Journal of The Korean Society of Agricultural Engineers (한국농공학회논문집)

The Korean Society of Agricultural Engineers (한국농공학회)
권호 표지
DOI QR코드

DOI QR Code

Satellite-based Hybrid Drought Assessment using Vegetation Drought Response Index in South Korea (VegDRI-SKorea)

식생가뭄반응지수 (VegDRI)를 활용한 위성영상 기반 가뭄 평가

  • Nam, Won-Ho (National Drought Mitigation Center, University of Nebraska-Lincoln) ;
  • Tadesse, Tsegaye (National Drought Mitigation Center, School of Natural Resources, University of Nebraska-Lincoln) ;
  • Wardlow, Brian D. (Center for Advanced Land Management Information Technologies, School of Natural Resources, University of Nebraska-Lincoln) ;
  • Jang, Min-Won (Department of Agricultural Engineering, Institute of Agriculture & Life Science, Gyeongsang National University) ;
  • Hong, Suk-Young (National Academy of Agricultural Science, Rural Development Administration)
  • Received : 2015.01.13
  • Accepted : 2015.04.29
  • Published : 2015.07.30
Download PDF
⟨ Previous Next ⟩

Abstract

The development of drought index that provides detailed-spatial-resolution drought information is essential for improving drought planning and preparedness. The objective of this study was to develop the concept of using satellite-based hybrid drought index called the Vegetation Drought Response Index in South Korea (VegDRI-SKorea) that could improve spatial resolution for monitoring local and regional drought. The VegDRI-SKorea was developed using the Classification And Regression Trees (CART) algorithm based on remote sensing data such as Normalized Difference Vegetation Index (NDVI) from MODIS satellite images, climate drought indices such as Self Calibrating Palmer Drought Severity Index (SC-PDSI) and Standardized Precipitation Index (SPI), and the biophysical data such as land cover, eco region, and soil available water capacity. A case study has been done for the 2012 drought to evaluate the VegDRI-SKorea model for South Korea. The VegDRI-SKorea represented the drought areas from the end of May and to the severe drought at the end of June. Results show that the integration of satellite imageries and various associated data allows us to get improved both spatially and temporally drought information using a data mining technique and get better understanding of drought condition. In addition, VegDRI-SKorea is expected to contribute to monitor the current drought condition for evaluating local and regional drought risk assessment and assisting drought-related decision making.

Keywords

References

  1. Ahn, S.R., J.W. Lee, and S.J. Kim, 2014. Analysis of 2012 spring drought using meteorological and hydrological indices and satellite-based vegetation indices. Journal of Korea National Committee on Irrigation and Drainage 21(1): 78-88 (in Korean).
  2. Brown, J.F., B.D. Wardlow, T. Tadesse, M.J. Hayes, and B.C. Reed, 2008. The vegetation drought response idex (VegDRI): a new integrated approach for monitoring drought stress in vegetation. GIScience & Remote Sensing 45(1): 16-46. https://doi.org/10.2747/1548-1603.45.1.16
  3. Broxton, P.D., X. Zeng, D. Sulla-Menashe, and P.A. Troch, 2014. A global land cover climatology using MODIS data. Journal of Applied Meteorology and Climatology 53: 1593-1605. https://doi.org/10.1175/JAMC-D-13-0270.1
  4. Hayes, M.J., O.V. Wilhelmi, and C.L. Knutson, 2004. Reducing drought risk: bridging theory and practice. Natural Hazards Review 5(2): 106-113. https://doi.org/10.1061/(ASCE)1527-6988(2004)5:2(106)
  5. Hayes, M., M. Svoboda, N. Wall, and M. Widhalm, 2011. The Lincoln declaration on drought indices: universal meteorological drought index recommended. Bulletin of the American Meteorological Society 92: 485-488. https://doi.org/10.1175/2010BAMS3103.1
  6. Hong, S.Y., J. Hur, J.B. Ahn, J.M. Lee, B.K. Min, C.K. Lee, Y.H. Lee, K.D. Lee, S.H. Kim, G.Y. Kim, and K.M. Shim, 2012. Estimating rice yield using MODIS NDVI and meteorological data in Korea. Korean Journal of Remote Sensing 28(5): 509-520 (in Korean). https://doi.org/10.7780/kjrs.2012.28.5.4
  7. Hunt, E.D., M. Svoboda, B. Wardlow, K. Hubbard, M. Hayes, and T. Arkebauer, 2014. Monitoring the effects of rapid onset of drought on non-irrigated maize with agronomic data and climate-based drought indices. Agricultural and Forest Meteorology 191: 1-11. https://doi.org/10.1016/j.agrformet.2014.02.001
  8. Jang, M.W., S.H. Yoo, and J.Y. Choi, 2007. Analysis of spring drought using NOAA/AVHRR NDVI for North Korea. Journal of the Korean Society of Agricultural Engineers 49(6): 21-33 (in Korean). https://doi.org/10.5389/KSAE.2007.49.6.021
  9. Jeong, S.T., K.C. Jang, S.Y. Hong, and S.K. Kang, 2011. Detection of irrigation timing and the mapping of paddy cover in Korea using MODIS images data. Korean Journal of Agricultural and Forest Meteorology 13(2): 69-78 (in Korean). https://doi.org/10.5532/KJAFM.2011.13.2.069
  10. Kim, G.S., and J.P. Kim, 2010. Analysis of spatial-temporal variability of NOAA/AVHRR NDVI in Korea. Journal of the Korean Society of Civil Engineers 30(3B): 295-303 (in Korean).
  11. Kim, G.S., and H.G. Park, 2010. Estimation of drought index using CART algorithm and satellite data. Journal of the Korean Association of Geographic Information Studies 13(1): 128-141 (in Korean).
  12. Kwon, H.J., S.C. Shin, and S.J. Kim, 2005. Climatic water balance analysis using NOAA/AVHRR satellite images. Journal of the Korean Society of Agricultural Engineers 47(1): 3-9 (in Korean). https://doi.org/10.5389/KSAE.2005.47.1.003
  13. Na, S.I., J.H. Park, and J.K. Park, 2012. Development of Korean paddy rice yield prediction model (KRPM) using meteorological element and MODIS NDVI. Journal of the Korean Society of Agricultural Engineers 54(3): 141-148 (in Korean). https://doi.org/10.5389/KSAE.2012.54.3.141
  14. Nam, W.H., J.Y. Choi, S.H. Yoo, and B.A. Engel, 2012a. A real-time online drought broadcast system for monitoring soil moisture index. KSCE Journal of Civil Engineering 16(3): 357-365. https://doi.org/10.1007/s12205-012-1357-3
  15. Nam, W.H., J.Y. Choi, S.H. Yoo, and M.W. Jang, 2012b. A decision support system for agricultural drought management using risk assessment. Paddy Water Environment 10(3): 197-207. https://doi.org/10.1007/s10333-012-0329-z
  16. Nam, W.H., M.J. Hayes, D.A. Wilhite, T. Tadesse, M.D. Svoboda, and C.L. Knutson, 2014. Drought management and policy based on risk assessment in the context of climate change. Magazine of the Korean Society of Agricultural Engineers 56(2): 2-15 (in Korean).
  17. Nam, W.H., M.J. Hayes, D.A. Wilhite, and M.D. Svoboda, 2015. Projection of temporal trends on drought characteristics using the standardized precipitation evapotranspiration index (SPEI) in South Korea. Journal of the Korean Society of Agricultural Engineers 57(1): 37-45 (in Korean). https://doi.org/10.5389/KSAE.2015.57.1.037
  18. Olson, D.M., and E. Dinerstein, 2002. The Global 200: priority ecoregions for global conservation. Annals of the Missouri Botanical Garden 89(2): 199-224. https://doi.org/10.2307/3298564
  19. Otkin, J., M. Shafer, M. Svoboda, B. Wardlow, M. Anderson, C. Hain, and J. Basara, 2014. Facilitating the use of drought early warning information through interactions with agricultural stakeholders. Bulletin of the American Meteorological Society, in press, doi: 10.1175/BAMS-D-14-00219.1.
  20. Park, J.K., B.S. Kim, S.Y. Oh, and J.H. Park, 2013. Applicability of vegetation indices from Terra MODIS and COMS GOCI imageries. Journal of the Korean Society of Agricultural Engineers 55(6): 47-55 (in Korean). https://doi.org/10.5389/KSAE.2013.55.6.047
  21. Park, J.S., and K.T. Kim, 2009. Evaluation of MODIS NDVI for drought monitoring: focused on comparison of drought index. The Journal of GIS Association of Korea 17(1): 117-129 (in Korean).
  22. Rulequest, 2013. An overview of Cubist. RuleQuest Research Pty Ltd, St Ives, NSW, Australia. available at http://www.rulequest.com/cubist-win.html.
  23. Shin, H.J., R. Ha, M.J. Park, and S.J. Kim, 2010. Estimation of spatial evapotranspiration using the relationship between MODIS NDVI and morton ET -for Chungjudam watershed-. Journal of the Korean Society of Agricultural Engineers 52(1): 19-24 (in Korean). https://doi.org/10.5389/KSAE.2010.52.1.019
  24. Shin, S.C., and C.J. Kim, 2003. Application of normalized difference vegetation index for drought detection in Korea. Journal of the Korean Water Resources Association 36(5): 839-849 (in Korean). https://doi.org/10.3741/JKWRA.2003.36.5.839
  25. Shin, S.C., and M.S. Eoh, 2004. Analysis of drought detection and propagation using satellite data. Journal of The Korean Society of Hazard Mitigation 4(2): 61-69 (in Korean).
  26. Shin, S.C., J. Soo, K.T. Kim, J.H. Kim, and J.S. Park, 2006. Drought detection and estimation of water deficit using NDVI. Journal of the Korean Association of Geographic Information Studies 9(2): 102-114 (in Korean).
  27. Smith, A.D., and Katz, R.W., 2013. US billion-dollar weather and climate disasters: data sources, trends, accuracy and biases. Natural Hazards 67: 387-410. https://doi.org/10.1007/s11069-013-0566-5
  28. Sur, C.Y., K.J. Kim, W.J. Choi, J.H. Shim, and M.H. Choi, 2014. Drought assessments using satellite-based drought index in Korea: southern region case in 2013. Journal of The Korean Society of Hazard Mitigation 14(3): 127-131 (in Korean). https://doi.org/10.9798/KOSHAM.2014.14.3.127
  29. Svoboda, M., D. LeComte, M. Hayes, R. Heim, K. Gleason, J. Angel, B. Rippey, R. Tinker, M. Palecki, D. Stooksbury, D. Miskus, and S. Stephens, 2002. The drought monitor. Bulletin of the American Meteorological Society 83(8): 1181-1190. https://doi.org/10.1175/1520-0477(2002)083<1181:TDM>2.3.CO;2
  30. Svoboda, M.D., B.A. Fuchs, C.C. Poulsen, and J.R. Nothwehr, 2015. The drought risk atlas: enhancing decision support for drought risk management in the United States. Journal of Hydrology, in press, doi:10.1016/j.jhydrol.2015.01.006.
  31. Swain, S., B.D. Wardlow, S. Narumalani, T. Tadesse, and K. Callahan, 2011. Assessment of vegetation response to drought in Nebraska using Terra-MODIS land surface temperature and normalized difference vegetation index. GIScience & Remote Sensing 48(3): 432-455. https://doi.org/10.2747/1548-1603.48.3.432
  32. Tadesse, T., D.A. Wilhite, S.K. Harms, M.J. Hayes, and S. Goddard, 2004. Drought monitoring using data mining techniques: a case study for Nebraska, USA. Natural Hazards 33: 137-159. https://doi.org/10.1023/B:NHAZ.0000035020.76733.0b
  33. Tadesse, T., J.F. Brown, and M.J. Hayes, 2005. A new approach for predicting drought-related vegetation stress: integrating satellite, climate, and biophysical data over the U.S. central plains. ISPRS Journal of Photogrammetry & Remote Sensing 59: 244-253. https://doi.org/10.1016/j.isprsjprs.2005.02.003
  34. Tadesse, T., B.D. Wardlow, M.J. Hayes, and M.D. Svoboda, 2010. The vegetation outlook (VegOut): a new method for predicting vegetation seasonal greenness. GIScience & Remote Sensing 47(1): 25-52. https://doi.org/10.2747/1548-1603.47.1.25
  35. Tadesse, T., G.B. Demisse, B. Zaitchik, and T. Dinku, 2014. Satellite-based hybrid drought monitoring tool for prediction of vegetation condition in eastern Africa: a case study for Ethiopia. Water Resources Research 50: 2176-2190. https://doi.org/10.1002/2013WR014281
  36. Tadesse, T., B.D. Wardlow, J. Brown, M. Hayes, M. Svoboda, B. Fuchs, and D. Gutzmer, 2015. Assessing the vegetation condition impacts of the 2011 drought across the U.S. southern great plains using the vegetation drought response index (VegDRI). Journal of Applied Meteorology and Climatology 54: 153-169. https://doi.org/10.1175/JAMC-D-14-0048.1
  37. Wells, N., S. Goddard, and M.J. Hayes, 2004. A self-calibrating palmer drought severity index. Journal of Climate 17: 2335-2351. https://doi.org/10.1175/1520-0442(2004)017<2335:ASPDSI>2.0.CO;2
  38. Wilhite, D.A., Hayes, M.J., Knutson, C., and Smith, K.H., 2000. Planning for drought: moving from crisis to risk management. Journal of the American Water Resources Association 36(4): 697-710. https://doi.org/10.1111/j.1752-1688.2000.tb04299.x
  39. Wilhite, D.A., M.D. Svoboda, and M.J. Hayes, 2007. Understanding the complex impacts of drought: a key to enhancing drought mitigation and preparedness. Water Resources Management 21: 763-774. https://doi.org/10.1007/s11269-006-9076-5