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Remote Sens. 2017, 9(3), 266; doi:10.3390/rs9030266

Habitat Mapping and Quality Assessment of NATURA 2000 Heathland Using Airborne Imaging Spectroscopy

1
Remote Sensing Department, Flemish Institute for Technological Research (VITO), Boeretang 200, 2040 Mol, Belgium
2
Research Institute for Nature and Forest (INBO), Kliniekstraat 25, 1070 Brussels, Belgium
3
Vision Lab, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk (Antwerp), Belgium
4
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, Droevendaalsesteeg 3, 6700 AA Wageningen, The Netherlands
5
Wageningen Environmental Research (Alterra), Droevendaalsesteeg 3, 6700 AA Wageningen, The Netherlands
6
Current affiliation: Institute of Avian Research (IFV), An der Vogelwarte 21, 26386 Wilhelmshaven, Germany.
7
Current affiliation: Research Foundation Flanders (FWO), Egmontstraat 5, 1000 Brussels, Belgium.
*
Author to whom correspondence should be addressed.
Academic Editors: Jose Moreno, Clement Atzberger and Prasad S. Thenkabail
Received: 23 January 2017 / Revised: 6 March 2017 / Accepted: 12 March 2017 / Published: 15 March 2017
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Abstract

Appropriate management of (semi-)natural areas requires detailed knowledge of the ecosystems present and their status. Remote sensing can provide a systematic, synoptic view at regular time intervals, and is therefore often suggested as a powerful tool to assist with the mapping and monitoring of protected habitats and vegetation. In this study, we present a multi-step mapping framework that enables detailed NATURA 2000 (N2000) heathland habitat patch mapping and the assessment of their conservation status at patch level. The method comprises three consecutive steps: (1) a hierarchical land/vegetation type (LVT) classification using airborne AHS imaging spectroscopy and field reference data; (2) a spatial re-classification to convert the LVT map to a patch map based on life forms; and (3) identification of the N2000 habitat type and conservation status parameters for each of the patches. Based on a multivariate analysis of 1325 vegetation reference plots acquired in 2006–2007, 24 LVT classes were identified that were considered relevant for the assessment of heathland conservation status. These labelled data were then used as ground reference for the supervised classification of the AHS image data to an LVT classification map, using Linear Discriminant Analysis in combination with Sequential-Floating-Forward-Search feature selection. Overall classification accuracies for the LVT mapping varied from 83% to 92% (Kappa ≈ 0.82–0.91), depending on the level of detail in the hierarchical classification. After converting the LVT map to a N2000 habitat type patch map, an overall accuracy of 89% was obtained. By combining the N2000 habitat type patch map with the LVT map, two important conservation status parameters were directly deduced per patch: tree and shrub cover, and grass cover, showing a strong similarity to an independent dataset with estimates made in the field in 2009. The results of this study indicate the potential of imaging spectroscopy for detailed heathland habitat characterization of N2000 sites in a way that matches the current field-based workflows of the user. View Full-Text
Keywords: hyperspectral; habitat mapping; species mapping; NATURA 2000; heathland; conservation status; classification; Calluna vulgaris; tree encroachment; grass encroachment hyperspectral; habitat mapping; species mapping; NATURA 2000; heathland; conservation status; classification; Calluna vulgaris; tree encroachment; grass encroachment
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Haest, B.; Vanden Borre, J.; Spanhove, T.; Thoonen, G.; Delalieux, S.; Kooistra, L.; Mücher, C.A.; Paelinckx, D.; Scheunders, P.; Kempeneers, P. Habitat Mapping and Quality Assessment of NATURA 2000 Heathland Using Airborne Imaging Spectroscopy. Remote Sens. 2017, 9, 266.

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