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Land Cover from ENVISAT MERIS

Land Cover from ENVISAT MERIS. Adrian Luckman & Laine Skinner Department of Geography, University of Wales Swansea, UK. Why does the SIBERIA project need Land Cover?. GHG accounting IIASA GIS approach Land cover defines basic GIS polygons Dynamic Vegetation Models (DVMs)

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Land Cover from ENVISAT MERIS

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  1. Land Coverfrom ENVISAT MERIS Adrian Luckman & Laine Skinner Department of Geography, University of Wales Swansea, UK

  2. Why does the SIBERIAproject need Land Cover? • GHG accounting • IIASA GIS approach • Land cover defines basic GIS polygons • Dynamic Vegetation Models (DVMs) • Land cover is primary data source for Plant Functional Type (PFT)

  3. Level 1 1Unproductive land 2Agricultural land 3Forestland 4Wetlands 5Grasslands and shrubs 11 Water 13 Soil & Rock 14 Urban & Industry 21 Arable, hayfields & pasture 31 Light Coniferous 32 Dark Coniferous 33 Soft Deciduous 34 Mixed Forest 35 Unproductive Needle-leaf Forest 36 Unproductive Broadleaf Forest 41 Wetlands • 51 Tundra – Lichen Moss • 52 Tundra - Heath • Steppe • Humid grasslands Level 2 Defining land cover in SIBERIA Land cover

  4. Previous work • Sergey Bartalev @ JRC • GLC2000 classification • SPOT Vegetation • 1km spatial resolution • How can we do better? • SIBERIA time frame • Better spatial resolution • Classes more appropriate to SIBERIA • Land cover change

  5. Archived Planned Land cover data sources Description 2001 2002 2003 2004 • MODIS • Data availability, quality and spectral bands • MERIS plus AATSR (for SWIR) • May have significant advantages for land cover AATSR~1000 Scenes MERIS~750 single scenes~170Gb per year MODIS~11Gb per year ~100Gb per year Monthly acquisitions from June to Sept. 16 day 1km composites 8 day 500m composites

  6. Histogram of forestry polygon sizes in S. Lake Baikal region VEGETATION MODIS MERIS Sqrt (polygon area) MERIS and land cover • MERIS Advantages: • European sensor • Future data availability • More appropriate spatial resolution for land cover

  7. Advantages of MERIS MODIS, 500m R:Red, G:Green, B:Blue MERIS, 300m R:Red, G:Green, B:Blue

  8. Land cover classificationmethod • Supervised classification • C5.0 (decision tree) • Training areas • Expert knowledge • Russian colleagues • Landsat TM analysis • Blue rectangles • Some classes from GLC2000 • 1000 training polygons

  9. Upscaling of information • Training polygons defined by • Russian forest database polygons • Obvious non-forest classes • E.g. urban • Verified by experts Landsat TM R:swir, G:NIR, B:red

  10. MERIS data requests • Original Quota • ~160 scenes • Would give only 10 days coverage • Thanks to ESA for extending the SIBERIA quotas • Data requests • June-Oct 03+04 • 1 day = ~15 • 10 days per month • Composite for cloud removal EOLI visualization Magenta indicates SIBERIA region

  11. SIBERIA in July from MERIS • Partial coverage • Reduced availability of training classes • Haze visible • We have yet to utilise ESA aerosol retrieval • Class labelling will be in error • But features will be discriminated • Following results very preliminary

  12. Result: Agricultural area MODIS, 500m MERIS, 300m

  13. Result: Agriculture-forest-tundra GLC2000, 1km MODIS, 500m MERIS, 300m

  14. Result: River in forest GLC2000, 1km MODIS, 500m MERIS, 300m

  15. Conclusions • Achievements with ENVISAT MERIS • Processing chain: CD to mosaics • >750 CDs expected per year • Preliminary classification • Demonstrates value of MERIS resolution • What we would like from ESA • Keep the data coming • Simpler data ordering and distribution? • FTP rather than CD • Automated and integrated ordering and processing • Future aims • Add:AATSR (SWIR) and ASAR (WS) • Siberia-wide land cover and change at 300m

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