Remote sensing image analysis systems and geographic information systems (GIS) show great promise for the integration of a wide variety of spatial information supporting tasks such as urban and regional planning, natural resource management, agricultural studies and topographic or thematic mapping. Current and future remote sensing programs are based on a variety of sensors that will provide timely and repetitive multisensor earth observation on a global scale. GIS offer efficient tools for handling, manipulating, analyzing and presenting spatial data that are required for sensible decision making in various areas. The Environmental Monitoring project may serve as a convincing example of the operational use of integrated GIS/remote sensing technologies. The overall goal of the project is to assess the capabilities of satellite remote sensing for the analysis of land use changes, especially in moor areas. These areas are recognized as areas crucial to the mission of the Department of Environment and, therefore, to be placed under an extended level of protection. It is of critical importance, however, to have accurate and current information about the ecological and economic state of these sensitive areas. In selected pasture and moor areas, methods for multisensor data fusion have being developed and tested. The results of this testing show which techniques are useful for pasture and moor monitoring at an operational level. A hierarchical method is used for extracting bog land classes with respect to the environmental protection goals. A highly accurate classification of the following classes was accomplished: deciduous- and mixed forest, coniferous forest, water, very wet areas, meadowland/farmland with vegetation, meadowland/farmland with partly vegetation, meadowland/ farmland without vegetation, peat quarrying with maximum of 50% vegetation, de- and regeneration stages. In addition, a change detection analysis is performed in comparison with the existing classification of 1994-96  and methods are developed to improve the classification strategy.