In recent years, remote sensing has been used to assessing water pollution distribution. In this study, water quality is analyzed using data collected by the Advanced Visible and Near Infrared Radiometer type-2 (AVNIR-2) of the Advanced Land Observing Satellite (ALOS) at various points in time. We carried out fuzzy regression analysis of the AVNIR-2 data and direct measurements of local water quality. The relationship between the water quality data and the AVNIR-2 data was analyzed by solving both the min and max problems. By comparing the maps of estimated water quality with actual distributions of water quality in the study area, we found that the method used in this study allows effective derivation of water quality conditions from AVNIR-2 data, which provides 10-m spatial resolution. Furthermore, by comparing the maps created using AVNIR-2 data collected at different times, we obtained results suggesting temporal changes in water quality. We also compared the results obtained using data collected by the optical sensor of the Landsat thematic mapper (TM) with 30-m resolution and those obtained using data collected by the active sensor of JERS-1 synthetic aperture radar (SAR), and examined the differences in classification results resulting from differences in resolution and sensors.

Remote sensing has been used to understand water distribution and pollution situation in recent years. This paper proposed an algorithm for analyzing water quality conditions using the data acquired by active sensor Synthetic Aperture Radar (SAR) of Japanese Earth Resources Satellite-1 (JERS-1). The proposed method has four steps. First, the gradations of the SAR data were conformed. Second, textures were calculated from the co-occurrence matrix of the SAR data. Third, the fuzzy regression analysis of the texture features and measurements for local water quality was done. The relation between water quality data and textures was calculated by using MIN problem and MAX problem. Finally, the estimation maps of water quality were obtained by using fuzzy level-slice processing. By comparing the estimation maps of water quality and realities in the study area, it was clear that the proposed method was possible to understand the water quality conditions effectively from the SAR data with single brightness information.

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To draw reconstruction plans following great earthquakes, it is necessary to quickly estimate the amount of disaster waste, with the use of remote sensing data affecting all subsequent processing. However, the digital number (DN) of each pixel represents the average land cover conditions, i.e., the information provided by a pixel should be represented as a one-pixel mixed-class ("mixel") instead of a one-pixel one-class. In a previous study, we proposed a method for unmixing mixels using the DNs and texture features from THEOS data. In this paper, we propose a method of land cover classification using RapidEye data, whose effectiveness was confirmed by our results.

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