geo

land GSE Land

Land Monitoring Core Service and Quality Assurance GMES Land User Meeting

Stockholm, Sweden – 27 May 2008

Steffen Kuntz

GSE Land / BOSS4GMES

GMES Land User Meeting, Stockholm, 26-27 May 2008

Overview

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Introduction to the LMCS Quality Assurance approach

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LMCS product examples

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Validation carried out by ETC LUSI

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Challenges © GMES Land consortium, 2008 2

GSE Land - Quality Assurance Approach The QA approach of GSE Land Information Services builds on four pillars 1. Product and service standards jointly set-up and agreed among GMES Stakeholders 2. Service Provider qualification scheme accepted by users organisations and service providers 3. Implementation scheme for the qualification elements performed by independent auditors (TÜV Süd) and trusted European experts as supervisors 4. Independent quality control of all mapping products led by ETC LUSI on behalf of the end users 3 © GMES Land consortium, 2008

GMES Land - QA Approach Overview ETC-LUSI / EEA © GMES Land consortium, 2008 4

Current LMCS Product Specification

1 1.1

1.2

1.3

1.4

2 2.1

2.2

2.3

2.4

3 16 3.2

3.2.1

3.2.2

3.2.3

3.2.4

3.3

3.3.1

3.3.2

3.3.3

3.3.4

3.3.5

4 4.1

4.2

GSELand No.

GSELand M2.1 Regional Land Cover Vectordata code Nomenclature

GSEL21_2005 11000 12000 13000 14000 21000 22000 23000 24000 31000 32100 32200 32300 32400 33100 33200 33300 33400 33500 41000 42000 M21 Mapping Units marked in orange Artificial surfaces Urban fabric Industrial, commercial and transport units Mine, dump and construction sites Artificial non-agricultural vegetated areas Agricultural areas Arable land Permanent crops Pasture Heterogeneous agricultural areas Forests and semi-natural areas Forests Shrubs and / or herbaceous Vegetation Natural Grasland Moors and Heathland Mediterranean Shrubs Transitional woodland -shrub Permanently non-vegetated areas Beaches, Dunes, Sands Bare rocks Sparsely vegetated areas Burnt areas Snow and ice Wetlands Inland wetlands Coastal wetlands 5 50000 Water

MinMU

1 ha 1 ha 1 ha 1 ha 5 ha 5 ha 5 ha 5 ha 5 ha 5 ha 5 ha 5 ha 5 ha 5 ha 5 ha 5 ha 5 ha 5 ha 5 ha 5 ha 5 ha

© GMES Land consortium, 2008 5

Examples for validated LMCS products

Weser (45.979 km²) • Germany Moselle-Sarre (27.360 km²) • France • Luxembourg • Germany Arable land Artificial, Non-Agricultural Vegetated Areas Bare rocks Beaches, dunes, sands Coastal wetlands Forests Heterogeneous Agricultural Areas Industrial, Commercial & Transport Areas Inland wetlands Mines, Dumps & Construction Sites Moors & Heathlands Natural grassland Pastures Permanent crops Sparsely vegetated areas Transitional Woodland & Scrub Urban fabric Water

6 © GMES Land consortium, 2008

Internal QA (before delivery to QA-team)

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Independent re-interpretation of satellite data (chief interpreter) Systematic sample grid ~ 1000 sample points / catchment Ancillary data used: Topomaps & Google Earth

Target accuracy: 80% +/- 3%

Site Internal QA QA team, cluster method QA team, cluster method buffered (10m) QA team, cluster method buffered (30m)

Weser Moselle Sarre

95,7% 86,6%

81,1% 79,6%, 87% 82,5% 88,6% 86,2%

7 © GMES Land consortium, 2008

QA methodology applied by ETC LUSI

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Validation = Reference interpretation by QA team based on independent, higher resolution data , considering all reference data provided

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Reference date = that of the satellite image used by SP Blind interpretation =

SP interpretation results are not considered

8 © GMES Land consortium, 2008

Data types used in the validation

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Hi-res image data (aerial photo, Ikonos, etc.) basis of identification and delineation of objects

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Topographic map data

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Other reference data

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Satellite image data (used by the SP) reference date of validation is that of the satellite image Challenge: Data (especially hi-res data and topomaps) were missing in certain cases 9 © GMES Land consortium, 2008

Validation method: Cluster sampling

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Validation unit of 2.5 x 2.5 km (M2.x) Validation unit of 1.25 x 1.25 km (M1.1) Clusters selected by Delphi

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Delineation of objects on hi-res data by QA team, considering sat. image date as reference date Ref. interpretation is cross-checked within QA team Ref. interpretation is sent to Delphi Delphi compares reference & SP interpretation Delphi calculates accuracy results Accuracy results sent to SP and QA team © GMES Land consortium, 2008 10

Example Results from LMCS Weser & Saar-Mosel

Target Accuracy: 80 % +/- 3 %

WESER

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Agreement (accuracy): 81.1% Largest disagreements:

– – –

(disagreement: 17.5%) 410-310: Forest classified as wetland – 2.6 % 230-210: Pasture classified as arable land – 2.2 % 210-230: Arable land classified as pasture – 2.2%

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SAAR-MOSEL Agreement (accuracy): Largest disagreements:

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79.6% (disagreement: 20.4%) 230-210: Arable land classified as pasture – 8.3% 210-230: Pasture classified as arable land – 1.3% 230-310: Forest classified as pasture – 1.2 % © GMES Land consortium, 2008 11

Summary

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The GMES Land QA approach is a first attempt towards standardised GMES products.

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Presently the approach is not a fixed method introduced by a prime or by ESA or by the EC but an iterative process including learning curves by all stakeholders leading finally to a European consensus

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Expected result : Consolidated QA approach which is accepted by all stakeholders (SPs, QA, users) and can be communicated to the public as a general proposal for EO based mapping products (i.e. the FTS and the GMES Core Service Land Monitoring 12 © GMES Land consortium, 2008

Critical issues for discussion

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Focus on the QA process as a whole!

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To analyse whole process and identify concerns and blockages by member states

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To improve throughput and cost efficiency To reduce ambiguities in service definitions

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To be able to use results as a real quality criteria for go / no go decisions of mapping products

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To increase overall acceptance among stakeholders 13 © GMES Land consortium, 2008