Evaluation of the Structure and Composition of Forests in Moscow Region Based on Field and Remote Sensing Data

The sequence and content of the main stages of determining the indicators of the structure and composition of forest communities based on satellite imagery of the Landsat system are described. It is shown that the application of quantitative processing methods for the interpolation of point data from field research, in particular canonical discriminant analysis, allows obtaining characteristics of the vegetation cover and investigating the factors of its biodiversity formation. The presented methods and results of the assessment of various parameters of the state of forests, their structure and typological composition can be integrated into the international network of the National Forest Inventory. Despite the difference in methodological approaches, there is a  principal possibility of harmonizing the data obtained with the data of the Global Earth Observation System of Systems. For the test territory in the central partof the Russian plain (western sector of the Moscow oblast), the results of a joint analysis of field research data, remote sensing dataand a  digital elevation model are presented. A  series of maps of the medium scale characterizing the spatial structure and composition of the forest cover of the study area was obtained.

1. Kapralov E.G., Koshkarev A.V., Tikunov V.S., Glazyrin V.V., Zavarzin A.V., Zamai S.S., Lur'e I.K., Okhonin V.A., Pyr'ev V.I., Ryl'skii I.A., Semin V.N., Serapinas B.B., Simonov A.V., Trofimov A.M., Fleis M.E., Yakubailik O.E., Yarovykh V.B. Geoinformatika [Geoinformatics]. Moscow: Akademiya Publ., 2010, part 2. 432 p.

2. Kendall M., Stuart A. Mnogomernyi statisticheskii analiz i vremennye ryady [Multivariate Statistical Analysis and Time Series]. Moscow: Nauka Publ., 1976. 736 p.

3. Knizhnikov Yu.F., Kravtsova V.I., Tutubalina O.V. Aerokosmicheskie metody geograficheskikh issledovanii [Aerospace Methods of Geographical Research]. Moscow: Akademiya Publ., 2004. 336 p.

4. Kozlov D.N., Puzachenko M.Yu., Fedyaeva M.V., Puzachenko Yu.G. Representation of spatial variation of landscape cover properties on the basis of remote sensing data Landsat and digital elevation model. Izv. Akad. Nauk, Ser. Geogr., 2008, no. 4, pp. 112–124. (In Russ.).

5. Kalmykova O.V. Kompleksnyi obzor GEOSS. Issledovanie arkhitektury postroeniya GEOSS [Comprehensive Review of GEOSS. Study of the GEOSS Architecture]. 2013. Available at: http://rusgnz.com/ mvk3doc/issledovanie_arkhitektury_postroenija_geoss_2013.pdf (accessed: 04.06.2019). (In Russ.).

6. Forest Plan of the Moscow Region. Moscow: Federal’noe Agentstvo Lesnogo Khozyaistva, 2010, vol. 1. 428 p. (In Russ.).

7. Obshchesoyuznye normativy dlya taksatsii lesov. Spravochnik [All-Union Standards for Forest Valuation. Handbook]. Moscow: Kosmos Publ., 1992. 496 p.

8. Pomerantsev A. Klassifikatsiya [Classification]. 2011. Available at: http://rcs.chemometrics.ru/Tutorials/ classification.htm (accessed: 04.06.2019) (In Russ.).

9. Puzachenko Yu.G. Matematicheskie metody v ekologicheskikh i geograficheskikh issledovaniyakh [Mathematical Methods in Ecological and Geographical Studies]. Moscow: Akademiya Publ., 2004. 408 p.

10. Puzachenko Yu.G. Science and the concept of sustainable development. In Landshaftnoe planirovanie: obshchie osnovaniya, metodologiya, tekhnologiya: tr. Mezhd. shkoly-konf. “Landshaftnoe planirovanie” [Landscape Planning: Basics, Methodology, Technology: Works of the Int. School-Conf. "Landscape Planning"]. Moscow: Mosk. Gos. Univ., 2006, pp. 4–80. (In Russ.).

11. Puzachenko Yu.G., Fedyaeva M.V., Kozlov D.N., Puzachenko M.Yu. Methodological grounds for mapping elementary geosystem processes. In Sovremennye estestvennye i antropogennye protsessy v pochvakh i geosistemakh [Modern Natural and Anthropogenic Processes in Soils and Geosystems]. Moscow: Ross. Akad. S–kh Nauk, 2006, pp. 13–52. (In Russ.).

12. Savorskii V.P., Tishchenko Yu.G. Global Earth observation system of systems - state and current activities. Sovremennye Probl. Distantsionnogo Zondirovaniya Zemli iz Kosmosa, 2010, vol. 7, no. 2, pp. 193–199. (In Russ.).

13. Tyurin Yu.N., Makarov A.A. Statisticheskii analiz dannykh na komp'yutere [Statistical Analysis of Data on a Computer]. Moscow: INFRA-M Publ., 1998. 528 p.

14. Chernen'kova T.V., Morozova O.V. Classification and mapping of coenotic diversity of forests. Lesovedenie, 2017, no. 4, pp. 243–255. (In Russ.).

15. Olsson H., Nilsson M., Persson A. GEOSS possibilities and challenges related to nationwide forest monitoring. In Proc. of the ISPRS Commission VII Term Symposium, Enschede, The Netherlands. 2006, vol. 811. Available at: http://www.isprs.org/proceedings/ XXXVI/part7/PDF/219.pdf (accessed: 04.06.2019).

16. Tomppo E. The Finnish national forest inventory. In Forest inventory. Kangas A., Maltamo M., Eds. Dodrecht: Springer, 2006, pp. 179–194. doi 10.1007/1-4020-4381-3_11

17. Tomppo E., Olsson H., Ståhl G., Nilsson M., Hagner O., Katila M. Combining national forest inventory field plots and remote sensing data for forest databases. Remote Sens. Environ., 2008, vol. 112, no. 5, pp. 1982–1999.

18. Tomppo E., Katila M., Mäkisara K., Peräsaari J. The Multi-Source National Forest Inventory Of Finland — Methods And Results 2011, vol. 319 of Working Papers of the Finnish Forest Research Institute. 2014. 224 p. Available at: http://www. metla.fi/julkaisut/workingpapers/2014/mwp319.htm (accessed: 15.05.2019).

19. Webster R., Burrough P.A. Multiple discriminant analysis in soil survey. J. Soil Sci., 1974, vol. 25, no. 1, pp. 120–134.