Evaluating the Efficiency of Vegetation Indices in Analyzing Drought Using MODIS Images: A Case Study Qom, Isfahan, Chaharmahal Bakhtiari, and Markazi Provinces

Document Type : Original Article

Authors

1 Associated Professor, Department of Geography, Faculty of Human Sciences, Golestan University, Gorgan, Iran

2 Human Sciences College, Golestan University, Gorgan

3 Geography Department, Human Sciences College, Golestan University, Gorgan

4 mapping department, non-profit Lampi Gorgani Institute, Gorgan

‎10.22052/deej.2024.254106.1039

Abstract

Introduction: As a natural disaster, drought may occur in any climate. In recent decades, widespread severe droughts have continuously affected Iran, imposing detrimental effects on the country’s various economic sectors, including agriculture, environment, and water resources. Today, vegetation indices obtained from remote sensing are widely used to identify and analyze meteorological droughts. Remote sensing technology enables near-real-time monitoring of drought conditions by analyzing high-resolution spectral data, allowing for pixel-level calculations over large geographic areas. The Iranian provinces of Qom, Isfahan, Chaharmahal Bakhtiari, and Markazi are among those regions whose drought conditions have frequently been warned about within the last few years. Therefore, as the study of drought in these four provinces bears special significance due to the sensitivity of the provinces and the large population they accommodate, the current research selected the provinces as its study areas.Material and methods: this study set out to investigate the correlation between SPI, NDVI, and EVI that were obtained from MODIS images from 2011 to 2020, seeking to monitor drought in central regions of Iran. To this end, changes made over a period of 10 years were identified using the images of the Modis satellite sensor and the precipitation data collected from the synoptic stations located in the study area. In this regard, four months (April, May, June, and July) were selected as sample periods by reviewing the data collected from the existing stations using the standardized precipitation index (SPI) model. This study selected MODIS Terra MOD13A2 imagery from 2011 to 2020 due to its high temporal resolution, broad spectral coverage, ease of access, and the absence of atmospheric and geometric correction requirements. This dataset was chosen to ensure the capture of both wet and dry periods. Subsequently, the Standardized Precipitation Index (SPI) was compared with the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). Moreover, the Pearson correlation coefficient was used to determine the correlation between the SPI meteorological drought index and remote sensing indices.Results: According to the study’s results, the correlation between SPI NDVI and EVI was found to be 0.832 and -0.149, respectively. In general, the results indicated that in areas with insufficient precipitation data and poor distribution of drought monitoring, remote sensing, and NDVI data can be used to monitor vegetation changes. Moreover, the results of drought monitoring revealed that during the ten-year study period, severe droughts occurred in some years. For instance, severe drought and extremely wet periods occurred in 2020 and 2011, respectively. On the other hand, the results of the correlation between SPI and remote sensing indices suggested that SPI had the highest correlation with NDVI at the level of 0.01. The results of this study can effectively contribute to the decisions made by decision-makers in monitoring, investigating, and resolving drought conditions.Discussion and conclusion: Vegetation characteristics, the studied time period, soil characteristics, and the distribution and intensity of precipitation are important factors involved in the establishment of the highest correlation coefficient between the NDVI and the SPI during the delay period. Satellite indices show a remarkable correlation with each other in terms of detecting the magnitude of change, and the highest correlation between satellite indices and terrestrial indices is found in the NDVI-SPI pair. Therefore, the NDVI is used to monitor meteorological drought. Compared to point meteorological methods (precipitation recording stations), satellite images offer greater advantages, including the number of sampling points, wider coverage area, higher time resolution, and lower cost. Therefore, remote sensing knowledge is suggested for drought monitoring. Generally, remote sensing data and NDVI are suggested as appropriate indices to be used for monitoring vegetation changes in areas with insufficient rain gauge data and inappropriate distribution of drought monitoring.

Keywords

Main Subjects


  1. Alawi Panah, S.K. (2010). Application of Remote Sensing in Earth Sciences (Soil Sciences). University of Tehran Press. Third Edition, 478 pages (in Persian).
  2. Alawi panah, S.K. (2014). Application of remote sensing in the earth sciences (soil). University of Tehran press. 4th edition, 479 Pages. (In Farsi).
  3. Alizadeh, A. (2014). Applied Hydrology. Imam Reza University Press, thirty- eighth edition, 941 pages.
  4. Azizi, Q., & Safarrad, T. (2012). Analysis of wind characteristics during ENSO phases, case study; 1997, 2008 and 2010. Journal of Climatological Research, 3(9), 82-70 (in Persian).
  5. Caccamo, G., Chisholm, L.A., Bradstock, R.A., & Puotinen, M.L., (2011). Assessing the sensitivity of MODIS to monitor drought in high biomass Remote Sensing of Environment, 115: 2626-2639.
  6. Ceccato, P., Flasse, S., Tarantola, S., Jacquemoud, S., & Gregoire, J. M. (2001). Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sensing of Environment, 77, Pp. 22–33.
  7. Chen, Z., Grasby, S.E., & Osadetz, K.G. (2004). Relation between climate variability and groundwater levels in the upper carbonate aquifer, southern Manitoba Canada. Journal of Hydrology, No. 290, 43–62.
  8. Chopra, (2006). Droyght Risk Assessment using Rimote sensing and GIS: Acase study of Gujora. M. scthesis, Itc University
  9. Dracup, J.A., Lee, K.S. J.R. & Paulson, E.G. (1980). On the definition of Water Resource Res, 16(2), 297-302PP.
  10. Dutta, D., Kundu, A., Patel, N.R., Saha, S.K., & Siddiqui, A.R., (2015). Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI). J. Remote Sens. Sp. Sci., 18(1), 53–63; Elsevier.
  11. Elhag, K., & Zhang, W., (2018). Monitoring and Assessment of Drought Focused on Its Impact on Sorghum Yield over Sudan by Using Meteorological Drought Indices for the Period 2001–2011. Remote Sensing, Vol 10 (8), 1231 p.
  12. Fensholt, R., Langanke, T., Rasmussen, K., Reenberg, A., Prince, S.D., Tucker, C., Scholes, R.J., Le, Q.B., Bondeau, A., Eastman, R., Epstein, H., Gaughan, A.E., Hellden, U., Mbow, C., Olsson, L., Paruelo, J., Schweitzer, Ch., Seaquist, J., & Wessels, K. (2012). Greenness in semi-arid areas across the globe 1981- 2007- an Earth Observing Satellite based analysis of trends and drivers. Remote Sensing of Environment, 121: 144-158.
  13. Fiorillo, F., & Guadagno, F.M. (2010). Karst spring discharges analysis in relation to drought periods, using the SPI. Water Resources Management, 24(9): 1867-1884.
  14. Ghafourian, H., Sanaeinejad, S.H., & Davari, K., (2014). Study to determine suitable areas for drought monitoring using TRMM satellite data (Case study: Khorasan Razavi province (water and soil). Agricultural Sciences and Industries (3). 639-648 (in Persian).
  15. Ghanar, A. (2001). Drought Assessment Using NOAA Images in East Azerbaijan, West Azerbaijan and Ardabil Province. Tarbiat Modares University Master Thesis. 89 p (in Persian).
  16. Hamzeh, S., Farahani, Z., Mahdavi, S., Chatar Abgon, O., & Golam Niah, M. (2017). Temporal and Spatial Monitoring of Agricultural Drought Using Remote Sensing Data, Case Study: Markazi Province of Iran. Journal of Spatial Analysis of Environmental Hazards, 4(3), 70-53 (in Persian).
  17. Hodel, E. (2012). Analysing Land Cover Change in Mongolia Using Terra MODIS Satellite Data superviso Hans Hurni, Masterarbeit der Philosophisch. Universität
  18. Ji, L., & Peters, A.J. (2003). Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sensing of Environment, 87, 85-98pp.
  19. Ji, L., & Peters, A.J. (2004). A Spatial Regression Procedure for Evaluating the Relationship between AVHRR-NDVI and Climate in the Northern Great Plains. J. Remote Sensing, 25, 297-311.
  20. Khodaei, M., Shad, R., Maghsoudi Mehrani, Y., & Ghaemi, M. (2016). Determining an optimal index of several remote sensing sensors in order to improve the real-time drought monitoring process in areas with heterogeneous land cover. Echo Hydrology, Volume 3, Number 3, Tehran. (in Persian)
  21. khoosh Akhlaq, F., Ranjbar, F., Toulabi, S., Moqbel, M., & Mamasoompour Asmakoush, J. (2010). Study of Drought in the Water Year 2007-2008 and Its Effects on Agricultural Water Resources (Case Study: Marvdasht County). Journal of Geography, No. 24, 136-119 (in Persian)
  22. Kogan, F.N. (2000). Global drought detection and impact: Assessment from apace, In Wilhite Editor Drought a Global Assessment, 1, 197-206.
  23. Li, B., Tang, H., & Chen, D. (2009). Drought Monitoring Using the Modified Temperature/Vegetation Dryness Index, 2nd International Congress on Image and Signal Processing, 17-19 October, China.
  24. Liu, C. L., & Wu, J.J. (2008). Crop drought monitoring using MODIS NDVI over Mid- Territory of China, International Geosciense and Remote Sensing Symposium.
  25. Mahmoudzadeh, A.H., Saghafian, B., & Mokhtari, A. (2008). Correlation between Drought Index SPI and NDVI Index of Fereydunshahr Region, Third Water Resources Management Conference. University of Tabriz. Faculty of Civil Engineering. 8 pages (in Persian).
  26. Parviz, L., Khayyat Khalqi, M., Valizadeh, K., Iraqi Nejad, S., & Irannejad, P. (2012). Evaluation of the efficiency of indicators resulting from remote sensing technology in assessing meteorological drought; Case Study: Sefidrood Watershed, Geography and Development Quarterly, May 2011, 9(22), 147 -164 (in Persian).
  27. Pei, F., Li, X., Liu, X., & Lao, C. (2013). Assessing the impacts of droughts on net primary productivity in China. Journal of Environmental Management, 114, 362–371.
  28. Rostami, A., Bzane, M., & Raini, M. (2016). Spatial and temporal monitoring of agricultural drought using Modis imagery and remote sensing technology. Journal of Soil and Water Science, 27, 213-226 (in Persian).
  29. Roswintiarti, O., Oarwati, S., & Anggraini, N. (2010). Potential Drought Monitoring over Agriculture Area in Java Island, Indonesia, Indonesian National Institute of Aeronautics and Space (LAPAN), Progress Report of SAFE Prototype Year.
  30. Soltani, M., Soltani, A., Kalhehui, M., & Soleimani, K. (2019). Regional Drought Monitoring Using Landsat Images, Study Area: Kermanshah County. Geographical Information Quarterly (Sepehr), 28(109), 138-146 (in Persian).
  31. Tabari, H., Abghari, H., & Hosseinzadeh Talaee, P. (2012). Temporal trends and spatial characteristics of drought and rainfall in arid and semiarid regions of Iran. Process, 26 (22), 3351–3361; Wiley Online Library.
  32. Yiar, Mohammadi, P. (2005). The Necessity of combing Geographic Information Systems and Remote Sensing in Drought Monitoring, Scientific Journal of Drought and Drought, No. 18 (in Persian).
  33. Yildirim, T., & Aşik, Ş. (2018). Index-based Assessment of Agricultural Drought using Remote Sensing in the Semi-arid Region of Western Turkey. Journal of Agricultural Sciences, 24, 510-516.
  34. Zambrano, F., Lillo-Saavedra, M., Verbist, K., & Lagos, O. (2016). Sixteen Years of Agricultural Drought Assessment of the BioBío Region in Chile Using a 250 m Resolution Vegetation Condition Index (VCI). Remote Sensing, 8, 530, 1-20.