May 22nd 2014 ESA, ESRIN | S2 Science 2014
Regionalized and application-specific
compositing
- a review of requirements, opportunities and
challenges
Patrick Griffiths & Patrick Hostert
Geography Department,
Humboldt University Berlin
Joanne White & Mike Wulder
Canadian Forest Service
http://www.hu-geomatics.de
Phone: +49 30 2093 - 6894
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Image compositing now allows to create cloud free, radiometrically consistent image datasets over large areas from the Landsat archive:
Cloud reduction no longer main objective
Surface reflectance allows integration of L4, L5, L7 (, L8)
Compositing strategies are shaped by specific information needs, the
regional context and data constraints:
Annual deforestation vs. agricultural change mapping
Land use, phenology & ecosystem dynamics
Climate, cloud cover & data availability
Global WELD, Roy et al. 2014
Pixel-based compositing
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Varying regional data availability
Total number of scenes acquired by L5 in 1987
Total number of scenes acquired by L7 in 2010
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Parametric scoring approach to determine best-pixel observation:
Griffiths, P., van der Linden, S., Kuemmerle, T., & Hostert, P. (2013). A Pixel-Based Landsat Compositing
Algorithm for Large Area Land Cover Mapping. IEEE Journal of Selected Topics in Applied Earth Observations and
Remote Sensing, 6, 2088-2101.
White, J., Wulder, M., Hobart, G.W., Luther, J.E., Hermosilla, T., Griffiths, P., Coops, N.C., Hall, R.J., Hostert, P., Dyk,
A., & Guindon, L. (in review). Pixel-based image compositing for large-area dense time series applications and
science. Canadian Journal of Remote Sensing.
Determination of best-pixel considering different parameters:
Acquisition year, day-of-year balance annual & seasonal consistency
Distance-to-clouds, thermal temperature, atmospheric opacity, sensor, local solar illumination angle, etc. optimize radiometric consistency
Best-pixel compositing
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Different compositing flavors for best observations:
Annual best-observation composite
Best-pixel compositing
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Different compositing flavors for best observations:
Multi-year best-observation composite
Best-pixel compositing
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Different compositing flavors for best observations:
Proxy-value best-observation composite
Best-pixel compositing
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Utilizing good observations:
Spectral-temporal variability metrics
Best-pixel compositing
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Land cover & crop type mapping
Requirement: two seasonal observations within 2009
25 footprints, 572 images
No topography, few clouds > low data constraints
Pampas, Argentina
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Approach:
Spring (DOY 60) & Fall (DOY 258) composites for 2009
Variability metrics
Extensive field-based training data
Pampas, Argentina
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Seasonal consistency:
Spring composite
Fall composite
Pampas, Argentina
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Land cover map
Pampas, Argentina
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Overall accuracy 81% for 10 classes
High class specific accuracies for corn (92%), soybean (86%), others
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Canada: National Terrestrial Ecosystem Monitoring System (NTEMS)
To support national programs and reporting obligations (e.g., NFI, Carbon
Accounting)
2010
Very large area, 1224 footprints (terrestrial)
>605,000 scenes in Canadian archive
Requirement: annual land cover, land cover change, forest structure
Shorter growing season in north but substantial 80% across track overlap
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National multi-year composite (2009, 2010, 2011)
Canada: NTEMS
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2003
August 1 ± 30 days
Annual
composite Areas with no
observations
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Proxy value
composite
Areas with persistent no data are
assigned a synthetic value, which is
determined using a trajectory of
available values for the pixel.
2003
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Reconstruction of land change histories
Different requirements: forest vs. agricultural dynamics
~5,000 images, 1984-2012, 32 footprints
Strong topography, frequent cloud cover > moderate constraints
Carpathians, Eastern Europe
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Spring composite
Fall composite
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Carpathians, Eastern Europe
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Carpathians, Eastern Europe
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Best-observation composite, target year 2000
Andes, Ecuador
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Data scarcity!
17 footprints, 1538 images, 1984-2012
Cloud free pixel observations (median=40)
Andes, Ecuador
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Annual consistency
Andes, Ecuador
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Summary
Applications & information needs drive compositing decisions:
Need to balance annual & seasonal consistency
Maintain radiometric consistency
Preserve synopticity
Global gradients in data availability:
Importance of USGS archive consolidation efforts
Need for global acquisition strategies
Need to explore strategies for data scarce regions:
Data fusion approaches hold great potential
Multi-mission & multi-sensor approaches
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Summary L8 and L7 acquisitions, June 1st – January 31st, 2014
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Implications for Sentinel-2
Great potential for complementary S2-L8 data use:
Merged surface reflectance product…
Need for rigorous cross-calibration
Need for comparable L1 processing
Need for joint application-focused research
User requirements for S2:
Free data
Systematic global terrestrial acquisition and archive
Standard analysis-ready product
Easy data access
On the long run:
Bring the algorithms to the data..
Seamless temporal-spatial data queries via virtual constellations
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Thank you!
This research is partly funded by the German Federal
Ministry for Economic Affairs and Energy (BMWi)
through the SenseCarbon project (FKZ 50EE1254)
We acknowledge support through the German
Aeronautics and Space Research Centre (LDR) and
Humboldt-University Berlin
This research contributed to the Global Land Project
and the Landsat Science Team
www.hu-geomatics.de/projects/sensecarbon
Research in support of the “National Terrestrial
Ecosystem Monitoring System (NTEMS): Timely
and detailed national cross-sector monitoring for
Canada” project was jointly funded by the
Canadian Space Agency (CSA) Government
Related Initiatives Program (GRIP) and the
Canadian Forest Service (CFS) of Natural
Resources Canada.
We acknowledge contributions of Geordie Hobart,
Txomin Hermosilla, Nicholas Coops
http://www.hu-geomatics.de
Phone: +49 30 2093 - 6894
35
References:
Griffiths, P., Mueller, D., Kuemmerle, T., & Hostert, P. (2013). Agricultural land change in the Carpathian
ecoregion after the breakdown of socialism and expansion of the European Union. Environmental Research
Letters.
Griffiths, P., van der Linden, S., Kuemmerle, T., & Hostert, P. (2013). A Pixel-Based Landsat Compositing
Algorithm for Large Area Land Cover Mapping. IEEE Journal of Selected Topics in Applied Earth Observations
and Remote Sensing, 6, 2088-2101.
Griffiths, P., Kuemmerle, T., Baumann, M., Radeloff, V.C., Abrudan, I.V., Lieskovsky, J., Munteanu, C.,
Ostapowicz, K., & Hostert, P. (2013). Forest disturbances, forest recovery, and changes in forest types across
the Carpathian ecoregion from 1985 to 2010 based on Landsat image composites. Remote Sensing of
Environment.