OBJECT BASED IMAGE ANALYSIS TO SUPPORT ENVIRONMENTAL MONITORING UNDER THE EUROPEAN HABITAT DIRECTIVE: A CASE STUDY FROM DECOVER

Abstract. DeCOVER serves as a national extension of the European Global Monitoring for Environment and Security (GMES) initiative. It was initiated to develop land cover information services adapted to German user needs. One of its three service developments pillars is the application of Remote Sensing to support environmental monitoring schemes under the European Habitats Directive.
Within two DeCOVER test sites located in North-Rhine Westphalia/Germany an object-based indicator classification approach is currently being developed to monitor heath habitats of importance under the Habitats Directive. While many previous Remote Sensing projects have focused on the discrete classification of habitat types to replace fieldwork, our approach is embedded in a strong operational context to a) focus and direct fieldwork efforts by pre-field visit assessment of habitat changes (change detection) and b) support fieldwork by contributing quality parameters and GIS-ready geometries.
Using Geoeye satellite data (VHR component) and RapidEye satellite images (Multi-temporal HR component) together with existing habitat and biotope maps (knowledge and post-classification component) an image analysis approach is realised using object-based classification routines based on data mining tools to derive training information. To extract meaningful objects of heath-, sand- and grassland from the VHR-data, training sample areas have to be assigned. Thresholds and appropriate features for describing these samples are analysed by statistical algorithms and are used in the following classification. A multi-temporal approach for the acquisition of tree habitat areas integrates two RapidEye scenes into the classification process. To validate classification accuracies and potential transects were sampled in the field and analyzed for their structural composition using top view field photos of 1m². First results demonstrate the realistic option to directly support the fieldwork or reduce its post-processing costs.