Eco-Engineering
Online ISSN : 1880-4500
Print ISSN : 1347-0485
ISSN-L : 1347-0485
Volume 27, Issue 4
Displaying 1-2 of 2 articles from this issue
Orginal papers
  • Tao Tonglaga, Yo Shimizu, Kenji Omasa
    2015 Volume 27 Issue 4 Pages 111-116
    Published: October 31, 2015
    Released on J-STAGE: January 07, 2016
    JOURNAL FREE ACCESS
    The differences in the improved Temperature Vegetation Dryness Index (iTVDI) and the Normalized Difference Vegetation Index (NDVI) between eleven land cover types in northwestern part of the Kanto region of Japan were analyzed using Landsat5 TM data and a vegetation map. The results showed that there were significant differences in iTVDI and NDVI values among the land cover types. The NDVI values were highest in the forest regions including evergreen forest, deciduous forest and mixed forest, while the iTVDI values were relatively lower in comparison to other land cover types, except for water. The iTVDI values of the grassland, cropland and orchard were higher while their NDVI values were not very low. And the iTVDI value of the paddy field was much higher than that of the water. In order to identify the reason, these land cover types were further classified into two classes respectively, the paddy field was classified into water-covered and not water-covered, and the other land cover types were classified into the full-covered and partialcovered. Consequently, the iTVDI values of the water-covered region of the paddy field and the well-vegetated region of the other three types were reduced. These results suggest that different land cover types cause the differences of iTVDI values and NDVI values due to the different fraction of vegetation cover and transpiration rates.
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Short communication
  • Kinya Uchida, Fumiki Hosoi, Kenji Omasa
    2015 Volume 27 Issue 4 Pages 117-121
    Published: October 31, 2015
    Released on J-STAGE: January 07, 2016
    JOURNAL FREE ACCESS
    It is important to examine forest trees in each species for understanding forest status and managing them. SAR (Synthetic Aperture Radar) has been used to forest survey. SAR can obtain structural information of trees, since its wavelength is long enough to penetrate into the tree canopy and the microwave reflects at stems and other large branches. This shows possibility of SAR to classify forest vegetation into each species based on the tree structural information. In this paper we tried to classify forest trees into coniferous and broad leaved ones using a polarimetric SAR image. First, we derived a coherency matrix for each pixel from the scattering matrix of the SAR data. The image was then decomposed into powers of four scattering models, based on the elements of the coherency matrix. The images of four powers were classified into 5 classes (coniferous, broad leaved, water, field, and urban area). To exclude mismatching area between the SAR image and ground validation data, we built a mask which excludes slopes that are on the opposite side of the mountain ridge as seen from the satellite. In addition to the four-component decomposition, we applied the three-component decomposition method and H, A, α decomposition method for the classification. As a result, the values for overall accuracy were 79%, 65% and 47% and the Kappa coefficient values were 0.51, 0.3 and 0.12 for the four-component decomposition, three component decomposition and H, A,α decomposition methods, respectively.
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