PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Seasonality and long-term trends of NDVI values in different land use types in the eastern part of the Baltic Sea basin

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This study analyses changes in Normalized Difference Vegetation Index (NDVI) values in the eastern Baltic region. The main aim of the work is to evaluate changes in growing season indicators (onset, end time, time of maximum greenness and duration) and their relationship with meteorological conditions (air temperature and precipitation) in 1982–2015. NDVI seasonality and long-term trends were analysed for different types of land use: arable land, pastures, wetlands, mixed and coniferous forests. In the southwestern part of the study area, the growing season lasts longest, while in the northeast, the growing season is shorter on average by 10 weeks than in the other parts of the analysed territory. The air temperature in February and March is the most important factor determining the start of the growing season and the air temperature in September and October determines the end date of the growing season. Precipitation has a much smaller effect, especially at the beginning of the growing season. The effect of meteorological conditions on peak greenness is weak and, in most cases, statistically insignificant. At the end of the analysed period (1982–2015), the growing season started earlier and ended later (in both cases the changes were 3–4 weeks) than at the beginning of the study period. All these changes are statistically significant. The duration of the growing season increased by 6–7 weeks.
Słowa kluczowe
Czasopismo
Rocznik
Strony
171--181
Opis fizyczny
Bibliogr. 41 poz., map., rys., wykr.,
Twórcy
  • Institute of Geosciences, Vilnius University, Vilnius, Lithuania
  • Institute of Geosciences, Vilnius University, Vilnius, Lithuania
  • Institute of Geosciences, Vilnius University, Vilnius, Lithuania
  • Institute of Geosciences, Vilnius University, Vilnius, Lithuania
Bibliografia
  • 1. Akima, H., Gebhardt, A., 2016. Akima: Interpolation of Irregularly and Regularly Spaced Data R package version 0.6-2.
  • 2. Aasa, A., Jaagus, J., Ahas, R., Sepp, M., 2004. The influence of atmospheric circulation on plant phenological phases in Central and Eastern Europe. Int. J. Climatol. 24, 1551-1564. https://doi.org/10.1002/joc.1066
  • 3. Ahas, R., Jaagus, J., Aasa, A., 2000. The phenological calendar of Estonia and its correlation with mean air temperature. Int. J. Biometeorol. 44, 159-166. https://doi.org/10.1007/s004840000069
  • 4. Brooks, P.D., McKnight, D., Elder, K., 2004. Carbon limitation of soil respiration under winter snowpacks: Potential feedbacks between growing season and winter carbon fluxes. Glob. Change Biol. 11 (2), 231-238. https://doi.org/10.1111/j.1365-2486.2004.00877.x
  • 5. Chen, X., Hu, B., Yu, R., 2005. Spatial and temporal variation of phenological growing season and climate change impacts in temperate eastern China. Glob. Change Biol. 11, 1118-1130. https://doi.org/10.1111/j.1365-2486.2005.00974.x
  • 6. Chybicki, A., Kulawiak, M., Lubniewski, Z., 2016. Characterizing surface and air temperature in the Baltic Sea coastal area using remote sensing techniques and GIS. Pol. Marit. Res. 23, 3-11. https://doi.org/10.1515/pomr- 2016- 0001
  • 7. Dabrowska-Zielinska, K., Kogan, F., Ciolkosz, A., Gruszczynska, M., Kowalik, W., 2002. Modelling of crop growth conditions and crop yield in Poland using AVHRR-based indices. Int. J. Remote Sens. 23, 1109-1123. https://doi.org/10.1080/01431160110070744
  • 8. Fu, Y.H., Piao, S., Op, de Beeck, Cong, M., Zhao, N., Zhang, H., Menzel, Y., Janssens, A., IA, 2014. Recent spring phenology shifts in western Central Europe based on multiscale observations. Glob. Ecol. Biogeogr. 23 (11), 1255-1263. https://doi.org/10.1111/geb.12210
  • 9. Gessner, U., Naeimi, V., Klein, I., Kuenzer, C., Klein, D., Dech, S., 2013. The relationship between precipitation anomalies and satellite-derived vegetation activity in Central Asia. Glob. Planet Change 110, 74-87. https://doi.org/10.1016/j.gloplacha.2012.09.007
  • 10. Hatfield, J.L., Prueger, J.H., 2015. Temperature extremes: effect on plant growth and development. Weather Clim. Extremes 10, 4-10. https://doi.org/10.1016/j.wace.2015.08.001
  • 11. Huete, A., Didan, K., Miura, T., Rodriguez, E.P., Gao, X., Ferreira, L.G., 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195-213. https://doi.org/10.1016/S0034-4257(02)00096-2
  • 12. Jaagus, J., Briede, A., Rimkus, E., Remm, K., 2014. Variability and trends in daily minimum and maximum temperatures and in the diurnal temperature range in Lithuania, Latvia and Estonia in 1951—2010. Theor. Appl. Climatol. 118, 57-68. https://doi.org/10.1007/s00704-013-1041-7
  • 13. Jablonska, K., Kwiatkowska-Falińska, A., Czernecki, B., Walawender, J.P., 2015. Changes in spring and summer phenology in Poland - responses of selected plant species to air temperature variations. Pol. J. Ecol. 63 (3), 311-319. https://doi.org/10.3161/15052249PJE2015.63.3.002
  • 14. Jackson, R.D., Huerte, A.R., 1991. Interpreting vegetation indices. Prev. Vet. Med. 11, 185-200. https://doi.org/10.1016/S0167-5877(05)80004-2h
  • 15. Jeong, S.J., Ho, C.H., Gim, H.J., Brown, M.E., 2011. Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982-2008. Glob. Change Biol. 17, 2385-2399. https://doi.org/10.1111/j.1365-2486.2011.02397.x
  • 16. Jin, H., Jönsson, A.M., Olsson, C., Lindström, J., Jönsson, P., Eklundh, L., 2019. New satellite-based estimates show significant trends in spring phenology and complex sensitivities to temperature and precipitation at northern European latitudes. Int. J. Biometeorol. 63 (6), 763-775. https://doi.org/10.1007/s00484-019-01690-5
  • 17. Kogan, F.N., 1997. Global drought watch from space. Bull. Am. Meteorol. Soc. 78, 621-636. https://doi.org/10.1175/1520-0477(1997)078〈0621:GDWFS〉2.0.CO;2
  • 18. Kogan, F.N., 2001. Operational Space Technology for Global Vegetation Assessment. Bull. Am. Meteorol. Soc. 82 (9), 1949-1964. https://doi.org/10.1175/1520-0477(2001)082〈1949: OSTFGV〉2.3.CO;2
  • 19. Linderholm, H.W., 2006. Growing season changes in last century. Agr. Forest Meteorol. 137, 1-14. https://doi.org/10.1016/j.agrformet.2006.03.006
  • 20. Liu, Q., Piao, S., Janssens, I.A., Fu, Y., Peng, S., Lian, X., Ciais, P., Myneni, R.B., Peñuelas, J., Wang, T., 2018. Extension of the growing season increases vegetation exposure to frost. Nat. Commun. 9, 1-8. https://doi.org/10.1038/s41467-017-02690-y
  • 21. Myneni, R.B., Ramakrishna, R., Nemani, R., Running, S.W., 1997. Estimation of global Leaf Area Index and Absorbed Par Using Radiative Transfer Models. IEEE Trans. Geosci. Remote Sens. 35 (6), 1380-1393. https://doi.org/10.1109/36.649788
  • 22. Park, T., Ganguly, S., Tømmervik, H., Euskirchen, E.S., Høgda, K.A., Karlsen, S.R., Brovkin, V., Nemani, R.R., Myneni, R.B., 2016. Changes in growing season duration and productivity of northern vegetation inferred from long-term remote sensing data. Environ. Res. Lett. 11 (8), 1-11. https://doi.org/10.1088/1748-9326/11/8/084001
  • 23. Pettorelli, N., Vik, J.O., Mysterud, A., Gaillard, J.M., Tucker, C.J.,Stenseth, N.C., 2005. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trend. Ecol. Evol. 20 (9), 503-510. https://doi.org/10.1016/j.tree.2005.05.011
  • 24. Piao, S., Mohammat, A., Fang, J., Cai, Q., Feng, J., 2006. NDVI —based increase in growth of temperate grasslands and its responses to climate changes in China. Glob. Environ. Change. 16 (4), 340-348. https://doi.org/10.1016/j.gloenvcha.2006.02.002
  • 25. Piao, S., Friedlingstein, P., Ciais, P., Viovy, N., Demarty, J., 2007. Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Global Biogeochem. Cy. 21 (3), 1-11. https://doi.org/10.1029/2006GB002888
  • 26. Rimkus, E., Stoneviˇcius, E., Kilpys, J., Maˇciulyt ̇e, V., Valiukas, D.,2017. Drought identification in the Eastern Baltic region using NDVI. Earth Syst. Dynam. 8 (3), 627-637. https://doi.org/10.5194/esd-8-627-2017
  • 27. Roerink, G.J., Menenti, M., Soepboer, W., Su, Z., 2003. Assessment of climate impact on vegetation dynamics by using remote sensing. Phys. Chem. Earth. 28, 103-109. https://doi.org/10.1016/S1474-7065(03)00011-1
  • 28. Ruiz—Pérez, G., Vico, G., 2020. Effects of Temperature and Water Availability on Northern European Boreal Forests. Front. For. Glob. Change. 3, 1-18. https://doi.org/10.3389/ffgc.2020.00034
  • 29. Singh, R.P., Oza, S.R., Pandya, M.R., 2006. Observing long-term changes in rice phenology using NOAA-AVHRR and DMSP-SSM/I satellite sensor measurements in Punjab, India. Curr. Sci. 91 (9), 1217-1221. http://www.jstor.org/stable/24094100
  • 30. Singh, R.P., Roy, S., Kogan, F., 2003. Vegetation and temperaturę condition indices from NOAA AVHRR data for drought monitoring over India. Int. J. Remote Sens. 24, 4393-4402. https://doi.org/10.1080/0143116031000084323
  • 31. Stöckli, R., Vidale, P.L., 2004. European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset. Int. J. Remote Sens. 25, 3303-3330. https://doi.org/10.1080/01431160310001618149
  • 32. Tateishi, R., Ebata, M., 2004. Analysis of phenological change patterns using 1982-2000 Advanced Very High Resolution Radiometer (AVHRR) data. Int. J. Remote Sens. 25 (12), 2287-2300. https://doi.org/10.1080/01431160310001618455
  • 33. Törmä, M., Rankinen, K., Härmä, P., 2007. Using phenological information derived from MODIS-data to aid nutrient modeling. International Geoscience and Remote Sensing Symposium (IGARSS). 2298-2301. https://doi.org/10.1109/IGARSS.2007.4423300
  • 34. White, M.A., de Beurs, K.M., Didan, K., Inouye, D.W., Richardson, A.D., Jensen, O.P., O‘Keefe, J., Zhang, G., Nemani, R.R., Van Leeuwen, W.J.D., Brown, J.F., de Wit, A., Schaepman, M., Lin, X., Dettinger, M., Bailey, A.S., Kimball, J., Schwartz, M.D., Baldocchi, D.D., Lee, J.T., Lauenroth, W.K., 2009. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982—2006. Glob. Change Biol. 15, 2335-2359. https://doi.org/10.1111/j.1365-2486.2009.01910.x
  • 35. Wu, D., Zhao, X., Liang, S., Zhou, T., Huang, K., Tang, B., Zhao, W., 2015. Time-lag effects of global vegetation responses to climate change. Glob. Change Biol. 21, 3520-3531. https://doi.org/10.1111/gcb.12945
  • 36. Zhang, X., Friedl, M.A., Schaaf, C.B., 2006. Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): Evaluation of global patterns and comparison with in situ measurements. J. Geophys. Res. 111, G04017. https://doi.org/10.1029/2006JG000217
  • 37. Zhang, X., Friedl, M.A., Schaaf, C.B., Strahler, A.H., Hodges, J.C.F., Gao, F., Reed, B.C., Huete, A., 2003. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 84 (3), 471-475. https://doi.org/10.1016/S0034-4257(02)00135-9
  • 38. Zhang, X., Tan, B., Yu, Y., 2014. Interannual variations and trends in global land surface phenology derived from enhanced vegetation index during 1982-2010. Int. J. Biometeorol. 58 (4), 547-564. https://doi.org/10.1007/s00484-014-0802-z
  • 39. Zhao, J., Zhang, H., Zhang, Z., Guo, X., Li, X., Chen, C., 2015. Spatial and temporal changes in vegetation phenology at middle and high latitudes of the Northern Hemisphere over the past three decades. Remote Sens. 7, 10973-10995. https://doi.org/10.3390/rs70810973
  • 40. Zhao, L., Dai, A., Dong, B., 2018. Changes in global vegetation activity and its driving factors during 1982-2013. Agr. Forest Meteorol. 249, 198-209. https://doi.org/10.1016/j.agrformet.2017.11.013
  • 41. Zhou, L., Tucker, C.J., Kaufmann, R.K., Slayback, D., Shabanov, N.V., Myneni, R.B., 2001. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. J. Geophys. Res. 106 (D17), 20069-20083. https://doi.org/10.1029/2000JD000115
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-17074f80-dd59-4452-9680-faa70e49beb4
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.