Application of remote sensing methods and GIS technologies for the classification of lands in the Pushkin district of St. Petersburg
https://doi.org/10.21266/2079-4304.2021.235.84-102
Abstract
GIS technologies and methods of automated classification of remote sensing data are actively used in many countries in forest inventory, forest management planning and assessment of the state and characteristics of forests. The objectives of the study were to develop a methodology and conduct an automated classification of land categories for a large administrative-territorial unit based of remote sensing methods and GIStechnologies. The object of the study was the territory of the Pushkin district of St. Petersburg. On the territory of the Pushkin district are located a significant number of green zones (parks, squares, gardens, forest belts), some of which are included in the list of monuments protected by UNESCO. Also on the territory of the district are located industrial enterprises, large industrial zones, agriculture is developed. Lansat-8 satellite images and a set of maps for the study area were used as initial materials. GIS ArcGIS and MapInfo, programs ENVI and Trimble eCognition were used to collect, visualize and process data. Field work on the selection of reference (training) samples included the selection of sample plots in nature, photography, and determination of coordinates. The Landsat images were classified according to the results of two main operations – automated interpretation by the maximum likelihood method and determination of the vegetation indices of the land categories classes. After performing field verification, as well as performing processing and aggregation operations, the final thematic map of the classes of land categories in the Pushkin region was formed and the final tables of the distribution of areas by municipalities were obtained. The presented methodology, associated with the processing and interpretation of remote sensing materials by means of GIS technologies, can be considered as a modern tool for landscape analysis, state (national) forest inventory, and various types of territory monitoring.
About the Authors
Thanh Quyet PhanRussian Federation
PHAN Thanh Quyet – PhD student
194021. Institute per. 5. St. Petersburg
Trong Tai Nguyen
Russian Federation
NGUYEN Trong Tai – PhD student
194021. Institute per. 5. St. Petersburg
A. S. Alekseev
Russian Federation
ALEKSEEV Aleksandr Sergeevich – DSc (Geography), professor, head of the department of forest inventory, management and GIS
194021. Institute per. 5. St. Petersburg
A. V. Lyubimov
Russian Federation
LYUBIMOV Aleksandr V. – DSc (Agricultural), professor of the department of forest inventory, management and GIS
194021. Institute per. 5. St. Petersburg
V. L. Sergeeva
Russian Federation
SERGEEVA Valeria L. – PhD (Biological), associate professor of the department of forest inventory, management and GIS
194021. Institute per. 5. St. Petersburg
D. M. Chernikhovskii
Russian Federation
CHERNIKHOVSKII Dmitrii M. – PhD (Agricultural), associate professor of the department of forest inventory, management and GIS
194021. Institute per. 5. St. Petersburg
References
1. Ali M.S., Vorobev O.N., Kurbanov E.A. Decision tree algorithm for forest classification of Syrian Arab Republic with the use of Sentinel-2 image. Vestnik of Volga State University of Technology. Series: Forest. Ecology. Nature Management, 2020, is. 1 (45), pp. 5–30. DOI: 10.25686/2306-2827.2020.1.5. (In Russ.)
2. Bartalev S., Egorov V., Zharko V., Loupian E., Plotnikov D., Khvostikov S., Shabanov N. Land cover mapping over Russia using Earth observation data. Moscow: Russian Academy of Sciences’ Space Research Institute, 2016, 208 p. (In Russ.)
3. Belova E.I., Ershov D.V. Assessing reforestation on clear cuts based on Landsat time series. Lesovedenie, 2015, is. 5, pp. 339–345. (In Russ.)
4. Belova E.I., Ershov D.V. Using Landsat time series for assessing reforestation on clear cuts in Bryansk region. Forest science issues, 2019, is. 2(4), pp. 1–20. DOI: 10.31509/2658-607x-2019-2-4-1-20. (In Russ.)
5. Vorobev O.N., Kurbanov E.A., Demisheva E.N., Menshikov S.A., Smirnova L.N. Algorithm for reviling the phenological parameters of forest cover on the base of time series of satellite data. Vestnik of Volga State University of Technology. Series: Forest. Ecology. Nature Management, 2019a, is. 1 (41), pp. 5–20. DOI: 10.25686/2306-2827.2019.1.5 (In Russ.)
6. Vorobev O.N., Kurbanov E.A., Demisheva E.N., Menshikov S.A., Ali M.S., Smirnova L.N., Tarasova L.V. Remote monitoring of forest ecosystems sustainability. Yoshkar-Ola: Volga State University of Technology, 2019b, 166 p. (In Russ.)
7. Vorobev O.N., Kurbanov E.A. Remote monitoring of vegetation regeneration dynamics on burnt areas of Mari Zavolzhje forests. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, is. 14(2), pp. 84–97. DOI: 10.21046/2070-7401-2017-14-2-84-97. (In Russ.)
8. Vorobyev O.N., Kurbanov E.A., Gubayev A.V., Leznin S.A., Polevshikova Y.A. Remote monitoring of forest burnt areas in Mari Zavolzhje. Vestnik of Volga State University of Technology. Series: Forest. Ecology. Nature Management, 2012, is. 1, pp. 12–22. (In Russ.)
9. Vorobyev O.N., Kurbanov E.A., Polevshikova Y.A., Leznin S.A. Assessment of dynamics and disturbance of forest cover in the Middle Povolzhje by Landsat images. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2016, vol. 13, is. 4, pp. 124–134. DOI: 10.21046/2070-7401-2016-13-3-124-134. (In Russ.)
10. Zhirin V.M., Knyazeva S.V., Eydlina S.P. Application of space images for reforestation dynamics evaluating on the plane territories. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2011, vol. 8, is. 2, pp. 208–216. (In Russ.)
11. Neshataev M.V., Neshataev V.Yu. Combined method of vegetation mapping (on the example of the Lapland reserve). Izvestia Sankt-Peterburgskoj lesotehniceskoj akademii, 2012, is. 201, pp. 29–40. (In Russ.)
12. Rees W.G. Physical Principles of Remote Sensing. Moscow: Technosphera, 2006, 336 p. (In Russ.)
13. 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. Tyumen State University Herald. Natural Resource Use and Ecology, 2018, vol. 4, is. 1, pp. 37–49. DOI: 10.21684/2411-7927-2018-4-1-37-49. (In Russ.)
14. Tokareva O.S. Processing and interpreting of Earth remote sensing data. Tomsk: Tomsk Polytechnic University Publishing House, 2010, 148 p. (In Russ.)
15. Chandra A.M., Ghosh S.K. Remote Sensing and Geographic Information Systems. Moscow: Technosphera, 2008, 312 p. (In Russ.)
16. Sharikalov A.G., Yakutin M.V. The analysis of taiga ecosystems condition applying automatic decoding method. Izvestiya of Altai State University. Earth sciences, 2014, is. 3-1 (83), pp. 123–127. DOI: 10.14258/izvasu(2014)3.1-22 (In Russ.)
17. Schowengerdt R.A. Remote sensing. Methods and models of image processing. Moscow: Technosphera, 2010, 560 p. (In Russ.)
18. Chu T., Guo X., Takeda K. Remote sensing approach to detect post-fire vegetation regrowth in Siberian boreal larch forest. Ecological Indicators, 2016, is. 62, pp. 32–46. DOI:10.1016/j.ecolind.2015.11.026
19. Franklin S.E. Remote sensing for sustainable forest management. LEWIS PUBLISHERS Boca Raton London New York Washington, 2001, 407 p.
20. Frazier R.J., Coops N.C., Wulder M.A. Boreal Shield forest disturbance and recovery trends using Landsat time series. Remote Sensing of Environment, 2015, is. 170, pp. 317–327. DOI: 10.1016/j.rse.2015.09.015
21. Maguire D.J., Goodchild M.F., Rhind D.W., Geographical information systems: principles and applications. New York: Wiley, Harlow, England, 1991, 649 p.
22. Ramachandran B., Justice C.O, Abrams M.J. Land Remote Sensing and Global Environmental Change NASA's Earth Observing System and the Science of ASTER and MODIS. Springer Science+Business Media, LLC, 2011, 873 p.
23. Vilaa J.P.S., Barbosa P. Post-fire vegetation regrowth detection in the Deiva Marina region (Liguria-Italy) using Landsat TM and ETM+ data. Ecological Modelling, 2010, is. 210, pp. 75–84.
Review
For citations:
Phan T.Q., Nguyen T.T., Alekseev A.S., Lyubimov A.V., Sergeeva V.L., Chernikhovskii D.M. Application of remote sensing methods and GIS technologies for the classification of lands in the Pushkin district of St. Petersburg. Izvestia Sankt-Peterburgskoj lesotehniceskoj akademii. 2021;(235):84-102. (In Russ.) https://doi.org/10.21266/2079-4304.2021.235.84-102