1. Department of Geography
Using Impervious Surface as a spatially explicit Proxy Measure of CO2 Emssions
Dr. Paul C. Sutton
Department of Geography
University of Denver
AAG presentation
April 2010
Washington DC

2. Outline http://www.purdue.edu/eas/carbon/vulcan/index.php
Brief overview of DMSP OLS nighttime lights data product
Mapping & Estimating Impervious Surface
Multiple Regression using Landscan Population and Nighttime Lights
Validation using High Resolution Aerial Photography
Brief overview of gridded CO2 emission data for U.S.
Vulcan CO2 data
Correlation Analysis of ISA and DMSP with CO2 emissions
Sources: Commercial, Cement, Industrial, Mobile, Residential, Utilities, Aircraft, Non-Road transportation, Total
Scales: 10 km, 20 km, 30 km, 50 km, 100 km (1 degree x 1 degree)
Mapping & Monetization of National Ecological Deficits and Surpluses
Regression Model, Error Pattern, Global estimates

3. The Satellite System Defense Meteorological Satellite Program
Operational LineScan System (DMSP OLS)
Two sun-synchronous polar orbiting satellites (865 km orbit)
Observations at 1) ~ Dawn & Dusk, 2) ~ Noon & Midnight
Pixel Size: smoothed ~2.4 km2, fine ~ 0.5 km2, Swath Width ~3000 km
Two Bands: 1) Panchromatic VNIR, 2) Thermal Infrared
Dynamic Range: VNIR more than 4 orders of magnitude larger than traditional sensors optimized for daytime observation
(e.g. sees light from reflected moonlight to reflected sunlight)
Data available from early 1970’s to Present, Digital Archive est. in 1992
Data Products derived from imagery (hyper-temporal mosaicing):
% cloud cover, % light observed, Fires, Lantern Fishing,
Gas Flares, City Lights, Radiance Calibrated City Lights,
Atmospherically corrected radiance calibrated city lights

4. Example of Cloud Screening over Italy
VNIR over Italy Thermal over Italy

5. City Lights Data Product for Conterminous United States

6. Mapping Impervious Surface

7. Impervious Surface Area (ISA) Data Product for Conterminous United States

8. New Question: How good a Proxy is ISA or Nighttime lights for CO2 Emissions?
Data Source: Spatially Explicit Accounting of CO2 emission sources
obtained from The Vulcan Project
CO2 Emissions Tons of CO2 emitted
Per year per
10 x 10 km
Grid Cell

9. CO2 Emissions Map from Mobile Sources
423,140,828 Tons of Carbon Per Year ~28% of Total U.S. CO2 emissions

10. CO2 Emssions Map from Utilities
586,694,308 Tons of Carbon Per Year ~39% of Total U.S. CO2 emissions

11. Aggregate ALL Datasets to Several Spatial Scales
~20 km x ~20 km grid cells
~30 km x ~30 km grid cells
~50 km x ~50 km grid cells
1 degree x 1 degree km grid cells

12. Correlation Matrix Assessed for all Scales
10 km x 10 km
20 km x 20 km
30 km x 30 km
50 km x 50 km
1º x 1º

13. Correlation (R) Matrix 10 km x 10 km

14. Correlation (R) Matrix 20 km x 20 km

15. Correlation (R) Matrix 30 km x 30 km

16. Correlation (R) Matrix 50 km x 50 km

17. Correlation (R) Matrix 1º x 1º

18. These Models are applied Globally
Note: A global product was generated using both DMSP OLS and Landscan Population Data

19. Using 10 km Resolution Lights aggregated to Nations in Ln vs. Ln
(Not very satisfying….)

20. Using 1 deg x 1 deg Data and Aggregating to National Level

21. Summary & Conclusions
Nighttime Lights and derived Data products such as ISA (Impervious Surface) are significant proxy measures of CO2 emissions.
However, They are better proxies of some forms of CO2 emissions than others (transportation yes, Cement not so much)
U.S. parameterization may not generalize well to other nations.
“Several Error prone measures are better than one”

22. Questions?