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First name SURNAMEPosition
Place, date
Name of the entity
Copernicus land monitoring portfolio
Hans DUFOURMONTProject 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
2
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6 services use Earth
Observation data to deliver …
SentinelsGMES USERS
Coordinated Data Access System
GMES ServicesGMES Services
Contributing missions
In situ observations
Sentinel 1/2/3/4/5 & Jason-CS seriesSentinel 1/2/3/4/5 & Jason-CS series
GMES Space Component
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 typeTree
cover density (Semi-)
naturalGrassland
Wetlands
Water bodies
Corine Land Cover 2012
Image mosaics
UrbanAtlas
IMDTimeSeries
Riparian Zones
Natura2000
EU-DEM
EU-hydro
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CORINE LAND COVERpan-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, 2012MMU, 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)
Sou
rce:
EC
FP
7 ge
olan
d2
Source: European Environment AgencyHRL data produced under EEA: GMES
Initial operations 2011 – 2013Background image: Google Earth
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HRL Imperviousness
CORINE Land Cover44 thematic classes
Minimum mapping unit: 25ha + 5ha change
HRL ImperviousnessContinuous 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: ProductionBuilt-up area mask
Calibrated biophysical variables (NDVI)
Multispectral satellite images
IRS-LISS III imagesSource: ISRO, GAF
Dat
a pr
oduc
ed b
y G
eoV
ille
Gm
bH
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)
Hun
gary
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.
Hun
gary
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.
Prod
uced
usi
ng p
rodu
cts
© A
ntrix
Cor
pora
tion
Lim
ited
2012
. Dis
trib
utio
n by
GAF
AG
, Ger
man
y, a
ll rig
hts
rese
rved
. Pro
vide
d un
der
EC/E
SA G
SC-D
A.
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Production workflow
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TCD estimation
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2. Image clusteringClustering: “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> , ... }
orEITHER <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 ATLASLocal component
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Evolution of FUAs in Urban Atlas
UA2006 UA2012
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Urban Atlas 2012Nomenclature
UA2012 nomenclature
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Sentinel-2 imageCopenhagen
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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 AREASLocal 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 2000Local 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-DEMComputed with 934038 pointsMean error: -0.0272mStd: 2,272m
ORIGINAL EU-DEMComputed with 991179 pointsMean error: -0,56mStd: 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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DISSEMINATIONland.copernicus.eu
