DOI: 10.5061/dryad.9h9q2
title: Data from: Habitat mapping of coastal wetlands using expert knowledge and Earth observation data

description: Long-term habitat mapping and change detection are essential for the
 management of coastal wetlands as well as for evaluating the impact of
 conservation policies. Earth observation (EO) data and techniques are a
 valuable resource for long-term habitat mapping. Although the use of EO
 data is well developed for the automatic production of land cover (LC)
 maps, this is not the same for habitat maps, which are highly related to
 biodiversity. In a previous paper, we used the Food and Agricultural
 Organization (FAO) Land Cover Classification System (LCCS) environmental
 attributes (e.g. water quality, lithology, soil surface aspect) for
 LC-to-habitat class translation. However, these environmental attributes
 are often not openly available, not updated or are missing. This paper
 offers an alternative, knowledge-based solution to automatic habitat
 mapping. When only expert rules and EO data are used, the final overall
 map accuracy, which is obtained by comparing reference ground truth
 patches to the ones depicted in the output map, is lower (75·1%) than the
 accuracy obtained using environmental attributes alone (97·0%). Some
 ambiguities that still remain in habitat discrimination are resolved by
 integrating the use of LCCS environmental attributes (if available) and
 expert rules. In this paper, we use very high-resolution (VHR) satellite
 data and LIDAR data. LC classes are labelled according to the LCCS
 taxonomy, which offers a framework to integrate EO data with in situ and
 ancillary data. Output habitat classes are labelled according to the
 European Habitats Directive (92/43 EEC Directive) Annex I habitat types
 and Eunis habitat classification. Two Natura 2000 coastal wetland sites in
 southern Italy are considered. Synthesis and applications. In this paper,
 we study the exploitation of ecological rules on vegetation pattern, plant
 phenology and habitat geometric properties for automatic translation of
 land cover (LC) maps to habitat maps in coastal wetlands. The methodology
 is useful for relatively inaccessible sites (e.g. wetlands) as it does not
 require in-field campaigns (generally costly) but only the elicitation of
 ecological expert rules. This can support site (e.g. Natura 2000) managers
 in long-term automatic habitat mapping. Habitat changes can be
 automatically detected by comparing map pairs, and trends can be
 quantified. This is particularly useful to satisfy the commitments of the
 European Habitats Directive (92/43/EEC), which requires Member States to
 take measures to maintain as, or restore to, favourable conservation
 status those natural habitat types and species of community interest that
 are listed in the Annexes to the Directive.
description: Worldview-2 image for Le
 Cesine site acquired on 2013-06-18Worldview-2 satellite
 image for Le Cesine site acquired on 2013-06-18. RGB-Visible composition.
 Spatial Resolution: 2 meters. - VHR satellite data were provided through
 the European Space Agency (ESA) Data Warehouse Policy within the framework
 of the FP7/SPA.2010.1.1-04 EU project titled "BIOdiversity
 multi-source monitoring system: from Space tO Species (BIO_SOS)",
 www.biosos.eu -WV-2_2013-06-18_LECESINE_RGB.tifWorldview-2 image for Le Cesine site acquired on 2013-11-07Worldview-2 satellite image of Le Cesine site acquired on 2013-11-07. RGB-Visual composition. Spatial resolution: 2 meters. - VHR satellite data were provided through the European Space Agency (ESA) Data Warehouse Policy within the framework of the FP7/SPA.2010.1.1-04 EU project titled "BIOdiversity multi-source monitoring system: from Space tO Species (BIO_SOS)", www.biosos.eu -WV-2_2013-11-07_LECESINE_RGB.tifLand Cover map for Le Cesine in FAO-LCCS taxonomyLand Cover map for Le Cesine in FAO-LCCS taxonomy. Spatial resolution: 2 meters. The README file contains the look-up.LCCS_CESINE_rast_2m.tifHabitat Map for Le CesineHabitat Map for Le Cesine site produced applying pattern zonation rules as Expert Knowledge. Spatial Resolution: 2 meters. The README file contains the look-upHabitatMap_Cesine_ExpertKnowledge_patternzonation.tif