Landsat TM Based Land-cover Analysis of Cholwon (South Korea) and Wonsan (North Korea)

  • Published : 2002.01.31

Abstract

The land-cover of two regions of South and North Korea included in one Landsat TM scene was investigated by comparing different seasons and different band data over the multiple land-cover types. The relationships between the intensities of two bands in the 2-D plot are mainly linear in band2 versus band1 and band3 versus band1, polygonal sporadic in band5 versus band1 and band7 versus band1, and almost tri-polarized in band4 versus band3. The 2-D plot of band4/band3 shows the best capability to discriminate different main land-cover such as water, vegetation and dry soil. Some discriminations are not clear between city and dry field, or mountain and plain field in the scene of September. The digital number data of band4 from vegetated zones show stronger reflectance in September rather than April, while other band values tend to be lager in April than in September over each land-cover. NDVI presents high value in both regions in September. However the image of Wonsan area in April suggests weak vigor of vegetation in comparison with Cholwon area. Band ratios are very effective in eliminating the influence of the complex topography. The proper pairing of the band ratio improved the discrimination capability of the land-cover; band5/band2 for dry soil, band4/band3 for vegetation and band1/band7 for the water. The RGB combination of the three band ratio pairs showed the best results in the discrimination of the land-cover of Wonsan, Cholwon and even the Demilitarized Zone.

Keywords

References

  1. Photogrammetric Engineering and Remote Sensing v.63 no.4 Evaluating the uncertainty of area estimates derived from Fuzzy Land-cover classification Canters, F.
  2. IEEE TRansactions Geoscience and Remote Sensing v.22 no.3 A Physically-Based Transformation of Thematic Mapper Data- The TM Tasseled Cap. Crist, E.P.;Cicone, R.C. https://doi.org/10.1109/TGRS.1984.350619
  3. Image Interpretation in Geology (2nd ed.) DA.
  4. SPIE 1966 annual meeting, Space Imaging EOSAT, 303-254-2103 Four-meter resolution multispectral satellite data and its implications for crop monitering and distribution mapping Dykstra, J.
  5. Photogrammetric ENgineering and Remote Sensing v.48 no.11 The relationships between reflectance in the Landsat wavebands and the composition of an Australian semi-arid shrub rangeland Grantz, R.D.;Gentle, M.R.
  6. Ecological Engineering v.6 Digital Interpretation and management of land-cover ad case of Cyprus Gumbricht, T.;McCarthy, J.;Mahlander, C. https://doi.org/10.1016/0925-8574(95)00065-8
  7. Remote Sensing of Environment v.21 An assessment of Landsat MSS and TM data for urban and near-urban Land-cover digital classification Haack, B. https://doi.org/10.1016/0034-4257(87)90053-8
  8. Photogrammetric Engineering and Remote Sensing v.62 no.2 Comparison of Three Method for Mapping Tundra with Landsat Digital Data Joria, P.E.;Jorgenson, J.C.
  9. Photogrammetric Engineering and Remote Sensing v.63 no.1 Supervised classification of Landsat Thematic Mapper Imagery in a semi-arid range-land by non-parametric Discriminant Analysis Knick, S.T.;Rotenberry, J.T.;Zariello, T.J.
  10. Remote Sensing and Image Interpretation (3rd ed.) Lillesand, T.M.;Kiefer, R.W.
  11. International Journal of Remote Sensing v.6 no.5 Spectral characteristics of the Landsat Thematic Mapper sensors Markham, B.;Barker, J.L. https://doi.org/10.1080/01431168508948492
  12. Photogrammetric Engineering and Remote Sensing v.63 no.1 Aquatic Macrophyte Modelling using GIS and logistic multiple regression Narumaloni, S.;Jensen, J.R.;Althausen, J.D.;Barkhalters, S.;Mackey Jr., H.E.
  13. International Journal of Remote Sensing v.7 no.12 Calibration of Landsat data for sparsely vegetated semi-arid range lands Pech, R.P.;Davis, A.W.;Lamacraft, R.R. https://doi.org/10.1080/01431168608948964
  14. Photogrammetric Engineering and Remote Sensing v.54 no.12 An Enhanced classification approach to change detection in semi-arid environments Pilon, P.G.;Howarth, P.J.;Bullock, R.A.
  15. Western Finland. Aquatic Botany v.21 A Landsat study of the aquatic vegetation of the Lake Luodonjarvi Reservoir Raitala, J.;Lenpinen, J. https://doi.org/10.1016/0304-3770(85)90075-0
  16. Photogrammetric Engineering and Remote Sensing v.48 no.5 Computation with physical values from Landsat Digital data Robinove, C.J.
  17. IEEE Transactions Geoscience and Remote Sensing v.29 no.1 The Least-Squares mixing model to generate Fraction images derived from remote sensing multi-spectral data Shimabukuro, Y.E.;James, A.S. https://doi.org/10.1109/36.103288
  18. International Journal of Remote Sensing v.10 no.6 Digital change detection techniques using remotel-sensed data Singh, A. https://doi.org/10.1080/01431168908903939
  19. The application of Landsat TM data to Geological Interpretation and Land-cover Classification in Cholwon Shin, Kwang Soo
  20. GIS v.2 Monitoring Urban Development of hangzhou City by using multi-temporal TM data Su, Y.;Chen, S.;Zhao, Y.;Chen, L.
  21. Fundamentals of Geological and Environmental Remote Sensing Vincent, R.K.