Remote sensing of scattered Natura 2000 habitats using a one-class classifier |
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| Affiliation: | 1. Institute for Geography and Geoecology, KIT Karlsruhe, Kaiserstr. 12, 76131 Karlsruhe, Germany;2. Institute of Geography, FAU Erlangen-Nuernberg, Wetterkreuz 15, 91058 Erlangen, Germany;3. Institute for Geoinformatics and Remote Sensing, University of Osnabrueck, Barbarastraße 22b, 49076 Osnabrueck, Germany;1. Laboratory of Aquatic Ecology, Evolution and Conservation, University of Leuven, Charles Deberiotstraat 32, Leuven 3000, Belgium;2. Tour du Valat Research Centre for Mediterranean Wetlands, Le Sambuc, Arles 13200, France;3. Laboratoire de Botanique, Mycologie et Environnement, Université Mohammed V, Faculté des Sciences de Rabat, 4 Avenue Ibn Battouta, Rabat BP 1014, RP, Morocco;1. Department of Electronics, Quaid-i-Azam University, Islamabad, Pakistan;2. Center for Advanced Mathematics and Physics, National University of Science and Technology, Islamabad, Pakistan;1. Universidad Politécnica de Madrid, Research Group SILVANET, Ciudad Universitaria, 28040, Madrid, Spain;2. Universidad Católica de Ávila, 05005, Avila, Spain;3. University of Eastern Finland, Faculty of Forest Sciences, P. O. Box 111, Joensuu, Finland;4. Universidad Carlos III de Madrid, Department of Thermal and Fluids Engeneering, Madrid, Spain |
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| 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 km2 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. |
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| Keywords: | Natura 2000 Maxent Multispectral remote sensing Nature conservation Habitat types One-class classification |
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