Using Impervious Surface as a spatially explicit Proxy Measure of CO 2 Emssions Dr. Paul C. Sutton Dr. Sharolyn Anderson Dr. Sharolyn Anderson Department.

Slides:

Advertisements

Similar presentations

Access to near real time global visible and thermal band mosaics from NGDC Chris Elvidge and Edward H. Erwin, NOAA-NGDC

Advertisements

VIIRS Data Evaluation Over Thailand and Japan February 13, 2012 Chair, Chris Elvidge NOAA-NGDC 1.

DEPARTMENT OF GEOMATIC ENGINEERING Mapping Anthropogenic Activities from Earth Observation Data Christopher Doll, Jan-Peter Muller Workshop on Gridding.

How fossil fuel CO 2 uncertainty impacts estimates of carbon exchange and variability Kevin Gurney, Yang Song, Jianhua Huang, Kevin Coltin, Alex Garden.

VIIRS LST Uncertainty Estimation And Quality Assessment of Suomi NPP VIIRS Land Surface Temperature Product 1 CICS, University of Maryland, College Park;

Assessment of black carbon in the Arctic: new emission inventory of Russia, model evaluation and implications Kan Huang 1, Joshua S. Fu 1,2, Xinyi Dong.

Global Mapping of Impervious Surface Area Benjamin Tuttle* - CIRES, University of Colorado, Boulder Christopher D Elvidge, PhD - NOAA-National Geophysical.

An Exploration of using Nighttime Satellite Imagery from the DMSP OLS for Mapping Population and Wealth in Guatemala Paul C.Sutton Department.

Relationships between Nighttime Imagery and Population Density for Hong Kong Qing Liu Paul C. Sutton Christopher D. Elvidge Asia Pacific Advanced Network.

Improving intraurban land use characterization with nighttime imagery Sharolyn Anderson, Assistant Professor, University of Denver Paul C. Sutton, Associate.

Multispectral Remote Sensing Systems

The Real Wealth of Nations: Mapping and Monetizing the Human Ecological Footprint Dr. Paul C. Sutton Presentation for The School of Natural Resources University.

Fusion of SeaWIFS and TOMS Satellite Data with Surface Observations and Topographic Data During Extreme Aerosol Events Stefan Falke and Rudolf Husar Center.

Estimation of GDP at Sub-National Scales using Nighttime Satellite Imagery Paul Sutton Department of Geography University of Denver April, 2005 Presentation.

Paving the Planet: Mapping and Monetizing Human Impact on the Earth Presentation Footprint Forum June 11, 2010 Colle di Val d’Elsa Paul C. Sutton Department.

Mapping the Dollar value of natural production and the Dollar cost of human consumption globally Paul C. Sutton & Benjamin Tuttle Department of Geography.

Detector Configurations Used for Panchromatic, Multispectral and Hyperspectral Remote Sensing Jensen, 2000.

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.

Progress in Empirical Measurement of the Urban Environment: An exploration of the theoretical and empirical advantages of using Nighttime Satellite Imagery.

Introduction, Satellite Imaging. Platforms Used to Acquire Remote Sensing Data Aircraft Low, medium & high altitude Higher level of spatial detail Satellite.

An Empirical Environmental Sustainability Index derived solely from Nighttime Satellite Imagery and Ecosystem Service Valuation Paul Sutton

Page 1 1 of 21, 28th Review of Atmospheric Transmission Models, 6/14/2006 A Two Orders of Scattering Approach to Account for Polarization in Near Infrared.

Meteorological satellites – National Oceanographic and Atmospheric Administration (NOAA)-Polar Orbiting Environmental Satellite (POES) Orbital characteristics.

An Overview of Methods for Estimating Urban Populations Using Nighttime Satellite Imagery Paul Sutton Department of Geography University.

Preliminary Estimation of Annual Gas Flaring Volumes in South and East Asia Chris Elvidge NOAA – National Geophysical Data Center 325 Broadway, E/GC2 Boulder,

Hyperspectral Satellite Imaging Planning a Mission Victor Gardner University of Maryland 2007 AIAA Region 1 Mid-Atlantic Student Conference National Institute.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Correction of Vegetation Time Series for Long Term Monitoring Marco Vargas¹.

VIIRS Data of Japan Izumi Nagatani AFFRIT, MAFF Japan APAN33 EM-WG Workshop in Chiang Mai, February 13,2012 VIIRS Data Evaluation over Thailand and Japan.

High-resolution global CO 2 emissions from fossil fuel inventories for 1992 to 2010 using integrated in-situ and remotely sensed data in a fossil fuel.

Change Detection In Satellite Observed Nightime Lights Chris Elvidge, Earth Observation Group NOAA National Geophysical Data Center (NGDC). Boulder, Colorado.

Estimation of poverty rates based on satellite observed nighttime lights Chris Elvidge, NOAA National Geophysical Data Center (NGDC) Boulder, Colorado.

Satellite Imagery and Remote Sensing NC Climate Fellows June 2012 DeeDee Whitaker SW Guilford High Earth/Environmental Science & Chemistry.

The earth at night Source:

MODIS: Moderate-resolution Imaging Spectroradiometer National-Scale Remote Sensing Imagery for Natural Resource Applications Mark Finco Remote Sensing.

Prospects for Improved Global Mapping of Development Using VIIRS Data Chris Elvidge Earth Observation Group NOAA-NESDIS National Geophysical Data Center.

Collaborative Tool for Collecting Reference Data on the Density of Constructed Surfaces Worldwide Chris Elvidge NOAA-NESDIS National Geophysical Data Center.

Scale Effect of Vegetation Index Based Thermal Sharpening: A Simulation Study Based on ASTER Data X.H. Chen a, Y. Yamaguchi a, J. Chen b, Y.S. Shi a a.

Digital Numbers The Remote Sensing world calls cell values are also called a digital number or DN. In most of the imagery we work with the DN represents.

Diagnosing Climate Change from Satellite Sounding Measurements – From Filter Radiometers to Spectrometers William L. Smith Sr 1,2., Elisabeth Weisz 1,

VIIRS Global DNB Product Status Chris Elvidge Earth Observation Group NOAA National Geophysical Data Center Kim Baugh, Feng-Chi Hsu, Mikhail Zhizhin CIRES,

U.S. Department of the Interior U.S. Geological Survey Multispectral Remote Sensing of Benthic Environments Christopher Moses, Ph.D. Jacobs Technology.

The Spatial GINI Coefficient

Christine Urbanowicz Prepared for NC Climate Fellows Workshop June 21, 2011.

With the rapid development of the Chinese economy in the last two decades, more and more people have relocated to cities and adjacent areas from rural.

Remote Sensing. Vulnerability is the degree to which a system is susceptible to, or unable to cope with, adverse effects of climate change, including.

Satellite-derived Sea Surface Temperatures Corey Farley Remote Sensing May 8, 2002.

1 of 26 Characterization of Atmospheric Aerosols using Integrated Multi-Sensor Earth Observations Presented by Ratish Menon (Roll Number ) PhD.

Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry

What is the key science driver for using Ocean Colour Radiometry (OCR) for research and applications? What is OCR, and what does it provide? Examples of.

Goal: to understand carbon dynamics in montane forest regions by developing new methods for estimating carbon exchange at local to regional scales. Activities:

Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.

SGM as an Affordable Alternative to LiDAR

Validation of Satellite-derived Clear-sky Atmospheric Temperature Inversions in the Arctic Yinghui Liu 1, Jeffrey R. Key 2, Axel Schweiger 3, Jennifer.

VIIRS Nighttime Lights Creating the Next Generation of Global Remotely-Sensed Nighttime Lights Products From VIIRS Day/Night Band (DNB) Data Chris Elvidge.

1 GLIMPSING THE FIRST PRODUCTS FROM VIIRS Dr. Wayne Esaias NASA GSFC Thomas F. Lee Jeffrey Hawkins Arunas Kuciauskas Kim Richardson Jeremy Solbrig Naval.

Impervious Surface Area of the Conterminous United States Christopher D. Elvidge John B. Dietz Paul S. Sutton.

NOAA Northeast Regional Climate Center Dr. Lee Tryhorn NOAA Climate Literacy Workshop April 2010 NOAA Northeast Regional Climate.

Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry

Methane Retrievals in the Thermal Infrared from IASI AGU Fall Meeting, 14 th -18 th December, San Francisco, USA. Diane.

Japan Meteorological Agency, June 2016 Coordination Group for Meteorological Satellites - CGMS Non-Meteorological Application for Himawari-8 Presented.

A Remote Sensing Approach to Measuring Artificial Night Time Light at North Carolina Sea Turtle Nesting Beaches Chelsey Stephenson College of Earth, Ocean,

Validation of Satellite-derived Lake Surface Temperatures

Toru Kouyama Supported by SELENE/SP Team HISUI calibration WG

Digital Numbers The Remote Sensing world calls cell values are also called a digital number or DN. In most of the imagery we work with the DN represents.

Lunar reflectance model based on SELENE/SP data

Igor Appel Alexander Kokhanovsky

Paul C. Sutton & Benjamin Tuttle Department of Geography

Department of Geography

Paul C. Sutton & Benjamin Tuttle Department of Geography

VALIDATION OF FINE RESOLUTION LAND-SURFACE ENERGY FLUXES DERIVED WITH COMBINED SENTINEL-2 AND SENTINEL-3 OBSERVATIONS IGARSS 2018 – Radoslaw.

Presentation transcript:

Using Impervious Surface as a spatially explicit Proxy Measure of CO 2 Emssions Dr. Paul C. Sutton Dr. Sharolyn Anderson Dr. Sharolyn Anderson Department of Geography University of Denver Dr. Chris Elvidge & Kim Baugh (NOAA) Tilotamma Ghosh & Ben Tuttle EARSeL Workshop Bonn, Germany November 2009

Outline Brief overview of DMSP OLS nighttime lights data productBrief overview of DMSP OLS nighttime lights data product Mapping & Estimating Impervious SurfaceMapping & 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.Brief overview of gridded CO2 emission data for U.S. –Vulcan CO2 data – Correlation Analysis of ISA and DMSP with CO2 emissionsCorrelation 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 estimatesRegression Model, Error Pattern, Global estimates

Two sun-synchronous polar orbiting satellites (865 km orbit) Observations at 1) ~ Dawn & Dusk, 2) ~ Noon & Midnight Pixel Size: smoothed ~2.4 km 2, fine ~ 0.5 km 2, 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 The Satellite System Defense Meteorological Satellite Program Operational LineScan System (DMSP OLS)

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

City Lights Data Product for Conterminous United States

Mapping Impervious Surface

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

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

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

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

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 cells1 degree x 1 degree km grid cells

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

Correlation (R) Matrix 10 km x 10 km

Correlation (R) Matrix 20 km x 20 km

Correlation (R) Matrix 30 km x 30 km

Correlation (R) Matrix 50 km x 50 km

Correlation (R) Matrix 1º x 1º

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

Impervious Surface Area is a reasonable proxy measure of Ecological Footprint Impervious Surface Area per Person vs. ecological footprint per person for 149 countries using Ecological Footprint Data from Wackernagel et. Al.

Questions?