Journal of the Korean earth science society (한국지구과학회지)

The Korean Earth Science Society (한국지구과학회)
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Monitoring of Lake area Change and Drought using Landsat Images and the Artificial Neural Network Method in Lake Soyang, Chuncheon, Korea

Landsat 영상 및 인공 신경망 기법을 활용한 춘천 소양호 면적 및 가뭄 모니터링

  • Eom, Jinah (Research Institute For Earth Resources, Kangwon National University) ;
  • Park, Sungjae (Department of Smart Regional Innovation, Kangwon National University) ;
  • Ko, Bokyun (Division of Science Education, Kangwon National University) ;
  • Lee, Chang-Wook (Division of Science Education, Kangwon National University)
  • 엄진아 (강원대학교 지구자원연구소) ;
  • 박성재 (강원대학교 스마트지역혁신학과) ;
  • 고보균 (강원대학교 과학교육학과) ;
  • 이창욱 (강원대학교 과학교육학과)
  • Received : 2020.02.17
  • Accepted : 2020.04.22
  • Published : 2020.04.30
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Abstract

Drought is an environmental disaster typically defined as an unusual deficiency of water supply over an extended period. Satellite remote sensing provides an alternative approach to monitoring drought over large areas. In this study, we monitored drought patterns over about 30 years (1985-2015), using satellite imagery of Lake Soyang, Gangwondo, South Korea. Landsat images were classified using ISODATA, maximum likelihood analysis, and an artificial neural network to derive the lake area. In addition, the relationship between areas of Lake Soyang and the Standardized Precipitation Index (SPI) was analyzed. The results showed that the artificial neural network was a better method for determining the area of the lake. Based on the relationship between the SPI value and changes in area, the R2 value was 0.52. This means that the area of the lake varied depending on SPI value. This study was able to detect and monitor drought conditions in the Lake Soyang area. The results of this study are used in the development of a regional drought monitoring program.

가뭄은 일반적으로 장기간에 걸쳐 물 공급이 부족하여 나타나는 환경 재앙 중 하나로 대부분 넓은 지역에 걸쳐 나타난다. 원격탐사 자료는 이러한 넓은 지역에서 나타나는 가뭄 모니터링에 적합한 방법이다. 따라서 이 연구에서는 강원도 소양호 지역의 Landsat 위성 영상 자료를 활용하여 약 30년(1985-2015) 동안의 소양호 면적을 산출하고 이를 가뭄 패턴과 분석하였다. 특히 ISODATA, Maximum likelihood 및 인공신경망을 활용하여 Landsat 영상을 분류하여 소양호 면적을 산출하였다. 또한 가뭄 패턴을 분석하기 위하여 산출된 호수 면적과 소양호 지역의 강수량을 활용한 표준강수지수(Standardized Precipitation Index: SPI)와의 상관관계를 분석하였다. 영상 분류 연구 결과, ISODATA, Maximum likelihood 및 인공신경망 방법 중에서 호수 면적 산출의 최적의 방법은 인공신경망 방법임을 알 수 있었다. 또한, 인공신경망 방법을 적용하여 산출한 호수 면적과 SPI와의 상관관계 분석 결과 R2 값이 0.52를 가진다. 즉, SPI지수가 낮을 때 호수 면적이 감소하는 것을 알 수 있었다. 즉 호수 면적 변화를 통하여 소양호 지역의 가뭄 상태 감지 및 모니터링이 가능하다는 것을 알 수 있었다. 이 연구는 향후 지역 가뭄 모니터링 프로그램 개발 등에 사용이 가능할 것이다.

Keywords

References

  1. Anderson, M.C., Hain, C., Wardlow, B., Pimstein, 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, 2025-2044. https://doi.org/10.1175/2010JCLI3812.1
  2. Anderson, M.C., Norman, J., Mecikalski, J., Otkin, J., and Kustas, W., 2007, A climatological study of evapotranspiration and moisture stress across the continental US based on thermal remote sensing: 2. Surface moisture climatology. Journal of Geophysical Ressearch, 112, D111112.
  3. Bhalme, H.N. and Mooley, D.A., 1980, Large-scale droughts/floods and monsoon circulation. Monthly Weather Review, 108(8), 1197-1211. https://doi.org/10.1175/1520-0493(1980)108<1197:LSDAMC>2.0.CO;2
  4. Cho, S.-H., Sohn, W.-M., Shin, S.-S., Song, H.-J., Choi, T.-G., Oh, C.-M., Kong, Y., and Kim, T.-S., 2006, Infection status of pond smelts, Hypomesus olidus, and other freshwater fishes with trematode metacercariae in 6 large lakes. The Korean Journal of Parasitology, 44(3), 243-246. https://doi.org/10.3347/kjp.2006.44.3.243
  5. Cohen, W.B., Spies, T.A., Alig, R J., Oetter, D.R., Maiersperger, T.K., and Fiorella, M., 2002, Characterizing 23 years (1972-95) of stand replacement disturbance in Western Oregon forests with Landsat imagery, Ecosystems, 5(2), 122-137. https://doi.org/10.1007/s10021-001-0060-X
  6. Ebaid, H.M., 2015, Real time drought monitoring using Remote Sensing approaches A Case Study: Western desert of Kharga and Dakhla Regions. International Journal of Geomatics and Geosciences, 6(3), 1638-1652.
  7. Heo, W.M., Kim, B., Kim, Y., and Cho, K.S., 1998, Storm runoff of phosphorus from nonpoint sources into Lake Soyang and transportation of turbid water mass within the lake. Korean Journal of Limnology, 31, 1-8.
  8. Huang, H., Zhou, H., Wang, P., Wu, W., and Yang, S., 2012, Monitoring southwest drought of China using HJ-1A/B and Landsat remote sensing data. Proceeding of SPIE, Land Surface Remote Sensing, 8524, 852410.
  9. Jin, Y., Zhu, J., Sung, S., and Lee, D.K., 2017, Application of Satellite Data Spatiotemporal Fusion in Predicting Seasonal NDVI. Korean Journal of Remote Sensing, 33(2), 149-158. https://doi.org/10.7780/kjrs.2017.33.2.4
  10. Kim, B. and Kim, Y., 2004, Phosphorus Cycle in a Deep Reservoir in Asian Monsoon Area (Lake Soyang, Korea) and the Modeling with a 2-D Hydrodynamic Water Quality Model. Korean Journal of Limnology, 37(2), 205-212.
  11. Kim, B., Choi, K., and Shim, S., 1997, Storm runoff of phosphorus from nonpoint sources. Proceeding of the International Symposium on the preservation of water quality and the management of watershed in rivers and lakes. National Institute of Environmental Research, Korea.
  12. Kim, Y. and Jim, B., 2006, Application of a 2-Dimensional Water Quality Model (CE-QUAL-W2) to the Turbidity Interflow in a Deep Reservoir (Lake Soyang, Korea). LAKE RESERV MANAGE, 22(3), 213-222. https://doi.org/10.1080/07438140609353898
  13. Klisch, A., Atzberger, C., and Luminari, L., 2005, Satellitebased Drought Monitoring in Kenya in an Operational Setting. 36th International Symposium on Remote Sensing of Environment, Berlin, Germany.
  14. Kogan, F.N., 1997, Global drought watch from space. Bulletin of the American Meteorological Society, 78, 621-636. https://doi.org/10.1175/1520-0477(1997)078<0621:GDWFS>2.0.CO;2
  15. Lee, J.H., Seo, J.W., and Kim, C.J., 2012, Analysis on Trends, Periodicities and Frequencies of Korean Drought Using Drought Indices, Journal of Korea Water Resources Association, 45(1), 75-89. https://doi.org/10.3741/JKWRA.2012.45.1.75
  16. Lillesand, T.M. and Keifer, R.W., 1994, Remote Sensing and Image Interpretation, John Wiley & Sons.
  17. McKee, T.B., Doesken, N.J., and Kleist, J., 1993, The relationship of drought frequency and duration to time scales. Proceeding of Eighth Conference on Applied Climatology, American Meteorological Society, Anaheim, CA, 17-22 January.
  18. Mckee, T.B., Doeskin, N.J., and Kleist, J., 1995, Drought monitoring with multiple time scales. Proceeding of Ninth Conference on Applied Climatology, American Meteorological Society, Boston, MA, 15-20 January.
  19. Ortiz, M.J., Formaggio, A.R., and Epiphanio, J.C.N., 1997, Classification of croplands through integration of remote sensing, GIS, and historical database, International Journal of Remote Sensing, 18(1), 95-105. https://doi.org/10.1080/014311697219295
  20. Parihar, S.M., Sarkar, S., Dutta, A., Sharma, S., and Dutta, T., 2013, Characterizing wetland dynamics: a postclassification change detection analysis of the East Kolkata Wetlands using open source satellite data, Geocarto International, 28(3), 273-287. https://doi.org/10.1080/10106049.2012.705337
  21. Palmer, W.C., 1965, Meteorological drought, Research Paper, U.S. Department of Commerce Weather Bureau, Washington, DC.
  22. Palmer, W.C., 1968, Keeping track of crop moisture conditions, nationwide: the crop moisture index. Weatherwise, 21(4), 156-161. https://doi.org/10.1080/00431672.1968.9932814
  23. Park, J.Y., Yoo, J.Y., Chi, M., and Kim, T.-W., 2011, Evaluation of Drought Risk in Gyeonsang-do Using EDI. Journal of the Korean Society of Civil Engineers, 31(3B), 243-252. https://doi.org/10.12652/KSCE.2011.31.3B.243
  24. Petropoulos, G., Carlson, T.N., Wooster, M.J., and Islam, S., 2009, A review of Ts/VI remote sensing based methods for the retrieval of land surface energy fluxes and soil surface moisture. Progress in Physical Geography, 33(2), 224-250. https://doi.org/10.1177/0309133309338997
  25. Saravanan, K. and Sasithra, S., 2014, Review on classification based in artificial neural networks, International Journal of Ambient Systems and applications, 2(4), 11-18. https://doi.org/10.5121/ijasa.2014.2402
  26. Shafer, B.A. and Dezman, L.E., 1982, Development of a surface water supply index (SWSI) to assess the severity of drought conditions in snowpack runoff areas. Proceeding of 50th Annual Western Snow Conference, Colorado State University, Fort Collins, CO, USA, 19-23 April.
  27. Shin, S.C., Hwang, M.H., Ko, I.H., and Lee, S.J., 2005, Application of Areal Drought Detection Using the Satellite Data. Proceeding of Korean Society of Civil Engineers, Jeju, Korea.
  28. Tou, J.T. and Gonzalez, R.C., 1974, Pattern recognition principles, Addison-Wesley.
  29. Van Rooy, M.P., 1965, A rainfall anomaly index independent of time and space. Notes, 14, 43-48.
  30. Wilson, E.H. and Sader, S.A., 2002, Detection of forest harvest type using multiple dates of Landsat TM imagery, Remote Sensing of Environment, 80(3), 385-396. https://doi.org/10.1016/S0034-4257(01)00318-2
  31. Zhou, Q., Li, B., and Kurban, A., 2008, Trajectory analysis of land cover change in arid environment of China, International Journal of Remote Sensing, 29(4), 1093-1107. https://doi.org/10.1080/01431160701355256