The Development of the Method for Updating Land Surface Data by Using Multi-temporally Archived Satellite Images

This study focuses on the problem of "time" affecting spatial data. The most substantial temporal problem for comprehensive use of spatial data is “time inconsistency”. Reducing “time inconsistency” within individual data, among multiple data, and between data and the real world can be achieved by frequent updating. The utilization of remote sensing is an effective method for updating various land surface data (e.g. land use, vegetation, soil and geology). The periodicity of usable data acquisition is quite stable for Synthetic Aperture Radar (SAR) data, because of its weather independence. The aim of this study was to develop a method for frequent updating of spatial data by taking advantage of the stable periodicity of multitemporal SAR images. Firstly, periodical and multitemporal SAR images were integrated by time series analysis. Following this process, temporal changes were restructured as a change process model and Speckle noise was reduced. The change process model described the trend of land surface changes. Secondly, newly acquired SAR image was assimilated into the database to strengthen the stability of the change process model. At this time, changes in land surface data were detected by comparing the change in the newly acquired image with the change process model. The effectiveness of this method was evaluated through comparison with actual spatial data. It is hoped that change detection in near real-time will be achieved using these procedures.