Toshibumi Sakata

Increasing trend of biologically active solar ultraviolet-B irradiance in mid-latitude Japan in the 1990s

Ground-based global solar ultraviolet-B (UV-B: 290 to 320 nm) irradiance has been measured by a narrow band UV-B radiometer at Tokai University, Hiratsuka, Japan (35°218N, 139°168E) for a 10-year period from October 1990 to September 2000. A precise calibration of the UV-B radiometer was periodically performed, and the yearly decay in sensitivity was found to be 23.7%. Using this decay rate, the measured UV-B irradiance was corrected, and the long-term trends of the UV-B irradiance were estimated. When the seasonal variation was eliminated by taking 12-month moving averages, an increasing trend in the UV-B irradiance was demonstrated to be 1.57 % per year. Moreover, to re- move quasibiennial oscillation (QBO), 26-month moving averages were applied to the UV-B irradiance normalized by the global total (300 to 3000 nm) solar irradiance. An increasing trend in the normalized UV-B irradiance was found to be 1.22 % per year. In winter, the clearly increas- ing trend of the UV-B irradiance was statistically significant, although the increasing tendency of the UV-B irradiance in other seasons (spring, summer, and fall) is not clear. A significant inverse correlation was con- firmed between the UV-B irradiance normalized by the global total solar irradiance and the effective ozone amount defined as total ozone amount 3secu, where u is the solar zenith angle. These findings give supporting evidence for a direct relationship between solar UV-B irradiance and the stratospheric ozone amount. In conclusion, the increasing trend of global solar UV-B irradiance, especially in winter, was confirmed in response to stratospheric ozone loss in mid-latitude Japan in the 1990s.

Evidence for uptake of 8-methoxypsoralen and 5-methoxypsoralen by cellular nuclei

The fluorescent appearance of oral mucosa cells treated with 8-methoxypsoralen (8-MOP) and 5-methoxypsoralen (5-MOP) was observed by means of fluorescence microscopy. Fluorescence at the nuclei was weakened in 8-MOP-treated cells, while it was intensified in 5-MOP-treated cells. These findings were consistent with changes in the fluorescence intensities on association of the psoralen derivatives with DNA in aqueous solution. This intensity change of fluorescence and also the blue shift of the fluorescence maximum of the derivatives on association suggested that the environment around the psoralen molecules is as little polar as methanol. From the results of these fluorescence microscopic observations and spectroscopic analysis of fluorescence of derivatives interacting with DNA during equilibrium dialysis, we concluded that 8-MOP, as well as 5-MOP, is incorporated by nuclei of human cells.

ESTIMATION OF AN INDEX OF HYDROPHOBICITY OF DNA INTERIOR USING 5‐METHOXYPSORALEN AS A FLUORESCENT PROBE

The index of hydrophobicity of DNA interior was estimated by measuring fluorescence spectra of psoralen derivatives associated with DNA. The environment around 5‐MOP associated with DNA was as hydrophobic (Dk= 34) as methanol, suggesting that the molecules reside at the space between the base‐pairs in B‐form DNA. This is also true for 8‐MOP. Thus, planar and aromatic molecules of 5‐ and 8‐MOP are more stable in the interior of DNA than in aqueous medium due to hydrophobic affinity.

Evaluations of unsupervised methods for land‐cover/use classifications of landsat TM data

Abstract Supervised classification methods have been mainly used for land‐cover/use classifications from the view point of classification accuracy, especially in the area where detailed land use dominates as in Japan. However, for high ground resolution image data such as Landsat TM and SPOT HRV data, it has been clarified that the classification accuracy using supervised classifications is lower than what was expected. One of the major reasons of this phenomenon may be caused by the difficulty with selecting sufficient training data. There is a possibility to solve this problem by using an unsupervised learning method because of its independent sampling characteristics. However, quantitative evaluations of performances of unsupervised classification methods for high resolution satellite data are not yet established. In this study, classification accuracies of unsupervised classification methods were evaluated for Landsat TM data with comparison to a conventional supervised maximum likelihood classificati...

Detection of ocean wave movements after the Northern Sumatra earthquake using SPOT images

Ocean wave movements slightly less than 3.5 hours after the northern Sumatra earthquake of December 26, 2004 were detected by the utilization of SPOT images. There exists the observation time lag of around 2.7 seconds between multispectral and panchromatic scenes of Banda Aceh, Sumatra taken right after the earthquake. These scenes can be considered to be two consecutive acquisition images covering the same area within the time lag, though the ground resolutions of those images are different. Applying this principle, moving ocean waves can be detected by the simple way using overlapping multispectral and panchromatic images in the same size and at the same coordinate. This paper is to introduce a new method of satellite remote sensing application in ocean disaster monitoring which has not been considered yet. Keywords-Detection; wave, movwment; tsunami, Sumatra

Comparison of multi-temporal image classification methods

One of the promising methods which can be thought to increase classification accuracies in remote sensing is the use of multi-temporal images. The authors propose multi-temporal image classification methods using backpropagation networks and fuzzy neural networks as classifiers and two kinds of classification models based on co-occurrence matrix as spatial information source. They are compared with conventional methods such as the likelihood addition method, the likelihood majority method and the Dempster-Shafer rule method.

An evaluation of JPEG compression for on-line satellite images transmission

Image compression is a key technology to realize on-line satellite image transmission economically and quickly. Among various image compression algorithms, the JPEG algorithm is the international standard for still color image compression. In this study, various kinds of satellite images were compressed with the JPEG algorithm. The relation between compression ratio and image quality were evaluated. As for the image quality evaluation, both subjective evaluation and objective evaluation were performed. It was determined that all the test satellite images could be compressed to at least 1/10 of the original data volume preserving high visual image quality. The degradation of spatial distribution quality of the compressed images were evaluated using power spectrum of original and compressed images.<<ETX>>

A classification method using spatial information extracted by neural network

A land cover classification method using a neural network is applied for the purpose of utilizing spatial information. The adopted model of the neural network has a three layered architecture, and the training method of the network is the back-propagation algorithm. Co-occurrence matrices, which are extracted from original image data, are used for the input pattern to the neural network. To evaluate the method, classification was conducted with this method for images from the Landsat TM and SPOT HRV. Obtained classification accuracies were 7-12% higher than that of the conventional pixel-wise maximum likelihood method based on spectral information.<<ETX>>

Spatial land cover classification with the aid of neural network

A land cover classification method using a neural network was applied for the purpose of utilizing spatial information, which is expressed as a two-dimensional array of a co-occurrence matrix. The adopted neural network has three layers feed forward network architecture with back-propagation learning algorithm. In this study, the three kinds of neural network classification models were proposed. The first and the second model classifies each band image at the first stage, then performs final decision based on the first stage result. At the decision stage, arithmetic decision algorithm and second neural network are used by the first and the second model, respectively. The third model is a single stage classifier that enters all band information into the neural network for learning and classification at the same time. In order to evaluate proposed models, land cover classification using the proposed models and conventional pixel wise maximum likelihood method was conducted with Landsat TM and SPOT HRV data. As a result, the third model showed best performance, with accuracies about 4% to 6% higher than those of the classification result of the first and second model, and it showed about 17% to 27% higher than that of the maximum likelihood classification result. Finally, we examine the best performance of the neural network classification model for multitemporal remote sensing data classification, which was successful.

Influence of lossy data compression on satellite for terrain elevation measurement

PRISM, a panchromatic stereomatic sensor with 2.5 m resolution, is planed to be launched in 2003. Because the observing data rate of PRISM is very high, lossy data compression will be applied on board the satellite in order to decrease the downlink data rate to ground receiving stations. The object of this study is to evaluate influences of the lossy data compression, atmospheric effects and S/N of the sensor for terrain elevation measurement. As the results, the following conclusions were given. Firstly, low S/N increases mean elevation error under the restriction that forward/backward viewing vectors converge within two pixels. Secondly, data compression and the turbid atmosphere decrease the mean elevation error, preferably. Thirdly, a data compression ratio of 1/6 is recommended for the practical operation of PRISM. In this case, the mean elevation error /spl les/3 m can be achieved without relation of atmospheric condition and S/N.