한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제55권11호
  • /
  • Pages.855-863
  • /
  • 2022
  • /
  • 2799-8746(pISSN)
  • /
  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
권호 표지
DOI QR코드

DOI QR Code

위성 정보를 활용한 도심 지역 기온자료 지도화를 위한 인공신경망 적용 연구

A study of artificial neural network for in-situ air temperature mapping using satellite data in urban area

  • 전현호 (성균관대학교 글로벌스마트시티융합전공) ;
  • 정재환 (성균관대학교 건설환경연구소) ;
  • 조성근 (성균관대학교 수자원학과) ;
  • 최민하 (성균관대학교 수자원학과)
  • Jeon, Hyunho (Department of Global Smart City, Sungkyunkwan University) ;
  • Jeong, Jaehwan (Center for Built Environment, Sungkyunkwan University) ;
  • Cho, Seongkeun (Department of Water Resources, Sungkyunkwan University) ;
  • Choi, Minha (Department of Water Resources, Sungkyunkwan University)
  • 투고 : 2022.08.23
  • 심사 : 2022.10.05
  • 발행 : 2022.11.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구에서는 서울시 기온 지상관측 자료의 지도화를 위해 Artificial Neural Network (ANN)을 사용하였다. 지도화를 위한 보조자료로는 MODerate resolution Imaging Spectroradiometer (MODIS) 자료를 사용하였다. ANN 모델 설계를 위해 입력자료와 출력자료 간의 산점도 및 통계분석을 수행하였으며, 기온과의 상관성이 비교적 높게 나타나는 입력자료인 지표면온도, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI)와 시간(위성관측시각, Day of year), 위치(위도, 경도), 데이터 품질(운량)과 관련된 데이터 종류를 분류 및 조합하여 학습을 진행하였다. 기온자료와 상관성이 높은 데이터만으로 학습을 진행하였을 때 상관계수(r)와 Root Mean Squared Error (RMSE)의 평균값이 0.9667, 2.708℃로 우수한 성능을 보였다. 학습에 사용된 데이터의 종류가 추가될수록 더 우수한 학습 결과를 보였으며, 모든 데이터가 활용될 때에는 r과 RMSE의 평균값이 0.9840, 1.883℃로 가장 우수한 성능을 보였다. ANN 모델으로 생성한 서울시 기온 지도에서는 픽셀별 지형적 특성에 적절하게 기온이 산정된 것으로 판단되며, 추후 연구지역 확대 및 위성자료의 다양화를 통해 시단위 및 전국단위 기온 분포 분석 연구가 가능할 것이다.

In this study, the Artificial Neural Network (ANN) was used to mapping air temperature in Seoul. MODerate resolution Imaging Spectroradiomter (MODIS) data was used as auxiliary data for mapping. For the ANN network topology optimizing, scatterplots and statistical analysis were conducted, and input-data was classified and combined that highly correlated data which surface temperature, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), time (satellite observation time, Day of year), location (latitude, hardness), and data quality (cloudness). When machine learning was conducted only with data with a high correlation with air temperature, the average values of correlation coefficient (r) and Root Mean Squared Error (RMSE) were 0.967 and 2.708℃. In addition, the performance improved as other data were added, and when all data were utilized the average values of r and RMSE were 0.9840 and 1.883℃, which showed the best performance. In the Seoul air temperature map by the ANN model, the air temperature was appropriately calculated for each pixels topographic characteristics, and it will be possible to analyze the air temperature distribution in city-level and national-level by expanding research areas and diversifying satellite data.

키워드

과제정보

본 연구는 국토교통부/국토교통과학기술진흥원의 지원으로 수행되었음(과제번호 RS-2022-00155763). 본 연구는 2022년도 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원(NRF-2022R1A2C2010266)과 교육부 및 한국연구재단의 4단계 두뇌한국21 사업(4단계 BK21 사업)과 2021년도 정부(교육부)의 재원으로 한국연구재단 (NRF-2021R1A6A3A01087645)의 지원을 받아 수행된 연구임.

참고문헌

  1. Baik, J., Liaqat, U.W., and Choi, M. (2018). "Assessment of satelliteand reanalysis-based evapotranspiration products with two blending approaches over the complex landscapes and climates of Australia." Agricultural and Forest Meteorology, Vol. 263, pp. 388-398. https://doi.org/10.1016/j.agrformet.2018.09.007
  2. Defernez, M., and Kemsley, E.K. (1999). "Avoiding overfitting in the analysis of high-dimensional data with artificial neural networks (ANNs)." Analyst, Vol. 124, No. 11, pp. 1675-1681. https://doi.org/10.1039/A905556H
  3. Hao, Y., Baik, J., and Choi, M. (2019). "Developing a soil water index-based Priestley - Taylor algorithm for estimating evapotranspiration over East Asia and Australia." Agricultural and Forest Meteorology, Vol. 279, 107760. https://doi.org/10.1016/j.agrformet.2019.107760
  4. Harmay, N.S.M., and Choi, M. (2022). "Effects of heat waves on urban warming across different urban morphologies and climate zones." Building and Environment, Vol. 209, 108677. https://doi.org/10.1016/j.buildenv.2021.108677
  5. Hunter, D., Yu, H., Pukish III, M.S., Kolbusz, J., and Wilamowski, B.M. (2012). "Selection of proper neural network sizes and architectures - A comparative study." IEEE Transactions on Industrial Informatics, Vol. 8, No. 2, pp. 228-240. https://doi.org/10.1109/TII.2012.2187914
  6. Jee, J.B., Lee, K.T., and Choi, Y.J. (2014). "Analysis of land surface temperature from MODIS and Landsat satellites using by AWS temperature in capital area." Korean Journal of Remote Sensing, Vol. 30, No. 2, pp. 315-329. https://doi.org/10.7780/KJRS.2014.30.2.13
  7. Lawrence, S., and Giles, C.L. (2000). "Overfitting and neural networks: conjugate gradient and backpropagation." Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, IEEE, Como, Italy, Vol. 1, pp. 114-119.
  8. Mendelsohn, R., Kurukulasuriya, P., Basist, A., Kogan, F., and Williams, C. (2007). "Climate analysis with satellite versus weather station data." Climatic Change, Vol. 81, No. 1, pp. 71-83.
  9. Ministry of the Interior and Safety (MOIS) (2021). Korea, accessed 28 July 2022, .
  10. National Geographic Information Institute (NGII) (2019). The national atlas of Korea I. pp. 68-75.
  11. Nichol, J.E., Fung, W.Y., Lam, K.S., and Wong, M.S. (2009). "Urban heat island diagnosis using ASTER satellite images and 'in situ'air temperature." Atmospheric Research, Vol. 94, No. 2, pp. 276-284. https://doi.org/10.1016/j.atmosres.2009.06.011
  12. Niclos, R., Puchades, J., Coll, C., Barbera, M. J., Perez-Planells, L., Valiente, J.A., and Sanchez, J.M. (2021). "Evaluation of Landsat-8 TIRS data recalibrations and land surface temperature splitwindow algorithms over a homogeneous crop area with different phenological land covers." ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 174, pp. 237-253. https://doi.org/10.1016/j.isprsjprs.2021.02.005
  13. Park, J., Baik, J., and Choi, M. (2017). "Satellite-based crop coefficient and evapotranspiration using surface soil moisture and vegetation indices in Northeast Asia." Catena, Vol. 156, pp. 305-314. https://doi.org/10.1016/j.catena.2017.04.013
  14. Peterson, T.C. (2003). "Assessment of urban versus rural in situ surface temperatures in the contiguous United States: no difference found." Journal of Climate, Vol. 16, No. 18, pp. 2941-2959. https://doi.org/10.1175/1520-0442(2003)016<2941:AOUVRI>2.0.CO;2
  15. Richter, R. (1997). "Correction of atmospheric and topographic effects for high spatial resolution satellite imagery." International Journal of Remote Sensing, Vol. 18, No. 5, pp. 1099-1111. https://doi.org/10.1080/014311697218593
  16. Rigo, G., Parlow, E., and Oesch, D. (2006). "Validation of satellite observed thermal emission with in-situ measurements over an urban surface." Remote Sensing of Environment, Vol. 104, No. 2, pp. 201-210. https://doi.org/10.1016/j.rse.2006.04.018
  17. Sobrino, J.A., Caselles, V., and Coll, C. (1993). "Theoretical splitwindow algorithms for determining the actual surface temperature." Il Nuovo Cimento C, Vol. 16, No. 3, pp.219-236. https://doi.org/10.1007/BF02524225
  18. Tewari, M., Yang, J., Kusaka, H., Salamanca, F., Watson, C., and Treinish, L. (2019). "Interaction of urban heat islands and heat waves under current and future climate conditions and their mitigation using green and cool roofs in New York City and Phoenix, Arizona." Environmental Research Letters, Vol. 14, No. 3, 034002. https://doi.org/10.1088/1748-9326/aaf431
  19. Vanhellemont, Q., and Ruddick, K. (2018). "Atmospheric correction of metre-scale optical satellite data for inland and coastal water applications." Remote Sensing of Environment, Vol. 216, pp. 586-597. https://doi.org/10.1016/j.rse.2018.07.015
  20. Weng, Q. (2009). "Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends." ISPRS Journal of Photogrammetry and Remote sensing, Vol. 64, No. 4), pp. 335-344. https://doi.org/10.1016/j.isprsjprs.2009.03.007
  21. Yu, Y., Privette, J.L., and Pinheiro, A.C. (2007). "Evaluation of splitwindow land surface temperature algorithms for generating climate data records." IEEE Transactions on Geoscience and Remote Sensing, Vol. 46, No. 1, pp. 179-192.
  22. Zheng, Y., Ren, H., Guo, J., Ghent, D., Tansey, K., Hu, X., Nie, J., and Chen, S. (2019). "Land surface temperature retrieval from sentinel-3A sea and land surface temperature radiometer, using a split-window algorithm." Remote Sensing, Vol. 11, No. 6, 650. https://doi.org/10.3390/rs11060650