Object-based classification of quick bird data using ancillary information for the detection of forest types and NATURA 2000 habitats

Abstract

The detection of forest types and habitats is of major importance for silvicultural management as well as for the monitoring of biodiversity in the context of NATURA 2000. For these purposes, the presented study applies an object-based classification method using VHR QuickBird data at a test site in the pre-alpine area of Bavaria (southern Germany). Additional geo-data and derived parameters, such as altitude, aspect, slope, or soil type, are combined with information about forest development and integrated into the classification using a fuzzy knowledgebase. Natural site conditions and silvicultural site conditions are considered in this rule-base. The results of the presented approach show higher classification accuracy for the classification of forest types using ancillary information than can be reached without additional data. Moreover, for forest types with very distinctly defined ecological niches (e. g. alluvial types of forest), a better characterisation and integration of rules is possible than for habitats with very wide ecological niches. Hence, classification accuracies are significantly higher when these rules are applied. In a second step NATURA 2000 habitat types and selected habitat qualities are derived from the classified forest types. However, the share of habitat qualities varies with an altering scale. This difficulty should be addressed in further research of NATURA 2000 monitoring.