Home JOURNAL HEADINGS Author Index SUBJECT INDEX INDEX OF ORGANIZATIONS Article Index
 
Arctic: ecology and economy
ISSN 2223-4594 | ISSN 2949-110X
Advanced
Search
RuEn
ABOUT|EDITORIAL|INFO|ARCHIVE|FOR AUTHORS|SUBSCRIBE|CONTACTS
Home » Archive of journals » Volume 14, No. 4, 2024 » Digital land cover mapping of the East Fennoscandian Arctic zone: case study of the south-eastern Kola Peninsula

DIGITAL LAND COVER MAPPING OF THE EAST FENNOSCANDIAN ARCTIC ZONE: CASE STUDY OF THE SOUTH-EASTERN KOLA PENINSULA

JOURNAL: Volume 14, No. 4, 2024, p. 617-629

HEADING: Regional problems

AUTHORS: Raevsky, B.V., Tarasenko, V.V.

ORGANIZATIONS: Department for Multidisciplinary Research of the Karelian Research Centre of Russian Academy of Sciences

DOI: 10.25283/2223-4594-2024-4-617-629

UDC: 528.71:58(470.21)

The article was received on: 22.05.2024

Keywords: multispectral satellite images, supervised classification, Landsat program, land cover, remote sensing data, interpretation

Bibliographic description: Raevsky, B.V., Tarasenko, V.V. Digital land cover mapping of the East Fennoscandian Arctic zone: case study of the south-eastern Kola Peninsula. Arktika: ekologiya i ekonomika. [Arctic: Ecology and Economy], 2024, vol. 14, no. 4, pp. 617-629. DOI: 10.25283/2223-4594-2024-4-617-629. (In Russian).


Abstract:

The conducted research has shown that interpretation of medium-resolution multispectral images of the Landsat satellite program by supervised classification methods made it possible to form a spatial model of the land cover of the southeastern part of the Kola Peninsula with acceptable accuracy. Based on the assessment of the thematic and positional aspects of the reliability of interpretation, the researchers have concluded that such a supervised classification algorithm as the “minimum distance” method showed good reliability of the interpretation results, especially with respect to woody vegetation. Using this algorithm for an area of ​​more than 4 million hectares, a reliable spatial model of the land/vegetation cover has been developed, which is informative in a wide range of image scales. Analysis of spatial information has revealed that typical northern taiga coniferous and deciduous stands are represented to the southwest of the northern boundary of the sparse island distribution of permafrost. To the east of this boundary, woody vegetation exists in the form of separate massifs of pre-tundra sparse forests and along watercourses. In the area of ​​massive-island distribution of permafrost, there is practically no woody vegetation, or it is found only in the valleys of large rivers.


Finance info: The research was supported from the federal budget under state assignment to the Karelian Research Center of Russian Academy of Sciences (No. FMEN-2022-0014).

References:

1. Ramsai W. Uber die geologische Entwicklung der Halbinsel Kola in der Quarterarz. Fennia, 1898, no. 16.

2. Federal law “About governmental support of business activity in Arctic zone of Russian Federation” from July 13, 2020 no. 193-ÔÇ. Available at: https://www.consultant.ru/document/cons_doc_LAW_357078/. (In Russian).

3. Koroleva N. E. Vegetation of Murmansk region as a component of biodiversity. Proceedings of Bauman Moscow State Technical University, 2009, vol. 12, no. 1, pp. 153—166. (In Russian).

4. Avrorin N. A., Kachurin M. Kh., Korovkin A. A. Study results concerning vegetation of Khibin mountins. Publications of Productive Forces Investigation Commission USSR Academy of Sciences. Kolsky series, 1936, iss. 11, pp. 3—95. (In Russian).

5. Bobrova L. I., Kachurin M. Kh. Outline of Monche-tundra vegetation. Publications of Productive Forces Investigation Commission USSR Academy of Sciences. Kolsky series, 1936, iss. 2, pp. 95—121. (In Russian).

6. Chernov E. G. Vegetation map of Kolsky peninsula on a scale of 1:1 000 000 with comments. Candidate (Phd) of biology thesis. Kirovsk, 1953, 274 p. (In Russian).

7. Atlas of Murmansk region, 1971. Available at: https://kolamap.ru/img/1971/1971.html. (In Russian).

8. Koroleva N. E., Loshkareva A. R. Field geobotanical investigations as the first stage in developind of new geobotanic map of Kola peninsula. InterCarto/InterGIS 17: sustainable development; theory of GIS and practice experience. Proceedings of international conference. Barnaul, 2011, P. 131—134. (In Russian).

9. Koroleva N. E., Loshkareva A. R. Evaluation of geobotanically mapped key plot at t the border of zonal tundra and forest tundra of Kolsky peninsula. Transactions of Karelian Research Centre RAS, 2013, no. 32, pp. 3—21. (In Russian).

10. Chernenkova T. V., Puzachenko M. Yu., Basova E. V., Koroleva N. E. Coenotic diversity and vegetation mapping of central part of Murmansk region. Geobotanic mapping, 2015, June, pp. 78—94. (In Russian).

11. Bartalev S. A., Egorov V. A., Zharko V. O., Loupian E. A., Plotnikov D. E., Khvostikov S. A., Shabanov N. V. Land cover mapping over Russia using Earth observation data. Moscow, IKI, 2016, 208 p.

12. Jaroshenko A. U., Dobrynin D. A., Egorov A. V., Juravleva I. V., Manisha A. E., Potapov P. V., Turubanova S. A., Hakimulin E. V. Forests of central and northern Russia. Map on a scale 1:4 500 000. Moscow, 2008. Available at: http://forestforum.ru/info/map_for_print.pdf. (In Russian).

13. Ershov D. V., Gavrilyuk E. A., Karpukhina D. A., Kovganko K. A. New map of vegetation cover of the central part of European Russia based on high resolution space data. Doklady Akademii nauk, 2015, vol. 464, no. 5, pp. 639—641. (In Russian).

14. Forest plan of Murmansk region. Available at: https://mpr.gov-murman.ru/documents/lesplan/. (In Russian).

15. Forest plan of Republic of Karelia. Available at: https://gov.karelia.ru/upload/iblock/ffb/12_2_562_704.pdf. (In Russian).

16. Shikhov A. N., Gerasimov A. P., Ponomarchuk A. I., Perminova E. S. Thematic decryption and interpretation of space images of medium and high spatial resolution. Perm, Perm State National Research Univ., 2020, 191 p. (In Russian).

17. Evdokimov S. I., Mikhalap S. G. Determine the physical meaning of a combination of Landsat image channels to monitor the state of terrestrial and aquatic ecosystems. Annals of Pscov State Univ. Ser. “Natural and Mathematical Sciences”, 2010, no. 7, pp. 21—32. (In Russian).

18. Labutina I. A., Baldina E. A. Using remote sensing data to monitor protected areas ecosystems. Guidebook. Moscow, WWF Rossii, 2011, 88 p. (In Russian).

19. Bartalev S. A., Loupian E. A. R&D on methods for satellite monitoring of vegetation by the Russian Academy of Sciences’ Space Research Institute. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz cosmosa [Ñurrent problems in remote sensing of the earth from space], 2013, vol. 10, no. 1, pp. 197—214. (In Russian).

20. Komarova A. F., Zhuravleva I. V., Yablokov V. M. Open source multispectral data and basic methods of remote sensing in vegetation cover investigations. Principles of ecology, 2016, no. 1, pp. 40—74. (In Russian).

21. Chuvieco E. Fundamentals of satellite remote sensing: an environmental approach. Second Edition. Boca Raton/London. New York, CRC Press Taylor & Francis, 2016, 468 p.

22. Jensen J. R. Introductory digital image processing: a remote sensing perspective. Pearson Series in Geographic Information Science. [S. l.], 2015, 656 p.

23. Shridhar D., Prapti D., Alvarinho J. A. Comprehensive Review on Pixel Oriented and Object Oriented Methods for Information Extraction from Remotely Sensed Satellite Images with a Special Emphasis on Cryospheric Applications. Advances in Remote Sensing, 2015, vol. 4, no. 3, pp. 177—195. DOI: 10.4236/ars.2015.43015.

24. Raevsky B. V., Tarasenko V. V., Petrov N. V. Inventory of the current state and changes in vegetation cover of the Onega Peninsula using staggered Landsat images. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz cosmosa [Ñurrent problems in remote sensing of the earth from space], 2021, vol. 18, no. 5, pp. 145—155. DOI: 10.21046/2070-7401-2021-18. (In Russian).

25. Raevsky B. V., Tarasenko V. V., Petrov N. V. Inventory of the Kostomukshskiy Strict Nature Reserve vegetation using Landsat images. Sovremennye problemy distantsionnogo zondirovania Zemli iz cosmosa [Ñurrent problems in remote sensing of the earth from space], 2022, vol. 19, no. 3, pp. 47—61. DOI: 10.21046/2070-7401-2021-18.

26. Kurbanov E. A., Vorobyov O. N. Remote methods in forestry: a textbook. Yoshkar-Ola, Volga State Technological Univ., 2020, 266 p. (In Russian).

27. Fassnacht F. E., Latifi H., Stereńczak K., Modzelewska A., Lefsky M., Waser L. T. Review of studies on tree species classification from remotely sensed data. Remote Sensing of Environment, 2016, vol. 186, pp. 64—87.

28. Haapanen R., Ek A. R., Bauer M. E., Finley A. O. Delineation of forest/non forest land use classes using nearest neighbor methods. Remote Sensing of Environment, 2004, vol. 89, pp. 265—271.

29. Malysheva N. V. Automatic interpretation of forests’ remote sensing data. Guidebook. Moscow, MGUL publish., 2012, 154 p. (In Russian).

30. Geocryology map of Russia on a scale of 1:2 500 000. Ed. By E. D. Ershov. Moscow, Moscow Univ. edition, 1996. (In Russian).


Download »


© 2011-2024 Arctic: ecology and economy
DOI 10.25283/2223-4594