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Habitat mapping of coastal wetlands using expert knowledge and Earth observation data
Author(s) -
Adamo Maria,
Tarantino Cristina,
Tomaselli Valeria,
Veronico Giuseppe,
Nagendra Harini,
Blonda Palma
Publication year - 2016
Publication title -
journal of applied ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.503
H-Index - 181
eISSN - 1365-2664
pISSN - 0021-8901
DOI - 10.1111/1365-2664.12695
Subject(s) - habitat , environmental resource management , habitats directive , land cover , water framework directive , earth observation , wetland , remote sensing , ground truth , thematic map , biodiversity , environmental science , land use , geography , ecology , computer science , cartography , satellite , water quality , engineering , machine learning , biology , aerospace engineering
Summary 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.