PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Study of Climate Impact on Vegetation Cover in Kherson Oblast (Ukraine) Using Normalized Difference and Enhanced Vegetation Indices

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Remote sensing is a convenient tool for the study of vegetation cover conditions and dynamics using normalized difference and enhanced vegetation indices. Determination of the connection between weather and vegetation indices plays an important role in better understanding peculiarities of ecosystems reaction to changing climate conditions. The study devoted to the evaluation of annual and long-term dynamics under vegetation cover conditions, and its reaction to the climate factor, was performed through the establishment of the link between remote sensing information (smoothed time series data on normalized and enhanced vegetation indices) and results of on-land hydrometeorological observations for air temperature and precipitation amounts in Kherson oblast of Ukraine during the period from 2012 to 2019 by the means of linear regression analysis of the data. The values of the studied vegetation indices (Terrain MODIS NDVI and MODIS EVI 250 m smoothed time series) were calculated and generalized by the means of GDAL raster analysis toolkit in QGIS 3.10. Statistical data processing was performed using BioStat v7 software. It was found that there is a strong tendency towards the enhancement of vegetation in the region year by year. Climate has strong effect on the vegetation, and the main input belongs to air temperature, while precipitation amounts cannot be considered as a driving force of changes in the growth of vegetation. Enhanced vegetation index seems to be more reliable for the estimation of vegetation cover conditions in comparison to normalized difference vegetation index.
Słowa kluczowe
Rocznik
Strony
126--135
Opis fizyczny
Bibliogr. 46 poz., rys., tab.
Twórcy
  • Institute of Irrigated Agriculture of NAAS, Naddniprianske, 73483 Kherson, Ukraine
Bibliografia
  • 1. De Myttenaere, A., Golden, B., Le Grand, B., & Rossi, F. 2016. Mean absolute percentage error for regression models. Neurocomputing, 192, 38–48. DOI: 10.1016/j.neucom.2015.12.114
  • 2. Ding, M., Zhang, Y., Liu, L., Zhang, W., Wang, Z., & Bai, W. 2007. The relationship between NDVI and precipitation on the Tibetan Plateau. Journal of Geographical Sciences, 17, 259–268. DOI: 10.1007/s11442–007–0259–7
  • 3. Eduarda, M. D. O., de Carvalho, L. M., Junior, F. W. A., & de Mello, J. M. 2007. The assessment of vegetation seasonal dynamics using multitemporal NDVI and EVI images derived from MODIS. pp. 1–5. In: 2007 International Workshop on the Analysis of Multi-temporal Remote Sensing Images; IEEE. DOI: 10.1109/MULTITEMP.2007.4293049
  • 4. Elmore, A. J., Mustard, J. F., Manning, S. J., & Lobell, D. B. 2010. Quantifying vegetation change in semiarid environments: precision and accuracy of spectral mixture analysis and the normalized difference vegetation index. Remote Sensing of Environment, 73, 87–102. DOI: 10.1016/S0034–4257(00)00100–0
  • 5. Everitt, B. 1998. The Cambridge Dictionary of Statistics. Cambridge University Press: UK New York.
  • 6. Gao, B. C. 1996. NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257–266. DOI: 10.1016/S0034–4257(96)00067–3
  • 7. Gurung, R. B., Breidt, F. J., Dutin, A., & Ogle, S. M. 2009. Predicting enhanced vegetation index (EVI) curves for ecosystem modeling applications. Remote Sensing of Environment, 113, 2186–2193. DOI: 10.1016/j.rse.2009.05.015
  • 8. Gutman, G. G. 1991. Vegetation indices from AVHRR: An update and future prospects. Remote Sensing of Environment, 35, 121–136. DOI: 10.1016/0034–4257(91)90005-Q
  • 9. Heiberger, R. M., & Neuwirth, E. 2009. Polynomial regression. pp. 269–284. In: R Through Excel , Springer: New York, USA. DOI: 10.1007/978–1-4419–0052–4_11
  • 10. Hobbs, T. J. 1997. Atmospheric correction of NOAA11 NDVI data in the arid rangelands of Central Australia. International Journal of Remote Sensing, 18, 1051–1058. DOI: 10.1080/014311697218566
  • 11. Huete, A. R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295–309. DOI: 10.1016/0034–4257(88)90106-X
  • 12. Huete, A., Justice, C., & Van Leeuwen, W. 1999. MODIS vegetation index (MOD13). Algorithm theoretical basis document 3(213).
  • 13. Jiang, D., Zhang, Y., & Lang, X. 2011. Vegetation feedback under future global warming. Theoretical and Applied Climatology, 106, 211–227. DOI: 10.1007/s00704–011–0428–6
  • 14. Jiang, Z., Huete, A. R., Didan, K., & Miura, T. 2008. Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment, 112, 3833–3845. DOI: 10.1016/j.rse.2008.06.006
  • 15. Karkauskaite, P., Tagesson, T., & Fensholt, R. 2017. Evaluation of the plant phenology index (PPI), NDVI and EVI for start-of-season trend analysis of the Northern Hemisphere boreal zone. Remote Sensing, 9, 485. DOI: 10.3390/rs9050485
  • 16. Kim, Y., Huete, A. R., Miura, T., & Jiang, Z. 2010. Spectral compatibility of vegetation indices across sensors: band decomposition analysis with Hyperion data. Journal of Applied Remote Sensing, 4, 043520. DOI: 10.1117/1.3400635
  • 17. Koslowsky, D. 1993. The influence of viewing geometry on annual variations of NDVI. pp. 1140 – 1142. In: Proceedings of IGARSS’93-IEEE International Geoscience and Remote Sensing Symposium; IEEE. DOI: 10.1109/IGARSS.1993.322136
  • 18. Lange, M., Dechant, B., Rebmann, C., Vohland, M., Cuntz, M., & Doktor, D. 2017. Validating MODIS and Sentinel-2 NDVI products at a temperate deciduous forest site using two independent groundbased sensors. Sensors, 17, 1855. DOI: 10.3390/s17081855
  • 19. Li, Z., Li, X., Wei, D., Xu, X., & Wang, H. 2010. An assessment of correlation on MODIS-NDVI and EVI with natural vegetation coverage in Northern Hebei Province, China. Procedia Environmental Sciences, 2, 964–969. DOI: 10.1016/j.proenv.2010.10.108
  • 20. Lijun, Z., Zengxiang, Z., Tingting, D., & Xiao, W. 2008. Application of MODIS/NDVI and MODIS EVI to extracting the information of cultivated land and comparison analysis. Transactions from the Chinese Society of Agricultural Engineering, 24, 167–172. DOI: 10.3969/j.issn.1002–6819.2008.3.033
  • 21. Lillesaeter, O. 1982. Spectral reflectance of partly transmitting leaves: laboratory measurements and mathematical modeling. Remote Sensing of Environment, 12, 247–254. DOI: 10.1016/0034–4257(82)90057–8
  • 22. Lykhovyd, P. V. 2020. Sweet corn yield simulation using normalized difference vegetation index and leaf area index. Journal of Ecological Engineering, 21, 228–236. DOI: 10.12911/22998993/118274
  • 23. Lykhovyd, P. V. 2018. Global warming inputs in local climate changes of the Kherson region: Current state and forecast of the air temperature. Ukrainian Journal of Ecology, 8, 39–41. DOI: 10.15421/2018_307
  • 24. Marsett, R. C., Qi, J., Heilman, P., Biedenbender, S. H., Watson, M. C., Amer, S., Weltz, M., Goodrich, D., & Marsett, R. 2006. Remote sensing for grassland management in the arid southwest. Rangeland Ecolohy & Management, 59, 530–540. DOI: 10.2111/05–201R.1
  • 25. Martínez-López, J., Carreño, M. F., Palazón-Ferrando, J. A., Martínez-Fernández, J., & Esteve, M. A. 2014. Remote sensing of plant communities as a tool for assessing the condition of semiarid Mediterranean saline wetlands in agricultural catchments. International Journal of Applied Earth Observation, 26, 193–204. DOI: 10.1016/j.jag.2013.07.005
  • 26. Matsushita, B., Yang, W., Chen, J., Onda, Y., & Qiu, G. 2007. Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: a case study in high-density cypress forest. Sensors, 7, 2636–2651. DOI: 10.3390/s7112636
  • 27. Miura, T., Nagai, S., Takeuchi, M., Ichii, K., & Yoshioka, H. 2019. Improved characterisation of vegetation and land surface seasonal dynamics in central Japan with Himawari-8 hypertemporal data. Scientific Reports, 9, 1–12. DOI: 10.1038/s41598–019–52076-x
  • 28. Morales, R. M., Miura, T., & Idol, T. 2008. An assessment of Hawaiian dry forest condition with fine resolution remote sensing. Forest Ecology and Management, 255, 2524–2532. DOI: 10.1016/j.foreco.2008.01.049
  • 29. Moreira, A., Fontana, D. C., & Kuplich, T. M. 2019. Wavelet approach applied to EVI/MODIS time series and meteorological data. ISPRS Journal of Photogrammetry and Remote Sensing, 147, 335–344. DOI: 10.1016/j.isprsjprs.2018.11.024
  • 30. Moreno, J. J. M., Pol, A. P., Abad, A. S., & Blasco, B. C. 2013. Using the R-MAPE index as a resistant measure of forecast accuracy. Psicothema, 25, 500–506. DOI: 10.7334/psicothema2013.23
  • 31. Oliver, M. A. 2010. Geostatistical applications for precision agriculture. Springer Science & Business Media, London New York. DOI: 10.1007/978–90–481–9133–8
  • 32. Qiu, J., Yang, J., Wang, Y., & Su, H. 2018. A comparison of NDVI and EVI in the DisTrad model for thermal sub-pixel mapping in densely vegetated areas: a case study in Southern China. International Journal of Remote Sensing, 39, 2105–2118. DOI: 10.1080/01431161.2017.1420929
  • 33. Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA special publication, 351, 309.
  • 34. Schultz, P. A., & Halpert, M. S. 1993. Global correlation of temperature, NDVI and precipitation. Advances in Space Research, 13, 277–280. DOI: 10.1016/0273–1177(93)90559-T
  • 35. Seber, G. A., & Lee, A. J. 2012. Linear regression analysis. Vol. 329. John Wiley & Sons.
  • 36. Seelan, S. K., Laguette, S., Casady, G. M., & Seielstad, G. A. 2003. Remote sensing applications for precision agriculture: A learning community approach. Remote Sensing of Environment, 88, 157–169. DOI: 10.1016/j.rse.2003.04.007
  • 37. Taylor, R. 1990. Interpretation of the correlation coefficient: a basic review. Journal of Diagnostic Medical Sonography, 6, 35–39. DOI: 10.1177/875647939000600106
  • 38. Tsimring, L. S. 2014. Noise in biology. Reports on Progress in Physics, 77, 026601. DOI: 10.1088/0034–4885/77/2/026601
  • 39. Ushkarenko, V. O. 1994. Irrigated Agriculture. Urozhai: Kyiv, Ukraine.
  • 40. Vozhehova, R., Kokovikhin, S., Lykhovyd, P., Vozhehov, S., & Drobitko, A. (2018): Artificial croplands and natural biosystems in the conditions of climatic changes: Possible problems and ways of their solving in the South Steppe zone of Ukraine. Research Journal of Pharmaceutical, Biological and Chemical Sciences, 9, 331–340.
  • 41. Wang, J., Rich, P. M., & Price, K. P. 2003. Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA. International Journal of Remote Sensing, 24, 2345–2364. DOI: 10.1080/01431160210154812
  • 42. Wang, Z., Liu, C., & Huete, A. 2003. From AVHRRNDVI to MODIS-EVI: Advances in vegetation index research. Acta Ecologica Sinica, 23, 979–987.
  • 43. Wardlow, B. D., & Egbert, S. L. 2010. A comparison of MODIS 250-m EVI and NDVI data for crop mapping: a case study for southwest Kansas. International Journal of Remote Sensing, 31, 805–830. DOI: 10.1080/01431160902897858
  • 44. Woodward, F. I., Lomas, M. R., & Betts, R. A. 1998. Vegetation-climate feedbacks in a greenhouse world. Philosophical Transactions of The Royal Society B Biological Sciences, 353, 29–39. DOI: 10.1098/rstb.1998.0188
  • 45. Zou, K. H., Tuncali, K., & Silverman, S. G. 2003. Correlation and simple linear regression. Radiology, 227, 617–628. DOI: 10.1148/radiol.2273011499
  • 46. Zoungrana, B. J. B., Conrad, C., Amekudzi, L. K., Thiel, M., & Da, E. D. 2015. Land use/cover response to rainfall variability: A comparing analysis between NDVI and EVI in the Southwest of Burkina Faso. Climate, 3, 63–77. DOI: 10.3390/cli3010063
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d135d309-8dcb-41c3-b39c-688effe870b1
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.