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Using Impervious Surface as a spatially explicit Proxy Measure of CO 2 Emssions

Using Impervious Surface as a spatially explicit Proxy Measure of CO 2 Emssions. Dr. Paul C. Sutton Department of Geography University of Denver AAG presentation April 2010 Washington DC. Outline. Brief overview of DMSP OLS nighttime lights data product

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Using Impervious Surface as a spatially explicit Proxy Measure of CO 2 Emssions

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  1. 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 • 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 • http://www.purdue.edu/eas/carbon/vulcan/index.php • 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 SystemDefense 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 http://www.purdue.edu/eas/carbon/vulcan/index.php CO2 Emissions Tons of CO2 emitted Per year per 10 x 10 km Grid Cell

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

  10. CO2 Emssions Mapfrom 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 Lightsaggregated to Nations in Ln vs. Ln (Not very satisfying….)

  20. Using 1 deg x 1 deg Data andAggregating 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?

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