マルチセンサーデータによる中国温帯地域における生物保護区の植生タイプの分類

DOI Web Site 参考文献33件
  • 劉 〓〓
    千葉大学環境リモートセンシング研究センター
  • 建石 隆太郎
    千葉大学環境リモートセンシング研究センター
  • 近藤 昭彦
    千葉大学環境リモートセンシング研究センター
  • 竹内 延夫
    千葉大学環境リモートセンシング研究センター

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  • Vegetation types classification of a temperate biosphere reserve in China by multisensor satellite imagery.

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Vegetation cover types on Changbai mountain, a natural biosphere reserve (2000km2) in northeast China, were derived by using multisensor satellite imagery combined with Landsat TM and JERS1 UPS. DEM data were used for improving classification accuracy. Cover types were classified into 20 groups. Bands 4 and 5 of Landsat TM image acquired on July 18, 1997 and band 1 of JERS1 UPS image acquired on Feb. 4, 1997 were fused to a false color image for the final output, and maximum likelihood supervised classification was performed. Data fused either with multitemporal (OPS) or with multisensor (TM+OPS) showed a high accuracy of identification, comparing to individual images. The overall accuracy of classification of individual images of OPS presented less than 40%, and TM less than 70%, while the fused data set provided an accuracy higher than 73% which was raised to 83% by post classification including filtering and verification with DEM.<BR>There were 5 vegetation zones on the mountain, from the base to the peak, deciduous forest zone, mixed forest zone, conifer forest zone, birch forest zone, and tundra zone. Spruce-fir conifer forest was the most dominant (nearly 50%) vegetation type, followed with mixed forest (10%) and larch forest (8%) . Classification accuracy was not only determined by data fusion from different sensors of different resolutions, but was also affected by image composite from different seasons. In winter images, the contrast between cover types in life-form level, e.g. evergreen and summer-green, or forest and meadow, is enhanced, while more detailed information of spectral characteristics of plant communities can be extracted from summer images. For boreal vegetation, autumn imagery is considered useful for discriminating cover types, because the leaf color in this season is significantly diversified. It is concluded that, for vegetation consisting of conifer and deciduous species, multitemporal imagery fused from phenologically different data is meaningful for creating vegetation maps with high accuracy.

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