The spectral characteristics of rice plants subject to water deficiency were analysed and compared with healthy rice plants. Under water deficiency conditions, the reflectance of visible bands of the rice plants increased significantly, while the reflectance of near- and middle-infrared bands decreased. For the early detection of water deficiency, the visible band of Landsat ETM+ is more sensitive than the infrared bands. Band-3 showed the highest reflectance differences between rice under water-deficient and healthy conditions. The use of the vegetation index can distinguish rice under water deficiency more clearly than when only using a single band. The Ratio Vegetation Index (RVI) is the best vegetation index for both early detection of water deficiency and distinguishes the two rice conditions.

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Recently, generation of the orthophoto using digital aerial photograph is performed for the maintenance of spatial information data for wide areas by middle scale. For orthophoto generation, digital elevation model (DEM) by the stereo matching methods is widely used. For the efficiency of orthophoto generation, mismatching should be removed and accuracy should be improved effectively in stereo matching process.
With this motive, new method which is called “Reverse-Projection Method” to remove mismatching points on DEM generation is proposed. The most remarkable points of the method are its ability to detect mismatching points and improve accuracy of DEM using only neighbor stereo models on the strip or the next strip, and the effectiveness of the method is discussed in this paper.

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Stable supply of high quality crop is an urgent requirement for rice cropping facilities in Japan. To cope with this demand, it is absolutely essential to evaluate rice leaf's chlorophyll content by measuring the SPAD values. But it requires lots of elaboration to measure the SPAD values for wide area in paddy. Therefore, the objective of this study is to resolve this problem by estimating the rice leaves SPAD values through remote sensing. Specifically, we made use of a trial observation equipment using hyperspectral sensor mounted on a radio-controlled helicopter, and calculated new indices LVI_pure and LVI_mixture using the obtained data to estimate SPAD values. As a result, the relations between the SPAD value and the estimation index exhibits high correlation : R²=0.885 for LVI_pure and R²=0.927 for LVI_mixture, which facilitated to obtain “the estimated SPAD value image” from the obtained data.

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An updated and accurate database of land use and cover change is essential for global environmental change research. Analysis of multi-year time series of land surface attributes and their seasonal evolutions provide broader views of land surface change. This study investigated land use changes based on characterizing temporal vegetation dynamics corresponding to land use types by applying the wavelet-filtered MODIS EVI images from 2000 to 2009. The temporal pattern analysis was able to detect the actual timing of change event, either by conversion of land use or vegetation growth ; however, such outstanding capability of the method in this research was limited due to mixtures of land covers in MODIS GIFOVs of which spatial dimension are 250 m by 250 m. We identified several significant change patterns in the periods ; those have been indicated as land use changes. On the other hand, the temporal pattern analysis showed the need to evaluate effectiveness of the method in several sites where pattern changes were detected, but actually land uses have not been changed. The performance of the approach needs to be assessed with a reference data set by field survey in the next stage of research.

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