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

patrick.griffiths@geo.hu-berlin.de

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

patrick.griffiths@geo.hu-berlin.de

http://www.hu-geomatics.de

Phone: +49 30 2093 - 6894

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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.