한국지리정보학회지 (Journal of the Korean Association of Geographic Information Studies)

  • 제25권4호
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  • Pages.1-18
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  • 2022
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  • 1226-9719(pISSN)
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  • 2287-6952(eISSN)
한국지리정보학회 (The Korean Association of Geographic Information Studies)
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경기도 지역에 대한 MODIS 위성영상 및 지점자료기반 가뭄지수의 비교·분석

Comparison and Analysis of Drought Index based on MODIS Satellite Images and ASOS Data for Gyeonggi-Do

  • 강유진 (인하대학교 대학원 토목공학과) ;
  • 김형수 (인하대학교 사회인프라공학과) ;
  • 김동현 (인하대학교 대학원 스마트시티공학과) ;
  • 왕원준 (인하대학교 대학원 스마트시티공학과) ;
  • 이하늘 (인하대학교 대학원 스마트시티공학과) ;
  • 서민호 ((주)지오씨엔아이 공간정보기술연구소) ;
  • 정윤재 ((주)지오씨엔아이 공간정보기술연구소)
  • Yu-Jin, KANG (Department of Civil Engineering, Inha University) ;
  • Hung-Soo, KIM (Department of Civil Engineering, Inha University) ;
  • Dong-Hyun, KIM (Department of Civil Engineering, Inha University) ;
  • Won-Joon, WANG (Department of Civil Engineering, Inha University) ;
  • Han-Eul, LEE (Department of Civil Engineering, Inha University) ;
  • Min-Ho, SEO (Geospatial Research Center, GEO C&I., Co., Ltd.) ;
  • Yun-Jae, CHOUNG (Geospatial Research Center, GEO C&I., Co., Ltd.)
  • 투고 : 2022.10.14
  • 심사 : 2022.10.28
  • 발행 : 2022.12.31
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초록

현재 우리나라 기상청에서는 6개월 누적강수량 기준인 SPI6(standardized precipitation index 6)을 이용하여 기상가뭄을 지역별로 평가하고 있다. 하지만, SPI는 69개 기상관측소의 강수량만을 고려하여 산정되는 지수로 복합적인 이유로 나타나는 가뭄사상은 정확하게 판단하지 못하고 있는 실정이다. 따라서, 본 연구의 목적은 강수량만을 고려한 SPI와 강수량, 식생지수 및 기온을 복합적으로 고려하는 SDCI(Scaled Drought Condition Index)를 경기도 지역을 대상으로 산정 및 비교하고자 하였다. 또한, SPI와 SDCI의 비교를 통해 산정된 결과를 활용하여 지점자료기반 가뭄지수와 위성영상기반 가뭄지수의 장단점을 파악하고자 하였다. SDCI를 산정하기 위해 MODIS(MODerate resolution Imaging Spectroradiometer) 위성영상자료, 종관기상관측(ASOS) 자료 및 크리깅 기법을 사용하였다. 강수량의 지속기간은 2014년의 8개 시점에 대해 1개월, 3개월, 6개월을 각각 적용하여 SDCI1, SDCI3, SDCI6을 산정하였다. SDCI 산정 결과, SPI와 달리 약 두달 전부터 가뭄양상을 나타내기 시작하여 경기도 시군별 가뭄에 대해서 잘 드러냈다. 이를 통해, 위성영상자료와 지점자료의 결합이 가뭄지수 변화 양상에 있어서 효율성을 높였으며, 기존의 건조 지역과 더불어 습윤 지역에 대해 가뭄예측 가능성을 증대시켰음을 파악할 수 있었다.

Currently, the Korea Meteorological Administration evaluates the meteorological drought by region using SPI6(standardized precipitation index 6), which is a 6-month cumulative precipitation standard. However, SPI is an index calculated only in consideration of precipitation at 69 weather stations, and the drought phenomenon that appears for complex reasons cannot be accurately determined. Therefore, the purpose of this study is to calculate and compare SPI considering only precipitation and SDCI (Scaled Drought Condition Index) considering precipitation, vegetation index, and temperature in Gyeonggi. In addition, the advantages and disadvantages of the station data-based drought index and the satellite image-based drought index were identified by using results calculated through the comparison of SPI and SDCI. MODIS(MODerate resolution Imaging Spectroradiometer) satellite image data, ASOS(Automated Synoptic Observing System) data, and kriging were used to calculate SDCI. For the duration of precipitation, SDCI1, SDCI3, and SDCI6 were calculated by applying 1-month, 3-month, and 6-month respectively to the 8 points in 2014. As a result of calculating the SDCI, unlike the SPI, drought patterns began to appear about 2-month ago, and drought by city and county in Gyeonggi was well revealed. Through this, it was found that the combination of satellite image data and station data increased efficiency in the pattern of drought index change, and increased the possibility of drought prediction in wet areas along with existing dry areas.

키워드

과제정보

본 결과물은 환경부의 재원으로 한국환경산업기술원의 습지생태계 가치평가 및 탄소흡수 가치증진 기술개발사업의 지원을 받아 연구되었습니다(2022003640003).

참고문헌

  1. Anderson, M.C., Hain, C., Wardlow, B., Pimsetin, A., Mecikalski, J.R., and Kustas, W.P. 2011. Evaluation of Drought Indices Based on Thermal Remote Sensing of Evapotranspiration over the Continental United States. Journal of Climate. 24(8):2025-2044. https://doi.org/10.1175/2010JCLI3812.1
  2. Choi, J.G. 2019. Drought Characteristics Analysis Using Standard Precipitation Index(SPI)(Case Study in Chungbuk and Neighboring Areas). Doctoral Thesis of Korea National University of Transportation. 174pp.
  3. Du, L., Tian, Q., Yu, T., Meng, Q., Jancso, T., Udvardy, P., and Huang, Y. 2013. A Comprehensive Drought Monitoring Method Integrating MODIS and TRMM Data. International Journal of Applied Earth Observation and Geoinformation. 23(1):245-253. https://doi.org/10.1016/j.jag.2012.09.010
  4. Hernandez, E.A. and Uddameri, V. 2013. Standardized Precipitation Evaporation Index (SPEI)-based Drought Assessment in Semi-arid South Texas. Environmental Earth Sciences. 71(6):2491-2501.
  5. Hong, S.Y., Hur, J.N., Ahn, J.B., Lee, J.M., Min, B.K., Lee, C.K., Kim, Y.H., Lee, K.D., Kim, S.H., Kim, G.Y., and Shim, K.M. 2012. Estimating Rice Yield Using MODIS NDVI and Meteorological Data in Korea. Korean Journal of Remote Sensing. 28(5):509-20. https://doi.org/10.7780/KJRS.2012.28.5.4
  6. Hong, S.Y., Na, S.I., Lee, K.D., Kim, Y.S., and Baek, S.C. 2015. A Study on Estimating Rice Yield in DPRK Using MODIS NDVI and Rainfall Data. Korean Journal of Remote Sensing. 31(5):441-448. https://doi.org/10.7780/KJRS.2015.31.5.8
  7. Jeong, J.H., Kim, D.E., and Choi, M.H. 2017. A Study on the Utilization of Geostationary Ocean Color Imager on Communication, Ocean and Meteorological Satellite for Drought Monitoring. Journal of the Korean Society of Hazard Mitigation. 17(3):69-77. https://doi.org/10.9798/KOSHAM.2017.17.3.69
  8. Jung, J.H. 2021. A Study on Development and Application of Bigdata Drought Index. Master Thesis of Seokyeong University. 125pp.
  9. Jung, J.H., Park, D.H., and Ahn, J.H. 2020. Drought evaluation using unstructured data: a cases study for Boryeong area. Journal of Korea Water Resources Association. 53(12):1203-1210. https://doi.org/10.3741/JKWRA.2020.53.12.1203
  10. Kim, D.J. 2017. Feasibility Study on Agricultural Drought Index for Field Scale Applications. Doctoral Thesis of Kyung Hee University. 96pp.
  11. Kim, H.Y., Zhang, X.H., Cho, J.H., and Joo, J.G. 2020. Analysis of Regional Drought Correlation Using Drought Indicators. Journal of the Korean Society of Hazard Mitigation. 20(6):283-290.
  12. Kim, J.H. 2017. A Comparative Study of Meteorologic Drought Index and Socioeconomic Drought Index, with Focus on the Gangwon Region. Master Thesis of Kangwon National University. 108pp.
  13. Kim, J.H., Lee, S.H., and Kim, B.S. 2018. An Assessment of Past and Future Droughts in North Korea Using Standardized Precipitation Evapotranspiration Index. 2018 Crisis and Emergency Management : Theory and Praxis. 14(2):139-151.
  14. Kim, S.H. and Chung, E.S. 2017. Peak Drought Index Analysis of Cheongmicheon Watershed Using Meteorological and Hydrological Drought Index. Journal of Korea Water Resources Association. 50(1):65-73. https://doi.org/10.3741/JKWRA.2017.50.1.65
  15. Kim, S.W., Kim, M.K., Jung, D.H., and Yoo, D.G. 2019. Identifying the Critical Drought Impact Factor with Feature Selection Approaches. Journal of the Korean Society of Hazard Mitigation. 19(7):511-518. https://doi.org/10.9798/KOSHAM.2019.19.7.511
  16. Kogan, F.N. 1990. Remote Sensing of Weather Impacts on Vegetation in Non-homogeneous Areas. International Journal of Remote Sensing. 11(8):1405-1419. https://doi.org/10.1080/01431169008955102
  17. Kogan, F.N. 1995. Application of Vegetation Index and Brightness Temperature for Drought Detection. Advances in Space  Research. 15(11):91-100. https://doi.org/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. K-water. 2021. 가뭄정보분석 연간보고서. 환경부.
  20. Kwon, H.J., Lim, H.J. and Kim, S.J. 2007. Drought Assessment of Agricultural District using Modified SWSI. Journal of the Korean Association of Geographic Information Studies. 10(1):22-34.
  21. Kwon, J.J. 2013. Improvement of the Classification Standard of Drought Index for the Korean Peninsula. Master Thesis of Seokyeong University. 97pp.
  22. Lee, J.J. and Kim, C.J. 2011. Derivation of Drought Severity-Duration-Frequency Curves Using Drought Frequency Analysis. Journal of Korea Water Resources Association. 44(11):889-902. https://doi.org/10.3741/JKWRA.2011.44.11.889
  23. Lee, J.W. 2012. Drought Indices Assessment using the Drought Records and Development of the Modified Drought Index by Considering Effects of the Dry Period and Rainfall. Master Thesis of Kyungpook National University. 93pp.
  24. Lee, S.K. 2021. Development of Agricultural Water Demand and Supply Drought Index (AWDSDI). Doctoral Thesis of Kongju National University. 126pp.
  25. McKee, T.B., Doesken, N.J., and Kleist, J. 1993. The Relationship of Drought Frequency and Duration to Time Scales. 8th Conference on Applied Climatology, Aneheim, California, 179-184.
  26. Na, S.I., Park, C.W., So, K.H., Park, J.M., and Lee, K.D. 2017. Development of Garlic & Onion Yield Prediction Model on Major Cultivation Regions Considering MODIS NDVI and Meteorological Elemets. Korean Journal of Remote Sensing. 33(5):647-659. https://doi.org/10.7780/KJRS.2017.33.5.2.5
  27. Nam, W.H., Tsegaye Tadesse, Wardlow, B.D., Jang, M.W., and Hong, S.Y. 2015. Satellite-based Hybrid Drought Assessment using Vegetation Drought Response Index in South Korea (VegDRI-SKorea). Journal of the Korean Society of Agricultural Engineers. 57(4):1-9. https://doi.org/10.5389/KSAE.2015.57.4.001
  28. NDMI. 2016. Development of Fundamental Technologies for Disaster Analysis and Application Based on Mid-resolution Satellite Imagery. National Disaster Management Research Institute.
  29. Park, J.S., Kim, K.T., Lee, J.H., and Lee, K.S. 2006. Applicability of Multi-Temporal MODIS Images for Drought Assessment in South Korea. Journal of the Korean Association of Geographic Information Studies. 9(4):176-192.
  30. Park, M.J., Choi, Y.S., Shin, H.J., Lee, Y.J., and Yu, S.J. 2017. Applyinh Terra MODIS Satellite Image to Analysis of Current State of Upland Field. Journal of the Korean Association of Geographic Information Studies. 20(3):1~11. https://doi.org/10.11108/KAGIS.2017.20.3.001
  31. Park, S.Y., Lee, S.W., Kim, T.H., and Choi, J.M. 2020. Analysis of Seasonal Effects between Drought Intensity and the Frequency of Forest Fires. The Geographical Journal of Korea. 54(3):299-309. https://doi.org/10.22905/kaopqj.2020.54.3.8
  32. Rhee, J.Y., Im, J.H., and Carbone, G.J. 2010. Monitoring Agricultural Drought for Arid and Humid Regions using Multi-sensor Remote Sensing Data. Remote Sensing of Environment. 114(12):2875-2887.
  33. Ryu, J.S., Ahn, J.H., and Kim, S.D. 2012. An Application of Drought Severity-Area-Duration Curves Using Copulas-Based Joint Drought Index. Journal of Korea Water Resources Association. 45(10):1043-1050. https://doi.org/10.3741/JKWRA.2012.45.10.1043
  34. Shin, H.J., Park, M.J., Hwang, E.H., Chae, H.S., and Park, S.J. 2015. A Study of Spring Drought Using Terra MODIS Satellite Image. Journal of the Korean Association of Geographic Information Studies. 18(4):145-157. https://doi.org/10.11108/KAGIS.2015.18.4.145
  35. Shin, J.H., Kim, J.K., Yeom, M.K., and Kim, J.P. 2021. Analysis of Drought Vulnerable Areas using Neural-Network Algorithm. Journal of the Society of Disaster Information. 17(2):329-340. https://doi.org/10.15683/KOSDI.2021.6.30.329
  36. Shin, J.Y., Kim, J.E., Lee, J.H., and Kim, T.W. 2019. Meteorological Drought Outlook with Satellite Precipitation Data Using Bayesian Networks and Decision-making Model. Journal of Korea Water Resources Association. 52(4):279-289. https://doi.org/10.3741/JKWRA.2019.52.4.279
  37. UNIST. 2016. 우주핵심기술개발사업 최종보고서. 미래창조과학부 한국연구재단.
  38. Won, K.J. and Chung, E.S. 2016. Drought Analysis of Cheongmicheon Watershed Using Meteorological, Agricultural, and Hydrological Drought Indices. Journal of Korea Water Resources Association. 49(6):509-518. https://doi.org/10.3741/JKWRA.2016.49.6.509
  39. Yoo, J.Y., So, B.J., Kwon, H.H., and Kim, T.W. 2020. Development of Drought Map Based on Three-dimensional Spatiotemporal Analysis of Drought. Journal of the Korean Society of Civil Engineers. 40(1):25-33. https://doi.org/10.12652/KSCE.2020.40.1.0025
  40. Yoon, D.H. 2021. Estimation of High-Resolution Drought Indices Using Multiple-Satellite Images and Spatiotemporal Characteristics Analysis. Master Thesis of Hankyong National University. 72pp.
  41. Yoon, D.H., Nam, W.H., Lee, H.J., Hong, E.M., Kim, T.G., Kim, D.E., Shin, A.K., and Svoboda, M.D. 2018. Application of Evaporative Stress Index (ESI) for Satellite-based Agricultural Drought Monitoring in South Korea. Journal of the Korean Society of Agricultural Engineers. 60(6):121131.