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This paper proposes fusion analysis of high-resolution multispectral and panchromatic satellite imageries for forest type classification.We have shown the performance of forest type classification using panchromatic and multispectral high-resolution QuickBird satellite imageries separately. With texture features obtained from a panchromatic imagery, forest was classified into two types, such as coniferous and broad-leaved forests. On the other hand, with spectral features obtained from a multispectral imagery, forest was classified into six types, such as three coniferous, one broad-leaved and two mixed forests. These results showed that both texture and spectral features are effective for classification of forest types.In this paper, we apply the object-based classification using the common segments obtained from a pansharpen imagery to fusion and single imagery analysis in order to compare the difference only between texture and spectral features. The mean value of each texture and spectral feature from a segment is adopted in the supervised classification, the standard nearest neighbor method, using radiometrically corrected satellite imageries. We selected the contrast as texture feature, and normalized band values and differences between normalized band values as spectral features. From the comparison of the result with ones obtained from a single imagery analysis, we demonstrated that data fusion analysis exceeds a single imagery analysis in accuracy.

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This paper reports on the optical imaging polarimetry of geostationary satellite Express-AM5. Precise optical polarimetry was carried out using the Nayuta 2-m telescope at Nishi-Harima Astronomical Observatory and a polarimeter. V -band images were continuously taken in 312 minutes. The degree of polarization decreased with time until the minimum phase angle was reached at midnight, and then, it increased until morning. This change in the degree of polarization is well explained by the reflection of sunlight from monocrystalline solar panels of the satellite and possibly from other satellite components such as the antenna dishes and/or bus.

During the Great East Japan Earthquake Disaster, the medical team’s responses in Iwate Prefecture Disaster Emergency Operations Center experienced various difficulties, especially during the first 9 days. In this paper, we propose to objectively reveal problems of response activities from the viewpoint of operational information processing by the After Action Review, focusing on the activity logs in the time series (chronologies). By using Essential Elements of Information (EEI) as a framework of our analysis, we clarified the gap between the task that should be performed and the actual conditions in the operation of Japan Disaster Medical Assistance Team (DMAT), from the hyperacute phase to the subacute phase of medial responses.

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This paper investigates adequate selection of hyperspectral components for estimating the degree of soybean bacterial pustule infection. According to a filed report,the higher the disease damage level is,the less the yield is expected.Based on highresolution hyperspectral images for different levels of damaged soybean fields,we have investigated the relationship between the soybean weight averaged for 100 seeds and the hyperspectral changes.In order to estimate the degree of damages from the hyperspctral data,we have tested 3 methods:(i) Estimation using reflectance of a single band based on a linear regression analysis.The RMSE was 2.0g.(ii)Estimation using normalized vegetation indices also based on a linear regression analysis.The RMSE was 2.1g.(iii) Estimation using neural network(NN)of a single-layer perceptron which has input nodes corresponding to the hyperspectral bands and a single output node.The RMSE was 1.5g,for estimating 100 seeds weight ranged over 16.7g to 22.7g.This paper indicates that NN is the most accurate among three methods to estimate the degree of soybean bacterial pustule infection.