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Patrick Hostert Tobias Kuemmerle Dirk Pflugmacher

Synergies between future Landsat and European satellite missions for better understanding coupled human-environment systems. Patrick Hostert Tobias Kuemmerle Dirk Pflugmacher.

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Patrick Hostert Tobias Kuemmerle Dirk Pflugmacher

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  1. Synergies between future Landsatand European satellite missions forbetter understanding coupledhuman-environment systems Patrick Hostert Tobias Kuemmerle Dirk Pflugmacher Warren Cohen (USDA-FS), Patrick Griffiths (HU Berlin), Hermann Kaufmann (GFZ Potsdam, Germany), Robert Kennedy (Boston U), Pedro Leitão (HU Berlin), Volker Radeloff (U Madison), Achim Röder (U Trier, Germany), Ruth Sonnenschein (EURAC, Bolzano, Italy), Thomas Udelhoven (U Trier, Germany), Sebastian van der Linden(HU Berlin), Björn Waske (U Bonn, Germany) Landsat Science Team Meeting, Buellton, February 10-14, 2013

  2. Overall goals • Exploring dense and long time series • Creating Landsat-based products across large areas • Integrating Landsatwith future Sentinel-2 and EnMAPdata • Central and Eastern Europe, SE-Asia, S-America

  3. December 2012 – February 2013 • Testing Landsat-based time series analysis with yearly data and based on the full archive depth (since TM) • Focus on the Carpathian Mountains of Eastern Europe: LULCC mapping and development of LU intensity metrics • Focus on SE-Asia: improved forest degradation mapping for REDD+ • Focus on Brazil: understanding pasture/cropping dynamics in Amazonia and their implications for carbon fluxes

  4. Data transfer INPE - USGS INPE USGS

  5. Data transfer INPE - USGS INPE USGS

  6. Data transfer INPE - USGS INPE USGS

  7. Data transfer INPE - USGS INPE USGS

  8. Understanding pasture dynamics from deep time series 150 Images Stack – Single Pixel Profiles Intensive Pasture

  9. Understanding pasture dynamics from deep time series 150 Images Stack – Single Pixel Profiles IntensifiedPasture

  10. Understanding pasture dynamics from deep time series 150 Images Stack – Single Pixel Profiles ExtensifiedPasture

  11. Arising questions, remaining problems • How error-prone are some of the “traditional” analysis results in highly dynamic environments? • How reliable are business-as-usual scenarios / baselines e.g. for REDD+ based on “snapshots”? • What does that mean for quantifying “additionality” estimates? • We need better haze and smoke flagging in the wet tropics. • Deriving reliable and transferable metrics from deep time series in highly dynamic environments is challenging.

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