In the landcover classification using optical multi-spectral scanning data some mis-classifications of landcover categories occur due to the similarity of spectral characteristics. A method has been developed to correct the mis-classification of Open Land category into High Density category classified from Satellite Pour l'Observation de la Terre High Resolution Vidicon (SPOT-HRV) image by using roughness information derived from Japan Earth Resources Satellite 1 Synthetic Aperture Radar (JERS-1 SAR) image. Twenty four core clusters were made based on a clustering algorithm as supervised data. After labeling them to 8 landcover categories, HRV image was classified by Nearest Neighbor Classification (NNC) and Maximum Likelihood Classification (MLC) methods. The occurrence of mis-classification is detected by comparing the classified image with color aerial photograph and investigated using error matrix.
In the correction procedure, the pixel position of the High Density category of HRV is transformed into a position of SAR image using Affine transformation. The pixel positions of both images are in the Universal Transverse Mercator (UTM) coordinate system. The SAR pixels surrounding this transformed pixel position play an important part in the correction method and is termed SAR neighbor pixels.
Each digital value of SAR neighbor pixels is logarithmically transformed to classify the pixel to be rough or flat. When the logarithmically transformed value of the SAR neighbor pixels is greater than 59, the pixel is decided to be urbanized pixel. The proportion of the numbers of SAR pixels determined to be urbanized within SAR neighbor pixels is calculated and defined as an urbanized ratio. The mis-classification of HRV image is estimated based on the urbanized ratio. The HRV pixels classified to High Density category are changed to Open Land when the urbanized ratio is less than 0.05.
By comparing the landcover images before and after correction with the information interpreted from aerial photograph, it is seen that the category accuracy of Open Land category classified by NNC and MLC were increased to 92.1% from 66.9% and to 94.6% from 74.5%, respectively. Approximately 80% of Open Land pixels mis-classified into High Density of HRV can be corrected. The results show that the method proposed is useful for corrcting the mis-classification between High Density and Open Land caused by similarity of spectral characteristics.

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Land cover changes around lake basin have caused aquatic environmental degradation such as eutrophication or water pollution. In particular, increase of nitrogen and phosphorus components to the lake due to land cover changes of surrounding areas has been serious. Estimating influent loads from lake basin to the lake through rivers is very important in the management of lake environment. In this paper, the relation between total amount of nitrogen to the lake and land cover distribution was investigated with remote sensing. Lake Kasumigaura was selected as a test site for the case study.
LANDSAT MSS data from 1979, 1984 and 1990 were analyzed to produce land cover distribution map of the area. Land cover categories include urban area, paddy field, forest area and field. Lake basin areas were delineated with DEM for individual rivers to the lake. And the relation between total nitrogen of rivers and land cover distribution at the lake basin was analyzed. As a result, it was found that urban area and field in the land cover categories gave influence to the eutrophication of Lake Kasumigaura. Furthermore, in order to evaluate total annual pollution load (total nitrogen) to Lake Kasumigaura,
(1) basic unit of total nitrogen load for each land cover categoris
(2) influent annual total nitrogen to the lake were estimated from land cover change patterns from MSS data and from the total nitorgen concentration measured at each rivers to the lake.

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Importance of in-flight radiometric calibration of satellite sensors has been increased. A snow field is a candidate as a ground target for vicarious calibration and cross calibration in the visible to near-infrared region because of its high reflectance and spatial uniformity. This study focuses on the advantages and disadvantages of a snow field for in-flight calibration in order to show the validity of a snow field as a ground target. The reflectance data was obtained at the Sarobetsu and the Konsen Plateau areas in Hokkaido, Japan.The snow reflectance was changeable in terms of temporal stability due to a variety of snow grain size, water content and impurities. However, the reflectance of the snow field was nearly flat spectrally in the 400-900 nm region and very uniform spatially. For the some properties of the snow field, the percent uncertainties and the sources of uncertainty or errors for the calibration are estimated. As a result, the snow fields can be selected as a good ground target for vicarious calibration using simultaneous in-situ measurements with data acquisition by satellite sensors and for cross calibration between sensors on the same platform.

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In the present study, we propose a new index called a Vegetation-Soil-Water (VSW) index to monitor land cover conditions. The VSW index is defined as a natural extension of PVI for monitoring not only vegetationconditions but also soil and water conditions as well. VSW index is defined using a triangle on a NIR-Red scatter plot. The PVI measures only vegetation parameters, whereas the VSW index monitors vegetation, soil and water parameters simultaneously by measuring the distances in the scatter plot. Landsat Thematic Mapper (TM) scenes were used for the analyses. Wetland plants in Kushiro Mire were in the beginning of their growing season in June, and in their maximum growth stage in August. All the vegetation was dead in October. These scenes show how the vegetation changes seasonally and year by year for 5 years. We determined the end member spectral points for vegetation, soil and water (VSW) with an algorithm to fit a triangle to the spectral distribution. This automatic end member determination also standardizes the spectral responses of scenes. Scatter plots of the TM scenes on NIR-red axes were overlaid with the determined VSW end member points. The VSW index was used to monitor land cover change in the Kushiro wetland with Landsat TM images to evaluate the effectiveness of our approach for wetland monitoring. Changes in the vegetative components of the Kushiro wetland, both seasonally and over the full 5 year period we examined, could be easily resolved from these color composite images.

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[in Japanese]

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[in Japanese]

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