DOI QR코드

DOI QR Code

Evaluating the Agricultural Drought for Pre-Kharif Season in Bangladesh using MODIS Vegetation Health Index

MODIS VHI를 이용한 방글라데시 Pre-Kharif 시즌 농업가뭄의 평가

  • Mohammad, Kamruzzaman (Department of Agricultural Engineering, Gyeongsang National University) ;
  • Jang, Min-Won (Division of Agro-system Engineering and Institute of Agriculture and Life Science, Gyeongsang National University) ;
  • Hwang, Syewoon (Division of Agro-system Engineering and Institute of Agriculture and Life Science, Gyeongsang National University) ;
  • Jang, Taeil (Department of Rural Construction Engineering, Chonbuk National University)
  • Received : 2018.08.23
  • Accepted : 2018.09.28
  • Published : 2018.11.30

Abstract

This paper aimed to characterize the spatial and temporal pattern of agricultural drought in Pre-Kharif season using Vegetation Health Index (VHI) and illustrated drought characteristics in Bangladesh during 2001-2015. VHI was calculated from TCI (Temperature Condition Index) and VCI (Vegetation Condition Index) derived from MODIS Terra satellite data, LST (Land Surface Temperature) and EVI (Enhanced Vegetation Index), respectively. The finding showed that all drought-affected areas were experienced by mild, moderate, severe and extreme droughts in several years of Pre-Kharif seasons. Significant drought events were found in the year of 2002 and 2013. On average, Chittagong district covered the largest drought area in all drought stages, and the fraction of drought area was the highest in Sylhet and Rangpur for Pre-Kharif season. Finally, overlaying annual VHI raster maps resulted in that the most vulnerable district to agricultural drought were Sylhet, Rangpur, and Mymensingh in the northern and eastern regions of Bangladesh.

Keywords

References

  1. Ahmad, Q. K., and R. A. Warrick, 1996. The Implications of Climate and Sea Level Change for Bangladesh. Dordecht: Kluwer Academic Publisher.
  2. Ahmad, Q. K., and A. R. Chowdhury, 2008. Socio-Economic Implications of Climate Change for Bangladesh. Dhaka: Bangladesh Unnayan Parishad.
  3. Al-Mamun, A., N. F. Rahman, A. Aziz, A. Qayum, I. Hossain, S. A. I. Nihad, and S. Kabir, 2018. Identification of meteorological drought prone area in Bangladesh using Standardized Precipitation Index. Journal of Earth Science & Climate Change 9: 457. doi:10.4172/2157-7617.1000457.
  4. Amalo, L. F., R. Hidayat, and Haris, 2017. Comparison between remote-sensing based drought indices in East Java. Earth and Environmental Science 54(1): 012009. doi:10.1088/1755-1315/54/1/012009.
  5. Brown, J. F., B. C. Reed, M. J. Hyes, A. D. Wilhite, and K. Hubbard, 2002. A prototype drought monitoring system integrating climate and satellite data. Percora 15/Land Satellite Information IV/ ISPRS Commission I/FIEOS 2002.
  6. Christensen, J. H., B. Hewitson, A. Busuioc, A. Chen, X. Gao, I. Held, R. Jones, R.K. Kolli, W. T. Kwon, R. Laprise, V. Magana Rueda, L. Mearns, C. G. Menendez, J. Raisanen, A. Rinke, A. Sarr, and P. Whetton, 2007. Regional climate projections. In: Climate Change 2007: The Physical Science Basis, ed. H. L. Miller, Cambridge and New York: IPCC.
  7. Chopra, P., 2006. Drought risk assessment using remote sensing and GIS, a case study in Gujarat. M.Sc. Thesis, Netherlands: ITC.
  8. Dalezios, N. R., A. Blanta, N. V. Spyropoulos, and A. M. Tarquis, 2014. Risk identification of agricultural drought for sustainable agroecosystems. Natural Hazards and Earth System Sciences 14(9): 2435-2448. doi:10.5194/nhess-14-2435-2014.
  9. FAO (Food and Agriculture Organization of the United Nations), 2011. Irrigation in Southern and Eastern Asia in Figures: AQUASTAT Survey-2011. FAO Water Report No. 37, Rome: FAO.
  10. Gommes, R., and F. Petrassi, 1994. Rainfall variability and drought in Sub-Saharan Africa since 1960. FAO Agro Meteorology Series Working Paper No. 9, Rome: FAO.
  11. Ghaleb, F., M. Mario, and A. Sandra, 2015. Regional landsat-based drought monitoring from 1982 to 2014. Climate 3 (3): 563-577. doi:10.3390/cli3030563.
  12. Hassan, Q. K., and C. P. A. Bourque .2015. Development of a new wetness index based on RADARSAT-1 ScanSAR data. In Monitoring and Modeling of Global Changes: A Geomatics Perspective, ed. X. Yang. 301-315. Switzerland: Springer International Publishing.
  13. Huete, A., K. Didan, T. Miura, E. P. Rodriguez, X. Gao, and L. G. Ferreira, 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83: 195-213. doi:10.1016/S0034-4257(02)00096-2.
  14. Huete, A., K. Didan, W. van Leeuwen, T. Miura, and E. Glenn, 2011. MODIS vegetation indices. Remote Sensing and Digital Image Processing 11: 579-602. doi:10.1007/978-1-4419-6749-7_26.
  15. Jiao, W., L. Zhang, Q. Chang, D. Fu, Y. Cen, and Q. Tong, 2016. Evaluating and enhanced vegetation condition index (VCI) based on VIUPD for drought monitoring in the continental United States. Remote Sensing 8:1-21.
  16. Khalil, A., M. Wahab, M.K. Hassanein, B. Ouldbdey, B. Katlan, and Y. Essa, 2013. Drought monitoring over Egypt by using MODIS land surface temperature and normalized difference vegetation. Nature and Science 11(11): 116-122.
  17. Kogan, F. N., 1995. Application of vegetation index and brightness temperature for drought detection. Advances in Space Research 15(11): 91-100. doi:10.1016/0273-1177(95)00079-T.
  18. Kogan, F. N., 2001. Operational space technology for global vegetation assessment. Bulletin of the American Meteorological Society 82(9): 1949-1964. https://doi.org/10.1175/1520-0477(2001)082<1949:OSTFGV>2.3.CO;2
  19. Mohsenipour, M., S. Shahid, and E. Chung, 2018. Changing pattern of droughts during cropping seasons of Bangladesh. Water Resources Management 32(5): 1555-1568. doi:10.1007/s11269-017-1890-4.
  20. Moktari, M. H., 2005. Agricultural drought impact assessment using remote sensing: a case study Borkhar District- Iran, M.Sc. Thesis, Netherlands: ITC.
  21. Park, S., J. J. Feddema, and L. S. Egbert, 2004. Impacts of hydrologic soil properties on drought detection with MODIS thermal data. Remote Sensing of Environment 89(1): 53-62. doi:10.1016/j.rse.2003.10.003.
  22. Partheepan, K., and N. D. K. Dayawansa, 2008. A GIS and remote sensing modeling approach for drought risk assessment in Batticaloa district, Srilanka. Paper-14, Third South Asia Water Research Conference: Innovative Modeling Approaches for IWRM, May 2008, Dhaka, Bangladesh.
  23. Rahman, M. R., and H. Lateh, 2015. Climate change in Bangladesh: a spatiotemporal analysis and simulation of recent temperature and rainfall data using GIS and time series analysis model. Theoretical and Applied Climatology 128(1-2): 27-41. doi:10.1007/s00704-015-1688-3.
  24. Rahman, A., H. J. Jibanul, and A. M. Sohul, .2015. Regional variation of temperature and tainfall in Bangladesh: estimation of trend. Journal of Applied & Computational Mathematics 4: 245. doi:10.4172/2168-9679.1000245.
  25. Shahid, S., 2010a. Recent trends in the climate of Bangladesh. Climate Research 42(3): 185-193. doi:10.3354/cr00889.
  26. Shahid, S., 2010b. Spatio-temporal variation of aridity and dry period in term of irrigation demand in Bangladesh. American-Eurasian Journal of Agricultural & Environmental Sciences 7(4): 386-396.
  27. Unganai, L. S., and F. N. Kogan, 1998. Drought monitoring and corn yield estimation in Southern Africa from AVHRR data. Remote Sensing of Environment 63: 219-232. https://doi.org/10.1016/S0034-4257(97)00132-6
  28. Wang, H., H. Lin, and D. Liu, 2014. Remotely sensed drought Index and its responses to meteorological drought in Southwest China. Remote Sensing Letters 5(5): 413-422. doi:10.1080/21 50704X.2014.912768
  29. Xiao, X., B. Braswell, Q. Zhang, S. Boles, S. Frolking, and B. Moore, 2003. Sensitivity of vegetation indices to atmospheric aerosols: continental scale observations in Northern Asia. Remote Sensing of Environment 84(3): 385-392. doi:10.1016/S0034-4257(02)00129-3
  30. Zargar, A., R. Sadiq, and B. Naser, 2011. A review of drought indices. Environmental Reviews 19: 333-349. doi:10.1139/a11-013.