Estimation of Ukrainian forest cover (Western Polissia) using remote sensing data

1Movchan, DM
1State institution «Scientific Centre for Aerospace Research of the Earth” of the Institute of Geological Science of the National Academy of Sciences of Ukraine, Kyiv, Ukraine
Kosm. nauka tehnol. 2013, 19 ;(4):29–43
Publication Language: Ukrainian

The dynamics of biophysical parameters of forest cover is analysed on the basis of remote sensing data for Ukrainian Western Polissia to estimate the intensity of carbon absorption by the forest cover. Seasonal changes of the basic vegetation cover parameters (NDVI, EVI, LAI, FPAR, ET, GPP and NPP) from 2000 to 2011 are analysed using MODIS data. Our results show that seasonal variations of vegetation cover parameters are closely connected with seasonal growth of vegetation. Weather variables for the periods under consideration are studied. Some correlations between GPP and NPP and different vegetation parameters and climatic factors are estimated. Water use efficiency (WUE) as the ratio of GPP to evapotranspiration (ET) and carbon uptake efficiency as NPP/GPP ratio are calculated and analysed.

Keywords: carbon uptake, forest cover, remote sensing
1. National Atlas of Ukraine, 440 p. (Kartografija, Kyiv, 2008) [in Ukrainian].
2. Cramer W., Kicklighter D. W., Bondeau A., et al. Comparing global models of terrestrial net primary productivity (NPP): overview and key results,  Global Change Biology, 5, 1—15 (1999).
 3. Earth systems change over Eastern Europe,  Ed. by P. Y. Groisman, V. I. Lyalko, 487 p. (Akademperiodyka, Kyiv, 2012).
 4. Field C. B., Randerson J. T., Malmstrom C. M. Global net primary production – combining ecology and remote-sensing,  Remote Sens. Environ., 51, 74—88 (1995).
 5. Gobron N., Verstraete M. ECV T10: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR),  Essential Climate Variables. Rome: Global Terrestrial Observing System; (2008).
 6. Heimann M., Reichstein M. Terrestrial ecosystem carbon dynamics and climate feedbacks,  Nature, 451, 289—292 (2008).
7. Heinsch F. A., Reeves M., Votava P., et. al. User’s Guide GPP and NPP (MOD17A2/A3) Products NASA MODIS Land Algorithm. Version 2.0, December 2, 2003, 57 p.
 8. IPCC (2001). Climate change 2001: The scientific basis, 881 p. (Cambridge: University Press, 2001).
 9. IPCC (2007). Climate change 2007: The physical science basis, 996 p. (Cambridge: University Press, 2007).
10. Korzoun V. I., Sokolov A. A., Budyko M. I., et al. World water balance and water resources of the Earth. In: Studies and Reports in Hydrology (UNESCO), no. 25/United Nations Educational, Scientific and Cultural Organization, 75 – Paris (France); International Hydrological Decade, Moscow (USSR). USSR National Committee, 663 p. (1978).
11. Lu X., Zhuang Q. Evaluating evapotranspiration and wateruse efficiency of terrestrial ecosystems in the conterminous United States using MODIS and AmeriFlux data,  Remote Sens. Environ., 114, 1924—1939 (2010).
12. Mu Q., Zhao M., Running S. W., et al. Contribution of increasing CO2 and climate change to the carbon cycle in China’s ecosystems,  J. Geophys. Res., 113, G01018 (2008).
13. Mu Q., Zhao M., Running S. W. Improvements to a MODIS Global Terrestrial Evapotranspiration Algorithm,  Remote Sens. Environ., 115, 1781—1800 (2011).
14. Mu Q., Zhao M., Running W. Evolution of hydrological and carbon cycles under a changing climate,  Hydrol. Processes, 25, 4093—4102 (2011).
15. Prieto-Blanco A., North P., Barnsley M. J., et al. Sattelite-driven modelling of Net Primary Productivity (NPP): Theoretical analysis,  Remote Sens. Environ., 113, 137—147 (2009).
16. Prince S. D., Goward S. N. Global primary production: A remote sensing approach,  J. Biogeography, 22, 815—835 (1995).
17. Quegan S., Beer C., Shvidenko A., et al. Estimating the carbon balance of central Siberia using a landscape-ecosystem approach, atmospheric inversion and Dynamic Global Vegetation Models,  Global Change Biology, 17, 351—365 (2011).
18. Rouse J. W., Haas R. H., Schell J. A., et al. Monitoring vegetation systems in the great plains with ERTS, Third ERTS Symposium, NASA SP-351 I., 309—317 (1973).
19. Running S. W., Nemani R., Glassy J. M., et al. MODIS daily photosynthesis (PSN) and annual net primary production (NPP) product (MOD17), Algorithm Theoretical Basis Document, Version 3.0, April 29, 1999.
20. Running S. W., Thornton P. E., Nemani R., et al. Global terrestrial gross and net primary productivity from the Earth Observing System,  Methods in ecosystem science,  Eds O. E. Sala, R. B. Jackson, H. A. Mooney, R. W. Howarth, P. 44—57 (Springer, New York, 2000).
21. Smith B., Knorr W., Widlowski J., et al. Combining remote sensing data with process modeling to monitor boreal conifer forest carbon balances,  Forest Ecology and Management, 225, 3985—3994 (2008).
22. Strahler A. H., Friedl M., Zhang X., et al. The MODIS land cover and land cover dynamics products. Presentation at remote sensing of the Earth’s environment from TERRA in l’Aquila, 2002, Italy.
23. Wang Y., Woodcock C.E., Buermann W., et al. Evaluation of the MODIS LAI algorithm at a coniferous forest site in Finland, Remote Sens. Environ., 91, 114—127 (2004).
24. Wu C., Nui Z., Gao S. Gross primary production estimation from MODIS data with vegetation index and photosynthetically active radiation in maize,  J. Geophys. Res., 115 (2010), D12127.
25. Xiao X., Hollinger D., Aber J., et al. Sattelite-based modeling of gross primary production in an evergreen needle-leaf forest,  Remote Sens. Environ., 89, 519—534 (2004).
26. Zhao M., Heinsch F. A., Nemani R. R., et al. Improvements of the MODIS terrestrial gross and net primary production global data set,  Remote Sens. Environ., 95, 164—176 (2005).
27. Zhao M., Running S. W., Nemani R. R. Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial primary production to the accuracy of meteorological reanalyses,  J. Geophys. Res., 111, G01002 (2006).
28.  Zhao M., Running S.W. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009, Science, 329, 940—943 (2010). :#231F20; mso-ansi-language:EN-GB;mso-bidi-font-style:italic'>from dspace/bitstream/ 2014/39590/1/05-0851.pdf