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Enhancing Global Land Cover Mapping: GLC2000 Preliminary Version and Future Improvements

Explore the preliminary version of the GLC2000 global land cover product and potential enhancements. Learn about mapping methods, maintaining regional legends, and utilizing ancillary datasets for improved accuracy. Discover future steps towards a more detailed and updated global map.

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Enhancing Global Land Cover Mapping: GLC2000 Preliminary Version and Future Improvements

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  1. Towards a global Land Cover 2000 product: the preliminary version of the global product and how it can be improved Steffen Fritz GVM/JRC Thanks to: Etienne Bartholomé Alan Belward etc. .

  2. GLC2000 - The different windows

  3. Method used for mosaicing • The best window was chosen according to expert knowledge and ancillary data • Data was checked for georeferencing • All regional maps were re-classified into the global legend • Borders between the different datasets defined • Natural boundaries • When the two maps had identical land cover classes

  4. Method used for mosaicing cont. • Global map can be updated • If new regional versions become available • If borders between datasets (windows) change • If global legend changes

  5. Where are we? • Globe is mapped except: mainly islands and eastern part of Eurasia, North and South of 75 degrees latitude (Eurasia 72.3 degrees) • For most windows mapping has been completed • Global map will not change significantly except legend, gaps, updated versions, possible classification errors

  6. Further steps • Making the global product available – Proposal: • global version light – without regional legend • global version - with regional legend • How can we maintain the regional legends? • Each pixel gets a regional legend identifier • All regional legends provided in an excel file • Global legend and regional legend attributes • Globally consistent regional classes could have the same name • Makes it an extremely ‘detailed’ product

  7. Instead of using borders between datasets use a rule based approach e.g. if 3 windows overlap use classification where 2 agree e.g. If one class is better mapped in one dataset (e.g. forest) use it for the whole overlap otherwise map until border line Advantage: global map is improved Disadvantage: border between windows might be visible Further Steps cont.

  8. Globally consistent Urban class? Night time luminosity as an option This has been done for India, South America, Africa, Eurasia Possibility of using ancillary datasets e.g. for Europe (Pelkom, Global Insight) Similar issue with water bodies (e.g. Europe) Further issues

  9. Identify further user needs (aggregated version, forest/non-forest map, etc.) What can be extracted from GLC2000/ were are the limits and error bars Validation Which areas are well mapped/ How can we show that? How does GLC2000 compare to other global products / regional products Further work

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