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

M. Adamo, C. Tarantino, V. Tomaselli, V. Kosmidou, Z. Petrou, I. Manakos, R.M. Lucas, C.A. Mücher, G. Veronico, C. Marangi, C. Pasquale, P. Blonda

Research output: Contribution to journalArticleAcademicpeer-review

30 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1045-1067
JournalLandscape Ecology
Volume29
Issue number6
DOIs
Publication statusPublished - 2014

Keywords

  • soil-salinity
  • harmonization
  • datasets

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