Effects of Seasonal and Perennial Climatic Fluctuations on Geographical Distribution of the Enhanced Vegetation Index EVI of Arable Land in the Bryansk Region

Grigory V. LOBANOV; Marina V. AVRAMENKO; Anna Yu. CHAROCHKINA; Nikolay N. DROZDOV

doi:10.14505//jemt.v10.3(35).21

Grigory V. LOBANOV Department of Geography, Ecology and Land Management Bryansk State University named after Academician I.G. Petrovsky Russian Federation
Marina V. AVRAMENKO Department of Geography, Ecology and Land Management Bryansk State University named after Academician I.G. Petrovsky Russian Federation
Anna Yu. CHAROCHKINA Department of Geography, Ecology and Land Management Bryansk State University named after Academician I.G. Petrovsky Russian Federation
Nikolay N. DROZDOV Department of Geography, Ecology and Land Management Bryansk State University named after Academician I.G. Petrovsky Russian Federation

DOI: https://doi.org/10.14505//jemt.v10.3(35).21

Abstract

This article discusses the patterns of geographical distribution of the enhanced vegetation index EVI within the Bryansk region (upper Dnieper basin, south-western Russia) in the spring months of 2010-2015. The factors of index distribution, based on agricultural land monitoring data in other regions, are described. The crucial role of abiotic (topography, soil) and biotic factors in the distribution of the EVI is shown. The generalized data of meteorological observations of 2010-2015 are presented; the effects of their high variability on the range of the EVI values and its geographical distribution are shown. Data on the differences in the EVI distribution in the spring months of 2010-2015 is presented, which are explained by the differing periods of phenological seasons, surface relief characteristics (flat and convex watersheds, drainage conditions), lithological composition and humus content in the upper horizons of arable soil. A qualitative relationship between spring changes in the EVI for arable land and the combination of edaphic factors of agro-landscape functioning is established and the mechanisms that ensure such differences are presented. The use of the EVI distribution patterns is justified in the years with different climatic conditions to identify arable lands with different surface topography and soil characteristics.

References

References
[1] Baumann, M., Ozdogan, M., Richardson, A.D.and Radeloff, V.C. 2017. Phenology from Landsat when data is scarce: Using MODIS and Dynamic Time-Warping to combine multi-year Landsat imagery to derive annual phenology curves. International Journal of Applied Earth Observation and Geoinformation, 54: 76-83.
[2] Cabello, J., et al. 2012. The role of vegetation and lithology in the spatial and inter-annual response of EVI to climate in drylands of Southeastern Spain. Journal of Arid Environments, 79: 76-83.
[3] Chen Y., et al. 2018. Mapping croplands, cropping patterns, and crop types using MODIS time-series data. International Journal of Applied Earth Observation and Geoinformation, 69: 133-147.
[4] da Silveira, H.L.F., et al. 2018. Use of MSI/Sentinel-2 and airborne LiDAR data for mapping vegetation and studying the relationships with soil attributes in the Brazilian semi-arid region. International Journal of Applied Earth Observation and Geoinformation, 73: 179-190.
[5] Dubovyk, O., et al. 2015. Monitoring vegetation dynamics with medium resolution MODIS-EVI time series at sub-regional scale in southern Africa. International Journal of Applied Earth Observation and Geoinformation, 38: 175-183.
[6] EarthData. https://search.earthdata.nasa.gov/search
[7] Emran, A., Roy, S., Bagmar, S.H., and Mitra, C. 2018. Assessing topographic controls on vegetation characteristics in Chittagong Hill Tracts (CHT) from remotely sensed data. Remote Sensing Applications: Society and Environment, 11: 198-208.
[8] Grunwald, S., Vasques, G.M., and Rivero, R.G. 2015. Chapter One – Fusion of Soil and Remote Sensing Data to Model Soil Properties Editor(s): D.L. Sparks. Advances in Agronomy, 131: 1-109.
[9] Guyet, T., and Nicolas, H., 2016. Long term analysis of time series of satellite images. Pattern Recognition Letters, 70: 17-23.
[10] Huete, A., et al. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83: 195-213.
[11] Huete, A.R., Justice, C., and van Leeuwen, W. 1999. MODIS Vegetation Index (MOD 13) Algorithm Theoretical Basis Document (ATBD). Version 3.0. EOS Project Office, NASA Goddard: Space Flight Center.
[12] Justice, O., et al. 2002. An overview of MODIS Land data processing and product status. Remote Sensing of Environment, 83: 3-15.
[13] Kern, A., et al. 2018. Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices. Agricultural and Forest Meteorology, 260-261: 300-320.
[14] Kubekova, S.N., Kapralova, V.I., Ibraimova, G.T. and Batyrbayeva, A.A. 2016. Enrichment wastes’ processing of manganiferous ores with the use of mechanochemical methods. International Journal of Environmental and Science Education, 11(11): 3855 - 4869.
[15] Mironova, S.I., Ivanov, V.V., Gavrilyeva, L.D. and Nikiforov, A.A. 2018. Persistence of Yakutia vegetation under the technogenic impact. Periodico Tche Quimica, 15(Special Issue 1): 18-26.
[16] Mondal, P., Jain, M., DeFries, R.S. and Galford, G.L. 2015. Christopher Small Sensitivity of crop cover to climate variability: Insights from two Indian agro-ecoregions. Journal of Environmental Management, 148: 21-30.
[17] Muhammad, S., Zhan, Y., Wang, L., Hao, P. and Niu, Z. 2016. Major crops classification using time series MODIS EVI with adjacent years of ground reference data in the US state of Kansas, Optik. International Journal for Light and Electron Optics, 127(3): 1071-1077.
[18] Nagy, A., Fehér, J. and Tamás, J. 2018. Wheat and maize yield forecasting for the Tisza river catchment using MODIS NDVI time series and reported crop statistics. Computers and Electronics in Agriculture, 151: 41-49.
[19] Piedallu, C., et al. 2018. Soil and climate differently impact NDVI patterns according to the season and the stand type, Science of The Total Environment, 2018, crop statistics. Computers and Electronics in Agriculture, 151: 41-49.
[20] Sokolov, N., Ezhov, S. and Ezhova, S. 2017. Preserving the natural landscape on the construction site for sustainable ecosystem. Journal of Applied Engineering Science, 15(4): 518-523.
[21] Sukhova, M.G., et al. 2018. Recreation and bioclimatic specifics of landscapes of the Central and South-Eastern Altai. Periodico Tche Quimica, 15(Special Issue 1): 537-547.
[22] Tsalyuk, M., Kelly, M. and Getz, W.M. 2017. Improving the prediction of African savanna vegetation variables using time series of MODIS products. ISPRS Journal of Photogrammetry and Remote Sensing, 131: 77-91.
[23] Wang, X., Gao, Q., Wang, C. and Yu, M. 2017. Spatiotemporal patterns of vegetation phenology change and relationships with climate in the two transects of East China. Global Ecology and Conservation, 10: 206-219.
[24] Wardlow, B., Egbert, St. L. and Kastens, J.H. 2007. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great. Remote Sensing of Environment, 108: 290-310.
[25] Zewdie, W., Csaplovics, E. and Inostroza, L. 2017. Monitoring ecosystem dynamics in northwestern Ethiopia using NDVI and climate variables to assess long term trends in dryland vegetation variability. Applied Geography, 79: 167-178.
[26] Zhang, X., Sun, R., Zhang, B. and Tong, Q. 2008. Land cover classification of the North China Plain using MODIS_EVI time series, ISPRS Journal of Photogrammetry and Remote Sensing, 63(4): 476-484.