1. Potential Landsat Contributions
for GEO-C Initiative
Zhiliang Zhu, USGS
24 October, 2017

2. The need for land and water data in global carbon observation
Land sector (both emissions and sinks) represents 20% of the total global budget
Uncertainty is highest for land sink
Better land data is needed, Landsat is the best choice, as it has been used broadly
USGS is working on making it even more useful/operational for EO
Global Carbon Project, Quéré et al 2016

3. History of Landsat
The longest and most comprehensive record of the Earth’s land and water surface

4. What makes Landsat useful to Carbon Observation?
Landsat Data Usages
(10/01/2015 through 09/30/2016)
Free, open, and easy access
Global acquisition
Calibrated, science-quality data
Resolution sufficient to distinguish between natural and human-induced land change
Continuous 45+ year archive

5. Potential Landsat contributions to carbon observation and GEO-C tasks
Land base representation for GHG inventories
Attribution for land use change
Specific fluxes e.g. wetlands, aquatic (rivers, lakes, reservoirs), and disturbance such as wildfire
GEO-C Tasks
User needs and policy interface
Data access and availability
Optimization of observational networks
Budget calculations and breakdowns

6. Current US approach for land base representation
EPA National GHG Inventory Report
Forest and agriculture inventories
Landsat-based mapping by USGS

7. Landsat is found to be the most suitable RS tool for land base representation
No discernable trend in accuracy with time or study area
There is general pattern between sensor types
Yu et al 2014, Meta-discoveries from a synthesis of satellite-based land-cover mapping research, IJRS

8. Emergence of Landsat models to track land use changes
Vegetation change tracker (VCT), Huang et al 2010
LandTrender, Kennedy et al 2010
Continuous change detection and classification (CCDC), Zhu and Woodcock 2014
Examples of the recent models:
Common concept of the models:

9. Emergence of Landsat models to track land use changes
Current results from the use of CCDC model, showing land change over the last 30 years in the U.S. Puget Sound region

10. How well do the models improve land use change tracking
How well do the models improve land use change tracking? – More data points = more accurate land base representation
(C. Huang, unpublished data on tree-height modeling with Landsat)

11. Landsat Essential Climate Variables - Wildfire
Area
Emission

12. Landsat Essential Variables – Dynamic Surface Water Extent

13. USGS efforts to make Landsat more operational for EO: Analysis Ready Data
Landsat data processed to a level that enables direct use in applications
Allows geospatial, multi-spectral, and multi-temporal manipulations for the purposes of data reduction, analysis, and interpretation
Consistent radiometric processing scaled to TOA and surface reflectance
Consistent geometry including spatial coverage and cartographic projection – e.g., pixels align through time
Metadata of sufficient detail on data provenance, geographic extent, and data quality

14. Building on the ARD: USGS Land Change Mapping, Analysis, Projection (LCMAP) Program
LCMAP is an institutional asset capable of producing national and global land use change maps at 30 meter resolution
Also is capable of on-demand data products
The program is scheduled to go operational in 2018 and we expect it will be a major contribution to tools needed for carbon observation

15. Prototyping LCMAP change products for ecosystem carbon tracking
1986
1995
2010

16. Summary
Landsat is uniquely suited to carbon observations: spatial resolution, temporal coverage, spectral characteristics, and USGS commitment to data quality
Landsat contributes to global carbon observations in several key areas: land base representation, land use attribution, and specific land fluxes
Improvements in Landsat data processing standard and modeling are poised to advance good practice guidance and other applications for carbon observation
As the result, supporting global carbon monitoring will further enhance the value of Landsat
Thank you!