Advertisement

Izvestiya, Atmospheric and Oceanic Physics

, Volume 53, Issue 9, pp 1029–1041 | Cite as

Study of Wetland Ecosystem Vegetation Using Satellite Data

  • E. A. Dyukarev
  • M. N. Alekseeva
  • E. A. Golovatskaya
The Use of Space Information about the Earth
  • 11 Downloads

Abstract

The normalized difference vegetation index (NDVI) is used to estimate the aboveground net production (ANP) of wetland ecosystems for the key area at the South Taiga zone of West Siberia. The vegetation index and aboveground production are related by linear dependence and are specific for each wetland ecosystem. The NDVI grows with an increase in the ANP at wooded oligotrophic ecosystems. Open oligotrophic bogs and eutrophic wetlands are characterized by an opposite relation. Maps of aboveground production for wetland ecosystems are constructed for each study year and for the whole period of studies. The average aboveground production for all wetland ecosystems of the key area, which was estimated with consideration for the area they occupy and using the data of satellite measurements of the vegetation index, is 305 g C/m2/yr. The total annual carbon accumulation in aboveground wetland vegetation in the key area is 794600 t.

Keywords

wetland ecosystems vegetation production vegetation index ground cover mapping 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alekseeva, M.N., Preis, Yu.I., and Dyukarev, E.A., Spatial structure of ground vegetation cover and the upper peat layer type in the northeastern branches of the Great Vasyugan Mire according to remote sensing and ground-based data, Izv. Tomsk. Politekh. Univ., 2015, vol. 326, no. 4, pp. 81–90.Google Scholar
  2. Anisimov, O.A., Zhil’tsova, E.L., and Razzhivin, V.Yu., Predictive modeling of plant productivity in the Russian Arctic using satellite data, Izv., Atmos. Ocean. Phys., 2015, vol. 51, no. 9, pp. 1051–1059.CrossRefGoogle Scholar
  3. Bartalev, S., Belward, A.S., Erchov, D., and Isaev, A.S., A new land cover map of Northern Eurasia, Int. J. Remote Sens., 2003, vol. 24, pp. 1977–1982.CrossRefGoogle Scholar
  4. Bazanov, V.A. and Berezin, A.E., Inventory of swamps in oil extraction areas of the Tomsk region, Vestn. Tomsk. Gos. Univ., 2006, no. 30, pp. 29–33.Google Scholar
  5. Boch M.S. and Mazing V.V., Ekosistemy bolot SSSR (Ecosystems of USSR Swamps), Leningrad: Nauka, 1979.Google Scholar
  6. Cherepanov, A.S. and Druzhinina, E.G., Spectral properties of vegetation and vegetation indices, Geomatika, 2009, no. 3, pp. 28–32.Google Scholar
  7. Dyukarev, E.A., Pologova, N.N., and Golovatskaya, E.A., Remote sensing technologies for identifying the structure forest–swamp complexes of the Bakcharskii key section, Zh. SFU: Tekh. Tekhnol., 2008, vol. 1, no. 4, pp. 334–345.Google Scholar
  8. Efremov, S.P., Efremova, T.T., and Melent’eva, N.V., Carbon reserves in wetland ecosystems, in Uglerod v ekosistemakh lesov i bolot Rossii (Carbon in Ecosystems of Forests and Swamps in Russia), Alekseev, V.A. and Berdsi, R.A., Eds., Krasnoyarsk, 1994, pp. 128–139.Google Scholar
  9. Elsakov, V.V. and Kulyugina, E.E., The vegetation cover of the Yugor Peninsula under climate changes of past decades, Issled. Zemli Kosmosa, 2014, no. 3, pp. 65–77.Google Scholar
  10. Golovatskaya, E.A. and Porokhina, E.V., Botanika s osnovami fitotsenologii. Biologicheskaya produktivnost' bolotnykh biogeotsenozov: Uch.-met. posobie (Botany with Basics of Phytocenology. Bioproductivity of Swamp Biogeocenoses: A Study Guide), Dyrin, V.A., Ed., Tomsk: Tomsk. ped. univ., 2005.Google Scholar
  11. Golovatskaya, E.A., Bioproductivity of oligotrophic and eutrophic swamps of the southern taiga subzone of West Siberia, Zh. SFU: Biol., 2009, vol. 2, no. 3, pp. 38–53.Google Scholar
  12. Golovatskaya, E.A. and Dyukarev, E.A., Method of spatial interpolation of bioproductivity of swamp ecosystems taking into account the structure of vegetation cover, in “Matematicheskoe modelirovanie v ekologii”, Mat. vtoroi nats. konf. s mezhdun. uch. (Mathematical Modeling in Ecology: Proceedings of the Secon National Conference with International Participation), Pushchino: IFKhiBPP RAN, 2011, pp. 72–74.Google Scholar
  13. Golubyatnikov, L.L. and Denisenko, E.A., Interrelation between the vegetation index and the climatic parameters and structural characteristics of vegetation cover, Izv., Atmos. Ocean. Phys., 2006, vol. 42, no. 4, pp. 484–496.CrossRefGoogle Scholar
  14. Hese, S. and Schmullius, C., High spatial resolution image object classification for terrestrial oil spill contamination mapping in West Siberia, Int. J. Appl. Earth Obs. Geoinf., 2009, vol. 11, no. 2, pp. 130–141.CrossRefGoogle Scholar
  15. Issledovanie prirodno-klimaticheskikh protsessov na territorii Bol’shogo Vasyuganskogo bolota (Study of the Natural and Climatic Processes in the Territory of the Great Vasyugan Mire), Novosibirsk: SO RAN, 2012.Google Scholar
  16. Krankina, O.N., Pflugmacher, D., Friedl, M., Cohen, W.B., Nelson, P., and Baccini, A., Meeting the challenge of mapping peatlands with remotely sensed data, Biogeoscience, 2008, vol. 5, pp. 1809–1820.CrossRefGoogle Scholar
  17. Krutikov, V.A., Polishchuk, Yu.M., Kozin, E.S., and Tokareva, O.S., Geoinformation support of the comprehensive monitoring of the Great Vasyugan Mire, in Bol’shoe Vasyuganskoe boloto. Sovremennoe sostoyanie i protsessy razvitiya (The Great Vasyugan Mire: The Current State and Development Processes), Kabanov, M.V., Ed., Tomsk: Inst. Opt. Atmos. SO RAN, 2002, pp. 73–79.Google Scholar
  18. Landshafty bolot Tomskoi oblasti (Swamp Landscapes of the Tomsk Region), Evseeva, N.S., Ed., Tomsk: Izd. Nauch.–tekh. lit., 2012.Google Scholar
  19. Lidzhieva, N.Ts., Ulanova, S.S., and Fedorova, N.L., Experience of the use vegetation index (NDVI) for determining the bioproductivity of arid zone phytocenoses on the example of the Chernye Zemli region, Izv. Sarat. Univ., Ser. Khim. Biol. Ekol., 2012, vol. 12, no. 2, pp. 94–96.Google Scholar
  20. Markham, B.L., Haque, M.O., Barsi, J.A., Micijevic, E., Helde, D.L., Thome, K.J., Aaron, D., and Czapla-Myers, J.S., Landsat-7 ETM+: 12 years on-orbit reflective-band radiometric performance, Geosci. Remote Sens., 2012, vol. 50, no. 5, pp. 2056–2062.CrossRefGoogle Scholar
  21. Medvedeva, M.A., Savin, I.Yu., Bartalev, S.A., Lupyan, E.A., The use of NOAA-AVHRR data for revealing the multiyear dynamics of vegetation in northern Eurasia, Issled. Zemli Kosmosa, 2011, no. 4, pp. 55–62.Google Scholar
  22. Pflugmacher, D., Krankina, O., Cohen, W.B., Friedl, M.A., Sulla-Menashe, D., Kennedy, R.E., Nelson, P., Loboda, T.V., Kuemmerle, T., Dyukarev, E., Elsakov, V., and Kharuk, V.I., Comparison and assessment of coarse resolution land cover maps for Northern Eurasia, Remote Sens. Environ., 2011, vol. 115, pp. 3539–3553.CrossRefGoogle Scholar
  23. Potapov, P., Turubanova, P.S. and Hansen, M.C., Regionalscale boreal forest cover and change mapping using Landsat data composites for European Russia, Remote Sens. Environ., 2011, vol. 115, no. 2, pp. 548–561.CrossRefGoogle Scholar
  24. Shevyrnogov, A.P., Chernetskii, M.Yu., and Vysotskaya, G.S., Multiyear trends of NDVI and temperature in the south of the Krasnoyarsk province, Issled. Zemli Kosmosa, 2012, no. 6, pp. 77–87.Google Scholar
  25. Sinyutkina, A.A., Classification of swamp geosystems of the Tomskoi region, Vestn. Tomsk. Gos. Univ., 2012, no. 357, pp. 192–195.Google Scholar
  26. Solano, R., Didan, K., Jacobson, A., and Huete, A., MODIS vegetation index user’s guide (MOD13 Series) Version 2.00, May 2010 (Collection 5). https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod13a1.Google Scholar
  27. Terekhin, E.A., Assessment of seasonal values of the vegetation index (NDVI) for detecting and analysis of the state of agricultural crop seeding, Issled. Zemli Kosmosa, 2015, no. 1, pp. 23–31.Google Scholar
  28. Wulder, M.A., Masek, J.G., Cohen, W.B., Loveland, T.R., and Woodcock, C.E., Opening the archive: How free data has enabled the science and monitoring promise of Landsat, Remote Sens. Environ., 2012, vol. 122, pp. 2–10.CrossRefGoogle Scholar
  29. Yashchenko, I.G., Alekseeva, M.N., and Svarovskaya, L.I., Geoinformation technologies for analyzing the river pollution by oil, Interekspo Geo-Sibir’, 2014, vol. 7, pp. 38–43.Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2017

Authors and Affiliations

  • E. A. Dyukarev
    • 1
  • M. N. Alekseeva
    • 2
  • E. A. Golovatskaya
    • 1
  1. 1.Institute of Monitoring of Climatic and Ecological Systems, Siberian BranchRussian Academy of SciencesTomskRussia
  2. 2.Institute of Petroleum Chemistry, Siberian BranchRussian Academy of SciencesTomskRussia

Personalised recommendations