Advanced SearchSearch Tips
Application of KOMSAT-2 Imageries for Change Detection of Land use and Land Cover in the West Coasts of the Korean Peninsula
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : Korean Journal of Remote Sensing
  • Volume 32, Issue 2,  2016, pp.141-153
  • Publisher : The Korean Society of Remote Sensing
  • DOI : 10.7780/kjrs.2016.32.2.7
 Title & Authors
Application of KOMSAT-2 Imageries for Change Detection of Land use and Land Cover in the West Coasts of the Korean Peninsula
Sunwoo, Wooyeon; Kim, Daeun; Kang, Seokkoo; Choi, Minha;
  PDF(new window)
Reliable assessment of Land Use and Land Cover (LULC) changes greatly improves many practical issues in hydrography, socio-geographical research such as the observation of erosion and accretion, coastal monitoring, ecological effects evaluation. Remote sensing imageries can offer the outstanding capability to monitor nature and extent of land and associated changes over time. Nowadays accurate analysis using remote sensing imageries with high spatio-temporal resolution is required for environmental monitoring. This study develops a methodology of mapping and change detection in LULC by using classified Korea Multi-Purpose Satellite-2 (KOMPSAT-2) multispectral imageries at Jeonbuk and Jeonnam provinces including protected tidal flats located in the west coasts of Korean peninsula from 2008 to 2015. The LULC maps generated from unsupervised classification were analyzed and evaluated by post-classification change detection methods. The LULC assessment in Jeonbuk and Jeonnam areas had not showed significant changes over time although developed area was gradually increased only by 1.97% and 4.34% at both areas respectively. Overall, the results of this study quantify the land cover change patterns through pixel based analysis which demonstrate the potential of multispectral KOMPSAT-2 images to provide effective and economical LULC maps in the coastal zone over time. This LULC information would be of great interest to the environmental and policy mangers for the better coastal management and political decisions.
Coastal monitoring;land use and land classification;Change detection;Unspurvised classification;KOMPSAT-2;
 Cited by
Angel, S., J. Parent, D.L. Civco, A. Blei, and D. Potere, 2011. The dimensions of global urban expansion: estimates and projections for al. countries, 2000-2050, Progress in Planning, 75: 53-107. crossref(new window)

Chan, J.C.W., K.P. Chan, and A.G.O. Yeh, 2001. Detecting the nature of change in an urban environment: a comparison of machine learning algorithms, Photogrammetric Engineering & Remote Sensing, 67(2): 213-225.

Ekercin, S., 2007. Multitemporal change detection on the SaltLake and it vicinity by integrating remote sensing andgeographic information system, PhD Thesis, Istanbul TechnicalUniversity, Institute of Science and Technology, Istanbul.

Ekercin, S. and C. Ormeci, 2008. An application to estimating soil salinity using satellite remote sensing data and real-time field sampling, Environmental Engineering Science, Academic Press.

Gose, E., R. Johnsbaugh, and S. Jost, 1996. Pattern Recognition and Image Analysis, Prentice Hall.

Hasse, J.E. and R.G. Lathrop, 2003. Land resource impact indicators of urban sprawl, Applied Geography, 23: 159-175. crossref(new window)

Kim, H.Y., T.J. Kim, and H. Lee, 2012. Brightness Value Comparison Between KOMPSAT-2 Images with IKONOS / GEOEYE-1 Images, Korean Journal of Remote Sensing, 28(2): 181-189 (in Korean with English abstract). crossref(new window)

Lee, H.J., J.H. You, and K.Y. Chang, 2009. Analysis for practical use as KOMPSAT-2 imagery for product of geo-spatial information, The Korean Society for Geospatial Information System, 17(1): 21-35 (in Korean with English abstract)

Lee, S. and J. Shan, 2003. Combining LIDAR elevation data and IKONOS multispectral imagery for coastal classification mapping, Marine Geodesy, 26(1-2): 117-127 (in Korean with English abstract). crossref(new window)

Potere, D., A. Schneider, S. Angel, and D.L. Civco, 2009. Mapping urban areas on a global scale: which of the eight maps now available is more accurate?, International Journal of Remote Sensing, 30(24): 6531-6558. crossref(new window)

Ridd, M.K. and J. Liu, 1998. A comparison of four algorithms for change detection in an urban environment, Remote Sensing of Environment, 63: 95-100. crossref(new window)

Singh, A., 1989. Digital change detection techniques using remotely-sensed data, International Journal of Remote Sensing, 10: 989-1003. crossref(new window)

Sohn, Y. and N.S. Rebello, 2002. Supervised and unsupervised spectral angle classifiers, Photogrammetric Engineering Remote Sensing, 68(12):1271-1280.

Squires, G.D., 2002. Urban sprawl: Causes, consequences, and policy responses, Washington D.C. Urban Institute Press.

Xu, M.X. and C.L. Wei, 2012. Remotely sensed image classification by complex network eigenvalue and connected degree, Computational and Mathematical Methods in Medicine, 2012: 632-703.

Yuan, F., M.E. Bauer, N.J. Heinert, and G. Holden, 2005. Multi-level land cover mapping of the Twin Cities (Minnesota) metropolitan area with multi-seasonal Landsat TM/ETM + data, Geocarto International, 20(2): 5-14.

Yuan, G.C., Y.J. Liu, M.F. Dion, M.D. Slack, L.F. Wu, S.J. Altschuler, and O.J. Rando, 2005. Genomescale identification of nucleosome positions in S. cerevisiae, Science, 309: 626-630. crossref(new window)

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. crossref(new window)