EAGLE data model for land monitoring Use cases and state ... · INSPIRE Conference, 04.-08. Sept...

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

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

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 …

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

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

Crown cover density [%]

Minimum tree height in [m]

2 4 6 8 10 12 14

Differences in „Forest“ definitions worldwide

Crown cover density [%]

Minimum tree height in [m]

2 4 6 8 10 12 14

100 Source: Comber et al. (2005)

Differences in „Forest“ definitions worldwide

About aromas and ingredients: Finding the favorite red wine

Do youremember yourfavorite wine?

Not sure, seems theychanged the

label…

About ingredients and aromas…

You know how it tasts/looks like,

but is difficult to describe

To describe taste of vine,

we use known aromas to

characterize it.

In the best case,

we understand each others

descriptions…

„Tropical Night “

What exactly is a tropical night ?

Meteorological Parameter

• Temperature

• Humidity

• Air Pressure

• Precipitation

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

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

Characterization

Ingredients Salt

E 510, …

Weight 1,5 Kilo

3 pounds

Color light

dark brown

Other details biocertificated

Characterization

Growth form homogeneous

heterogenous

Growth density slosed

sparcely

Soil condition wet

acidic

Use/Funktion Pasture

Recreation

Air traffic

Ecosystem type

Wetland, swamp

Semantic overlap between class definitions

Alpine Calluna heath, border between SE and FI

CLC 333

“Sparsely vegetated”

CLC 322

“Moors and heathland”

Both products correct,

inconsistent nomenclature

due to overlap of definitions

Semantic overlap between class definitions

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 ?

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

De-Composition of landscape

From classification to object-oriented description

Fotos: © Copyright Ursus Wehrli

Parameter

De-Composition of CORINE Land Cover classes

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

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

Structure of the EAGLE matrix

Matrix as semantic comparison tool

Description of landscape with …

Land Cover Components (LCC),

Characteristics (CH),

Land Use Attributes (LUA)

LU Attributes

(HILUCS)

LC Unit

(1..* LCCs)

ABIOTIC LCCs

(Artificial/Natural)

BIOTIC LCCs

(Vegetation)

WATER LCCs

ristic

Structure of the EAGLE data model

INSPIRE LC Data model

INSPIRE

data model

EAGLE UML model overview

INSPIRE

data model

Determining spatial reference objects

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

Grid cell: descriptive characterization, standardized spatial reference unit

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

Example: „Rural Settlement“

Online Tool: EAGLE Matrix population andcomparison tool (EMPACT)

EAGLE Matrix population and comparison tool(EMPACT)

Tools: EAGLE Matrix population andcomparison Tool (EMPACT)

European

National (A)

Land Monitoring

Regional (a)

Land Monitoring

National

Sub-National Level

National (B)

Land Monitoring

Regional (b)

Land Monitoring

Konzept

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

LPISHR

Layers

AtlasLUCAS

Integration schema for a future land monitoring framework in Europe

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

EAGLE Use Cases

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

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

NatFlo Mapping approach

Indicator orienteddescription of objectswith environmental parameters andcharacteristics derivedfrom input data

Database structure and management

Assigning EUNIS classes to landscape units

ontology basedreasoning

Semantic Analysis of the Feature Type Catalogue

„Recent Land Use“ in Land Surveying Data Model

in Germany

Dr. Christian Lucas

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

AdV Projekt LB/LN

EAGLE Matrix

Elements for

feature type

„Forest“

AdV Projekt LB/LN

Künstliche Oberflächen und

Bauwerke61%

Natürliches Oberflächenmaterial

Holzige Vegetation9%

Krautige Pflanzen10%

Sukkulenten, Kakteen und andere Pflanzen

Flechten, Moose, Algen

Flüssigwasserflächen6%

Festwasserflächen0,02%

Wasser und andere6%

- Frequency of LC-Components in entire Analysis

AdV Projekt LB/LN

- Frequency of LU-Components in entire Analysis

Primärer Wirtschaftssektor

Sekundärer Wirtschaftssektor

(Industrie und Handwerk)

Transportnetzwerke, Logistik und technische

Einrichtungen37%

Wohnen5%

Andere Nutzungen

Binnenwassernutzung0%

Andere7%

Tertiärer Wirtschaftssektor(Dienstleistungen)

AdV Projekt LB/LN

Frequency of LC-Components in feature type “Sport,

Leisure and Recreation”

Künstliche Oberflächen und Bauwerke

40%Natürliches Oberflächenmaterial

Holzige Vegetation24%

Krautige Pflanzen30%

Sukkulenten, Kakteen und andere Pflanzen

Flechten, Moose, Algen0%

Flüssigwasserflächen1%

Andere1%

Festwasserflächen

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

uta.heiden@dlr.de

Kunststoffdach

(Polyethylen)

Roter Dachziegel

Rote Splittflächen

4. Enable Synergies with other EO missions

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

Urban Surface Materials

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.

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

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

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

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

Website:

http://land.copernicus.eu/eagle

EAGLE Meeting, March 2015, Frankfurt am Main

EAGLE data model Version 2.3

Thank you for your attention!

EAGLE website:

Fedaral Statistical Office (DESTATIS)

Land Use Statistics

Stephan Arnold

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

stephan.arnold@destatis.de

www.destatis.de

EAGLE data model for land monitoring Use cases and state ... · INSPIRE Conference, 04.-08. Sept...

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