In recent times, it has become apparent that environmental information systems are useful only if they work with a high resolution in space. Detailed area information is required in order to identify environmental dangers, changes in the ecosystems and for planning of environmental protection or rehabilitation measures. This is also true for hydrological and water management systems dealing with water quantity and water quality. Also, hydrological modeling data sets are needed which are distributed in area, both as input to hydrological models and for determination of parameters of hydrological models. Therefore, the focus of this paper is on hydroecological data sets exposing a high resolution in space. In ecological and hydrological modeling, we need such data sets for many parameters so that the acquisition, storage and retrieval of such data forms a major problem, particularly since such multiple data sets are required also in a multi-temporal form. The handling of these data sets is reasonably done with the aid of Geographical Information Systems (GIS). Three types of area data with a high resolution in space will be discussed here: a) remote sensing data, b) Digital Elevation Models and c) digitized maps. Examples will be given in the paper for long-term changes in an environmental system for various parameters, e.g. urbanization, forest diseases, and reduction of agricultural areas. Historic changes of such parameters will be demonstrated as well as a method for forecasting changes in these environmental parameters for future conditions. The use of the overlay concept allows merging data from different sources (remote sensing, digital maps, Digital Elevation Models) with a high resolution in space. By merging of these different data sets, new information is generated having a new quality for monitoring or modeling environmental systems. Examples of such new products will be shown.
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- Integration of Remote Sensing Information, Digital Elevation Models and Digital Maps Within a GIS to Generate New Spatial Environmental Data Sets for Water Management Purposes
G. A. Schultz
- Springer Netherlands
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