Estimation of the rice-planted acreage is divided into two processes, discrimination of the rice-planted area and totalization of the riceplanted area. Satellite radar remote sensing is effective for discriminating the rice-planted area, because the growing season for rice in Japan, overlaps with the rainy season. Furthermore, the rice-planted area can be accurately totalized by referring to agricultural plots vector data. In order to improve the estimation accuracy of the rice-planted acreage, a method which discriminates the rice-planted area using satellite radar data and totalized the rice-planted area referring to agricultural plots vector data was proposed in this paper. The proposed method was applied to RADARSAT data to estimate the riceplanted acreage for a test site in Koshiji, Niigata prefecture. The estimated rice-planted acreage obtained by the above method was compared with that acquired by QuickBird data. As a result of accuracy verification, the accuracy of estimation using the above method was 85%.

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This paper proposes a new method of LIDAR data processing to automatically extract building and 3-D city modeling. This method exploits image processing technique including Hough transformation. The process of building extraction starts with DSM segmentation according to elevation values. Among these fragments, extracting process limits candidates of building fragments to those that have elevation higher than a specified threshold value and the DEM at the same location. Hough transformation is executed to boundary pixels of each candidate of building fragment and then fit to linear segments. Edges in corresponding parts of aerial images can also be used to refine boundaries of buildings. Each fragment is polygonized with these line segments. After the extraction, 3-D-building model is created with these polygons such that each polygon has vertical wall from the top of building to the ground (DEM) . Texture images are pasted onto each building, including walls. The most appropriate texture image is selected from aerial images according to geometry between building faces and external parameters of the photos. The method has been tested with LIDAR data of Shinjuku and Ginza area in Tokyo.

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It is very important to control the water stress at the growth stage of Satsuma Mandarin for high sugar content. It is known that a shift of the red edge wavelength of spectral reflectance to shorter wavelengths is caused in water stressed leaves. The authors investigated the transition of the water potential and the red edge wavelength of spectral reflectance in the actual fruit cultivation. Its period was from July to November in 2003. Consequently, the transition of the red edge wavelength was the same as the transition of the water potential in filmmulching on the ground surface of the fruit trees. Tow main results were obtained. First, an adequate correlation between the averaged sugar content in harvest fruit and amount of change of the red edge wavelength at the growth stage of fruit was indicated from the results of measurements. Second, it is assumed that the individual differences of mandarin trees, such as the amount of fruit, volume of tree, and nutritive condition, influence the red edge wavelength.

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This paper focuses on the evaluation of compositing algorithms of MODerate resolution Imaging Spectroradiometer (MODIS) . The atmospheric effects of channel 1-7 have been investigated by radiative transfer simulation using 6S code over variety of aerosol and climatic conditions. As a result, blue wavelength (channel 3) was found to be the most subject to atmospheric effects and its apparent reflectance values were higher than other channels. However, this method is subject to select cloud shadows as a optimum values. To avoid this problem, the combination of minimum blue constrained with thermal channel criteria is newly developed based on the assumption that the cloud shadow is cooler than normal pixels. These two approaches, to say, the minimum blue criterion (MinB) and the minimum blue and maximum thermal criteria (TMinB) have been tested along with five approaches including the maximum NDVI criterion (MaxN), the maximum temperature criterion (MaxT), the NDVI criteria followed by minimum scan angle (NMinS), the thermal criteria followed by minimum scan angle (TMinS) and the NDVI and thermal criteria followed by minimum scan angle method (NTMinS), to improve on the heritage of Advanced Very High Resolution Radiometer (AVHRR) . Then the 10 day composite images over Japan main islands have been generated using the above mentioned seven approaches. The comparative performances of these seven methods are judged from five criterion including cloud screening, satellite zenith angle, smoothness of the image, snow/ice identification and cloud shadow. It is concluded that the TMinB method produces fairly consistent results with possible refinements and more sophisticated methods to escape from cloud shadows.

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We have examined positioning availability and accuracy of OmniSTAR, a wide area DGPS service, and StarFire, a global DGPS service, comparing with RTK and the DGPS service by Japan Cost Guard (JCG) . The experiments were for utilization of GPS in urban area. There were four experiments, i.e. fixing antennas on known points, carrying antennas by hands and brief stop on known points, revolving antennas, and carrying antennas by a vehicle. Accuracy of StarFire and OmniSTAR are not higher than the JCD's DGPS, large errors were sometimes observed for StarFire. Availability of DGPS is much higher than RTK. But availability of OmniSTAR was sometimes decreased by microwave wireless facilities.

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A new method for image clustering with density maps derived from Self-Organizing Maps (SOM) is proposed together with a clarification of learning processes during a construction of clusters. It is found that the proposed SOM based image clustering method shows much better clustered result for both simulation and real satellite imagery data. It is also found that the separability among clusters of the proposed method is 16% longer than the existing k-mean clustering.

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A non-linear merge and split method for image clustering is proposed to overcome a complicated situation for the clusters distributions in a feature space which is relatively difficult to cluster with the linear approach like a k-means and ISODATA clustering algorithms. The effectiveness of the proposed clustering method is shown with the real remote sensing satellite imagery data.

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