Advertisement

Analysis of NVDI variability in response to precipitation and air temperature in different regions of Iraq, using MODIS vegetation indices

  • Afrah Daham
  • Dawei Han
  • Miguel Rico-Ramirez
  • Anke Marsh
Original Article

Abstract

Iraq, the land of two rivers, has a history that extends back millennia and is the subject of much archaeological research. However, little environmental research has been carried out, and as such relatively little is known about the interaction between Iraq’s vegetation and climate. This research serves to fill this knowledge gap by investigating the relationship between the Normalized Difference Vegetation Index (NDVI) and two climatic factors (precipitation and air temperature) over the last decade. The precipitation and air temperature datasets are from the Water and Global Change Forcing Data ERA-Interim (WFDEI), and the NDVI dataset was extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) at 250 m spatial resolution and 16 day temporal resolution. Three different climatic regions in Iraq, Sulaymaniyah, Wasit, and Basrah, were selected for the period of 2001–2015. This is the first study to compare these regions in Iraq, and one of only a few investigating vegetation’s relationship with multiple climatic factors, including precipitation and air temperature, particularly in a semi-arid region. The interannual, intra-annual and seasonal variability for each region is analysed to compare the different responses of vegetation growth to climatic factors. Correlations between NDVI and climatic factors are also included. Plotting annual cycles of NDVI and precipitation reveals a coherent onset, fluctuation (peak and decline), with a time lag of 4 months for Sulaymaniyah and Wasit (while for the Basrah region, high temperatures and a short rainy season was observed). The correlation coefficients between NDVI and precipitation are relatively high, especially in Sulaymaniyah, and the largest positive correlation was (0.8635) with a time lag of 4 months. The phenological transition points range between 3 and 4 month time lag; this corresponds to the duration of maturity of the vegetation. However, when correlated with air temperature, NDVI experiences an inverse relationship, although not as strong as that of NDVI and precipitation; the highest negative correlation was observed in Wasit with a time lag of 2 months (− 0.7562). The results showed that there is a similarity between temporal patterns of NDVI and precipitation. This similarity is stronger than that of NDVI and air temperature, so it can be concluded that NDVI is a sensitive indicator of the inter-annual variability of precipitation and that precipitation constitutes the primary factor in germination while the air temperature acts with a lesser effect.

Keywords

WATCH Forcing Data ERA-Interim (WFDEI) NDVI Precipitation Air temperature Vegetation Inter-annual Intra-annual Seasonal variability Rainfall indicators Air temperature indicators 

References

  1. Agha OMAM., Şarlak N (2016) Spatial and temporal patterns of climate variables in Iraq. Arab J Geosci 9:302CrossRefGoogle Scholar
  2. Amanollahi J, Kboodvanpour S, Makmom Abdullah A, Arif NL (2012). Influences of the heat and vegetation cover changes of Iraq deserts on dust storms using satellite images processing. In: Proceedings from the 6th international symposium on advances in science and technology, Kuala Lumpur, March, pp 21–25Google Scholar
  3. Amri R, Zribi M, Lili-Chabaane Z, Duchemin B, Gruhier C, Chehbouni A (2011) Analysis of vegetation behavior in a North African semi-arid region, Using SPOT-VEGETATION NDVI DATA. Remote Sens 3(12):2568–2590CrossRefGoogle Scholar
  4. Azooz A, Talal SK (2015) Evidence of climate change in Iraq. J Environ Protection Sustain Dev 1(2):66–73Google Scholar
  5. Bannari A, Morin D, Bonn F, Huete A (1995) A review of vegetation indices. Remote Sens Rev 13(1–2):95–120CrossRefGoogle Scholar
  6. Boyte SP, Wylie BK, Major DJ, Brown JF (2015) The integration of geophysical and enhanced moderate resolution imaging spectroradiometer normalized difference vegetation index data into a rule-based, piecewise regression-tree model to estimate cheatgrass beginning of spring growth. Int J Digit Earth 8(2):118–132CrossRefGoogle Scholar
  7. Brady NC, Weil RR (2000). Elements of the nature and properties of soilsGoogle Scholar
  8. Cantón Y, Solé-Benet A, de Vente J, Boix-Fayos C, Calvo-Cases A, Asensio C, Puigdefábregas J (2011) A review of runoff generation and soil erosion across scales in semiarid south-eastern Spain. J Arid Environ 75:1254–1261CrossRefGoogle Scholar
  9. Childs C (2004) Interpolating surfaces in ArcGIS spatial analyst. ArcUser July September 3235:569Google Scholar
  10. Ciampalini R, Follain S, Le Bissonnais Y (2012) LandSoil: a model for analysing the impact of erosion on agricultural landscape evolution. Geomorphology 175/176:25–37CrossRefGoogle Scholar
  11. Colditz RR, Conrad C, Wehrmann T, Schmidt M, Dech S (2008) TiSeG: A flexible software tool for time-series generation of MODIS data utilizing the quality assessment science data set. IEEE Trans Geosci Remote Sens 46(10):3296–3308CrossRefGoogle Scholar
  12. CSO (2008). Central Statistical Organization (CSO). IRAQ, Environmental Statistics. http://www.cosit.gov.iq/ar/env-stat/envi-stat. Accessed 26 Jan 2016
  13. Cuomo V, Lanfredi M, Lasaponara R, Macchiato M, Simoniello T (2001) Detection of interannual variation of vegetation in middle and southern Italy during 1985–1999 with 1 km NOAA AVHRR NDVI data. J Geophys Res 106:17863–17876CrossRefGoogle Scholar
  14. 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(6):1109–1123CrossRefGoogle Scholar
  15. Davenport ML, Nicholson SE (1993) On the relation between rainfall and the Normalized Difference Vegetation Index for diverse vegetation types in East Africa. Int J Remote Sens 14(12):2369–2389CrossRefGoogle Scholar
  16. Djamali M, Akhani H, Andrieu-Ponel V, Braconnot P, Brewer S, de Beaulieu JL, Fleitmann D, Fleury J, Gasse F, Guibal F, Jackson ST (2010) Indian summer monsoon variations could have affected the early-Holocene woodland expansion in the Near East. Holocene 20(5):813–820CrossRefGoogle Scholar
  17. Fadhil AM (2011) Drought mapping using Geoinformation technology for some sites in the Iraqi Kurdistan region. Int J Digital Earth 4(3):239–257CrossRefGoogle Scholar
  18. FAO (2003). Food Agriculture Organization of the United Nations. Special report FAO/WFP crop, food supply and nutrition assessment mission to Iraq. http://www.fao.org/docrep/005/j0465e/j0465e00HTM. Accessed 21 Aug 2016
  19. FAO (2008). Food Agriculture Organization of the United Nations. IRAQ, Geography, Climate and Population. http://www.fao.org/nr/water/aquastat/main/index.stm. Accessed 11 Jan 2016
  20. FAO (2011). Food Agriculture Organization of the United Nations. Country Pasture/Forage Resource Profiles. Rome, Italy. http://www.fao.org/ag/agp/AGPC/doc/Counprof/Iraq/Iraq.html. Accessed 26 May 2016
  21. FAO (2013). Country programming framework 2013–2017, Report. http://www.fao.org/3/a-au666e.pdf. Accessed 03 Oct 2016
  22. FAO (2016). Agriculture And Livelihoods Needs Assessment, report. Available at: http://www.fao.org/fileadmin/user_upload/FAO-countries/Iraq/ToR/FAO_Assessment1.pdf. Accessed 21 May 2016
  23. 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. Global Planet Change 110:74–87CrossRefGoogle Scholar
  24. Harris I, Jones P, Osborn T, Lister D (2014) Updated high-resolution grids of monthly climatic observations–the CRU TS3. 10 Dataset. Int J Climatol 34(3):623–642CrossRefGoogle Scholar
  25. Hashemi SA (2011) Investigation of relationship between rainfall and vegetation index by using NOAA/AVHRR satellite images. World Appl Sci J 14(11):1678–1682Google Scholar
  26. Hou W, Gao J, Wu S, Dai E (2015) Interannual variations in growing-season NDVI and its correlation with climate variables in the southwestern karst region of China. Remote Sens 7(9):11105–11124CrossRefGoogle Scholar
  27. Huffman GJ, Bolvin DT, Nelkin EJ, Wolff DB, Adler RF, Gu G, Stocker EF (2007) The TRMM multisatellite precipitation analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8(1):38–55CrossRefGoogle Scholar
  28. Huffman GJ, Adler RF, Bolvin DT, Nelkin EJ (2010). The TRMM multi-satellite precipitation analysis (TMPA) satellite rainfall applications for surface hydrology (pp 3–22): Springer, BerlinCrossRefGoogle Scholar
  29. IAU. (2009). Inter-agency information and analysis unit (IAU). Wassit Governorate Profile, report. http://reliefweb.int/sites/reliefweb.int/files/resources/8A855E72FA11A388C12577EB004D8F22-Full_Report.pdf. Accessed 24 June 2017
  30. Jaradat A (2002) Agriculture in Iraq: resources, potentials, constraints, and research needs and priorities. Food Agric Environ 1(2):160–166Google Scholar
  31. Jones HG (2013). Plants and microclimate: a quantitative approach to environmental plant physiology. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  32. Kastens JH, Legates DR (2002) Time series remote sensing of landscape-vegetation interactions in the southern great plains. Photogramm Eng Remote Sens 68(10):1021–1030Google Scholar
  33. Kottek M, Grieser J, Beck C, Rudolf B, Rubel F (2006) World map of the Köppen-Geiger climate classification updated. Meteorol Z 15(3):259–263CrossRefGoogle Scholar
  34. Lambin EF (1996) Change detection at multiple temporal scales: seasonal and annual variations in landscape variables. Photogramm Eng Remote Sens 62(8):931–938Google Scholar
  35. Lee R, Yu F, Price K, Ellis J, Shi P (2002) Evaluating vegetation phenological patterns in inner Mongolia using NDVI time-series analysis. Int J Remote Sens 23(12):2505–2512CrossRefGoogle Scholar
  36. Li J, Lewis J, Rowland J, Tappan G, Tieszen L (2004) Evaluation of land performance in Senegal using multi-temporal NDVI and rainfall series. J Arid Environ 59(3):463–480CrossRefGoogle Scholar
  37. Liu J, Zhuang D, Luo D, Xiao X-m (2003) Land-cover classification of China: integrated analysis of AVHRR imagery and geophysical data. Int J Remote Sens 24(12):2485–2500CrossRefGoogle Scholar
  38. Lobo A, Marti JI, Gimenez-Cassina CC (1997) Regional scale hierarchical classification of temporal series of AVHRR vegetation index. Int J Remote Sens 18(15):3167–3193CrossRefGoogle Scholar
  39. Marsh A, Altaweel M (2018) The search for hidden landscapes in the Shahrizor: Holocene land use and climate in Northeastern Iraqi Kurdistan. In: Lawrence D, Altaweel M, Phillip G (eds) New agendas in remote sensing and landscape archaeology. University of Chicago, Oriental Institute. Chicago (In Press)Google Scholar
  40. Marsh A, Fleitmann D, Al-Manmi DAM, Altaweel M, Wengrow D, Carter R (2018) Mid- to late-Holocene archaeology, environment and climate in the northeast Kurdistan region of Iraq. The Holocene, JanuaryGoogle Scholar
  41. Marszelewski W, Skowron R (2006) Ice cover as an indicator of winter air temperature changes: case study of the Polish lowland lakes. Hydrol Sci J 51(2):336–349CrossRefGoogle Scholar
  42. Martínez B, Gilabert MA (2009) Vegetation dynamics from NDVI time series analysis using the wavelet transform. Remote Sens Environ 113(9):1823–1842CrossRefGoogle Scholar
  43. Martiny N, Camberlin P, Richard Y, Philippon N (2007) Compared regimes of NDVI and rainfall in semi-arid regions of Africa. Int J Remote Sens 27(23):5201–5223CrossRefGoogle Scholar
  44. Martiny N, Philippon N, Richard Y et al (2010) Predictability of NDVI in semi-arid African region. Theoret Appl Climatol 100:467–484CrossRefGoogle Scholar
  45. Matthews E (1982) Global vegetation and land use: new high-resolution data bases for climate studies. J Clim Meteorol 22:474–487CrossRefGoogle Scholar
  46. Mennis J (2001) Exploring relationships between ENSO and vegetation vigour in the south-east USA using AVHRR data. Int J Remote Sens 22(16):3077–3092CrossRefGoogle Scholar
  47. Mkhabela M, Bullock P, Raj S, Wang S, Yang Y (2011) Crop yield forecasting on the Canadian Prairies using MODIS NDVI data. Agric For Meteorol 151(3):385–393CrossRefGoogle Scholar
  48. Moulin S, Kergoat L, Viovy N, Dedieu G (1997) Global-scale assessment of vegetation phenology using NOAA/AVHRR satellite measurements. J Clim 10(6):1154–1170CrossRefGoogle Scholar
  49. Najmaddin PM, Whelan MJ, Balzter H (2017) Application of satellite-based precipitation estimates to rainfall-runoff modelling in a data-scarce semi-arid catchment. Climate 5:32CrossRefGoogle Scholar
  50. Nicholson SE, Davenport ML, Malo AR (1990) A comparison of the vegetation response to rainfall in the Sahel and East Africa, using normalized difference vegetation index from NOAA AVHRR. Clim Change 17(2–3):209–241CrossRefGoogle Scholar
  51. Pachauri RK, Meyer LA (eds.) (2014) Limate change: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change [Core Writing Team]. IPCC, Geneva, 151Google Scholar
  52. Prasad VK, Badarinath K, Eaturu A (2008) Effects of precipitation, temperature and topographic parameters on evergreen vegetation greenery in the Western Ghats, India. Int J Climatol 28(13):1807–1819CrossRefGoogle Scholar
  53. Prince S (1991) A model of regional primary production for use with coarse resolution satellite data. Int J Remote Sens 12(6):1313–1330CrossRefGoogle Scholar
  54. Qader SH, Atkinson PM, Dash J (2015) Spatiotemporal variation in the terrestrial vegetation phenology of Iraq and its relation with elevation. Int J Appl Earth Obs Geoinf 41:107–117CrossRefGoogle Scholar
  55. Roerink G, Menenti M, Soepboer W, Su Z (2003) Assessment of climate impact on vegetation dynamics by using remote sensing. Phys Chem Earth Parts A/B/C 28(1):103–109CrossRefGoogle Scholar
  56. Rousvel S, Armand N, Andre L, Tengeleng S, Alain TS, Armel K (2013) Comparison between vegetation and rainfall of bioclimatic ecoregions in central Africa. Atmosphere 4(4):411–427CrossRefGoogle Scholar
  57. Running SW, Nemani RR, Heinsch FA, Zhao M, Reeves M, Hashimoto H (2004) A continuous satellite-derived measure of global terrestrial primary production. AIBS Bull 54(6):547–560Google Scholar
  58. Schneider U, Becker A, Finger P, Meyer-Christoffer A, Ziese M, Rudolf B (2014) GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theoret Appl Climatol 115(1–2):15–40CrossRefGoogle Scholar
  59. Schnepf RD (2004). Iraq agriculture and food supply: background and issuesGoogle Scholar
  60. Sellers P, Tucker C, Collatz G, Los S, Justice C, Dazlich D, Randall D (1994) A global 1 by 1 NDVI data set for climate studies. Part 2: The generation of global fields of terrestrial biophysical parameters from the NDVI. Int J Remote Sens 15(17):3519–3545CrossRefGoogle Scholar
  61. Suzuki R, Xu J, Motoya K (2006) Global analyses of satellite-derived vegetation index related to climatological wetness and warmth. Int J Climatol 26(4):425–438CrossRefGoogle Scholar
  62. Wang J, Price K, Rich P (2001) Spatial patterns of NDVI in response to precipitation and temperature in the central Great Plains. Int J Remote Sens 22(18):3827–3844CrossRefGoogle Scholar
  63. Wang J, Rich P, Price K (2003) Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA. Int J Remote Sens 24(11):2345–2364CrossRefGoogle Scholar
  64. Weedon GP, Balsamo G, Bellouin N, Gomes S, Best MJ, Viterbo P (2014) The WFDEI meteorological forcing data set: WATCH forcing data methodology applied to ERA-Interim reanalysis data. Water Resour Res 50(9):7505–7514CrossRefGoogle Scholar
  65. Zoungrana BJ-B, Conrad C, Amekudzi LK, Thiel M, Da ED (2014) Land use/cover response to rainfall variability: A comparing analysis between NDVI and EVI in the Southwest of Burkina Faso. Climate 3(1):63–77CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Afrah Daham
    • 1
  • Dawei Han
    • 1
  • Miguel Rico-Ramirez
    • 1
  • Anke Marsh
    • 2
  1. 1.Department of Civil EngineeringUniversity of BristolBristolUK
  2. 2.Institute of Archaeology University College LondonLondonUK

Personalised recommendations