Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 21, 2024
Number 1 Number 2
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
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, 2023, Vol. 20, No. 2, pp. 97-112

Development of pseudo-surfaces for ground cover monitoring: the case of western Russian Karelia

V.V. Tarasenko 1 , B.V. Raevsky 1 
1 Karelian Research Centre RAS, Petrozavodsk, Russia
Accepted: 23.03.2023
DOI: 10.21046/2070-7401-2023-20-2-97-112
The results of supervised classification of temporary alternate Landsat images (Landsat ET 1988 and Landsat OLI 2021) were treated by generating a number of pseudo-surfaces. Decoding of multispectral space images was carried out by means of the classic minimum distance method based on specially selected training sample (392 regions of interest), which was subdivided into 28 classes and 9 groups of them. This classification gave the results with a comparatively high level of reliability with 71–77 % of overall accuracy and 0.62–0.65 of Cohen’s kappa. A method for generating pseudo-surfaces by groups of vegetation cover and man-made zones classes was developed and applied to the parts of Kostomuksha municipality, Kalevalsky and Muezersky regions’ territories (totally about 800 thousand hectares). The original method for creating pseudo-surfaces was realized by forming regular grid (100×100 m) where each cell centroid contains information about the percentage of objects’ area belonging to various groups. Particularly a number of pseudo-surfaces have been created for various types of vegetation communities of the Kostomukshsky Strict Nature Reserve. The results of applying pseudo-surfaces for native vegetation types and human transformed areas were discussed addressing the problems of monitoring the territory which combines deeply transformed zones and natural boreal landscapes. The obtained results can be used to form actual and reliable thematic cartographic products using GIS technologies, including interactive maps and publishing data in the Internet.
Keywords: ground cover monitoring, pseudo-surfaces, remote sensing data, special protected area, GIS technologies, thematic mapping, interactive maps
Full text

References:

  1. Baratalev S. A., Loupian E. A., Research and development of the IKI RAS on the development of methods of satellite monitoring of vegetation cover, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2013, Vol. 10, No. 1, pp. 197–214 (in Russian).
  2. Bartalev S. A., Ershov D. V., Loupian E. A., Tolpin V. A., Possibilities of using the VEGA satellite service to solve various problems of monitoring terrestrial ecosystems, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 1, pp. 49–56 (in Russian).
  3. Bartalev S. A., Egorov V. A., Zharko V. O., Loupian E. A., Plotnikov D. E., Khvostikov S. A., Shabanov N. V., Sputnikovoe kartografirovanie rastitel’nogo pokrova Rossii (Land cover mapping over Russia using Earth observation data. Moscow), Moscow: IKI RAN, 2016, 208 p. (in Russian).
  4. Vorobyov O. N., Kurbanov E. A., Monitoring of the state of vegetation cover in the territory of the Republic of Mari El using Envisat MERIS, Vestnik moskovskogo gosudarstvennogo universiteta lesa — Lesnoi vestnik, 2013, No. 7, pp. 42–45 (in Russian).
  5. Vorobiev O. N., Kurbanov E. A., Gubaev A. V., Demisheva E. N., Method of stepwise classification of satellite images for thematic mapping of forests, Annals of the Volga State Technical University. Ser.: Forest. Ecology. Nature management, 2015, No. 4(28), pp. 57–72 (in Russian).
  6. Gromtsev A. N., Osnovy landshaftnoi ekologii evropeiskikh taezhnykh lesov Rossii (Fundamentals of landscape ecology of the European taiga forests of Russia), Petrozavodsk: Karelian Research Centre RAS, 2008, 238 p. (in Russian).
  7. Evdokimov S. I., Mikhalat S. G., Determining of the physical meaning of band combinations of Landsat images for monitoring terrestrial and aquatic ecosystems, Vestnik Pskovskogo gosudarstvennogo universiteta, Ser.: Estestvennye i matematicheskie nauki, 2015, No. 7, pp. 21–32 (in Russian).
  8. Komarova A. F., Zhuravleva I. V., Yablokov V. M., Open-source multispectral remote sensing data for the investigation of plant communities, Principles of ecology, Petrozavodsk: Petrozavodsk State University, 2016, No. 1, pp. 40–74 (in Russian), DOI: 10.15393/j1.art.2016.4922.
  9. Kurbanov E. A., Vorobyov O. N., Distantsionnye metody v lesnom khozyaistve (Remote methods in forestry), Yoshkar-Ola: Volga State Technical Univ., 2020, 266 p. (in Russian).
  10. Kurbanov E. A., Nureeva T. V., Vorobyov O. N., Gubayev A. V., Leznin S. A., Miftakhov T. F., Nezamayev S. A., Polevshchikova Yu. A., Remote monitoring of disturbances in forest cover, reforestation and afforestation of Mari Zavolzhje, Annals of Mari El State Technological University, 2011, No. 3, pp. 17–24 (in Russian).
  11. Kurbanov E. A., Vorobyov O. N., Gubayev A. V., Lezhnin S. A., Polevshchikova Yu. A., Demisheva E. N., Four decades of forest research with the use of Landsat images, Annals of Permskiy State Technical University, 2014, No. 1(21), pp. 18–32 (in Russian).
  12. Labutina I. A., Baldina E. A., Ispol’zovanie dannykh distantsionnogo zondirovaniya dlya monitoringa ekosistem OOPT: metodicheskoe posobie (Using remote sensing data to monitor protected areas ecosystems: a methodological guidebook), Moscow: Vsemirnyi fond dikoi prirody (WWF of Russia), 2011, 88 p. (in Russian).
  13. Loupian E. A., Savorskiy V. P., Basic products of Earth remote sensing data processing, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 2, pp. 87–96 (in Russian).
  14. Nikitina (Shevchuk) Yu. G., Olzoev B. N., Space image-based study of baikal region landscape anthropogenic transformation (by example of olkhon island) (by example of Olkhon Island), Annals of Irkutsk State Technical University, 2014, No. 2(85), pp. 67–74 (in Russian).
  15. Nikitina A. D., Knyazeva S. V., Gavrilyuk E. A., Tikhonova E. V., Eidlina S. P., Koroleva N. V., Mapping the dynamics of the vegetation cover of the Kursha Kosa National Park based on the materials of the space survey Alos and Sentinel-2, Forest science issues, Issue “Aerospace methods and GIS technologies in forestry, forestry and ecology”, 2019, Vol. 2(3), pp. 1–21 (in Russian), DOI: 10.31509/2658-607x-2019-2-3-1-21.
  16. Raevsky B. V., The structure of forest land and forest stands of the kostomukshsky strict nature reserve compared to kalevalsky national park, Proc. Karelian Research Centre RAS, 2017, No. 4, pp. 3–14 (in Russian), DOI: 10.17076/them532.
  17. Raevsky B. V., Tarasenko V. V., Petrov N. V., Inventory of the kostomukshskiy strict nature reserve vegetation using landsat images, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 3, pp. 47–61 (in Russian), DOI: 10.21046/2070-7401-2022-19-3-47-61.
  18. Skripko V. V., Golovina A. A., Geo-information support of ecological monitoring of specially protected natural areas, Annals of modern science, 2016, No. 1, pp. 149–152 (in Russian).
  19. Soromotin A. V., Brodt L. V., Monitoring of vegetation cover during the development of oil and gas fields according to the Landsat multispectral survey data, Annals of Tyumen State University, Ecology and environmental management, 2018, Vol. 4, No. 1, pp. 37–49 (in Russian), DOI: 10.21684/2411-7927-2018-4-1-37-49.
  20. Styshov M. S., Troitskaya N. I., Organizatsiya ekologicheskogo monitoringa na osobo okhranyaemykh prirodnykh territoriyakh (Organization of ecological monitoring within special protected areas), Moscow: Zapovednaya Rossiya, WWF, 2017, 138 p. (in Russian).
  21. Khadyeva V. S., Gubin V. N., Shalkevich F. E., Bogdasarov M. A., Metody distantsionnykh issledovanii v geologii i geografii (Methods of remote research in geology and geography), 2015, 210 p. (in Russian), https://rep.brsu.by/handle/123456789/4548.
  22. Shokin Yu. I., Potapov V. P., Popov S. E., Giniyatullina O. L., Satellite radar interferometry: information computer aspects, Computing technologies, 2016, Vol. 21, No. 1, pp. 141–151 (in Russian).
  23. Yaldygina N. B., Use of ENVI software for solving forest management issues, Geomatic, 2011, No. 3, pp. 34–39 (in Russian).
  24. Vertical Mapper: User Guide, Version 3.7, Pitney Bowes Software Inc., 2009, 213 p.