yu., Belyaev B. I., Katkovsky L. V. et al., Classification of water surfaces by spectra measured from the ISS during the Uragan space experiment, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, V. 16, No. 6, pp. 201–208 (in Russian), DOI: 10.21046/2070-7401-2019-16-6-201-208.
  11. Bruchkouskaya S. I., Litvinovich G. S., Bruchkousky I. I., Katkovsky L. V., Algorithm for second-order diffraction correction in the spectrometer with a concave diffraction grating, Zhurnal prikladnoi spektroskopii, Minsk, 2019, V. 86, No. 4, pp. 620–627 (in Russian).
  12. Lagutin B. M., Naglyadnaya matematicheskaya statistika: uchebnoe posobie (Visual mathematical statistics: Tutorial), Moscow: BINOM, Laboratoriya znanii, 2007, 472 p. (in Russian).
  13. Martinov A. O. (2022a), Algorithm for automatic determination of spectral lines in aberration-prone images during wavelength calibration of imaging spectrometers, Materialy 20-i Mezhdunarodnoi konferentsii “Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa”(Proc. 20th Intern. Conf. “Current Problems in Remote Sensing of the Earth from Space”), Moscow: IKI RAS, 2022, p. 135 (in Russian), DOI: 10.21046/20DZZconf-2022a.
  14. Martinov A. O. (2022b), Classification of norway spruce drying states on the basis on reflection spectra, J. Belarusian State University. Physics, 2022, No. 3, pp. 26–38, (in Russian), DOI: 10.33581/2520-2243-2022-3-26-38.
  15. Martinov A. O., Litvinovich G. S., Gulyaeva S. I., Smolentseva L. A., Raskazov I. V., Method for mathematical correction of the ghosts in an image after reflection from the beam splitter plate, J. Belarusian State University. Physics, 2024, No. 3, pp. 25–40 (in Russian).
  16. Nauchnye osnovy tekhnologii vozdelyvaniya ozimykh zernovykh kul’tur, rapsa i kukuruzy (Scientific foundations of technologies for cultivating winter grain crops, rapeseed, and corn), A. A. Autko, F. I. Privalov (eds.), Minsk: Belaruskaya navuka, 2021, 495 p. (in Russian).
  17. Natsyyanal’ny atlas Belarusі (National Atlas of Belarus), Minsk: Belkartagrafiya, 2024, 348 p. (in Belarusian).
  18. Pochvy Respubliki Belarus’ (Soils of the Republic of Belarus), V. V. Lapa (ed.), Minsk: IVTs Minfina, 2019, 632 p. (in Russian).
  19. Semenenko N. N., Torfyano-bolotnye pochvy Poles’ya: transformatsiya i puti ehffektivnogo ispol’zovaniya (Peat-bog soils of Polesia: transformation and ways of effective use), Minsk: Belaruskaya navuka, 2015, 281 p. (in Russian).
  20. Smeyan N. I., Tsytron G. S., Klassifikatsiya, diagnostika i sistematicheskii spisok pochv Belarusi (Classification, diagnostics and taxonomic list of soils of Belarus), Minsk: RUP “Institut pochvovedeniya i agrokhimii”, 2007, 220 p. (in Russian).
  21. Smolentseva L. A., Scientific and technical support experiments of PJSC RSC “Energia” using the ground segment of planning and express analysis of research results, Sovremennye problemy raketnoi i kosmicheskoi tekhniki: sbornik statei sektsii “Problemy raketnoi i kosmicheskoi tekhniki” v ramkakh 58 Nauchnykh chtenii (Proc. “Modern Problems of Rocket and Space Technology”: 58th Scientific readings, section “Problems of Rocket and Space Technology”), Kazan, 2024, pp. 92–101 (in Russian).
  22. Tsytron G. S., Bubnova T. V., Drobysh S. V., Gorbacheva E. V., Use of indicators of spectral reflective capacity of the sod-podzolic soils in the diagnostic of their agrogenic transformation’s degree, Soil Science and Agrochemistry, 2011, No. 1, pp. 29–36 (in Russian).
  23. Chervan A. N., Davidovich Yu. S., Typology of geosystems of Belarusian Polesie, J. Belarusian State University. Geography and Geology, 2024, No. 2, pp. 3–23 (in Russian).
  24. Shalkevich F. E., Kuryanovich M. F., Tematicheskoe deshifrirovanie: posobie (Thematic interpretation: manual), Minsk: BSU, 2022, 131 p. (in Russian).
  25. Andrew A. L., Eigenvectors of certain matrices, Linear Algebra and its Applications, 1973, V. 7, Iss. 2, pp. 151–162, DOI: 10.1016/0024-3795(73)90049-9.
  26. Bai Z. D., Miao B. Q., Pan G. M., On asymptotics of eigenvectors of large sample covariance matrix, The Annals of Probability, 2007, V. 35, No. 4, pp. 1532–1572, DOI: 10.1214/009117906000001079.
  27. Belgiu M., Drăguţ L., Random Forest in remote sensing: A review of applications and future directions, ISPRS J. Photogrammetry and Remote Sensing, 2016, V. 114, pp. 24–31, DOI: 10.1016/j.isprsjprs.2016.01.011.
  28. Biau G., Scornet E., A random forest guided tour, TEST, 2016, V. 25, pp. 197–227, DOI: 10.1007/s11749-016-0481-7.
  29. Estornell J., Martí-Gavilá J. M., Sebastiá M. T., Mengual J., Principal component analysis applied to remote sensing, Modelling in Science Education and Learning, 2013, V. 6, pp. 83–89, DOI: 10.4995/msel.2013.1905.
  30. Han H., Guo X., Yu H., Variable selection using mean decrease accuracy and mean decrease Gini based on random forest, 2016 7 th IEEE Intern. Conf. on Software Engineering and Service Science (ICSESS), IEEE, 2016, pp. 219–224, DOI: 10.1109/ICSESS.2016.7883053.
  31. Jolliffe I. T., Principal Component Analysis, New York: Springer-Verlag, 2002, 488 p., DOI: 10.1007/b98835.
  32. Jolliffe I. T., Cadima J., Principal component analysis: a review and recent developments, Philosophical Trans. Royal Soc. A: Mathematical, physical and Engineering Sciences, 2016, V. 374, Article 20150202, DOI: 10.1098/rsta.2015.0202.
  33. Kumar S., Efficient K-mean clustering algorithm for large datasets using data mining standard score normalization, Intern. J. Recent and Innovation Trends in Computing and Communication, 2014, V. 2, Iss. 10, pp. 3161–3166.
  34. Mestre X., Improved estimation of eigenvalues and eigenvectors of covariance matrices using their sample estimates, IEEE Trans. Information Theory, 2008, V. 54, No. 11, pp. 5113–5129, DOI: 10.1109/TIT.2008.929938.
  35. Palacios-Orueta A., Ustin S. L., Remote sensing of soil properties in the Santa Monica Mountains. I. Spectral analysis, Remote Sensing of Environment, 1998, V. 65, No. 2, pp. 170–183, DOI: 10.1016/S0034-4257(98)00024-8.
  36. Ran X., Xi Y., Lu Y. et al., Comprehensive survey on hierarchical clustering algorithms and the recent developments, Artificial Intelligence Review, 2022, V. 56, pp. 8219–8264, DOI: 10.1007/s10462-022-10366-3.
  37. Uddin M. P., Mamun M. A., Hossain M. A., PCA-based feature reduction for hyperspectral remote sensing image classification, IETE Technical Review, 2021, V. 38, No. 4, pp. 377–396, DOI: 10.1080/02564602.2020.1740615.
  38. Yin L., Zhang C., Liu K., Wang X., Low-tillering winter wheat cultivars are more adaptable to late sowing, J. Agricultural Science, 2020, V. 158, No. 10, pp. 870–883, DOI: 10.1017/S002185962100023X.