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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

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Page 1: EAGLE data model for land monitoring Use cases and state ... · INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg Information on landscape described with three separate blocks:

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

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paceArnold et al.: EAGLE Concept

INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

Content

Background and Motivation

Criteria and Structure of Data Model

Semantic decomposition

EAGLE use cases

Summary

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paceArnold et al.: EAGLE Concept

INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

Preamble: Who is „EAGLE“?

EAGLE = EIONET Action Group on Land Monitoring in Europe

Participants are

Land Monitoring experts and

Representatives of National Reference Centres (NRC)

for Land Cover in the EEA´s EIONET

(European Environmental Information and Observation Network)

Established in 2009 as self-initiative

Focus on object-oriented data modelling

Open „membership“ based on own commitment

Firstly no external funding, meanwhile supported by EEA funding, waiting

for continuation …

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paceArnold et al.: EAGLE Concept

INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

Harmonisation and integration of different data sources on land cover and land use require:

Clear and non-overlapping class definitions

Synchronized time interval of data capture

Comparable minimum mapping units

Similar data quality

Background and Motivation

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INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

Many applications of LC/LU data lead to various different classification systems (on national or European level)

Effects :

Mixture of LC and LU classes

Specific fields of work have own emphasis on thematic

categories

Lack of comparability between nomenclatures hamper

exchange of information between data sets

Background and Motivation

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Crown cover density [%]

Minimum tree height in [m]

2 4 6 8 10 12 14

20

40

60

80

100

Differences in „Forest“ definitions worldwide

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paceArnold et al.: EAGLE Concept

INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

Crown cover density [%]

Minimum tree height in [m]

2 4 6 8 10 12 14

20

40

60

80

100 Source: Comber et al. (2005)

Differences in „Forest“ definitions worldwide

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paceArnold et al.: EAGLE Concept

INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

About aromas and ingredients: Finding the favorite red wine

Do youremember yourfavorite wine?

Not sure, seems theychanged the

label…

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paceArnold et al.: EAGLE Concept

INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

About ingredients and aromas…

You know how it tasts/looks like,

but is difficult to describe

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paceArnold et al.: EAGLE Concept

INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

About ingredients and aromas…

To describe taste of vine,

we use known aromas to

characterize it.

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INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

About ingredients and aromas…

In the best case,

we understand each others

descriptions…

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12

„Tropical Night “

What exactly is a tropical night ?

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Meteorological Parameter

13

• Temperature

• Humidity

• Air Pressure

• Precipitation

We describe itwith measurableparameters. Likewise we shouldbe able to do so with Land Cover.

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INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

Ein Brot (dt.) is a bread (en.) est un pain(fr.) é pane (it.) es pan (sp.) ist ein Brot …

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paceArnold et al.: EAGLE Concept

INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

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INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

Characterization

Ingredients Salt

Wheat

Water

Yiest

E 510, …

Weight 1,5 Kilo

3 pounds

Color light

dark brown

Other details biocertificated

vegan

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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

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Semantic overlap between class definitions

18

© Norwegian Forest and Landscape Institute.

Alpine Calluna heath, border between SE and FI

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INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

CLC 333

“Sparsely vegetated”

CLC 322

“Moors and heathland”

Both products correct,

but:

inconsistent nomenclature

due to overlap of definitions

Semantic overlap between class definitions

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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 ?

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INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

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

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INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

De-Composition of landscape

From classification to object-oriented description

Fotos: © Copyright Ursus Wehrli

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INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

LC

Parameter

LU

CH

De-Composition of CORINE Land Cover classes

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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.

Structure of the EAGLE matrix

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I. LCC block II. LUA block III. CH block

CLC

cla

sses

Structure of the EAGLE matrix

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INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

Matrix as semantic comparison tool

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Description of landscape with …

Land Cover Components (LCC),

Characteristics (CH),

Land Use Attributes (LUA)

CH

- ...

- ...

- ...

- ...

CH

- ...

- ...

- ...

- ...

LU Attributes

(HILUCS)

LC Unit

(1..* LCCs)

ABIOTIC LCCs

(Artificial/Natural)

BIOTIC LCCs

(Vegetation)

WATER LCCs

CH

- ...

- ...

Ch

ara

cte

ristic

s

CH

+

Structure of the EAGLE data model

+ + +

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INSPIRE LC Data model

INSPIRE

data model

EAGLE

data model

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paceArnold et al.: EAGLE Concept

INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

EAGLE UML model overview

INSPIRE

data model

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Determining spatial reference objects

Polygons: single objects, distinct feature types, individual are sizes

Grid cell: descriptive characterization, standardized spatial reference unit

Fol

ie

30

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Land cover components (LCC):

Conventional buildings

Trees, broad leaved

Herbaceous plants

Open sealed surfaces

Land use attributes (LUA):

Permanent residantial

Agriculture; own consumption

Road transportation network

Further characteristics (CH):

Soil sealing degree = 35%

Built-up pattern = discontinuous, single houses

Agricultural measure: Mowing

© B. Kosztra

Example: „Rural Settlement“

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Online Tool: EAGLE Matrix population andcomparison tool (EMPACT)

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EAGLE Matrix population and comparison tool(EMPACT)

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EAGLE Matrix population and comparison tool(EMPACT)

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paceArnold et al.: EAGLE Concept

INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

Tools: EAGLE Matrix population andcomparison Tool (EMPACT)

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INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

European

Level

National (A)

Land Monitoring

Regional (a)

Land Monitoring

National

CLC

CLC

National

&

Sub-National Level

National (B)

Land Monitoring

Regional (b)

Land Monitoring

EAGLE

Konzept

[C O P E R N I C U S]

LPISHR

Layers

Urban

AtlasLUCAS

Integration schema for a future land monitoring framework in Europe

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INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

Vision

Implementation Land Monitoring Concept fit for multi-

purposes

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

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EAGLE Use Cases

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Copernicus Land Monitoring Service

Development of future CORINE Land Cover+ (CLC+)

Detailed core data set as basis for downstream products

CLC CLC+

EAGLE DM

CLC Core

CLC Rural

Grid

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Institute for Agroecology

NATFLORemote sensing based landscape objects for natureconservation

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

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NatFlo Mapping approach

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Indicator orienteddescription of objectswith environmental parameters andcharacteristics derivedfrom input data

Database structure and management

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Assigning EUNIS classes to landscape units

ontology basedreasoning

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Semantic Analysis of the Feature Type Catalogue

„Recent Land Use“ in Land Surveying Data Model

in Germany

Dr. Christian Lucas

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Schleswig-Holstein. Der echte Norden. 45

AdV Projekt LB/LN

Komponenten

OA 2 2 1 0 3

OA 2 2 2 1 1

OA 1 1 0 0 1

AAA-AnwendungsschemaAAA-Anwendungsschema

AAA-FachschemaAAA-Fachschema

AAA-BasisschemaAAA-Basisschema

OAB LB/LNOAB LB/LN

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Schleswig-Holstein. Der echte Norden. 46

AdV Projekt LB/LN

EAGLE Matrix

Elements for

feature type

„Forest“

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Schleswig-Holstein. Der echte Norden. 47

AdV Projekt LB/LN

Künstliche Oberflächen und

Bauwerke61%

Natürliches Oberflächenmaterial

14%

Holzige Vegetation9%

Krautige Pflanzen10%

Sukkulenten, Kakteen und andere Pflanzen

0%

Flechten, Moose, Algen

0%

Flüssigwasserflächen6%

Festwasserflächen0,02%

Wasser und andere6%

- Frequency of LC-Components in entire Analysis

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Schleswig-Holstein. Der echte Norden. 48

AdV Projekt LB/LN

- Frequency of LU-Components in entire Analysis

Primärer Wirtschaftssektor

21%

Sekundärer Wirtschaftssektor

(Industrie und Handwerk)

15%

Transportnetzwerke, Logistik und technische

Einrichtungen37%

Wohnen5%

Andere Nutzungen

2%

Binnenwassernutzung0%

Andere7%

Tertiärer Wirtschaftssektor(Dienstleistungen)

20%

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AdV Projekt LB/LN

Frequency of LC-Components in feature type “Sport,

Leisure and Recreation”

Künstliche Oberflächen und Bauwerke

40%Natürliches Oberflächenmaterial

5%

Holzige Vegetation24%

Krautige Pflanzen30%

Sukkulenten, Kakteen und andere Pflanzen

0%

Flechten, Moose, Algen0%

Flüssigwasserflächen1%

Andere1%

Festwasserflächen

0%

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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

[email protected]

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Kunststoffdach

(Polyethylen)

Roter Dachziegel

Rote Splittflächen

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4. Enable Synergies with other EO missions

Metadata – Land cover classificationUse of EAGLE Model: Extension of HierarchicalLand Cover Components

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Urban Surface Materials

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Urban Surface Materials – Application Potential

• Urbane Land Cover Mapping

(Roessner et al. 2001, Xu and Gong 2007, Heiden et al. 2012, Okujeni et al. 2013, Priem and

Canters 2016)

• Detection of Hazardous Materials: z.B. Asbest

(Clark et al. 2001, Marino et al. 2001, Bassani et al. 2007, Cavalli et al. 2009,

Frassy et al. 2014)

• Urban Planing / Spatial Indicators

(Heiden et al. 2003, Bochow et al. 2007, Heldens et al. 2009,

Demarchi et al. 2012, Heiden, et al. 2012, Behling et al. 2015)

• Condition of urban Vegetation (Lacan and McBride 2008, Zhang and Qiu 2012, Alonzo et al. 2014,

Delegido et al. 2014, Gu et al. 2015, Alonzo et al. 2016, Brackx et al. 2017)

• Urban micro climate / heat spots

(Oltra-Carrió et al. 2012, Sobrino et al. 2012a, He et al. 2015, Heldens et al. 2017)

• Etc.

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INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

SummaryThe Eagle concept …

Instrument for semantic analysis and comparisonof 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 approachin the European land monitoring process,

supported by EEA, observed by Eurostat

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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 futureEuropean Land Monitoring Framework. In: R. Lasaponara, L. Masini and M. Biscione (Eds.), Towards Horizon2020: Earth Observation and Social Perspectives. 33th EARSeLSymposium Proceedings, S. 551-568. 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

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EAGLE Use Cases

Masterplan of AdV (german association of land surveying authorities):

separation and completion of LC and LU features types

Natur- und Umwelt Monitoring System Nordrhein-Westfalen (NUMO

NRW)

COBWEB project (citizen science data collection)

Hungarian test case on CLC data derivation based on national data

sources through EAGLE concept

LandSense, IIASA

IGN Spain: EAGLE geometric test case

… <you could be the next in this list> Project

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Website:

http://land.copernicus.eu/eagle

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EAGLE Meeting, March 2015, Frankfurt am Main

EAGLE data model Version 2.3

http://land.copernicus.eu/eagle

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paceArnold et al.: EAGLE Concept

INSPIRE Conference, 04.-08. Sept 2017, Kehl/Strasbourg

Thank you for your attention!

EAGLE website:

http://land.copernicus.eu/eagle

Fedaral Statistical Office (DESTATIS)

Land Use Statistics

Stephan Arnold

Telefon: +49 (0) 228 / 99 643 8612

[email protected]

www.destatis.de