EIONET Action Group on Land Monitoring in Europe The EAGLE concept – a paradigm shift in European land monitoring Move from CORINE Land Cover towards a new harmonized approach of object-oriented landscape mapping Barbara Kosztra Institute of Geodesy, Cartography and Remote Sensing (FÖMI) Hungary Stephan Arnold Federal Statistical Office (Destatis) Germany Elise Järvenpää & Markus Törmä Finnish Environment Institute (SYKE) Finland 4a Characterizing bread 7 EAGLE KEY MESSAGES 1 Given situation • Many applications lead to numerous classification systems • Most classification systems contain mixture of LC and LU information • Redundant data collection on EU and national level • Lack of compatibility between European and national datasets • Harmonization between top-down and bottom-up approches is needed to meet... different data collection methods, different scales, tailored-to-purpose class definitions, lack of completeness for either LC or LU information Classification • Possible applications of the EAGLE model: Bread ≠ Bread Characterization • Analytical decomposition of class definitions Outer Appearence • weight • size • shape Inner details • grainsize • density Ingredients • salt • wheat / rye • water • yiest • E 510, … Color • light • dark Other characteristics • Bio-certified • GMO-free Use • Food, Alimentation • Semantic translation between different classification systems • Collection and mapping of land related information Conceptual basis for harmonized future European Land Monitoring Framework 4b Characterizing grassland 8 LandIntegration monitoring vision Scheme of Europeanframework Land Monitoring Framework 2 Seed questions • How to strictly separate LC and LU information in a model? Classifications • European Level C O P E R Urban CLC Atlas N I C HR Layers • What information can be captured by remote sensing? • How to apply object-oriented data modelling for European and national land monitoring? U S BioPhys Par I A C S LUCAS LPIS Characterization Growth structure • homogenous • heterogenous Growth density • closed • sparse Moisture • Wet soil • Surface water Use • agriculture • conservation • sports Management • mowing • grazing Ecosystem type • Inland marsh EAGLE Concept • How to decompose and recombine LC and LU information? National Level National (A) Land Monitoring 3 Criteria for data model Sub-National Level • Object-oriented description instead of classification • Separation of LC and LU information • Describe land cover in a mutually exclusive and comprehensive way • Scale independence • Semantic translation between classification systems • INSPIRE compliance • Support both bottom-up and top-down initiatives National CLC Regional (a) Land Monitoring National (B) Land Monitoring Regional (b) Land Monitoring 9 Structure of EAGLE data model 5 Decomposing objects Structure of the EAGLE data of model Structure the Land Cover Dataset (1..* LCUs) Describing an object by decomposition into components and further characterization EAGLE data model LUA Land Use Attribute (HILUCS EAGLE-extended) CH Storage of parameterized data • Handling of multi-dimensional /-temporal aspects • Flexibility for extension by new model elements + Characteristics ABIOTIC LCCs Artificial/Natural • LCU Land Cover Unit (1..* LCCs) + + Grassland ≠ pasture ≠ lawn ≠ natural grassland CH + -- ...... - ... BIOTIC LCCs Vegetation CH + - WATER LCCs ... ... ... ... CH + - ... ... ... ... Land Cover Unit (LCU) is formed by one or several Land Cover Components (LCC), further described with Characteristics (CH), and with one or many Land Use Attribute (LUA). © Ursus Wehrli 6 Structure of EAGLE matrix III. Landscape Characteristics II. Land Use Attributes I. Land Cover Components 6a Decomposing built-up area 6b Decomposing agricultural land 6c Decomposing wetland I. Land cover components (LCC): - conventional buildings - broadleaved trees - herbaceous plants - open sealed surfaces I. Land cover components (LCC): - regular graminaceous - broadleaved trees - bushes I. Land cover components (LCC): - inland water body - reeds - regular shrubs II. Land use attributes (LUA): - permanent residential - agriculture for commercial / for own consumption - road network II. Land use attributes (LUA): - agriculture for commercial production - forestry II. Land use attributes (LUA): - nature conservation, protected site III. Characteristics (CH): - soil sealing degree = 35% - spatial pattern = discont. single houses Photo © György Büttner III. Characteristics (CH): - ecosystem type = inland marshes - salinity = fresh water - water regime = permanent - spatial pattern = mosaic Photo © Barbara Kosztra III. Characteristics (CH): - linear landscape pattern = hedgerows - cultivation pattern = crop rotation Photo © György Büttner Use case I. Enhancement of CLC nomenclature guidelines TASK APPROACH Mixture of LC, LU, other characteristics and thresholds in CLC definitions results in difficulties to match national features types to European classes in a bottom-up CLC production process. Defining elements in CLC classes need to be identified and categorized through semantic analysis. The EAGLE matrix is used to decompose CLC classes to LCC, LUA, CH and to reveal inconsistencies like semantic gaps and overlaps in the class definitions. Example: CLC 111 Continuous urban fabric “Areas mainly occupied by dwellings and buildings used by administrative/public utilities. Most of the land is covered by structures and the transport network. Buildings, roads and artificially surfaced areas cover more than 80 % of the total surface. Nonlinear areas of vegetation and bare soil are exceptional. ” LC / LU / CH / Threshold RESULT New structure of CLC class definitions: „This heading is applicable / not applicable for“ expressing the applicable landscape situations and land uses „This heading includes / excludes“ expressing the relevant land cover components Use case II. Extractable LC information from Sentinel-2 Satellite data from the up-coming Sentinel-2 mission are useful for land monitoring purposes. By visual interpretation or automatic analysis the imagery can be used to detect and monitor the following landscape properties of EAGLE data model: Abiotic • Artificial/Sealed: GIO Land HRL Soil sealing • Size of the buildings or nature type using visual interpretation • Natural/Consolidated: Bare rock and Hardpan could be possible to classify Vegetation / Biotic • Woody / Herbaceous / Lichen&Moss / Succulent -classification should be possible • Woody/Trees: Coniferous & Broadleaf • SpeciesType: would require spectrometer • CrownCoverDensity: HRL Forest • ForestHistoryType & ClearCut: multiyear time series • Start & EndGrowingSeason: seasonal time series of e.g. NDVI • Wetness: Tasselled Cap-transformation • Irrigation & Fertilizing: visual interpretation • AgriculturalCultivationType/PermanentGrassland: HRL Grassland Water • Liquid: HRL Water • Further division of Liquid is possible using visual interpretation • Solid: PermanentSnow & IceAndGlacier Other • Spatial pattern: Partly possible to automatize, but generally requires visual interpretation Conclusion Sentinel-2 kind of satellite images are not able to provide all information for EAGLE data model, so other information sources like existing GIS databases, in-situ measurements or interpretation of VHR-images are also needed. Results presented in the poster contain contribution from all members of the voluntary EAGLE group: Gebhard Banko, Christoph Perger (Austria), Tomas Soukup (Czech Republic), Markus Törmä , Elise Järvenpää (Finland), Stephan Arnold, Michael Bock, Stefan Kleeschulte, Andreas Littkopf (Germany), Barbara Kosztra, Gergely Maucha (Hungary), Gerard Hazeu (The Netherlands), Geir-Harald Strand (Norway), Julián Delgado Hernández, Roger Milego, César Martínez Izquierdo, Alejandro Simon Colina, Nuria Valcarcel Sanz (Spain), Charlotte Steinmeier (Switzerland), Geoff Smith (UK) Contact EAGLE web: http://sia.eionet.europa.eu/EAGLE EAGLE e-mail: [email protected] Authors: [email protected], [email protected], [email protected], [email protected]
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