Much local government has been using a large scale digital map with Geographic Information System (GIS). However, the updating method of a map is not established yet. The purpose of this study is the real-time renewal of the digital map for local government by using Remote Sensing and RTK-GPS. This concept was defined as REAL TIME GIS. This system has the problem that RTK-GPS measuring data is Japanese Geodetic Datum 2000 (JGD2000) of WGS-84, but most of the digital maps of local government are still Tokyo Datum of old geodetic system. It is necessary to transform an old geodetic system to a new one. In this study, the coordinate transformation methods were compared Affine Transformation with TKY2JGD. Moreover, the number and arrangement of control points were changed, coordinates were converted by Affine Transformation. In this paper, the parameters which were calculated by Affine Transformation were called “High-Accuracy Regional Parameter (HARP)”. As a result, TKY2JGD has a maximum 15cm error. Affine Transformation has 2cm errors using 4 control points at the corner of unit. It is suggested that the process of REAL TIME GIS and HARP should be introduced to the work of local government.
The objective of this study is to find areas where landslides may occur in the near future by using satellite remote sensing data and thematic-map data related to landslides. We used Landsat TM data, geological maps, and inclination angles to predict landslide areas. As a result, we conclude that NVI data which was calculated from satellite data, geological types, and inclination angles are important factors to extract areas where landslides may occur.
An inference of landslide areas using digital elevation data and Landsat TM band 6 data has been performed on the basis of the assumption that the occurrence of landslides is closely related to the amount of underground-water and the topographic features of watersheds. It is shown that it is possible to distinguish between dangerous landslide areas and non-landslide areas by using spatial features of watersheds, such as the area, mean slop and shape factor, and the ground surface temperature obtained from Landsat TM data.
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