To meet the demand of monitoring water pollution in China, Information Center of State Environmental Protection of China (ICSEP) and Institute of Remote Sensing Applications, Chinese Academy of Sciences (IRSA,CAS) have carried out a project to utilize the data extracted from Environment and Hazard Monitoring Constellation. This project is to build the first Remote-sensing and Environmental Monitoring System (REMS) in China. The most important component of REMS is the Hyperspectral-Environmental Database (HED). This paper is to describe the architecture and mechanism of HED. HED consists of five parts: Environmental backgrounds, Spectrums, Hyperspectral images, Basic geographic information and Environmental quality data. The interactions and relationships among the five parts are depicted. The workflow of HED assisting REMS is delineated. A preliminary research in Taihu Lake based on HED is also described in this paper.
Remote sensing data, especially the hyperspectral remote sensing data, characterize their great quantities. So how to deal wtih these data is a focus. Database has solved the problem of storing, searching, updating and maintaining of the data, but it is not satisfactory in disposing them. In recent years, the technology of data warehouse has great development. It can re-integrate, synthesize and separate the data of database, and use the searching pattern of multiple dimensions to realize data mining (DM). This technolgoy has been widely used in commerce to analyze the inner relationship of the numerous data and makes some remarkable achievements in decision supporting. Data warehouse and Data mining technology have been used in GIS. This article would give a set of complete steps and some general methods in using the DM to analyze the remote sensing data, especially in hyperspectral data. And it tries to do some preliminary exploration in using it to deeply analyze the potential relations among the acquired spectra, images and biology parameters of the experiments and get some anticipated possible results.
In some complicated terrain area, such a loess plateau of China, it is very difficult to get higher accuracy of landuse classification only depending on the traditional spectral statistics methods, especially the image pixel size is much larger than the geomorphology units. In order to improve the image classification results, large scale relief map has been used to create the digital geomorphology model(DGM). DGM can be used to do the pixel unmixing works, specially reducing the influence of terrain shadow. Applying fuzzy mathematics theory, the DGM has been used to correct the digital image classification result, so as to create more accurate landuse map. In addition, this method is also helpful to find some minor objects in low spatial resolution images.