EAGLE data model for land monitoring - Use cases and state of play

Transcription

1 EAGLE data model for land monitoring - Use cases and state of play Stephan Arnold, Barbara Kosztra, Gebhard Banko, Geoff Smith, Christoph Perger, Michael Bock, Geir-Harald Strand, Cesar Martinez, Julian Delgado EAGLE Group

2 Content Background and Motivation Criteria and Structure of Data Model Semantic decomposition EAGLE use cases Summary

3 Background and Motivation Harmonisation and integration of different data sources on LC / LU require: Clear and non-overlapping class definitions Synchronized time interval of data capture Comparable minimum mapping units Similar data quality

4 Background and Motivation Broad variety of applications and fields of work for LC/LU Specific emphasises on feature type subgroups result in Many different classification systems for LC/LU (on national or European level) Effects: Mixture of LC and LU classes Lack of comparability between nomenclatures hamper exchange of information between data sets

5 Differences in Forest definitions worldwide 100 Source: Comber et al. (2005) Crown cover density [%] 20 Minimum tree height in [m]

6 About ingredients and aromas

7 About aromas and ingredients: Finding the favorite red wine Do you remember your favorite wine? Not sure, seems they changed the label

8 Characterization Growth form homogeneous heterogenous Growth density slosed sparcely Soil condition wet dry acidic Use/Funktion Pasture Recreation Sport Air traffic Ecosystem type Wetland, swamp

9 Semantic overlap between class definitions Alpine Calluna heath, border between SE and FI Norwegian Forest and Landscape Institute. 9

10 Semantic overlap between class definitions SE: CLC 333 Sparsely vegetated FI: CLC 322 Moors and heathland Both interpretation correct, but: inconsistent nomenclature due to overlap of definitions

11 Classification and Interpretation Same term different meaning Same content different name + difference in interpreting the class definitions or tolerance of class definition => Comparable / Non-comparable data?

12 Criteria for data model Clear separation between LC and LU Scale independent Object-oriented description instead of classification Complete coverage of themes LC and LU Modelling of temporal phenomena Applicable on national and European level

13 Structure of the EAGLE matrix Information on landscape described with three separate blocks: I.) LAND COVER Components LCC Abiotic (Artificial + Natural), Vegetation, Water Surfaces II.) LAND USE Attributes LUA Agriculture, Forestry, Residential, Transportation etc. III.) CHARACTERISTICS CH spatial pattern, bio-physical parameters, cultivation measures, land management practices, status/condition etc.

14 CLC classes INSPIRE Conference, Sept 2017, Kehl/Strasbourg Structure of the EAGLE matrix I. LCC block II. LUA block III. CH block

15 Vision Implementation Land Monitoring Concept fit for multipurposes Object-oriented data model is applicable for 2 main approaches: Semantic comparison of definitions between different classification systems or single classes Descriptive characterization of landscape for collection and mapping of LC/LU for future initiatives

16 Integration schema for a future land monitoring framework in Europe [C O P E R N I C U S] European Level CLC Urban Atlas HR Layers LPIS LUCAS EAGLE Konzept National & Sub-National Level National (A) Land Monitoring Regional (a) Land Monitoring National CLC Regional (b) Land Monitoring National (B) Land Monitoring

17 De-Composition of landscape From classification to object-oriented description Fotos: Copyright Ursus Wehrli

18 De-Composition of CORINE Land Cover classes LC LU CH Parameter

19 Structure of the EAGLE data model Description of landscape with Land Cover Components (LCC), Characteristics (CH), Land Use Attributes (LUA) CH LC Unit (1..* LCCs) LU Attributes (HILUCS) ABIOTIC LCCs (Artificial/Natural) CH Characteristics BIOTIC LCCs (Vegetation) CH WATER LCCs CH

20 INSPIRE LC Data model INSPIRE data model EAGLE data model

21 EAGLE UML model overview INSPIRE data model

22 Example: Rural Settlement Land Cover Unit: village CH: Soil sealing degree 35% LCC: Conventional buildings CH: Built-up pattern discontinuous single houses LUA: Permanent residantial LCC: Trees, broad leaved CH: crown cover density 15%, av tree height 10m LCC: Herbaceous plants LUA: Agriculture; commercial production / own consumption CH: cultivation type: arable land / permanent grassland LCC: Open sealed surfaces LUA: Road transportation network B. Kosztra

23 EAGLE Matrix population and comparison tool (EMPACT)

24 EAGLE Use Cases

25 Copernicus Land Monitoring Service Development of future CORINE Land Cover+ (CLC+) Detailed core data set as basis for downstream products CLC CLC + EAG LE DM CLC Core Grid CLC Rur al

26 NATFLO Remote sensing based landscape objects for nature conservation Rhineland-Palatinate, Ministry of Environment [ ] Input data: Topographic reference data and digital terrain and surface models (land surveying authority) Combination of aerial and satellite imagery Object-based analysis and segmentation of aerial images and HR-nDSM Institute for Agroecology

27 NatFlo Mapping approach

28 Assigning EUNIS classes to landscape units ontology based reasoning

29 Semantic Analysis of the Feature Type Catalogue Recent Land Use in Land Surveying Data Model in Germany Dr. Christian Lucas

30 AdV Projekt LB/LN AAA-Anwendungsschema AAA-Basisschema AAA-Fachschema Komponenten OA OA OA OAB LB/LN Schleswig-Holstein. Der echte Norden. 30

31 AdV Projekt LB/LN EAGLE Matrix Elements for feature type Forest Schleswig-Holstein. Der echte Norden. 31

32 AdV Projekt LB/LN - Frequency of LC-Components in entire Analysis Natürliches Oberflächenmaterial 14% Holzige Vegetation 9% Krautige Pflanzen 10% Sukkulenten, Kakteen und andere Pflanzen 0% Künstliche Oberflächen und Bauwerke 61% Wasser und andere 6% Flüssigwasserflächen 6% Flechten, Moose, Algen Festwasserflächen 0% 0,02% Schleswig-Holstein. Der echte Norden. 32

33 Mapping of Urban Surfaces based on Hyperspectral Satellite Imagery Copernicus Forum Berlin 2017 Land Monitoring 2.0 Von der Klassifizierung zur objektorientierten Charakterisierung der Landschaft Uta Heiden, Marianne Jilge, Wieke Heldens

34 Use 4. of Enable EAGLE Synergies Model: with other EO missions Metadata Land cover classification Extension of Hierarchical Land Cover Components

35 Urban Surface Materials

36 The Eagle concept is Summary Instrument for semantic analysis and comparison of class definitions not a new classification system, but vehicle for semantic harmonisation and transformation, is INSPIRE compliant, can provide flexible framework for future mapping initiatives, helps to avoid redundant data capture, applicable on raster or polygon data, follows principle of integrating bottom-up / top-down approach in the European land monitoring process, supported by EEA, observed by Eurostat

37 Literatur on EAGLE Arnold, S., B. Kosztra, G. Banko, G. Smith, G. Hazeu, M. Bock, N. Valcarcel Sanz (2013):The EAGLE concept A vision of a future European Land Monitoring Framework. In: R. Lasaponara, L. Masini and M. Biscione (Eds.), Towards Horizon 2020: Earth Observation and Social Perspectives. 33th EARSeL Symposium Proceedings, S EARSeL and CNR, Matera. Arnold, S., Kosztra, B., Banko, G., Milenov, P., Smith, G., Hazeu, G. (2014): Explanatory Documentation of the EAGLE Concept. EEA, Copenhagen. Arnold, Smith, Hazeu, Kosztra, Perger, Banko, Strand, Valcarcel-Sanz, Bock (2015):The EAGLE Concept - A Paradigm Shift in Land Monitoring. In: Ahlqvist, Fritz, Janowicz (Eds.): Land Use and Land Cover Semantics - Principles, Best Practices, and Prospects,Taylor & Francis, CRC Press

38 EAGLE Use Cases AdV (german association of land surveying authorities): semantic analysis of feature type catalogue, introduction of new LC types Natur- and Env. Monitoring Nordrhein-Westfalen (NUMO NRW) Land Information System Austria (LISA) DLR: Detecting Urban Surfaces on Hyperspectral Satellite Imagery COBWEB project (citizen science data collection) Hungarian test case on CLC data derivation based on national data sources through EAGLE concept LandSense, IIASA, (citizen science project) IGN Spain: EAGLE geometric test case <you could be the next in this list> Project

39 Website:

40 EAGLE data model Version 2.3 EAGLE Meeting, March 2015, Frankfurt am Main

41 Thank you for your attention! EAGLE website: Fedaral Statistical Office (DESTATIS) Land Use Statistics Stephan Arnold Telefon: +49 (0) 228 / stephan.arnold@destatis.de