Spatiotemporal Assessment of Vegetation Indices and Land Cover for Erbil City and Its Surrounding Using Modis Imageries

Shwan O. Hussein 1 , Ferenc Kovács 1 , and Zalán Tobak 1
  • 1 Department of Physical Geography and Geoinformatics, University of Szeged, Egyetem u. 2-6, H-6722 , Szeged, Hungary

Abstract

The rate of global urbanization is exponentially increasing and reducing areas of natural vegetation. Remote sensing can determine spatiotemporal changes in vegetation and urban land cover. The aim of this work is to assess spatiotemporal variations of two vegetation indices (VI), the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), in addition land cover in and around Erbil city area between the years 2000 and 2015. MODIS satellite imagery and GIS techniques were used to determine the impact of urbanization on the surrounding quasi-natural vegetation cover. Annual mean vegetation indices were used to determine the presence of a spatiotemporal trend, including a visual interpretation of time-series MODIS VI imagery. Dynamics of vegetation gain or loss were also evaluated through the study of land cover type changes, to determine the impact of increasing urbanization on the surrounding areas of the city. Monthly rainfall, humidity and temperature changes over the 15-year-period were also considered to enhance the understanding of vegetation change dynamics. There was no evidence of correlation between any climate variable compared to the vegetation indices. Based on NDVI and EVI MODIS imagery the spatial distribution of urban areas in Erbil and the bare around it has expanded. Consequently, the vegetation area has been cleared and replaced over the past 15 years by urban growth.

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  • Bonan, G. 2002. Ecological Climatology. Cambridge University Press, p. 690.

  • Camberlin, P., Martiny, N., Philippon, N., Richard, Y. 2007. Determinants of the interannual relationships between remote sensed photosynthetic activity and rainfall in tropical Africa. Remote Sensing of Environment 106 (2), 199-216. DOI: 10.1016/j.rse.2006.08.009

  • Colditz, R., Conrad, C., Wehrmann, T., Schmidt, M., Dech, S. 2006. Generation and assessment of MODIS time series using quality information. IEEE International Geoscience and Remote Sensing Symposium, IGARSS, p.6. DOI: 10.1109/igarss.2006.200

  • Gregg, J., Jones, C., Dawson, T. 2003. Urbanisation effects on tree growth in the vicinity of New York City. Nature 424, 183-187. DOI: 10.1038/nature01728

  • Hameed, H. 2013. Water Harvesting In Erbil Governorate, Kurdistan Region, Iraq, Lund University.

  • 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 Sensing of Environment 83 (1-2), 195-213. DOI: 10.1016/s0034-4257(02)00096-2

  • Hussein, S. 2017. The geographical evolution of urban greenness in the city of Erbil based on Landsat imagery. Regional Committee of the Hungarian Academy of Sciences, Pécs, 232-239.

  • Imhoff, M., Zhang, P., Wolfe, R., Bounoua, L. 2010. Remote sensing of the urban heat island effect across biomass in the continental USA. Remote Sensing of Environment, 114 (3), 504-513. DOI: 10.1016/j.rse.2009.10.008

  • Kurdistan Region Government, 2016. Ministry of agriculture and water resources, Iraq.

  • 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

  • Los, S., Weedon, G.P., North, P.R.J, Kaduk, J.D., Taylor, C.M., Cox, P.M. 2006. An observation-based estimate of the strength of rainfall- vegetation interactions in the Sahel. Geophysical Research Letters 33(16), DOI: 10.1029/2006GL027065

  • Lunetta, R.S., Knight, J.F., Ediriwickrema, J., Lyon, J.G., Worthy, L.D. 2006. Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sensing of Environment 105(2), 142-154. DOI: 10.1016/j.rse.2006.06.018

  • M’Ikiugu, M. M., Kinoshita, I., Tashiro, Y. 2012. Urban Green Space Analysis and Identification of its Potential Expansion Areas. Procedia - Social and Behavioral Sciences 35, 449-458. 10.1016/j.sbspro.2012.02.110

  • Mertes, C., Schneider, A., Sulla-Menashe, D., Tatem, A.J., Tan, B. 2015. Detecting change in urban areas at continental scales with MODIS data. Remote Sensing of Environment 158(1), 331-347. DOI: 10.1016/j.rse.2014.09.023

  • Myneni, R.B., Yang, W., Nemani, R.R., Huete, A.R., Dickinson, R.E. et al. 2007. Large seasonal changes in leaf area of amazon rainforests. PNAS 104(12), 4820-4823. DOI: 10.1073/pnas.0611338104

  • Neil, K., Wu, J. 2006. Effects of urbanisation on plant flowering phenology. Urban Ecosystem 9(3), 243-257. DOI: 10.1007/s11252-006-9354-2

  • Nicholson, S., Davenport, M, Malo, A. 1990. A comparison of the vegetation response to rainfall in the Sahel and East Africa, using normalized difference vegetation index from NOAA AVHRR. Climatic Change 17(2), 209-241. DOI: 10.1007/bf00138369

  • Rizzi, R., Rudorff, B., Shimabukuro Y., Doraiswami, P. 2006. Assessment of MODIS LAI retrievals over soybean crop in southern Brazil. International Journal of Remote Sensing 27(19), 4091-4100. DOI: 10.1080/01431160600851850

  • Schwarts, M., 2013. Phenology: An Integrative Environmental Science. Springer, London, p.602.

  • SOITM, 2013. Erbil City. Turkmen Human Rights: Research Foundation, New York.

  • Suepa, T., Jiaguo, Q., Lawawirojwong, S., Messina, J. 2016. Understanding spatio-temporal variation of vegetation phenology and rainfall seasonality in the monsoon Southeast Asia. Environmental Research 147, 621-629. DOI: 10.1016/j.envres.2016.02.005

  • Word Bank, 2015. Data Catalog. http://data.worldbank.org

  • Tucker, C, Slayback, D.A., Pinzon, J.E., Los, S.O., Myneni, R.B., Taylor, M.G. 2001. Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999. International Journal of Biometeorology 11(2), 184-190. DOI: 10.1007/s00484-001-0109-8

  • UNESCO, 2016. UNESCO World Heritage Centre. http://en.unesco.org/

  • United Nations Development Program, 2016. United Nations Development Program. http://www.undp.org/

  • United Nations, 2014. World urbanization prospects. New York: United Nations, p.32.

  • USGS, 2016. Vegetation Indices 16-Day L3 Global 250m. https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod13q1

  • Xiao, X., Zhang, J., Yan, H., Wu, W., Biradar, C. 2009. Land surface phenology: Convergence of satellite and CO2 eddy flux observation. In.: Normeets A. (ed.) Phenology of Ecosystem processes. Springer Sciences+Business Media, New York, 247-270. DOI: 10.1007/978-1-4419-0026-5_11

  • Yuan, F., Bauer, M. 2007. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sensing of Environment 106(3), 375-386. DOI: 10.1016/j.rse.2006.09.003

  • Zhang, X., Friedl, M., Schaaf, C. 2006. Global vegetation phenology from moderate resolution imaging Spectroradiometer (MODIS): evaluation of global patterns and composition with in situ measurements. Journal of Geophysical Research 111(G04017), 1-14. DOI: 10.1016/j.rse.2006.09.003

  • Zhang, X., Friedl, M., Schaaf, C., Strahler, A. 2005. Monitoring the response of the vegetation phenology to precipitation in Africa by coupling MODIS and TRMM instruments. Journal of Geophysical Research 110(D12), 1-14. DOI: 10.1029/2004jd005263

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