, Volume 24, Issue 3, pp 701–710 | Cite as

Use of balloon aerial photography for classification of Kushiro wetland vegetation, northeastern Japan

  • Michiru Miyamoto
  • Kunihiko Yoshino
  • Toshihide Nagano
  • Tomoyasu Ishida
  • Yohei Sato


Kushiro wetland in northeastern Japan is a Ramsar-designated wetland of international importance (1980) that is characterized by high biodiversity and spatial heterogeneity. These characteristics of the wetland also present innumerable challenges for mapping and monitoring such unique ecosystems. Recent advances in remote sensing technology have provided many sensors with different spatial and spectral scales and resolutions. However, they are still inadequate for mapping wetland vegetation at a large scale for various reasons, such as inadequate resolution and high costs. This study was designed to evaluate the potential of balloon aerial photography to acquire high resolution (15 cm pixel size) imagery for mapping wetland vegetation in the Akanuma marsh. We used a standard 28-mm non-metric camera (Nikon-F-801), which seven specific categories (species mixes) were successfully delineated. It was possible to classify small shrubs mixed with herbaceous plants; moss bogs with pools; dwarf shrubs with sedges; and moss with alpine plants. From this research, it seems that balloon aerial photography is a powerful tool for mapping temperate wetland vegetation, allowing classification of specific and typical vegetation types to the genus and species level.

Key Words

temperate wetland vegetation mapping balloon aerial photography mosaicking visual photo interpretation Akanuma marsh Kushiro wetland 


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Literature Cited

  1. Aber, J. S. and W. S. Aber. 2001. Potential of kite aerial photography for peatland investigations with examples from Estonia. Suoseura 52:45–56.Google Scholar
  2. Barrette, J., P. August, and F. Golet. 2000. Accuracy assessment of wetland boundary delineation using aerial photography and digital orthophotography. Photogrammetric Engineering and Remote Sensing 66:409–416.Google Scholar
  3. Buerkert, A., F. Mahler, and H. Marschner. 1996. Soil productivity management and plant growth in the Sahel: potential of an aerial monitoring technique. Plant and Soil 180:29–38.CrossRefGoogle Scholar
  4. Davis, J. L. and A. P. Annan. 1989. Ground-penetrating radar for high-resolution mapping of soil and rock stratigraphy. Geophysical Prospecting 37:531–551.CrossRefGoogle Scholar
  5. Derksen, C., J. Piwowar and E. Ledrew. 1997. Sea-ice melt-pond fraction as determined from low level aerial photographs. Arctic and Alpine Research 29:345–351.CrossRefGoogle Scholar
  6. Friedli, B., S. Tobias, and M. Fritsch. 1998. Quality assessment of restored soils: combination of classical soil science methods with ground penetrating radar and near infrared aerial Photography. Soil & Tillage Research 46:103–115.Google Scholar
  7. Garrison, J. L. and S. J. Katzberg. 2000. The Application of Reflected GPS Signals to Ocean Remote Sensing. Remote Sensing of Environment 73:175–187.CrossRefGoogle Scholar
  8. Gerard, B., A. Buerkert, P. Hiernaux, and H. Marschner. 1997. Non-destructive measurement of plant growth and nitrogen status of pearl millet with low-altitude aerial photography. Plant and Soil 43:993–998.Google Scholar
  9. Harvey, K. R. and G. J. E. Hill. 2001. Vegetation mapping of a tropical freshwater swamp in the Northern Territory, Australia: a comparison of aerial photographs, Landsat TM and SPOT satellite imagery. International Journal of Remote Sensing 22:2911–2925.CrossRefGoogle Scholar
  10. Honda, K. 1993. Kushiro Wetland, second edition. The Asahi Library, Asahi Shimbun, Tokyo, Japan.Google Scholar
  11. Inoue, Y. and S. Morinaga. 1995. Estimating spatial-distribution of plant-growth in a soybean field-based on remotely-sensed spectral imagery measured with a balloon system. Japanese Journal of Crop Science 64:156–158.Google Scholar
  12. Jennings, C. A., P. A. Vohs, and M. R. Dewey. 1992. Classification of wetland area along the upper Mississippi River with aerial videography. Wetlands 12:163–170.CrossRefGoogle Scholar
  13. Jensen, J. R., E. J. Christensen, and R. Sharits. 1984. National wetland mapping in South Carolina using airborne multispectral scanner data. Remote Sensing of Environment 16:1–12.CrossRefGoogle Scholar
  14. Johnston, R. M. and M. M. Barson. 1993. Remote sensing of Australian wetlands: an evaluation of Landsat TM data for inventory and classification. Australian Journal of Marine and Freshwater Research 44:223–232.CrossRefGoogle Scholar
  15. Juan, C., J. D. Jordan, and C. Tan. 2000. Application of airborne hyperspectral imaging in wetland delineation. Proceedings of The 21st Asian Conference on Remote Sensing, Taipei, Taiwan 2:834–839.Google Scholar
  16. Kadmon, R. and R. Harari-Kremer. 1999. Studying long-term vegetation dynamics using Digital Processing of Historical Aerial Photographs. Remote Sensing of Environment 68:164–176.CrossRefGoogle Scholar
  17. Koizumi, T., M. Murai, T. Koike, and H. Manabe. 1986. An Automated System and its Application for Aerial Photography Using Kite Balloon. Journal of The Japan Society of Photogrammetry and Remote Sensing 25:12–23.Google Scholar
  18. Lyon, J. G. 2001. Wetland Landscape Characterization: GIS, Remote Sensing, and Image Analysis, first edition. Ann Arbor Press, Chelsea, MI, USA.Google Scholar
  19. Miller, S., M. Birk, F. Schreier, and D. Hausamann. 1992. Airborne far-infrared heterodyne remote-sensing of stratospheric OH-A feasibility study. International Journal of Infrared and Millimeter Waves 13:1241–1268.CrossRefGoogle Scholar
  20. Miyamoto, M., Y. Yoshino, and K. Kushida. 2001. Relationship between canopy BRDF and physical parameters of 3D structure of vegetation in northern wetlands in Japan. Asian Journal of Geoinformatics 1:57–70.Google Scholar
  21. Nagano, T. 1990. Studies on mangrove in Thailand—an aerial photographic survey using a kite balloon. Japanese Society of Environment Control in Biology 28:119–124.Google Scholar
  22. Nakano, T., S. Kuniyoshi, and M. Fukuda. 2000. Temporal variation in methane emission from tundra wetlands in a permafrost area, northeastern Siberia. Atmospheric Environment 34:1205–1213.CrossRefGoogle Scholar
  23. Scarpace, F. L., B. K. Quirk, R. W. Keifer, and S. L. Wynn. 1981. Wetland mapping from digitized aerial photography. Photogrametric Engineering and Remote Sensing 47:829–838.Google Scholar
  24. Varotsos, C. A. and K. Y. Kondrat’ev. 2001. Experiment in integrated interpretation of remote sensing data and direct measurements of atmospheric ozone content. Earth Observation and Remote Sensing 16:511–526.Google Scholar
  25. Vincent, R. K. 1997. Fundamentals of Geological and Environmental Remote Sensing, first edition. Prentice Hall, Upper Saddle River, NJ, USA.Google Scholar
  26. Weyman, G. S., P. C. Jepson, and K. D. Sundreland. 1995. Do seasonal-changes in numbers of aerially dispersing spiders reflect population-density on the ground or variation in ballooning motivation. Oecologia 101:487–493.CrossRefGoogle Scholar
  27. Yamagata, Y. 1999. Advanced remote sensing technique for monitoring complex ecosystems: spectral indices unmixing and classification of wetlands. National Institute for Environmental Studies. Tsukuba, Japan. R-141.Google Scholar

Copyright information

© Society of Wetland Scientists 2004

Authors and Affiliations

  • Michiru Miyamoto
    • 1
  • Kunihiko Yoshino
    • 2
  • Toshihide Nagano
    • 3
  • Tomoyasu Ishida
    • 4
  • Yohei Sato
    • 5
  1. 1.National Institute Environmental StudiesJapan Society for the Promotion Science FellowTsukuba, IbarakiJapan
  2. 2.Institute of Policy and Planning ScienceThe University of TsukubaTsukuba, IbarakiJapan
  3. 3.Faculty of International Agricultural and Food StudiesTokyo University of AgricultureTokyoJapan
  4. 4.Faculty of AgricultureUtsunomiya UniversityUtsunomiyaJapan
  5. 5.Graduate School of Agricultural and Life SciencesThe University of TokyoTokyoJapan

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