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 of target habitats in a complex mosaic of vegetation types not relevant for conservation planning. Limiting the necessary ground reference to a small sample of target habitats would greatly reduce and therefore support the field mapping effort. We thus aim to answer in this study the question: can semi-automated remote sensing methods help to map such patches without the need of ground references from sites not relevant for nature conservation? Approaches able to fulfill this task may help to improve the efficiency of 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 four habitat types across a patchy landscape of 1000 km(2) near Munich, Germany. This task was conducted using the low number of 125 ground reference points only along with easily available multi-seasonal RapidEye satellite imagery. Encountered problems include the non-stationarity of habitat reflectance due to different phenological development across space, continuous transitions between the habitats and 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 simple and affordable approach can thus be recommended for a first survey of previously unmapped areas, as a tool for identifying potential gaps in existing habitat inventories and as a first check for changes in the distribution of habitats. (C) 2014 Elsevier B.V. All rights reserved.

Publication
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION

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