For the analysis of landscape changes, methods based on remote sensing data are often used. Although these methods now use Object-Based Image Analysis (OBIA) to go beyond a pixel-by-pixel comparison of images, remote sensing cannot capture the change in important semantic information that are not physical in nature, but required for planning processes, such as changes in land ownership or changes in zoning regulations. In contrast to the methods based on remote sensing data, this paper presents a novel method that enables large-scale change detection, documentation, and analysis at the level of individual landscape objects. The presented method is based on a spatio-temporal landscape information model. Even though the input data for the model are annual extracts of public authority information systems, the spatio-temporal representation of the data goes beyond the familiar "snapshot or object-lifetime approach" in Geographic Information Systems (GIS) by keeping the identity of landscape objects stable over time and tracking the evolution of each individual object. In addition to the conceptual model, which is specified using the Unified Modeling Language (UML) and the ISO 191xx family of standards, the paper will show how this approach can be implemented based on an objectrelational spatial database system. Several use cases demonstrate the benefits of the approach for planning.