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

The Korean Association of Geographic Information Studies (한국지리정보학회)
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Analysis of Land Cover Change in the Waterfront Area of Taehwa River using Hyperspectral Image Information

초분광 영상정보를 이용한 태화강 수계지역의 토지피복 변화분석

  • KIM, Yong-Suk (Dept. of Landscape Architecture Dong-A University)
  • 김용석 (동아대학교 디자인환경대학 조경학과)
  • Received : 2021.01.05
  • Accepted : 2021.01.22
  • Published : 2021.03.31
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Abstract

Land cover maps are used in various fields in urban expansion and development. This study analyzed the amount of land cover change over time using multi-sensor information, focusing on the waterfront area of the Taehwa River. In order to apply high-accuracy aerial hyperspectral images, patterns with Field-spectral were reviewed and compared with time series Digital map. The hyperspectral image was set as 13 land cover grades, and the time series digital map was classified into 7 and the waterfront area was classified into 5-6 grades and analyzed. As a result of analysis of the change in land cover of the digital map from the 1990s to 2010, it was found that forest areas were rapidly decreasing and Farmland and grassland were becoming urban. As for the land cover change(2010~2019) in the waterfront area(set 500m) analyzed through hyperspectral images, it was found that Farmland(1.4㎢), Forest(1.0㎢), and grassland (0.8㎢) were converted into urbanized and dried areas, and urbanization was accelerating around the Taehwa River waterfront. Recently, a lot of research has been conducted on the production of land cover maps using high-precision satellite images and aerial hyperspectral images, so it is expected that more detailed and precise land cover maps can be produced and utilized.

토지피복도는 도시의 확장과 개발에 있어 다양한 분야에서 활용되고 있다. 본 연구는 태화강 수계지역을 중심으로 멀티센서 정보를 이용하여 시계열적 토지피복 변화량을 분석하였다. 정확도가 높은 항공 초분광 영상을 적용하기 위하여 지상분광 스펙트럼과의 패턴을 검토하고, 시계열 수치지형도와 비교하였다. 초분광 영상은 13개의 토지피복 등급을 설정하였고, 시계열 수치지형도는 7개, 그리고 수계지역을 중심으로는 각각 5~6개 등급으로 분류하여 분석하였다. 1990년대에서 2010년까지 수치지형도의 토지피복 변화량 분석결과 산림지역이 빠르게 감소하고 농경 및 초지가 도시화되고 있는 것을 알 수 있었다. 초분광 영상을 통한 수계지역(500m 설정)의 토지피복변화(2010~2019)는 농업, 산림, 초지가 각각 1.4㎢, 1.0㎢, 0.8㎢가 시가지화 건조지역으로 변화되었으며 태화강 수계를 중심으로 도시화가 가속화되고 있음을 알 수 있었다. 최근 고정밀 위성영상과 항공 초분광 영상을 이용하여 토지피복도 제작에 대한 연구가 많이 이루어지고 있기 때문에 더욱 세분화되고 정밀한 토지피복도를 제작하여 활용할 수 있을 것으로 기대된다.

Keywords

References

  1. Angel, S., J. Parent, D.L. Civco, A. Blei, and D. Potere. 2011. The dimensions of global urban expansion: estimates and projections for all countries, 2000-2050. Progress in Planning 75: pp.53-107. https://doi.org/10.1016/j.progress.2011.04.001
  2. Cho, H.G. and Lee, K.S. 2014. Comparison between hyperspectral and multispectral images for the classification of coniferous species. Korean Journal of Remote Sensing 30(1):25-36. https://doi.org/10.7780/kjrs.2014.30.1.3
  3. Hochberg, E.J., M.J. Atkinson and S. Andrefouet. 2003. Spectral reflectance of coral reef bottom-types worldwide and implications for coral reef remote sensing. Remote Sensing of Environment 85:159-173. https://doi.org/10.1016/S0034-4257(02)00201-8
  4. Jensen J.R. 2005. Introductory Digital Image Processing: A Remote Sensing Perspective 3rd edition. Sigma Press Seoul Korea. p.580.
  5. Kim, T.W., Choi, D.J., We, G.J., and Suh, Y.C. 2013. Detection of small green space in an urban area using airborne hyperspectral imagery and spectral angle mapper. Journal of the Korean Association of Geographic Information Studies, 16(2):88-100. https://doi.org/10.11108/kagis.2013.16.2.088
  6. Lee, J.D., Bhang, K.J., and Joo, Y.D. 2016. Atmospheric Correction Effectiveness Analysis and Land Cover Classification Using Airborne Hyperspectral Imagery. The Journal of the Korea Contents Association 16(7):31-41. https://doi.org/10.5392/JKCA.2016.16.07.031
  7. Ma, S., Z. Tao, X. Yang, Y. Yu, X. Zhou and Z. Li. 2014. Bathymetry retrieval from hyperspectral remote sensing data in optical-shallow water. IEEE Transactions on Geoscience and Remote Sensing 52(2):1205-1212. https://doi.org/10.1109/TGRS.2013.2248372
  8. Myneni, R.B., F.G. Hall, P.J. Sellers and A.L. Marshek. 1995. The interpretation of spectral vegetaion indexes. IEEE Transactions on Geoscience and Remote Sensing 33(2):481-486. https://doi.org/10.1109/tgrs.1995.8746029
  9. Oh, K.Y., Lee, M.J., and No, W.Y. 2016. A Study on the Improvement of Subdivided Land Cover Map Classification System-Based on the Land Cover Map by Ministry of Environment. Korean Journal of Remote Sensing 32(2):105-118. https://doi.org/10.7780/kjrs.2016.32.2.4
  10. Park, H.L., and Choi, J.W. 2017. Accuracy evaluation of supervised classification by using morphological attribute profiles and additional band of hyperspectral imagery. Journal of Korean Society for Geospatial Information System 25(1):9-17. https://doi.org/10.7319/KOGSIS.2017.25.1.009
  11. Park, H.S. and Jang, D.H., 2020. Analysis of Changes in Urbanized Areas in Daejeon Metropolitan City by Detection of Changes in Time Series Landcover: Using Multi-temporal Satellite Images. Journal of the Association of Korean Geographers 9(1):177-190. https://doi.org/10.25202/jakg.9.1.12
  12. Ryu, J.H., Shin, J.I., Lee, C.S., Hong, S., Lee, Y.W., and Cho, J. 2017. Evaluating Applicability of Photochemical Reflectance Index using Airborne-Based Hyperspectral Image: With Shadow Effect and Spectral Bands Characteristics. Korean Journal of Remote Sensing 33(5_1):507-519. https://doi.org/10.7780/kjrs.2017.33.5.1.5
  13. Sunwoo, W., Kim, D., Kang, S., and Choi, M. 2016. Application of KOMSAT-2 Imageries for Change Detection of Land use and Land Cover in the West Coasts of the Korean Peninsula. Korean Journal of Remote Sensing 32(2):141-153. https://doi.org/10.7780/kjrs.2016.32.2.7
  14. Zhao, M., Z.H. Kong, F.J. Escobedo, and J. Gao. 2010. Impacts of urban forests on offsetting carbon emissions from industrial energy use in Hangzhou, China. Journal of Environmental Management 91:807-813. https://doi.org/10.1016/j.jenvman.2009.10.010