Scientific- Research Quarterly of Geographical Data (SEPEHR)

Scientific- Research Quarterly of Geographical Data (SEPEHR)

Investigating the sensitivity of NDVI and EVI vegetation indices to dry and wet years in arid and semi-arid regions (Case study: Sistan plain, Iran)

Document Type : Research Paper

Authors
Fatemeh Firouzi 1
Taghi Tavosi 2
Peyman Mahmoudi 3
1 PhD Student of climatology, Geography and Regional Planning Faculty, University of Sistan and Baluchestan, Zahedan, Iran.
2 Professor, Department of Physical Geography, Geography and Regional Planning Faculty, University of Sistan and Baluchestan, Zahedan, Iran
3 Assistant Professor, Department of Physical Geography, Geography and Regional Planning Faculty, University of Sistan and Baluchestan, Zahedan, Iran
10.22131/sepehr.2019.36621
Abstract
Extended Abstract
Introduction
With recent advances in satellite remote sensing productions in past few decades, several indices have been provided for the study of vegetation dynamics, and especially for the assessment of drought impacts. Among these, two vegetation indices -Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) - have gained the attention of various researchers. Therefore, the present study aims to investigate the reaction of these two vegetation indices (i.e. NDVI and EVI) to dry and wet years in a dry plain in Iran (i.e. Sistan plain in eastern Iran).
Materials & Methods
To assess the sensitivity of these indices to dry and wet years, two different databases were required. First, NDVI and EVI image base received from Terra satellite (MODIS sensor) for April, May and June 2000-2014, and downloaded from EOS website. Second, daily data base of Zabol synoptic meteorological station (for a statistical period of 30-years 1985-2014) received from Iran Meteorological Organization. After data acquisition, separate vegetation dynamics maps (for April, May and June) were produced for the study area based on the information derived through processing of MODIS sensor images (Terra satellite) using NDVI and EVI. Effective drought index (EDI) was used to determine the frequency of dry and wet years in Sistan plain.
Results & Discussion
Mapping of vegetation dynamics based on images received from MODIS sensor (Terra satellite) for a 15-year statistical period (2000 to 2014: April, May, and June) indicated that NDVI and EVI had significant differences in exhibiting the dynamics of vegetation in the study area. These differences were obvious in areas with average amount of vegetation (0.4-0.5 in both NDVI and EVI) and also in areas with sparse dispersed vegetation (0.3-0.4 in both NDVI and EVI). In average levels of vegetation, total area of vegetation calculated by EVI is​​ much higher than what is calculated by NDVI, while in sparse and dispersed vegetation, total area of vegetation calculated by NDVI is almost higher than EVI. Subsequently by selection of a dry (2010-2011) and a wet year (2005-2006), we compared changes in total area of vegetation (average and sparse) calculated by NDVI and EVI. Regarding the response of these two indices to dry and wet years, it was concluded that NDVI shows a better and more logical response during droughts, while EVI provides better results in wet years. However, it should be noted that the mean annual precipitation of Sistan plain is so low (59 mm per year) and its evapotranspiration is so high (4800 mm per year) that precipitation does not play a significant role in vegetation dynamics of this plain. Therefore, water flow in Helmand River, which is the lifeblood of this desert, is much more important than this limited precipitation in Sistan plain; hence, we can conclude that meteorological drought monitoring indices cannot reflect the relationship between drought and vegetation dynamics in Sistan plain, and this makes it difficult to compare NDVI and EVI in the region.
Conclusion
In general, it can be concluded that NDVI is a more suitable index for dynamics of vegetation in plains such as Sistan, whose life depends not on precipitation but on water running in the river. Because of the computational nature of EVI, it responds better in areas with dense vegetation. According to the vegetation type obtained from MODIS sensor images and field visits, NDVI is a better index for these types of plains.
Keywords
Drought Effective Index
Sistan Plain
MODIS Sensor
NDVI
EVI
  1. اکبری، مرتضی، 1382، ارزیابی و طبقه‌بندی بیابان‌زایی با تکنیک RS و GIS در منطقه خشک شمال اصفهان، به راهنمایی خواجه‌الدین جمال‌الدین، کریمیان اقبال، مصطفی، پایان‌نامه کارشناسی ارشد دانشگاه صنعتی اصفهان.
  2. باعقیده، محمد، 1386، بررسی و پایش خشکسالی‌های استان اصفهان با استفاده از تصاویر چند زمانه‌ای NOAA/AVHRR، شاخص NDVI و سیستم اطلاعات جغرافیایی (GIS)، به راهنمایی علیجانی بهلول و ضیائیان پرویز، دانشگاه تربیت معلم تهران.
  3. جلیلی، شیدا، 1384، مقایسه شاخص‌های ماهواره‌ای و هواشناسی در پایش خشکسالی‌ها (مطالعه موردی استان تهران)، به راهنمایی مرید سعید ، پایان‌نامه کارشناسی ارشد دانشگاه تربیت مدرس.
  4. جعفری، مصباح‌زاده؛ مهدی، طیبه؛ 1396، ارزیابی شاخص‌های پوشش گیاهی با استفاده از سنجش‌ازدور (مطالعه موردی: کرج)، اولین همایش ملی و بین‌المللی علوم محیط‌زیست، کشاورزی و منابع طبیعی، صص 10-1.
  5. شایگان، مهران؛1381،  تجزیه‌وتحلیل ریسک خشکسالی با استفاده از داده‌های سنجش‌ازدور و GIS (حوضه شیروان و قوچان)، به راهنمایی علی‌محمدی سراب عباس، پایان‌نامه کارشناسی ارشد دانشگاه تربیت مدرس.
  6. شمسی‌پور، علوی‌پناه، محمدی؛ علی‌اکبر، کاظم، حسین، 1389، بررسی کارایی شاخص‌های گیاهی و حرارتی ماهواره NOAA-AVHRR، فصلنامه علمی-پژوهشی تحقیقات مرتع و بیابان ایران، جلد17، شماره3، صص 465-445.
  7. مفاخری، شمسی‌پور، فلاحی خوشجی، کرمانی؛ امید، علی‌اکبر، مصطفی، آذر؛ 1395، تحلیل خشکسالی با استفاده از شاخص NDVI در دشت قروه و دهگلان، نشریه تحقیقات کاربردی علوم جغرافیا، سال16، شماره 41، صص 94-77.

8. Akhtari, R., Morid, S., Mahdian, M. H.,  Smakhtin, V, 2009, Assessment of areal interpolation methods for spatial analysis of SPI and EDI drought indices, Int. J. Climatol, 29, 135–145.

9. Akkartala, A., Turudua, O.,  Erbekb, F. S., 2005, Analysis of changes in vegetation biomass using multitemporal and multisensor satellite data, Istanbul Technical University, Faculty of Civil Engineering, Geodesy and photogrammetry Engineering Department Undergraduate program,34469.

10. Alwesabi, Mohammed ., 2012,  MODIS NDVI satellite data for assessing drought in Somalia during the period 2000-2011, Supervisor Lars Eklundh,  Physical Geography and Ecosystems Science, Lund University.

11. Ardö, J., Tagesson, T., Jamali, S., Khatir, A., 2017, MODIS EVI-based net primary production in the Sahel 2000–2014, Int J Appl Earth Obs Geoinformation, 65, 35–45.

12. Benedetti,R., Rossinip., T., 1994, Vegetation classification in the Mediterranean area by satellite data, International Journal of Remote Sensing, 3,583-596

13. Bhalme, H., Reddy, R., Mooley, D., Murty, B.V.R., 1981, Solar activity and Indian weather/climate Proc ,Indian Acad, Sci.-Earth Planetary, 90, 245–262.

14. Byun, H. R., Wilhite, D. A., 1999, Objective quantification of drought severity and duration. J. Climate, 12, 2747–2756.

15. Ceccato, P., Flasse, S., Tarantola, S., Jacquemoud, S., Grégoire, J.-M., 2001, Detecting vegetation leaf water content using reflectance in the optical domain, Remote Sens Environ, 77, 22–33.

16. Chen, P.Y., Srinivasan, R., Fedosejevs, G., Kiniry, J.R., 2003, Evaluating different NDVI composite techniques using NOAA-14 AVHRR data, International Journal of Remote Sensing, 24 (17), 3403–3412.

17. Diodato, N., Bellocchi, G., 2008, Modelling vegetation greenness responses to climate variability in a Mediterranean terrestrial ecosystem, Environ, 143, 147–159.

18. Fensholt, R., Sandholt, I., 2003, Derivation of a shortwave infrared water stress index from MODIS near-and shortwave infrared data in a semiarid environment,Remote Sens Environ, 87, 111–121.

19. Fuller,D.O.,1998, Trends in NDVI time series and their relation to rangland and crop production in sengal 1987-1993, INT.J.Remote Sensing, 10, 2013-2018.

20. Gao, B.-C., 1996, NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ, 58, 257–266.

21. Ghulam, A., Li, Z.-L., Qin, Q., Tong, Q., 2007, Exploration of the spectral space based on vegetation index and albedo for surface drought estimation, J. Appl. Remote Sens, 1, 1-13.

22. Groeneveld, D.P., Baugh, W.M., 2007,Correcting satellite data to detect vegetation signal for eco-hydrologic analyses, Journal of Hydrology, 344, 135–145.

23. Hardisky, M.A., Klemas, V., Smart, R.M., 1983, The influence of soil salinity growth form and leaf moisture on the spectral radiance of Spartina alterniflora canopies, Eng Rem. Sens, 49, 77–83.

24. Herrmann, S.M., Anyamba, A., Tucker, C.J., 2005, Exploring relationship between rainfall and vegetation dynamics in the Sahel using coarse resolution satellite data, Statement by the author, 79.

25. Hodel, Elias., 2012, Analysing Land Cover Change in Mongolia Using Terra MODIS Satellite Data supervisor Hans Hurni, Masterarbeit der Philosophisch, Universität Bern.

26. Hollinger, S., Isard, S., Welford, M., 1993, A new soil moisture drought index for predicting crop yields, In: Preprints, Eighth Conference on Applied Climatology, pp. 187–190.

27. Hunt, E.R., Rock, B.N., 1989, Detection of changes in leaf water content using nearand middle-infrared reflectances, Remote Sens Environ, 30, 43–54.

28. Jackson, T.J., Chen, D., Cosh, M., Li, F., Anderson, M., Walthall, C., Doriaswamy, P., Hunt, E.R., 2004, Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans, Remote Sens Environ, 92, 475–482.

29. Ji, L., Peters, A.J., 2003, Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices, Remote Sens Environ, 87, 85–98.

30. Jordan,C.F., 1969, Derivation of leaf area index from quality of light on the forest floor, Ecology, 50,663-666.

31. Kalamaras, N., Michalopoulou, H.,  Byun, H. R., 2010, Detection of drought events in Greece using daily precipitation, Hydrol. Res, 41 (2), 126–133.

32. Kassa, A., 1999, Drought risk monitoring for the sudan using ndvi 1982-1993, A Dissertation submitted to the University College London.

33. Kim, D. W., Byun, H. R., 2009, Future pattern of Asian drought under global warming scenario, Theor. Appl. Climatol, 98, 137–150.

34. Kimes, D., Markham, B., Tucker, C., McMurtrey, J., 1981, Temporal relationships between spectral response and agronomic variables of a corn canopy, Remote Sens Environ, 11, 401–411.

35. Kogan,F.N., 1990, Remote Sensing of weather impacts on vegetation in non-homogeneous areas, International Journal of Remote  Sensing, 11:1105-1419.

36. Kogan,F.N., 1997, Global drought watch from space Bulletin of the American, Meteorological Society, 78: 621-636.

37. Kogan,F.N., 1993,United States drought of late 1980s as seen by NOAA polar orbiting satellites, International Geoscience and Remote Sensing Symposium, 1:197-207.

38. Kogan,F.N., 1995, Drought of the late 1980s in the united states as derived from NOAA polar –orbiting satellite data, Bulletin of the American Meteorological Society, 76:655-668.

39. Kogan,F.N., 2000, Global drought detection and impact: Assessment from apace, In Wilhite Editor Drought a Global Assessment, 1: 197-206.

40. Lim, C., Kafatos, M., 2002, Frequency analysis of natural vegetation distribution using NDVI/AVHRR data from 1981 to 2000 for North America: Correlation with SOI, International Journal of Remote Sensing, 23, 3347- 3383.

41. Liu,W.T., Kogan, F.N., 1995, Monitoring regional drought using the vegetation condition index, International journal of Remote Sensing, 17: 2761-2782.

42. Maki, M., Ishiahra, M., Tamura, M., 2004, Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data, Remote Sens Environ, 90, 441–450.

43.Matsushita,B.,Wei.Y,Jin.C,Yuyichi.O., Guoyn.Q., 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 www.mdpi.org/sensors.

44. Mckee, T.B., Doesken,N.J, Kleist, J., 1993, The relationship of drought frequency and duration to time scales, Proceedings of the Eighth Conferences on Applied Climatology, American Meteorological Society, Boston,  179-184.

45. Morid, S., Smakhtin, V., Bagherzadeh, K., 2007, Drought forecasting using artificial neural networks and time series of drought indices, Int. J. Climatol, 27, 2103–2111.

46. Morid, S., Smakhtin, V., Moghaddasi, M., 2006, Comparison of seven meteorological indices for drought monitoring in Iran, Int. J. Climatol, 26, 971–985.

47. Mohanta, K., Nandi, D., 2017, Monitoring Vegetation and Land Surface Temperature Dynamics in Similipal Biosphere Reserve Odisha, Scientific Research, 100, 1344-1360.

48. Narasimhan, B., Srinivasan, R., 2005, Development and evaluation of soil moisture deficit index (SMDI) and evapotranspiration deficit index (ETDI) for agricultural drought monitoring, Agric For Meteorol, 133, 69–88.

49. NASA., 2012, MODIS-Specifications, http://modis.gsfc.nasa.gov/about/specifications.php accessed on1, 06.

50. Palmer, W.C., 1965, Meteorological drought. US Department of Commerce, Weather Bureau Washington, DC, USA.

51. Pandey, R.P., Dash, B.B., Mishra, S.K., Singh, R., 2008, Study of indices for drought characterization in KBK districts in Orissa (India), Hydrol,  22 ( 12), 1895–1907.

52. Peters, D., 2002, plant species dominance at a grassland-shrubland ecoton: an individual based gap dynamics model of herbaceous and woody species, Ecological Modeling, 1,5-32.

53. Pettorelli,N.,Vik.J.O., Mysterud, A.,Gaillard, J.M., Tucker, C.J., Stenseth,N.C., 2005, Using the satellite –derived NDVI to assess ecological responses to environmental change.J.Trends in ecology and evolution, 20(9), -13.

54. Qin, Y., Xiao, X., Dong, J., Zhou, Y., Zhu, Z., Zhang, G., Du, G., Jin, C., Kou, W., Wang, J., 2015, Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery,  J Photogrammetry Remote Sens, 105, 220–233.

55. Reed, B,C.,1992,Using remote sensing and Geographic Information System for analyzing, and scape/drought interaction, International Journal of Remote Sensing, 14:3495-3505.

56. Reichstein, M., Tenhunen, J.D., Roupsard, O., Ourcival, J.m., Rambal, S., Miglietta, F., Peressotti, A., Pecchiari, M., Tirone, G.,  Valentini, R., 2002, Severe drought effects on ecosystem CO2 and H2O fluxes at three Mediterranean evergreen sites: revision of current hypotheses Glob, Change Biol, 8, 999–1017.

57. Roudier, P., Mahe, G., 2010, Study of water stress and droughts with indicators using daily data on the Bani River (Niger basin, Mali), Int. J. Climatol,  30 ( 11),  1689–1705.

58. Salinas- Zavala, C.A., Douglas, A.V., Diaz, H.F., 2002, Interannual variability NDVI in northwest Mexico, Associated climatic mechanisms and ecological, Remote Sensing of Environment, 82, 417-30.

59. Shafer, B., Dezman, L., 1982, Development of a Surface Water Supply Index (SWSI) to assess the severity of drought conditions in snowpack runoff areas, In: Proceedings of the Western Snow Conference, pp. 164–175.

60. Shakir, M., Yulin, Z., Li, W., Pengyu, H., Zheng, N., 2015, Major crops classification using time series MODIS EVI with adjacentyears of ground reference data in the US state of Kansas, Optik, 1-7.

61. Shinoda,  M., Nandintsetseg, B.,  2013, Assessment of drought frequency duration, and severity and its impact on pasture production in Mongolia , Nat Hazards , 66, 995–1008.

62. Solano, R., Didan, K., Jacobson, A., Huete, A., 2010, MODIS Vegetation Indices (MOD13) C5

63. Song, Y., Ma, M., 2011, A statistical analysis of the relationship between climatic factors and the normalized difference vegetation index in China, Int. J. Remote Sens. 32, 3947–3965.

64. Srivastava, S.K., Jayarman, V., Nageswara Rao, p.p., Manikiam, B., Candrasekhar,M.G., 1996, Interlinkages of NOAA/AVHRR derived integrated NDVI seasonal precipitation and transpiration in dry land tropics, International Journal of Remote Sensing, 18, 2931-2952.

65. Tadesse, T., Brown, J.F., Hayes, M.J., 2005, A new approach for predicting droughtrelated vegetation stress: integrating satellite, climate, and biophysical data over the US central plains, ISPRS J. Photogrammetry Remote Sens, 59, 244–253.

66. Thenkabail, P. S., Gamage, M.S.D.N. and Smakhtin, V.U., 2003, The use of note Sensing Data for Drought Assessment and Monitoring in Southwest Asia, IWMI, Research Report, 85.

67. Tucker, C.J.,  Sellers, P. J., 1986, Satellite remote sensing of primary vegetation. International Journal of Remote Sensing, 7, 1395–1416.

68. Tucker, C.J., 1980, Remote sensing of leaf water content in the near infrared. Remote Sens Environ, 10, 23–32.

69. Tucker, C.J., VanPraet, C.L., Sharman, M.J., Van Ittersum, G., 1985, Satellite remote sensing of total herbaceous biomass production in the Senegalese Sahel: 1980–1984, Remote Sensing of Environment ,17,233–249.

70. Van Beek, E., Meijer, K., 2006, Integrated water resources management for the Sistan closed inland delta, Iran. Delft, Netherlands, Delft hydraulics. www.wldelft.nl/cons/area/rbm/wrpl/pdf/main_report_sistan_irwm.pdf.

71. Van Rooy, M., 1965, A rainfall anomaly index independent of time and space, Notos 14, 43–48.

72. Walesh,S.J.,1987,Comparison of NOAA AVHRR data to Meteorologic drought indices, Photogrammetric Engineering and Remote Sensing, 53:1069-1074.

73. Wan, Z., Wang, P., Li, X., 2004, Using MODIS land surface temperature and normalized difference vegetation index products for monitoring drought in the southern Great Plains, J Remote Sens, 25, 61–72.

74. Weghorst, K., 1996, The reclamation drought index: guidelines and practical applications, In: North American Water and Environment Congress & Destructive Water, ASCE, pp. 637–642.

75. Wilhite, D.A., Glantz, M.H., 1985, Understanding: the drought phenomenon: the role of definitions, Water Int, 10, 111–120.

76. Xiao, X., Boles, S., Liu, J., Zhuang, D., Liu, M., 2002, Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 Vegetation sensor data, Remote Sens. Environ, 82, 335–348.

77. Zhang, G., Xiao, X., Dong, J., Kou, W., Jin, C., Qin, Y., Zhou, Y., Wang, J., Menarguez, M.A., Biradar, C., 2015, Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data, ISPRS J. Photogrammetry Remote Sens, 106, 157–171.