ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, Vol. 14, No. 6, pp. 97-107

Revealing and mapping long-term NDVI trends for the analysis of climate change contribution to agroecosystems’ productivity dynamics in the Northern Eurasian forest-steppe and steppe

N.O. Telnova 1 
1 Institute of Geography RAS, Moscow, Russia
Accepted: 01.12.2017
DOI: 10.21046/2070-7401-2017-14-6-97-107
Northern Eurasian forest-steppe and steppe encompass a huge region where observed and projected climate change and in particular change in precipitation regime demonstrate high spatial heterogeneity. In this study, spatial and temporal variations of croplands and grasslands productivity in the main agricultural regions of Russia and adjacent countries are indicated by means of sum annual NDVI time series analysis. For the three decadal periods with different climatic and socio-economic conditions (1980s, 1990s and 2000s) we constructed time series of NDVI extracted from low-resolution remote sensing data (NOAA AVHRR, Terra MODIS) and time series of gridded climate data — precipitation and PDSI. Revealed non-parametric significant trends in sum annual NDVI were analyzed on the concordance of their signs with climate data trends for different forest-steppe and steppe ecoregions. Spatial analysis and resulted maps demonstrate the predominance of positive NDVI trends throughout the region for the 1980s under favorable climatic conditions whereas the 1990s are characterized with high spatially heterogeneous disagreement between signs of NDVI and climatic trends with more significant anthropogenic impact on general decline in agro-ecosystems’ productivity. In the 2000s the presence of extensive belt elongated through dry and deserted steppes from Lower Don basin to the Eastern Kazakhstan with stable negative NDVI trend under regional aridization verifies results of projected climate change in this region towards the middle of the 21 century.
Keywords: time series analysis, NDVI, precipitation, PDSI, forest-steppes and steppes, agricultural lands, climate change
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References:

  1. Zolotokrylin A. N., Titkova T. B., Cherenkova E. A., Vinogradova V. V., Trendy uvlazhneniya i biofizicheskikh parametrov zasushlivykh zemel’ Evropeiskoi chasti Rossii za period 2000–2014 gg. (Trends of moisture indexes and biophysical parameters of European Russia drylands for the period of 2000–2014), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2015, Vol. 12, No. 2, pp. 155–161.
  2. Karta “Zony i tipy poyasnosti rastitel’nosti Rossii i sopredel’nykh territorii”. Masshtab 1:8000000 (Map “Vegetation zones and altitudinal belts of Russia and adjacent territories”. Scale 1:8000000), Moscow: “Ekor”, 1999, 64 p.
  3. Mahambetov M.Zh. Otsenka protsessov vosstanovleniya degradirovannykh ekosistem Atyrauskoi oblasti: Diss. doktora filosofii (Assessment of rehabilitation processes in degraded ecosystems of Atyrau region: PhD thesis), Almaty: KNAU, 2016, 152 p.
  4. Savin I.Yu., Vrieling A., Analiz mnogoletnei dinamiki rastitel’nykh resursov na territorii Rossii po dannym NOAA AVHRR (Analysis of Agricultural Vegetation Dynamics in Russia Based on NOAA AVHRR Data), Issledovanie Zemli iz kosmosa, 2008, No. 5, pp. 74–82.
  5. Strashnaya A. I., Maksimenkova T. A., Chub O. V., O srokakh seva ozimykh kul’tur v usloviyakh izmeneniya klimata i ikh prognozirovanie v Privolzhskom federal’nom okruge (Seeding times of winter crops under the conditions of climate change and their forecast in the Privolzhskiy federal region), Trudy Gidromettsentra Rossii (Proceedings of The Hydrometeorological Center of Russia), 2011, Issue 345, pp. 175–193.
  6. Bai Z. G., Dent D. L., Olsson L., Schaepman M. E., Global assessment of land degradation and improvement. 1. Identification by remote sensing. Report 2008/01, Wageningen: International Soil Reference and Information Centre (ISRIC), 2008, 69 p.
  7. Bartholomé E., Belward A., Frédéric A., Bartalev S., Carmona-Moreno C., Eva H., Fritz S., Grégoire J., Mayaux P., Stibig H.-J. E.E., GLC 2000: Global Land Cover Mapping for the Year 2000: Project Status November 2002, European Commission, Joint Research Center, 2002, 66 p.
  8. Beck H. E., McVicar T. R., van Dijk A. I.J. M., Schellekens J., de Jeu R. A.M., Bruijnzeel L. A. Global evaluation of four AVHRR–NDVI data sets: Intercomparison and assessment against Landsat imagery, Remote Sensing of Environment, 2011, Vol. 115, No. 10, pp. 2547–2563.
  9. Dai A., The Climate Data Guide: Palmer Drought Severity Index (PDSI), 2017, Retrieved from https://climatedataguide.ucar.edu/climate-data/palmer-drought-severity-index-pdsi.
  10. Dronin N. M., Kirilenko A. P., Climate change, food stress, and security in Russia, Regional Environmental Change, 2011, Vol. 11, No. 1, pp. 167–178.
  11. Dronin N. M., Kirilenko A. P., Weathering the soviet countryside: The impact of climate and agricultural policies on Russian grain yields, 1958–2010, Soviet and Post-Soviet Review, 2013, Vol. 40, No. 1, pp. 115–143.
  12. Fensholt R., Langanke T., Rasmussen K., Reenberg A., Prince S. D., Tucker C., Scholes R. J., Le Q. B., Bondeau A., Eastman R., Greenness in semi-arid areas across the globe 1981–2007 — an Earth Observing Satellite based analysis of trends and drivers, Remote Sensing of Environment, 2012, Vol. 121, pp. 144–158.
  13. Fensholt R., Proud S. R., Evaluation of Earth Observation based global long term vegetation trends — Comparing GIMMS and MODIS global NDVI time series, Remote Sensing of Environment, 2012, Vol. 119, pp. 131–147.
  14. Hoaglin D. C., Mosteller F., Tukey J. W., Understanding robust and exploratory data analysis, New York: Wiley, 2000, 447 p.
  15. Huete A., Didan K., Leeuwen W. van Miura T., Glenn E., MODIS Vegetation Indices, Land Remote Sensing and Global Environmental Change. Remote Sensing and Digital Image Processing, New York: Springer, 2010, pp. 579–602.
  16. Mbow C., Fensholt R., Rasmussen K., Diop D., Can vegetation productivity be derived from greenness in a semi-arid environment? Evidence from ground-based measurements, Journal of Arid Environments, 2013, Vol. 97, pp. 56–65.
  17. Müller D., Jungandreas A., Koch F., Schierhorn F., Impact of Climate Change on Wheat Production in Ukraine: Agricultural policy report APD/APR/02/2016. Kyiv, 2016, 41 p.
  18. Neeti N., Eastman J. R., A Contextual Mann-Kendall Approach for the Assessment of Trend Significance in Image Time Series: A Novel Method for Testing Trend Significance, Transactions in GIS, 2011, Vol. 15, No. 5, pp. 599–611.
  19. Running S. W., Heinsch F. A., Zhao M., Reeves M., Hashimoto H. A continuous satellite-derived measure of global terrestrial production. Bioscience, 2004, Vol. 54, No. 6, pp. 547–560.
  20. Schneider U., Becker A., Finger P., Meyer-Christoffer A., Rudolf B., Ziese M., GPCC Full Data Reanalysis Version 7.0 at 0.5°: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data, 2015. DOI:10.5676/DWD_GPCC/FD_M_V7_050.
  21. Tucker C. J., Pinzon J. E., Brown M. E., Global Inventory Modeling and Mapping Studies (GIMMS) Satellite Drift Corrected and NOAA-16 incorporated Normalized Difference Vegetation Index (NDVI), Monthly 1981–2002, College Park, Maryland: Global Land Cover Facility, University of Maryland, 2004.