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Global land monitoring in Europe’s Earth observation programme (GMES)

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Presentation on theme: "Global land monitoring in Europe’s Earth observation programme (GMES)"— Presentation transcript:

1 Global land monitoring in Europe’s Earth observation programme (GMES)
Global land monitoring in Europe’s Earth observation programme (GMES) Alan Belward Joint Research Centre Ispra (VA) Italy “Europe has decided to develop its own operational Earth observation capacity in order to reflect the EU’s growing responsibility in European and world affairs” COM(2009) 223 final

2 A land service is underway
Outline Europe’s Earth Observation Programme (GMES)* was adopted 9th November 2010 This includes both spacecraft (Sentinels) and services (the GMES Initial Operations) A land service is underway ‘GMES is the main space contribution of the Union to tackle climate change’** *COM(2009) 223; The European Earth Observation Programme (GMES) and its Initial Operations **COM(2010) 614; An Integrated Industrial Policy for the Globalisation Era

3 An operational EO Programme
Overall framework space component (ESA coordination) in-situ component (EEA coordination) service component (EC coordination) land, marine, atmosphere, emergency response, climate change, security Funding leading up to the regulation 2003 – 2006 the EU and ESA spent €100m each on GMES projects 2007 – 2013 the EU budgeted €430m for GMES project work 2007 – 2013 the EU contributed €624m to the total ESA GMES space component budget of €2246m Funding for GMES Initial Operations (GIO) 2011 – 2013 the EU has allocated €107m to the implementation of the Regulation 2011 – 2013 the EU has redeployed an additional €43m from the research budget Sentinel 1 (C-band SAR) a and b €2.9bn Sentinel 2 (13 channel MSI) a and b Sentinel 3OLCI (Ocean and Land Colour Instrument) Swath width: 1270 km, Spatial sampling: 300 m 21 bands [ ] SLST (Sea and Land Surface Temperature) > Swath width: 1675 km (nadir) / 750 km(backwards) Spatial sampling: 500 m (VIS, SWIR), SRAL (Sentinel-3 Ku/C Radar Altimeter) POD (Precise Orbit Determination) > GPS, LRR and DORIS (3 cm final accuracy) Sentinel 2 (13 channel MSI) 443 nm– 2190 nm (including 3 bands for atmospheric corrections)> Spectral resolution: 15 nm– 180 nm > Spatial resolution: 10m, 20m and 60m 290 km swath Sentinel 1 (C-band SAR, 5m to 40m resolution 80km to 400 km swath €150m Sentinel 3 (21 channel OLCI) a and b Images courtesy of ESA

4 monitor implementation of this legislation;
COM(2009) 223 final “The GMES services will allow policy-makers in particular to: prepare national, European and international legislation on environmental matters, including climate change; monitor implementation of this legislation; “GMES is a tool for cooperation linked to development, humanitarian aid and emergency situations worldwide and, more specifically, with Africa”

5 Land monitoring service
Reference data: basic geographic framework Local component: 1 m resolution mapping of Urban Areas ( ) and extension to other ‘hot spots’ e.g. Biodiversity protection sites Pan-European component: Land Use / Cover Area mapping at 10 m resolution Global component: the terrestrial essential climate variables, modelling and ‘hot spots’ small water bodies Urban atlas (Dublin) Corine

6 The GCOS ECVs (2010 update) Implementation Plan for the Global Observing System for Climate in Support of the UNFCCC (2010 Update) FINAL DRAFT (v2.0) 19 July 2010 GCOS Secretariat GCOS-138 WMO/TD-No. 1523

7 Main FAPAR product providers.
Projects/Institution Input data Output product Retrieval Method References JRC-FAPAR ESA MERIS Top of Atmosphere (TOA) BRFs in blue, red and near-infrared bands Instantaneous green FAPAR based on direct incoming radiation Optimization Formulae based on Radiative Transfer Models Gobron et al (2000, 2006, 2008) NASA MODIS LAI/FPAR Surface reflectance in 7 spectral bands and land cover map. FAPAR with direct and diffuse incoming radiation Inversion of 3D Model versus land cover type with backup solution based on NDVI relationship) Knyazikhin et al. (1998b) MISR LAI/FPAR Surface products BHR, DHR & BRF in blue, green, red and near-infrared bands + CART FAPAR with direct and diffuse incoming radiation. Knyazikhin et al. (1998a) GLOBCARBON Surface reflectance red, near infrared, and shortwave infrared Instantaneous FAPAR (Black leaves) Parametric relation with LAI as function as Land cover type. Plummer et al. (2006) CYCLOPES Surface reflectance in the blue, red, NIR and SWIR bands FAPAR at 10:00 solar local time Neural network Baret et al (2007) LANDSAF Visible and Near-Infrared bands FAPAR Parametric relation Roujean and Breon (1995) JRC-TIP Broadband Surface albedo in visible and near-infrared bands. FAPAR & Green FAPAR for direct & diffuse incoming radiation Inversion of two-stream model using the Adjoint and Hessian codes of a cost function. Pinty et al. (2007) N. Gobron & M. M. Verstraete (2009) FAPAR: assessment report on available methodological standards and guides, GTOS-65

8 1998 onwards

9 April 2002 onwards


11 Satellite Derived FAPAR Anomalies Anomalies 1998 - 2009 - Base period 1998 - 2010
Gobron et al GRL

12 fAPAR Anomalies Gobron et al GRL

13 Relative fraction of land surface showing fAPAR anomaly
Gobron et al GRL

14 May 2010 fAPAR anomalies

15 June 2010 fAPAR anomalies

16 July 2010 fAPAR anomalies

17 Crossing scales ETM MERIS ETM 0.62 MERIS


19 Decision 4/CP.15

20 Source Bartholome and Belward JRC

21 Systematic sampling - 4016 sample sites
Tropical Latin America & Caribbean (LAC): 1230 sample sites South and Southeast Asia plus PNG and the Solomon Islands (SEA): 741 sample sites Sub-Saharan Africa (AFR):2045 sample sites -> 1990 2000 2010 Samples are 20km x 20km size

22 Acquisition dates for satellite imagery used for the “year 1990 period”

23 Data gaps “1990 / 2000” Data gaps “2005”

24 Cloud cover evaluation of TREES-3 sample sites for the “year 1990 period” (in percent)

25 Global monitoring of Tree Cover Changes : First results on East Africa
Tree cover loss Tree to other wooded land and other vegetation (red = deforestation green = aforestation) Other wooded land loss Other wooded land to other vegetation (orange = loss of other wooded land green = gain of other wooded land) Distribution of Land cover in 1990 (Source Brink and Bodart JRC)

26 August 12, 2008 vol. 105 no. Supplement 1 11498-11504
Photo credit Superbowl 45 was played here Feb 6th 2011 Cowboys Stadium in Arlington, Texas field by 48.8 meters = ca 0.53 ha. We estimate that the Brazilian portion of the Amazon Basin has (or had) 11,210 tree species that reach sizes >10 cm DBH (stem diameter at breast height). Of these, 3,248 species have population sizes >1 million individuals, and, ignoring possible climate-change effects, almost all of these common species persist under both optimistic and nonoptimistic scenarios. At the rare end of the abundance spectrum, however, neutral theory predicts the existence of ≈5,308 species with <10,000 individuals each that are expected to suffer nearly a 50% extinction rate under the nonoptimistic deforestation scenario and an ≈37% loss rate even under the optimistic scenario. Most of these species have small range sizes and are highly vulnerable to local habitat loss. In ensembles of 100 stochastic simulations, we found mean total extinction rates of 20% and 33% of tree species in the Brazilian Amazon under the optimistic and nonoptimistic scenarios, respectively. August 12, 2008 vol. 105 no. Supplement How many tree species are there in the Amazon and how many of them will go extinct? Stephen P. Hubbell*†‡, Fangliang He§, Richard Condit†¶, Luís Borda-de-Água*‖, James Kellner‖, and Hans ter Steege**

27 Deforestation Photo credit, JRC

28 Deforestation; humid tropics 5.8 mha/yr
Photo credit, JRC

29 Deforestation; humid tropics 5.8 mha/yr, 13 mha/yr globally
Photo credit, JRC

30 Deforestation; emissions ~ 1.2 Pg C yr–1
Photo credit, JRC …less than 3 seconds to clear a football field ha/yr ha/day ha/hour ha/minute ha/second pitch size/ha time/pitch/sec humid tropics deforestation only – no regrowth replanting 5.8 mha/yr humid tropics Achard et al. (2002), Science 297, 13 mha/yr globally FAO (2010) Global Forest Resource Assessment Key Findings Emissions; van der Werf et al, 2009, Nature BiogeoSciences

31 10 m 20 m 60 m Sentinel 2 simulations (Courtesy ESA)
Sentinel 2 bands (courtesy ESA)

32 Europe’s EO satellites - 23rd November 1977…
19th June 1981 Meteosat 2 22nd February 1986 SPOT 1 15th June 1988 Meteosat 3 6th March 1989 Meteosat 4 22nd January 1990 SPOT 2 2nd March 1991 Meteosat 5 17th July 1991 ERS-1 26th September 1993 SPOT 3 20th November 1993 Meteosat 6 21st April 1995 ERS-2 2nd September 1997 Meteosat 7 24th March 1998 SPOT 4 1st March 2002 ENVISAT 4th May 2002 SPOT 5 28th August 2002 Meteosat 8 27th October 2005 TopSat 21st December 2005 Meteosat 9 19th October 2006 MetOp-A 7th June 2007 COSMOSkyMed 15th June 2007 TerraSAR-X 9th December 2007 COSMOSkyMed 29th August 2008 RapidEyes 1 to 5 24th October 2008 COSMOSkyMed 29th July 2009 UKDMC2, Demios 1 5th November 2010 COSMOSkymed

33 ©CNES, ©DMCii, ©ASICosmoSkyMed, ©ESA, ©EUMETSAT, ©DLR
23rd November 1977…

34 Summary Continuity of observation is extremely likely (virtually certain up to launch…) Continuity of GCOS ECV generation is very likely(GEOSS, CEOS Working Groups Climate and WGCV for characterisation / validation) Global cloud free optical data sets at high-resolution for key historical epochs e.g are unlikely - but not exceptionally unlikely Data acquisition strategies and data policy need to (continue to) be tuned to global scales – priority areas are known Partnership is key; GEOSS, GCOS, CEOS Virtually certain  > 99% probability  Extremely likely  > 95% probability   Very likely  > 90% probability  Likely  > 66% probability  More likely than not  > 50% probability  About as likely as not  33 to 66% probability  Unlikely  < 33% probability  Very unlikely  < 10% probability  Extremely unlikely  < 5% probability  Exceptionally unlikely  < 1% probability 

35 Acknowledgements FAPAR; Nadine Gobron
TREES-3 optical remote sensing team; Frédéric Achard, René Beuchle, Hugh Eva, Hans-Juergen Stibig, Silvia Carboni, Rastislav Raši, François Donnay, Andreas Brink, Catherine Bodart, Philippe Mayaux, Dario Simonetti, Desirée Johansson, Ouns Kissiyar, Michael Vollmar FAO partners from the Forest Resource Assessment GMES Bureau staff Landsat Data Continuity Mission Science Team GCOS secretariat, steering committee and science panels

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