esa globcover due project - wur...validation campaign objectives: • the main objective of...

ESA GLOBCOVER DUE PROJECT

ESA GLOBCOVER DUE ProjectContents

•First bimonthly MERIS composite May-June 2005 : “Tapisserie de Montreux”

•Why Globcover ?

•Meris FRS data specs & processor status

•Globcover Consortium and processing lines description

•Calibration/Validation Campaign

•Globcover access and information

O. Arino, D. Gross, F. Ranera , M. Leroy, P. Bicheron, C. Brockmann, P. Defourny, C. Vancutsem, F. Achard, L. Bourg, J. Latham, A. Di Gregorio, R. Witt, M. Herold, S. Plummer, JL. Weber, L. Shouten

ESA EO Science and Applications Department : [email protected]

The GLOBCOVER InitiativePartnership: ESA - JRCPrograms: GOFC - IGBPUsers: FAO, UNEP, EEAObjectives: Global Land Cover Map 2005/2006 by using

ENVISAT MERIS data at 300m (15 bands) Outputs: 6 Bimonthly composites

Annual composite Land cover map

Schedule: Definition phase April 2004KO GLOBCOVER processing April 2005Calibration/Validation November 2007GLOBCOVER V1 February 20082nd User Consultation February 2008GLOBCOVER V2 April 2008

Implementation: ESA (ACRI, MEDIAS, UK-PAC) and JRC

Introduction : Why Globcover ?

A GOFC-GOLD & IGBP perspectives

GOFC-GOLD is an international program to monitor forest cover and land dynamics globallyStrong push to improve global land cover assessment (GEOSS, GCOS IP, IGOL …)Existing land data though are not yet satisfying:

Spatial detailThematic content and flexibilityRegular updates and change assessmentMissing link across scales (in-situ, regional, global)

GLOBCOVER considers existing data and known challenges harmonized, more detailed, flexible, and validated map productThematic legend compatible with LCCS

Why Globcover ?

Acquisition requirements:

All land areas, with a sufficient sampling to capture vegetation changes.

Seasons: to focus on period of interests: based on inputs from JRC (F. Achard)

Examples in South America :

Amazon region: acquisitions from October to January may be avoided (rainy season)

Temperate region: March to September may be avoided (Winter season)

Acquisition constraints

MERIS FR data acquisition

:

No systematic acquisition in FR mode (but systematic in RR mode)

Max. acquisition raised from 20 min. to 23 min. per orbit

Outside Artemis mask:Use of recorder, i.e.ASAR HR or MERIS FR

Artemis mask

Volume size :

MERIS L1 product : 36 GByte per day (180 min per day)

MERIS L1 product : ~20 TByte for the whole acquisition period

MERIS FR data acquisition status

Acquisition status: MERIS L0 data in ESA archive since 1st December 2004

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)

MERIS Geolocation Accuracy

Globcover Processing time requirements: 1 year of MERIS FR data geometrically corrected in about 20 days.

History(I) 2002, RMS absolute geolocation error:

251.24 ± 81 m.

(II) 2003, RMS absolute geolocation error368.39 ± 67 m.

(III) After 12th Dec. 2003, RMS absolute geolocation error

212 ± 22 m mainly due to resamplingto the nearest neighbourg.

(IV) 2006, with the Globcover processing chain: RMS absolute geolocation error

77.1 m (ca. 0.26 pixels)

Global Composites final result

• Urge cities (Cairo, Alexandria)

• Free water and swamp grasslands, and irrigated agriculture

• DesertGlobCover Annual composite

Nile Delta (Africa)


Kilimanjaro, Mehru(Africa)

• Montane Forest

• Closed Grassland

• Agriculture

GlobCover Annual composite


Amazonian Forest (S. America)


MERIS VGT

Can Tho (ASIA)


MERIS MODIS

N-Sumatra (Indonesia)

Tapisserie de Montreux

Result of the pre-processing pillar: Bimonthly composite March-April 2006

Consortium presentation

Magellium

Support Geometry

Noveltis

Support CloudScreen. Atm. Corr.

Medias-France

Support BRDF& Compositing

Brockmann-ConsultPreprocessing

Synoptics

SupportValidation

Intern. Experts

SupportValidation

UCLClassifications

Spacebel

Support Software& Production

Medias-FranceSoftware & Production

Medias-France

Management

The GLOBCOVER System

The GLOBCOVER System is based on three pillars :Pre-processingClassificationSoftware and Production

SOFTWARE &PRODUCTION

• A neural network relying on MOMO method has been selectedbecause of good validation results (ESA-Albedo Map project)

• The atmospheric correction is a complex processL1B radiance to reflectance correctionAerosol correction requires aerosol optical depth from MODIS-8 daysGaseous 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

• Updates to do for V2Improvement for the atmospherical using a new atmospherical auxiliarydataset:

• Building of 1 year of daily LARS products using 2005 MERIS-RR data and monthly LARS climatology as fallback

Atmospherical correction

• Two methods are used:1. Cloud Probability Method: atmospherical scheme MOMO (Preusker, Freie Universität Berlin) through a neural network

• CLEAR_1, CLOUD_1

2. Blue Bands Cloud Screening: global threshold using 443, 753, 760, 865 nm

• 4 states CLEAR_2, THIN_CLOUD_2, DENSE_CLOUD_2 and SNOW_2

Final Cloud detection combines both excluding snowResults validated through one month of METEO-France synopticaldataset (Europe/Africa) and a Cloud Toolbox (Noveltis-CNES)

• Update to do for 2007:Improvement of the cloud detection

• Better cloud shadow location using CTP• Pressure threshold depending on altitude• Using of a blue band climatology (building of MERIS FR-490 nm)

Cloud detection

BRDF Correction and compositing

• For BRDF correction, two methods are used:The MeanMean CompositeComposite (Vancutsem et al., IJRS 2002) isprocessed as a first reference Surface Spectral Reflectance on a compositing equal to 51days

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

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

• CompositingA mean is applied over the ‘valid’ Surface Spectral Reflectanceat 2 temporal frequencies

• Bimonthly• Annual

•• Key Key ideaidea: combine the high spatial consistency of classes obtainedfrom multispectral composite(s) with the great land cover discrimination provided by temporal profile analysis

•• PrinciplesPrinciples::Regionally-tuned approach based on 22 equal-reasoning areaMultispectral composites and reflectance time seriesTypology defined and documented using FAO Land CoverClassification System and as much as possible compatible with EEA CORINE Land Cover product and GLC2000, Africover..International experts inputs for the classification algorithm calibration

•• Challenges:Challenges:Consistent but regionally-tuned classification processing strategyAutomatic and repeatable processing chainBuilding on GLC2000 experience and previous LC products

Classification concept

Global Land CoverNon Validated version

Algortithm inprovements during Calibration campaign

First release : February 2008

Legend : 23 LCCS classes(46 including regional classes)

•• 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 classification pixel 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: Per3: Per--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

A priori stratification4 strata to cover Europe

First “calibrated” result

Classification – version 1 GLC2000

Globcover (300m) Corine LC (300m)

Globcover GLC2000

Globcover (300m) GLC2000 (1km)

Classification – version 1


GLC2000Globcover


Globcover GLC2000

Reference dataset based on existing LC data:

• GLC2000• Corine LC 2000• FAO- Africover product (2000)• US National LC database (USGS) 2005• National LC map of China (Chinese Academy of Sciences)• RDCongo LC map (UCL-Geomatics, 2000)• Australia Land use map (Australian Government)

Labelling procedure based on best previous products

Globcover 2005-2006 (300 m) Corine LC & GLC2000 (300 m)

But best exiting is not always relevant to label GLOBCOVER product

Romania

Validation Campaign

Objectives:• The main objective of validation is to allow a potential user to determine the

map's "fitness for use" for his or her application. • Validation – The process of assessing, by independent means, the quality of the

data products derived from the system outputs (WGCV).• Here we look at the thematic information only!• Assumed accuracy of 0.70

Concept• For obvious reasons no field surveys were foreseen to conduct the validation• Instead we decided to engage “regional experts” in the validation.• This implies that we accept a certain level of subjectivity.• Two sets of validation points are required (Calibration/Validation)

Sampling Strategy• Stratified random sampling• Minimum of 5 points per class

Validation ToolTwo dedicated tools :

1. A dedicated environment to fill in the data (in MS Access)2. Tailor made information (Virtual Earth) that allows quick

panning to the concerned validation point

1ST Tool 2nd Tool

1st tool: Access Application

Completing the toolImport the layers into the

mashup on Virtual Earth (VE)Import the coordinates of the

points into the Access application4 level of certainty (certain, fair,

ambiguous, none)Coordinates of the point

NDVI profiles can be generated (from SPOT VGT)

Links to VE and profilesUp to three land cover types

per point.One land cover type is

always the most dominant one.

2nd tool: Virtual EarthSecond tool:

A clickable URL that will open Virtual Earth and NDVI profile .

Validation status•Experiences with:

Sander Mucher, Allaard de Wit and Gerard Hazeu (Alterra)Philippe Mayaux (JRC)Carlos di Bella (INTA)

•Expert Workload defined for following weeks

•Many suggestions of these experts have been implemented

•Number of points that can be obtained:10-15 points per hourMaximum of 5 to 6 hours per dayAmounts to 250 to 450 points for a whole week per expert

Globcover Access ToolsESA Globcover Data access tool

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

Bit torrent / HTTP

Available 6 bi-monthly Reflectance1 annual Reflectance

Postel/Medias-France GlobCover Data access tool

http://postel.mediasfrance.org

HTTP

Thanks !

ESA/DUE Globcover websitehttp://www.esa.int/due/ionia/globcover

Please register to our Globcover Newsletter( contact point : [email protected] )Next issue in December 2007

Postel Globcover websitehttp://postel.mediasfrance.org

Globcover Information

mailto:[email protected]

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