Studying the vegetation impact of terrestrial ecosystems on reducing the carbon footprint in in the territory of the Russian Federation

Cover Page

Cite item

Abstract

Plant communities of terrestrial ecosystems of the Russian Federation are studied in terms of their ability to reduce the carbon footprint as a result of carbon dioxide sequestration. The classification of typical plant communities and the division of the territory depending on the climatic and regional characteristics is given, with further provision of values of the specific absorption capacity of growing plant communities according to the division presented. To assess the biomass of vegetation, as well as its dynamics of change, an analysis of the remote sensing method was carried out as the most preferred method for determining biomass in real time. The characteristics of currently used remote sensing systems, including IKONOS, Quickbird, Worldview, ZY-3, SPOT, Sentinel, Landsat and MODIS are given. The main indicators used for the indexation assessment of vegetation biomass are listed, with subsequent prediction based on them of the efficiency of carbon dioxide uptake by plant communities.

About the authors

Marina A. Pashkevich

Saint Petersburg Mining University

Email: mpash@spmi.ru
ORCID iD: 0000-0001-7020-8219

Dr.Sci. (Eng.), Head of the Department of Geoecology

2 21st Line, Saint Petersburg, 199106, Russian Federation

Anna E. Korotaeva

Saint Petersburg Mining University

Author for correspondence.
Email: s205056@stud.spmi.ru
ORCID iD: 0000-0002-0211-6782

postgraduate student

2 21st Line, Saint Petersburg, 199106, Russian Federation

References

  1. Di Vita G, Pilato M, Pecorino B, Brun F, D’Amico M. A Review of the role of vegetal ecosystems in CO2 capture. Sustain. 2017;9:1840. http://doi.org/10.3390/SU9101840
  2. Fyodorov BG, Moiseev BN, Sinyak YuV. Absorption capacity of Russian forests and carbon dioxide emissions by energy facilities. Problemy Prognozirovaniya. 2011; 126(3):127-42. (In Russ.)
  3. Akita N, Ohe Y. Sustainable forest management evaluation using carbon credits: from production to environmental forests. Forests. 2021;12(8):1-18. http://doi.org/10.3390/f12081016
  4. Cherepovitsyn AE, Sidorova AE, Smirnova AE. Feasibility of using CO2 sequestration technologies in Russia. Neftegazovoe Delo. 2013;(5):459-473. (In Russ.)
  5. Krasutsky BV. Absorption of carbon dioxide woods of Chelyabinsk region: modern ecological and economical aspects. Tyumen State Univ. Herald Nat. Resour. Use Ecol. 2018;4(3):57-68. http://doi.org/10.21684/2411-7927-2018-4-3-57-68
  6. Koroleva NE. The main types of plant communities “Russian Svalbard.” Trudy Karel’skogo Nauchnogo Centra RAN. 2016;(7):3-26. (In Russ.) http://doi.org/10.17076/bg323
  7. Bykova MV, Alekseenko AV, Pashkevich MA, Drebenstedt C. Thermal desorption treatment of petroleum hydrocarbon-contaminated soils of tundra, taiga, and forest steppe landscapes. Environю. Geochem. Health. 2021;43(6):2331-2346. http://doi.org/10.1007/S10653-020-00802-0
  8. Kurbatova AI. Analytical review of modern studies of changes in the biotic components of the carbon cycle. RUDN Journal of Ecology and Life Safety. 2020;28(4):428-438. (In Russ.) http://doi.org/10.22363/2313-2310-2020-28-4-428-438
  9. Zamolodchikov D, Grabovskiy V, Kurc V. Managing the carbon balance of Russia’s forests: past, present and future. Ustojchivoe Lesopol'zovanie. 2014;2(39):23-31. (In Russ.)
  10. Mancini MS, Galli A, Niccolucci V, Lin D, Bastianoni S, Wackernagel M, Marchettini N. Ecological footprint: refining the carbon footprint calculation. Ecol. Indic. 2016;61: 390-403. http://doi.org/10.1016/j.ecolind.2015.09.040
  11. Xu D, Wang H, Xu W, Luan Z, Xu X. LiDAR applications to estimate forest biomass at individual tree scale: opportunities, challenges and future perspectives. Forests. 2021;12(5):1-19. http://doi.org/10.3390/f12050550
  12. Calders K, Jonckheere I, Nightingale J, Vastaranta M. Remote sensing technology applications in forestry and REDD+. Forests. 2020;11(2):10-13. http://doi.org/10.3390/f11020188
  13. Chen L, Ren C, Zhang B, Wang Z, Xi Y. Estimation of forest above-ground biomass by geographically weighted regression and machine learning with sentinel imagery. Forests. 2018;9(10):1-20. http://doi.org/10.3390/f9100582
  14. Kumar L, Mutanga O. Remote sensing of above-ground biomass. Remote Sens. 2017;9(9):1-8. http://doi.org/10.3390/rs9090935
  15. Adamovich TA, Kantor GYa, Ashikhmina TYa, Savinykh VP. The analysis of seasonal and long-term dynamics of the vegetative NDVI index in the territory of the State Nature Reserve “Nurgush”. Teoreticheskaya i Prikladnaya Ecologiya. 2018;(1):18-24. (In Russ.)
  16. Ferwerda JG, Skidmore AK, Mutanga O. Nitrogen detection with hyperspectral normalized ratio indices across multiple plant species. Int. J. Remote Sens. 2005;26(18):4083-4095. http://doi.org/10.1080/01431160500181044
  17. Seward A, Ashraf S, Reeves R, Bromley C. Improved environmental monitoring of surface geothermal features through comparisons of thermal infrared, satellite remote sensing and terrestrial calorimetry. Geothermics. 2018;73:60-73. http://doi.org/10.1016/j.geothermics.2018.01.007
  18. Adão T, Hruška J, Pádua L, Bessa J, Peres E, Morais R, Sousa JJ. Hyperspectral imaging: a review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sens. 2017;9(11):1110. http://doi.org/10.3390/rs9111110
  19. Strizhenok AV, Ivanov AV. Ecological assessment of the current state of environmental components on the territory of the impact of cement production industry. J. Ecol. Eng. 2017;18(6):160-165. http://doi.org/10.12911/22998993/76850
  20. Kusumaning Asri A, Lee HY, Pan WC, Tsai HJ, Chang HT, Candice Lung SC, Su HJ, Yu CP, Ji JS, Wu CD, Spengler JD. Is green space exposure beneficial in a developing country? Landsc Urban Plan. 2021;215:104226. http://doi.org/10.1016/J.LANDURBPLAN.2021.104226
  21. John J, Jaganathan R, Dharshan Shylesh DS. Mapping of Soil moisture index using optical and thermal remote sensing. Lect. Notes Civ. Eng. 2022;171:759-767. http://doi.org/10.1007/978-3-030-80312-4_65
  22. Laefer DF. Harnessing remote sensing for civil engineering: then, now, and tomorrow. Lecture Notes in Civil Engineering. 2020;33:3-30.
  23. Liu N, Harper RJ, Handcock RN, Evans B, Sochacki SJ, Dell B, Walden LL, Liu S. Seasonal timing for estimating carbon mitigation in revegetation of abandoned agricultural land with high spatial resolution remote sensing. Remote Sens. 2017;9(6):545. http://doi.org/10.3390/rs9060545
  24. Chevrel S, Bourguignon A. Application of optical remote sensing for monitoring environmental impacts of mining: from exploitation to postmining. L. Surf. Remote Sens. Environ. Risks. Elsevier; 2016. p. 191-220. http://doi.org/10.1016/B978-1-78548-105-5.50006-2
  25. IUCN and WRI. A guide to the Restoration Opportunities Assessment Methodology (ROAM): assessing forest landscape restoration opportunities at the national or sub-national level. Switzerland: IUCN; 2014.
  26. Veludo G, Cunha M, Sá MM, Oliveira-Silva C. Offsetting the impact of CO2 emissions resulting from the transport of Maiêutica’s academic campus community. Sustainability. 2021;13:10227. https://doi.org/10.3390/su131810227
  27. Asner GP, Powell GVN, Mascaro J, Knapp DE, Clark JK, Jacobson J, Kennedy-Bowdoin T, Balaji A, Paez-Acosta G, Victoria E., Secada L., Valqui M, Hughes RF. High-resolution forest carbon stocks and emissions in the Amazon. Proc. Natl. Acad. Sci. USA. 2010;107(38):16738-16742. http://doi.org/10.1073/pnas.1004875107
  28. Pan Y, Birdsey RA, Fang J, Houghton R, Kauppi PE, Kurz WA, Phillips OL, Shvidenko A, Lewis SL, Canadell JG, Ciais Ph, Jackson RB, Pacala SW, McGuire AD, Piao S, Rautiainen A, Sitch S, Hayes D. A large and persistent carbon sink in the world’s forests. Science. 2011;333(6045):988-993. http://doi.org/10.1126/science.1201609
  29. Bernal B, Murray LT, Pearson TRH. Global carbon dioxide removal rates from forest landscape restoration activities. Carbon Balance Manag. 2018;13(1), 22. https://doi.org/10.1186/s13021-018-0110-8

Copyright (c) 2021 Pashkevich M.A., Korotaeva A.E.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

This website uses cookies

You consent to our cookies if you continue to use our website.

About Cookies