A rapidly increasing demand for accurate and updated geo-spatial information requires advanced techniques for
extracting and providing relevant information. The presented work was carried out in a 3654 km<sup>2</sup> sized area in the region
of Stuttgart/Germany, characterized by high dynamic growth and steady economic development. The project Biotope
Information and Management System (BIMS) provides and monitors aggregated spatial units relevant for regional
planning tasks. We discuss experiences from the first phase of the project, in which we developed an adaptive per-parcel
approach for delineating elementary units using SPOT-5 MS data (5 m GSD). The target geometry was pre-defined by
digital cadastre data from 2005, but not all existing boundaries were retained: some were dismissed, others introduced.
We followed a threefold strategy: (1) a parcel with internal homogeneity remains the same; (2) neighboring parcels with
similar spectral behavior are merged; (3) a single, heterogeneous parcel is split and new boundaries are generated. By
this, the initial number of units dropped to one fourth. The majority of the units were merged due to trans-boundary
homogeneity, one fourth was subdivided. Assets of this approach are its cost-efficiency, the high matching degree of the
produced geometry and the transferability to similar cases because of the standardized character of the data sets involved.
The main concept behind this paper is that pattern and processes are linked in a mutual way. In the last decades landscape ecology was dominated by quantitative descriptions (landscape metrics) of the landscape under concern and its components. Now there is a growing interest in the cause-effect-relationships between these environmental characteristics. High-resolution aerial photography hold an important amount of valuable information, but until recently only a little proportion of the entire information was usually used in scientific analyses due to conceptual and technical limitations. In this paper we present results derived with a multi-scale image segmentation approach and it is demonstrated how this approach allows for an identification of pattern at several scales simultaneously. First results testify that this is a suitable method for the delineation of meaningful landscape elements and subsequently for landscape monitoring, particularly if dealing with complex or small-scaled pattern. It is shown that hierarchically linked objects are more suitable for monitoring than pixels although the necessity for a comprehensive methodology for object-based change detection arises.