GlobCover Land Cover Version 2

O. Arino, P. Bicheron et al.

Partnership: ESA - JRCPrograms: GOFC - IGBPUsers: FAO, UNEP, EEAObjectives: A service for a Global Land Cover Map 2005/2006

by using MERIS data at 300mOutputs: Version 2 (December 2004 / June 2006)

10 bimonthly composites annual composite (year2005)demo composite (Dec2004/June2006)land cover map

A hardware & software system

Schedule: KO GlobCover April 2005GlobCover V1 13 February 2008GlobCover V2 18 August 20083rd User Consultation 9-10 September 2008

Implementation: ESA, UK PAC, MEDIAS, Brockmann Consult, UCL, ACRI

MERIS FR data acquisition status

Acquisition status: MERIS L0 data in ESA archive between 1st December 2004 and June 2006

MERIS FR data acquisition status

Number of valid observations V2

Geolocation accuracy:relative RMS error 51.6 m (ca. 0.17 pixel) absolute RMS error 77.1 m (ca. 0.26 pixel)

Improvement regardingSatellite attitude accuracyRelief deformationProjection map

Improved Geolocation Approach

Input : MER_FRS_1Restituted Attitude Files + Restituted Orbit (or DORIS)Instrum. Charact. Aux File (MER_INS)+DEM (Getasse, 30” res)

Output :MERIS FSG 1(same structure as FRS)Corrected lat/long/alt as additional bands for every pixelNo projection map

AMORGOS Tool (ACRI)

Combined with the Projection Tool (Medias)

Rainfed Cropland

Post-flooding or irrigated croplands

Mosaic cropland (50-70%) / vegetation (grassland/shrubland/forest) (20-50%)Mosaic vegetation (grassland/shrubland/forest) (50-70%) / cropland (20-50%)

Closed to open (>15%) broadleaved evergreen and/or semi-deciduous forest (>5m)Closed (>40%) broadleaved deciduous forest (>5m)

Open (15-40%) broadleaved deciduous forest/woodland (>5m)Closed (>40%) needle-leaved evergreen forest (>5m)Open (15-40%) needle-leaved deciduous or evergreen forest (>5m)

Closed to open (>15%) mixed broadleaved and needleaved forestMosaic forest or shrubland (50-70%) and grassland (20-50%)

Mosaic grassland (50-70%) and forest or shrubland (20-50%)

Closed to open (>15%) shrubland (<5m)

Closed to open (>15%) grassland

Sparse (<15%) vegetationClosed (>40%) broadleaved forest regularly flooded, fresh water

Closed (>40%) broadleaved semi-deciduous and/or evergreen forest regularly flooded, saline waterClosed to open (>15%) grassland or shrubland or woody vegetation on regularly flooded or waterlogged soil, fresh, brackish or saline waterArtificial surfaces and associated areas (Urban areas >50%)

Bare Areas

Water Bodies

Permanent Snow and Ice

No Data

23 classes - Compatible with the UN Land Cover classification System (LCCS)

Legend

ESA Globcover Data Access tool (GCAT)

http://www.esa.int/due/ionia/globcover

Bit torrent / HTTP

Available 10 bi-monthly Reflectance1 annual Reflectance (2005)

! Land ! Land CoverCover + PDM (Sept. 30) + PDM (Sept. 30) + Validation Report (Oct. 30) !+ Validation Report (Oct. 30) !

Postel/Medias-France GlobCover Data access tool

http://postel.mediasfrance.org

HTTP

The GLOBCOVER System

The GLOBCOVER System is based on three pillars :Pre-processing (Brockmann-Consult)Classification (Univ. Cath. Louvain)Software and Production (MEDIAS-France)

SOFTWARE &PRODUCTION

• Atmospheric correction: use of MOMO neural networkAerosol correction requires aerosol optical depth from LARS-RR climatology monthly/annual/back-upGaseous absorption correction

• Ozone from ECMWF auxiliary data present at MERIS L1b tie-points• O2 from B11/B10 ratio, H2O from B15/B14 ratio• LUTs for transmission assessment

• Cloud Detection: two methods are used:1. Cloud Probability Method: atmospherical scheme MOMO (Preusker, FreieUniversität Berlin) through a neural network

• CLEAR_1, CLOUD_1

2. Blue Bands Cloud Screening: global threshold at 443, 753, 760, 865 nm• 4 states CLEAR_2, THIN_CLOUD_2, DENSE_CLOUD_2 and SNOW_2

+ pressure threshold depending on altitude + cloud shadow location using CTP = Final Cloud detection (excluding snow )Results validated through one month of METEO-France synoptical dataset(Europe/Africa) and a Cloud Toolbox (Noveltis-CNES)

Cloud/Atmosph. correction

BRDF Correction/Compositing

• For BRDF correction, two methods are used:The MeanMean CompositeComposite (Vancutsem et al., IJRS 2002) is processed as a first reference Surface Spectral Reflectance on a compositing equalto 60 days

The CYCLOPESCYCLOPES (Hagolle et al., RSE 2004) method is then applied to detect ‘valid’ Surface Spectral Reflectance:

• Removal of outliers (residual thin clouds, aerosols, shadows) withan iterative procedure

• Compositing strategy: priority given to ‘Land’ status if ‘Flooded’ pixels present in the synthesis. Otherwisemajoritary status

•• StepStep 0: A priori stratification0: A priori stratificationSplit the world in 22 equal-reasoning regions from

ecological and remote sensing point of view

•• StepStep 1: For 1: For eacheach regionregion, per, per--pixel (sup./pixel (sup./unsupunsup) ) classification classification algorithmalgorithm

Homogeneous land cover classes

•• StepStep 2: Per2: Per--pixel temporal pixel temporal characterizationcharacterizationRobust temporal metrics computed per-object from bi-monthly multispectral composite and associated indices

•• StepStep 3: 3: PerPer--objectobject classification classification algorithmalgorithmConsistent unlabelled spectro- temporal classes

•• StepStep 4: 4: LabelingLabeling rulerule--basedbased procedureprocedureBased on best existing products and experience of an

international expert network, LCCS land cover classes

•• StepStep 5: Calibration5: CalibrationMERIS specific labeling thanks to interactive calibration

by a network of international experts

•• StepStep 6: Independent Validation6: Independent ValidationClassification accuracy

Classification scheme

0

0,5

1

1,5

2

2,5

3

3,5

4

4,5

1 4 7 10 13 16 19 22 25 28 31 34

temps

ND

VI

NDVIR (%)

n classes

x classes

clustering

Vegetatio

n

ExpertExpe

rts

& Ancillarydata

Each region is processed independently using regionally tuned classif. parameters

Tuning of classification parameters (LUTs)NB_ST Name_ST N NB_PIX DAY_DAY_ DAY_DAY_CH1 CH2 TEMP CH_PP MIN MAX NB_VAL DAY_DAY_DAYDAYCH1 CH2 X NB_PIX

1 Polar areas 20 5000 13 17 0 0 14,7,5,3 0 0 0 0 0 0 0 0 0 0 0 0 0 02 Canada 150 5000 13 17 0 0 14,7,5,3 0 1 14,12,7,5,3 1 1 3 0 0 0 0 0 0 50 50003 US 150 5000 8 13 17 20 14,7,5,3 14,7,5,3 1 0 0 0 0 13 18 1 24 14,7,5,3 14,7,5,3 50 50004 Central-America 80 5000 1 24 0 0 14,7,5,3 0 1 14,12,7,5,3 1 1 3 0 0 0 0 0 0 30 50005 Amazon 80 5000 1 24 0 0 14,7,5,3 0 0 0 0 0 0 0 0 0 0 0 0 0 06 South-America 200 5000 8 17 0 0 14,7,5,3 0 1 14,12,7,5,3 1 1 3 15 19 0 0 14,7,5,3 0 40 50007 North-west Eurasia 200 5000 8 20 0 0 14,7,5,3 0 1 0 0 0 0 12 15 0 0 14,7,5,3 0 50 50008 North-east Eurasia 200 5000 13 18 0 0 14,7,5,3 0 1 14,12,7,5,3 1 1 3 0 0 0 0 0 0 50 50009 Eurasia 200 5000 7 12 17 22 14,7,5,3 14,7,5,3 1 14,12,7,5,3 1 1 3 17 22 0 0 14,7,5,3 0 50 5000

10 England 20 5000 7 12 17 22 14,7,5,3 14,7,5,3 0 0 0 0 0 0 0 0 0 0 0 0 011 Mediterranea 200 1000 14 16 8 10 14,7,5,3 14,7,5,3 1 14,12,7,5,3 1 1 3 0 0 0 0 0 0 50 500012 Africa desert 20 1000 1 24 0 0 14,7,5,3 0 0 0 0 0 0 0 0 0 0 0 0 0 013 North-Africa 200 2000 19 24 0 0 14,7,5,3 0 1 14,12,7,5,3 1 1 3 0 0 0 0 0 0 50 500014 Central Africa 100 2000 1 24 0 0 14,7,5,3 0 1 0 0 0 0 4 12 19 24 14,7,5,3 14,7,5,3 40 500015 South-Africa 200 2000 8 15 0 0 14,7,5,3 0 1 14,12,7,5,3 1 1 3 0 0 0 0 0 0 50 500016 Madagascar 40 5000 1 24 0 0 14,7,5,3 0 0 0 0 0 0 0 0 0 0 0 0 0 017 Asia desert 30 5000 1 24 0 0 14,7,5,3 0 0 0 0 0 0 0 0 0 0 0 0 0 018 South-east Asia 200 2000 1 24 1 5 14,7,5,3 14,7,5,3 1 14,12,7,5,3 1 1 3 0 0 0 0 0 0 40 500019 Japan 80 2000 12 20 0 0 14,7,5,3 0 1 14,12,7,5,3 1 1 3 0 0 0 0 0 0 30 500020 Indonesia 200 2000 1 24 0 0 14,7,5,3 0 1 14,12,7,5,3 1 1 3 0 0 0 0 0 0 30 500021 Australia 40 5000 1 24 16 17 14,7,5,3 14,7,5,3 0 0 0 0 0 0 0 0 0 0 0 0 022 New-Zeland 40 5000 1 24 0 0 14,7,5,3 0 0 0 0 0 0 0 0 0 0 0 0 0 0

NB_ST Name_ST N NB_PIX DAY_DAY_ DAY_DAY_CH1 CH2 TEMP CH_PP MIN MAX NB_VAL DAY_DAY_DAYDAYCH1 CH2 X NB_PIX1 Polar areas 30 2000 12 18 0 0 14,7,5 0 0 0 0 0 0 0 0 0 0 0 0 0 02 Canada 250 2000 12 18 0 0 14,7,5 0 1 14,7,5 1 1 3 12 18 0 0 14,7,5 0 70 20003 US 250 2000 8 13 16 21 14,7,5 14,7,5 1 14,7,5 1 1 3 16 21 0 0 14,7,5 0 70 20004 Central-America 80 2000 1 24 0 0 14,7,5 0 0 0 0 0 0 0 0 0 0 0 0 0 05 Amazon 80 2000 1 24 0 0 14,7,5 0 0 0 0 0 0 0 0 0 0 0 0 0 06 South-America 200 2000 1 24 23 6 14,7,5 14,7,5 1 14,7,5 1 1 3 23 6 0 0 14,7,5 0 60 20007 North-west Eurasia 250 2000 11 16 0 0 14,7,5 0 1 14,7,5 1 1 3 11 16 0 0 14,7,5 0 60 20008 North-east Eurasia 250 2000 11 16 0 0 14,7,5 0 1 14,7,5 1 1 3 11 16 0 0 14,7,5 0 60 20009 Eurasia 200 2000 7 12 17 22 14,7,5 14,7,5 1 14,7,5 1 1 3 17 22 0 0 14,7,5 0 60 2000

10 England 40 2000 7 12 16 21 14,7,5 14,7,5 0 0 0 0 0 0 0 0 0 0 0 0 011 Mediterranea 200 2000 14 16 8 10 14,7,5 14,7,5 1 14,7,5 1 1 3 0 0 0 0 0 0 60 200012 Africa desert 60 2000 1 24 0 0 14,7,5 0 0 0 0 0 0 0 0 0 0 0 0 0 013 North-Africa 200 2000 19 24 0 0 14,7,5 0 1 14,7,5 1 1 3 19 24 0 0 14,7,5 0 60 200014 Central Africa 60 2000 1 24 0 0 14,7,5 0 0 0 0 0 0 0 0 0 0 0 0 0 015 South-Africa 200 2000 8 15 0 0 14,7,5 0 1 14,7,5 1 1 3 8 15 0 0 14,7,5 0 60 200016 Madagascar 100 2000 1 24 0 0 14,7,5 0 0 14,7,5 1 0 3 1 24 0 0 14,7,5 0 50 200017 Asia desert 60 2000 1 24 0 0 14,7,5 0 0 0 0 0 0 0 0 0 0 0 0 0 018 South-east Asia 250 2000 1 24 23 4 14,7,5 14,7,5 1 14,7,5 1 1 3 23 4 0 0 14,7,5 14,7,5 70 200019 Japan 60 2000 1 24 0 0 14,7,5 0 0 0 0 0 0 0 0 0 0 0 0 0 020 Indonesia 100 2000 1 24 0 0 14,7,5 0 0 0 0 0 0 0 0 0 0 0 0 0 021 Australia 100 2000 1 10 15 20 14,7,5 14,7,5 1 14,7,5 1 1 3 1 10 15 20 14,7,5 14,7,5 60 200022 New-Zeland 40 2000 1 24 0 0 14,7,5 0 0 0 0 0 0 0 0 0 0 0 0 0 0

•• PerPer--pixel classification pixel classification –– use of seasonal and/or annual compositesuse of seasonal and/or annual composites•• Temporal characterisation Temporal characterisation -- use of temporal metrics for 11 strata use of temporal metrics for 11 strata

•• Seasons• Use of temp. metrics• N/X classes

About the Calibration Phasesignificant tuning

Before After

About the Calibration Phase

GLOBCOVER Land cover V2much better spatial resolution!

Reference

Globcover V2

Calibration/Validation meetings with

• Chandra Giri, USGS• Rasim Latifovic, CCRS• Peter Caccetta, CSIRO • Philippe Mayaux, JRC• André Nonguierma, CA• Bruno Gérard, ICRISAT• Sergey Bartalev, IKI• Carlos Di Bella, INTAA• Gabriel Jaffrain, ETC-LUSI• Huang Lin, CAS• Valey Gond, CIRAD

About the Validation Phase

Validation More than 4300 points analysed containing3346 points of good level of certainty !

• GLOBCOVER versus homogeneousvalidation points for 6 main LC types from LCCS dichotomeous phase:

77.9%77.9%• GLOBCOVER versus homogeneous

validation points for 22 GLOBCOVER classes :

67.9%67.9%

Validation

• GLOBCOVER Land Cover V2 product is currently at the same level of accuracy than previous global products (e.g. GLC2000) but with a 10 times better resolution !!

• GLOBCOVER Land Cover is more than a Land Cover map…

• It is an automated and global classification chain successfully demonstrated at 300 m with embedded cumulative knowledgeprocess !

Conclusions