Remote sensing of scattered Natura 2000 habitats using a one-class classifier

Abstract

Mapping of habitats with relevance for nature conservation involves the identification of patches oftarget habitats in a complex mosaic of vegetation types not relevant for conservation planning. Limitingthe necessary ground reference to a small sample of target habitats would greatly reduce and thereforesupport the field mapping effort. We thus aim to answer in this study the question: can semi-automatedremote sensing methods help to map such patches without the need of ground references from sites notrelevant for nature conservation? Approaches able to fulfill this task may help to improve the efficiencyof large scale mapping and monitoring programs such as requested for the European Habitat Directive.In the present study, we used the maximum-entropy based classification approach Maxent to map fourhabitat types across a patchy landscape of 1000 km2near Munich, Germany. This task was conductedusing the low number of 125 ground reference points only along with easily available multi-seasonalRapidEye satellite imagery. Encountered problems include the non-stationarity of habitat reflectancedue to different phenological development across space, continuous transitions between the habitatsand the need for improved methods for detailed validation.The result of the tested approach is a habitat map with an overall accuracy of 70%. The rather simpleand affordable approach can thus be recommended for a first survey of previously unmapped areas, asa tool for identifying potential gaps in existing habitat inventories and as a first check for changes in thedistribution of habitats.