The Copernicus land monitoring service provides geographical information on land cover and on variables related, for instance, to the vegetation state or the water cycle. It supports applications in a variety of domains such as spatial planning, forest management, water management, agriculture and food security, etc. The service became operational in 2012. It consists of three main components: ◾A global component; ◾A Pan-European component; ◾A local component.

1. Title
First name SURNAME
Position
Place, date
Name of the entity
Copernicus land
monitoring
portfolio
Hans DUFOURMONT
Project manager Copernicus
land monitoring services
Copenhagen, 07.04.2016
European Environment Agency

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Outline
 Introduction: the Copernicus programme
 Portfolio overview
 Corine Land Cover
 High Resolution Layers
 Urban Atlas
 Riparian Zones
 Natura 2000 sites
 European Reference Data
 EU-DEM & EU-hydro
 Dissemination and access
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6 services use
Earth Observation
data to deliver …
Sentinels
Contributing missions
In situ
observations
Contributing missions
in-situ
…added-value products
Overall Architecture

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From global…
…to pan-European…
…to local
e.g. Vegetation dynamics, Bio-
physical parameters, energy
balance
e.g. bio-diversity, water bodies,
land-use, land change
e.g. urban land-use
Land Monitoring Service: JRC & EEA

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Copernicus land monitoring service
pan-European, local & RDA products
Imperviousness
Forest type
Tree
cover
density (Semi-)
natural
Grassland
Wetlands
Water bodies
Corine Land
Cover 2012
Image mosaics
Urban
Atlas
IMD
Time
Series
Riparian Zones
Natura2000
EU-DEM
EU-hydro

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CORINE LAND COVER
pan-European component
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CORINE Land Cover basics
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• Mapping ~permanent surface features at scale
1:100.000 based on physical characteristics
(changes > 1 year)
• MMU: 25 ha (5 ha for changes); MMW: 100 m
• Nomenclature: 5 main groups, three levels, 44
level-3 LU/LC classes (representing Europe)
• Basic data support: satellite imagery
• Ancillary (in-situ) data: national orthophotos,
topographic maps, VHR imagery…
• Implemented by national teams
• Inventories: 1990, 2000, 2006, 2012
MMU, MMW and nomenclature have not
changed since the beginning!

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HIGH RESOLUTION LAYERS
ON LC CHARACTERISTICS
pan-European component
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Copernicus and Earth observation satellites help to unveil
where and how fast cities are expanding
HRL Imperviousness
HRL Imperviousness 2012
Copenhagen city centre (20m full resolution)
Source:ECFP7geoland2
Source: European Environment Agency
HRL data produced under EEA: GMES
Initial operations 2011 – 2013
Background image: Google Earth

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HRL Imperviousness
CORINE Land Cover
44 thematic classes
Minimum mapping unit: 25ha + 5ha change
HRL Imperviousness
Continuous degree of imperviousness 0-100%
Resolution: 20m (intermediate) / 100m (final)
Source: European Environment Agency;
Source: European Environment Agency; Data produced by GeoVille GmbH
Oulu, Finland

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HRL Imperviousness: Production
 Per-pixel estimates of degree of imperviousness for EEA-33 + 6
 Source: optical, high-resolution bi-temporal satellite images
 Automated change detection from calibrated biophysical
variables (NDVI)
 Spatial resolution: 20m (intermediate) / 100m (final)
 Thematic accuracy: >85% at 1ha level
 Temporal resolution: 2006 / 2009 / 2012 status and changes

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HRL Imperviousness: Production
Built-up area mask
Calibrated biophysical
variables (NDVI)
Multispectral satellite
images
IRS-LISS III images
Source: ISRO, GAF
DataproducedbyGeoVilleGmbH
Degree of imperviousness
Imperviousness change
Image
processing
and
classification
models & tools

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HRL Forest: Specifications
Tree Cover Density (TCD)
(20m, 100m)
Dominant Leaf Type (20m)
Forest Type (FTY, 100m)
Hungary
IRS-LISS III
(18.08.2011)
IRS-LISS III
(18.08.2011)

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HRL Forest Specifications
Tree Cover Density (TCD)
(20m, 100m)
Dominant Leaf Type (20m)
Forest Type (FTY, 100m)
 No MMU (pixel resolution)
 Min. Mapping Width: 20m
 TCD range: (1)-100%
 Includes orchards, olive groves,
trees in urban context, etc.
Hungary
 MMU: 0.5 ha
 Min. Mapping Width: 20m
 TCD threshold: ≥10%-100%
 Support Layer (non-forest trees)
 100m prod. excludes orchards, olive
groves, trees in urban context, etc.
Producedusingproducts©AntrixCorporationLimited2012.
DistributionbyGAFAG,Germany,allrightsreserved.
ProvidedunderEC/ESAGSC-DA.

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Production workflow
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TCD estimation
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2. Image clustering
Clustering: “proba_cluster”
1. Image input 3. Estimation of the TCD for each pixel
Objective: use the Proba_cluster module to
produce a k-means classification of the
input IRS image into homogeneous
clusters.
The criteria and the number of clusters are
defined into an input parameter *.txt file.

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Statistical files: Proba_stats and Proba_plot
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Fig.: Ground data association with clusters: Field plot data:
“proba_plot”
Objective: Calculate the mean values of the spectral
bands for each cluster and associate a TCD value to
each cluster on the basis of the reference sample
points.
Input data: “input_file”_kmeans_out and other output
files generated automatically from Proba_cluster
module; “reference_data.txt”
Fig. : Cluster statistics calculation: “ ProbaStats”

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Automatic correction of the mask
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Objective: to produce a good and
reliable Forest Mask
automatically, with the
assistance of the Ancillary
Data, trying to make it as
smooth as possible for the
subsequent manual correction.
Input data:
 first mask, with “assigned”
forest clusters, TCD and
kmeans_out;
 Ancillary Data (SIOSE, CLC
layer and JRC maps).

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ProbaEstimates, the TCD
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Objective: Calculate the TCD value for all
the pixels inside the input IRS scene.
Input data: IRS image converted into *.ers
format, input parameter *.txt file, output
file of ProbaStats,
cluster_data_content_T.txt generated
automatically from Proba_plot module
Fig. : The total TCD for NAVARRA,
north of Spain.

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The Mask, Forest – No Forest
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Analysis of the assignment of the Forest/No Forest
values to the clusters by a scatterplot.
Objective: Assign the
Forest/No Forest value at the
clusters generated from the
IRS scene.
Input data: scatterplot
generated by the intersection
of the reflectance values
between RED and NIR band.
The Clusters classified in the
Vegetation Signature are
considered FOREST and will
constitute the forest mask,
NO Forest the others .

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Manual correction of the mask
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Note: if this step depends very much on the
previous and on the precision of the ancillary
data, the achievement of all the product
depends on this manually correction.
Also the total time depends on this step, and
especially on the problems that will be faced
during the interpretation and that will be
discussed afterwards.
Objective: Manual correction of
the Forest Mask.
Input data: mask, products of the
manual correction: omission
and commission Shape,
Ancillary Data.
Output data: the products of the
manual correction: shape of
omission and commission
polygons that will be applied
at the forest mask.

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Correction of the Forest Mask
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Objective: to produce a good
and reliable Forest Mask
automatically.
Input data: mask, TCD,
products of the manual
correction: omission and
commission layers with
related 1bit images
integration layers.
Method: a model with simply
condition:
CONDITIONAL { (<test1>)
<arg1> , (<test2>)
<arg2> , ... }
or
EITHER <arg1> IF ( <test> ) OR
<arg2> OTHERWISE

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From Forest Mask to TCD corrected
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Critical points in the workflow
The critical points for a “corrected” TCD corrected:
 presence of grass;
 bushes; Macchia Mediterranea
 shrubs;
 Irs and RE acquisition time;
 presence of dehesa.
The typical examples of errors that occur for lot 4, Mediterranean
Area, and that make production of the TCD long and
complicated are presented below ..
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HRL Forest: pan-European result
New Horizons for European and Global land monitoring - Copernicus
products and services ready to use, 19-20/10/2015, Copenhagen

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High Resolution Layer
Permanent Grasslands (Nantes, FR)

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High Resolution Layers
Permanent Wetlands + Water Bodies
(Confluence of Odiel and Tinto rivers,
Huelva SW Spain, FR)

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URBAN ATLAS
Local component
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Evolution of FUAs in Urban Atlas
UA2006 UA2012

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Urban Atlas 2012
Nomenclature
UA2012 nomenclature

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Sentinel-2 image
Copenhagen
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Urban Atlas

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Corine land cover 2012
Copenhagen area
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Urban Atlas 2012
CLC vs UA

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Aalborg (DK): Street tree layer close-up

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RIPARIAN AREAS
Local component
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Riparian ecosystem services
 Regulation of water flows
 Moderation of extreme events
 Erosion prevention
 Climate regulation
 Maintenance of soil fertility
 Maintenance of life cycles of migratory
species (incl. nursery service)
 Aesthetic information
 Recreation and tourism
 …
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© Clerici et al. 2011
International Commission for the
Protection of the Rhine; © Klaus
Wendling, MUFV Rheinland-Pfalz)

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Riparian zones: Land Cover mapping
Land Cover and Land Use (LC/LU)
DU043A Rhone and Coastal Mediterranean
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Delineation process of the riparian zone
• Complex workflow modelling potential riparian zone and
observable riparian zone, and combining both into one single
membership degree: Actual riparian zone
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Riparian zones: delineation 1/3
Potential riparian zone (vector)
DU043A Rhone and Coastal Mediterranean
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Riparian zones: delineation 2/3
Observable riparian zone (vector)
DU043A Rhone and Coastal Mediterranean
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Riparian zones: delineation 3/3
Actual riparian zone (vector)
DU043A Rhone and Coastal Mediterranean
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Riparian zones: Green Linear Elements
Green Linear Elements (GLE)
DU043A Rhone and Coastal Mediterranean
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NATURA 2000
Local component
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Focus for N2K mapping
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 Semi-natural and natural grasslands are important European
ecosystems that provide high biodiversity and a range of other
environmental and societal functions.
 Agriculture intensification and grassland management, land
abandonment, drainage, shrub encroachment, afforestation,
changing population structures and urbanisation are increasingly
threatening these valuable natural communities

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N2K grassland-rich sites: 5 grassland habitats types
6210, 6240, 6250, 6510 and 6520, including a 2km
buffer (covering approx. 160.000 km2)
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N2K mapping

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Basic procedure
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Input data:

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Land use & Land cover characterisation
• LC/LU classification follows
the MAES (Mapping and
Assessment of Ecosystems
and their Services)
ecosystem types and is fully
compatible with CLC and
Urban Atlas
• provides 62 thematic classes
• MMU 0.5ha
• MMW 10m
• CORE_03 SPOT-5/6 and
Pléiades data as main data
source
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Regensburg
production site

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Land use & Land cover characterisation
• LC/LU classification follows
the MAES (Mapping and
Assessment of Ecosystems
and their Services)
ecosystem types and is fully
compatible with CLC and
Urban Atlas
• provides 62 thematic classes
• MMU 0.5ha
• MMW 10m
• CORE_03 SPOT-5/6 and
Pléiades data as main data
source
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Regensburg
production site

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EUROPEAN REFERENCE
DATASETS
EU-DEM, EU-HYDRO
Reference Data Access
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 Pixel resolution: 25 meters
 Vertical accuracy of +/- 7 meters RMSE
 Projection: LAEA (EPSG:3035); ellipsoid GRS80,
vert. datum EVRS2000 geoid EGG08
 Source datasets: SRTM, ASTER GDEM and
Russian topographic maps
 Delivery format: GeoTIFF 32 bit,100x100 km tiles
 More than 75.000 artifacts detected and corrected
 Consistency with the EU-HYDRO coastline
 Burning EU-HYDRO water bodies into EU-DEM
 QC: statistical analysis, removal of artifacts & geo-
positioning errors, consistency with EU-HYDRO,
completeness
EU-DEM 2015

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 Visual check of the final result in the 100% of the Europe coast line, comparing
EU-DEM with EU-HYDRO features (Coastal_p)
 Visual check of the burning of river network and inland waters for the 5% of the
tiles
Any issues detected in the burning of river network and inland waters
EU-DEM 2015 Quality Control

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CORRECTED EU-DEM
Computed with 934038 points
Mean error: -0.0272m
Std: 2,272m
ORIGINAL EU-DEM
Computed with 991179 points
Mean error: -0,56m
Std: 2,85m
ICESat bias adjustment: Statistical measurements demonstrating that the
fundamental accuracy of EU-DEM has been improved:
EU-DEM 2015 VALIDATION

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EU-HYDRO update work-flow
REVISION OF VECTOR LAYERS:
• Manual revision of 100% geometry using
VHR SPOT-5 color-enhanced imagery of
2011-2013 as reference
• Complete revision of 100% of coastline and
islands
• Integration with GIO-Land Lot 6 layers
(Permanent Water bodies), adding objects
>1 ha, revising polygon boundaries
• Complete revision of attributes, linking to
WFD,
• ECRINS, National WB, INSPIRE, Global
Pfaffstetter ...
• Automated QC procedures on computed
datasets
•

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 River Network scale: 1:50,000 and better
 Projection: Lambert Azimuthal Equal-Area
(EPSG:3035); geographic Coordinate Reference
System: ETRS89
 Minimum Mapping Unit: 1 ha: photo-interpretation
Very High Resolution SPOT5/6 imagery (2.5 m
pixels), period 2011-2013
 River network: rivers (l/p), inland water bodies (p),
culverts (l), nodes, canals (l/p), ditches (l/p),
transitional waters (p), coastal polygon (p), river
basins (p)
 QC: positional & thematic accuracy, topological
consistency, completeness, INSPIRE conformity
EU-HYDRO 2015

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COASTLINE: 98 840 islands, 675 150 km
LAKES: 402 510 objects, 141 514 km2
RIVERS: 930 061 objects, 2 248 639 km
CANALS: 3 365 objects, 15 754 km
DITCHES: 2 351 objects, 7 730 km
EU-HYDRO 2015 COVERAGE

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River Basins in flat coastal areas:
- Precise match with River
Network;
- No “guessing” if drainage is too
low;
- Many “loose ends” of RN
draining into the sea without
separate Basins.
EU-HYDRO/EU-DEM CONSISTENCY

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DISSEMINATION
land.copernicus.eu

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land.copernicus.eu

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Data download and view: land.copernicus.eu
Questions and feedback: hans.dufourmont@eea.europa.eu
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