Expert knowledge for translating land cover/use maps to General Habitat Categories (GHC)

Authors Organisations
  • Maria Adamo(Author)
    National Research Council – Institute of Intelligent Systems for Automation (CNR-ISSIA)
  • Cristina Tarantino(Author)
    National Research Council – Institute of Intelligent Systems for Automation (CNR-ISSIA)
  • Valeria Tomaselli(Author)
    Institute of Biociences and BioResources
  • Vasiliki Kosmidou(Author)
    Information Technologies Institute (ITI),
  • Zisis Petrou(Author)
    Information Technologies Institute (ITI),
  • Ioannis Manakos(Author)
    Information Technologies Institute (ITI),
  • Richard Lucas(Author)
  • Caspar A. Mücher(Author)
    Wageningen University and Research Centre
  • Giuseppe Veronico(Author)
    Institute of Biociences and BioResources
  • Carmela Marangi(Author)
    Institute for Applied Mathematics
  • Vito De Pasquale(Author)
    Planetek Italia
  • Palma Blonda(Author)
    National Research Council – Institute of Intelligent Systems for Automation (CNR-ISSIA)
Type Article
Original languageEnglish
Pages (from-to)1045-1067
Number of pages23
JournalLandscape Ecology
Volume29
Issue number6
Early online date16 Apr 2014
DOI
Publication statusPublished - 01 Jul 2014
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Abstract

Monitoring biodiversity at the level of habitats and landscape is becoming widespread in Europe and elsewhere as countries establish international and national habitat conservation policies and monitoring systems. Earth Observation (EO) data offers a potential solution to long-term biodiversity monitoring through direct mapping of habitats or by integrating Land Cover/Use (LC/LU) maps with contextual spatial information and in situ data. Therefore, it appears necessary to develop an automatic/semi-automatic translation framework of LC/LU classes to habitat classes, but also challenging due to discrepancies in domain definitions. In the context of the FP7 BIO_SOS (www.biosos.eu) project, the authors demonstrated the feasibility of the Food and Agricultural Organization Land Cover Classification System (LCCS) taxonomy to habitat class translation. They also developed a framework to automatically translate LCCS classes into the recently proposed General Habitat Categories classification system, able to provide an exhaustive typology of habitat types, ranging from natural ecosystems to urban areas around the globe. However discrepancies in terminology, plant height criteria and basic principles between the two mapping domains inducing a number of one-to-many and many-to-many relations were identified, revealing the need of additional ecological expert knowledge to resolve the ambiguities. This paper illustrates how class phenology, class topological arrangement in the landscape, class spectral signature from multi-temporal Very High spatial Resolution (VHR) satellite imagery and plant height measurements can be used to resolve such ambiguities. Concerning plant height, this paper also compares the mapping results obtained by using accurate values extracted from LIght Detection And Ranging (LIDAR) data and by exploiting EO data texture features (i.e. entropy) as a proxy of plant height information, when LIDAR data are not available. An application for two Natura 2000 coastal sites in Southern Italy is discussed.

Keywords

  • biodiversity monitoring, General Habitat Categories, VHR satellite imagery