GSI has observed around Ito-City and Mt. Tsukuba using air-borne synthetic aperture radar (SAR) . It is found that accurate digital elevation models (DEMs) can be made, and we can produce a DEM with high data acquisition ratio even in steep topography by combining observations from several directions. As a result, DEMs with less than 10m accuracy in standard deviation were obtained from the both area and 94% of all the grids on the DEM around Mt. Tsukuba were collected through the combination. Further practices, such as, generating ortho images and 3-D images using visualized SAR data and the DEMs were also examined.
Multi-temporal Landsat TM data were utilized to classify the cultivated land located in the middle area of the Nagara River basin, Gifu prefecture. Color images of the vegetation indices including DVI, NDVI, SAVI and GVI were composed from respective multi-temporal VI images obtained from original Landsat TM data. The newly composed images clearly demonstrated the cultivated land such as paddy field, upland field and orchard. The distribution of the cultivated land related to the total river basin was also easily discriminated. However, by comparing the change profiles of the vegetation indices, SAVI was confirmed to be the most suitable index to reflect the vegetative conditions of major land-cover categories at different times, and the composed color image based on this index was thus applied for further classification analysis. As compared to the original TM images, classification results based on the composed SAVI image agreed much well with the related census data, with the classification accuracy for the paddy field and the upland field being especially high. The corresponding classification errors for these two land-cover categories were generally below 10%.
We studied the possible use of aerial high definition television (HDTV) images taken after the 1995 Kobe earthquake, to establish a methodology for an automated detection of building damage. The relationship between the degree of building damage and the color and edge indices from the aerial images were examined by image processing techniques. The characteristics of building damage were defined on the basis of hue, saturation, brightness and edge intensity. Using a threshold value of these parameters, the study areas were classified into damaged and undamaged pixels. A filter analysis was further conducted to these pixels and damaged buildings were identified. The estimated damage distribution by the proposed methodology agrees well with the field survey data and the visual inspection of the aerial HDTV and photographs.