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Collaborative Tool for Collecting Reference Data on the Density of Constructed Surfaces Worldwide

Collaborative Tool for Collecting Reference Data on the Density of Constructed Surfaces Worldwide. Chris Elvidge NOAA-NESDIS National Geophysical Data Center 325 Broadway, Boulder, Colorado 80305 USA Tel. 1-303-497-6121 Email: chris.elvidge@noaa.gov Ben Tuttle

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Collaborative Tool for Collecting Reference Data on the Density of Constructed Surfaces Worldwide

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  1. Collaborative Tool for Collecting Reference Data on the Density of Constructed Surfaces Worldwide Chris Elvidge NOAA-NESDIS National Geophysical Data Center 325 Broadway, Boulder, Colorado 80305 USA Tel. 1-303-497-6121 Email: chris.elvidge@noaa.gov Ben Tuttle Cooperative Institute for Research in Environmental Sciences University of Colorado, Boulder, Colorado USA and Department of Geography, University of Denver, Denver, Colorado USA Paul Sutton Department of Geography University of Denver, Denver, Colorado USA September 10, 2009

  2. Constructed Surfaces • Roads • Buildings • Vehicles • Sidewalks Collectively referred to as constructed surfaces or impervious surface area (ISA).

  3. Constructed Surfaces • People everywhere build for shelter, commerce and transportation. • Impervious materials are used in buildings to keep out water and enhance durability. • Roads are paved with impervious materials such as asphalt and cement for their ability to shed water, provide a smooth driving surface, and ease of maintenance. • There are large gradients in the density of constructed surfaces, ranging from zero to 100%. • These gradients track gradients in the density of population, economic activity and resource consumption.

  4. How Constructed Surfaces Impact the Environment • Flooding • Reduced groundwater recharge • Urban heat islands • Urban pollutants swept into waterways reduce surface water quality • Intense water discharges tends to incise stream banks • Habitat loss and fragmentation • Linked to reductions in biodiversity

  5. How Constructed Surface Density Grids Are Used • Flood prediction • Design of storm drainage systems (sizing and placement) • Evaluating human impacts on ecosystems • As a measure of the human ecological footprint • As an indicator of the spatial distribution of resource consumption

  6. How Constructed Surface Area Density Grids are made • Ground based surveys – time consuming and covering small areas. • Measurements from high spatial resolution airborne or satellite imagery – expanded coverage from ground surveys – but still limited in spatial extent. • Spectral analysis of moderate resolution satellite imagery (e.g. Landsat). Calibrate with high resolution imagery. Can produce grids of extended geographic areas – but no global product produced to date. • Model from coarse resolution data sources such as nighttime lights or population count. Calibrate using a subset of measurements made from higher spatial resolution imagery.

  7. Year 2000 Global Constructed Surface Area Density Grid • Paper: http://www.mdpi.com/1424-8220/7/9/1962 • Data: http://www.ngdc.noaa.gov/dmsp/download_global_isa.html • Nominally for year 2000 • Input 1: Radiance calibrated DMSP nighttime lights for year 2000 (30 arc second grid) • Input 2: Landscan 2004 population count (30 arc second grid) • Model calibrated using 30 meter resolution ISA densities of the USA derived from year 2000 Landsat (U.S. Geological Survey)

  8. USGS %ISA For Year 2000

  9. Radiance Calibrated DMSP Nighttime Lights For Year 2000

  10. Population Count For Year 2004DOE Landscan

  11. Modeled ISA From Lights and Population

  12. NGDC versus USGS %ISA

  13. Global Nighttime Lights

  14. Global Population Density Landscan U.S. Department of Energy Oak Ridge National Laboratory

  15. Global % ISA Year 2000

  16. Year 2000 Top Twenty Countries

  17. Shortcoming of the Year 2000 Grid • The model was calibrated using 30 meter resolution ISA densities of the USA derived from Landsat (U.S. Geological Survey). • It is likely that there is variation in the relationship between ISA density, lights and population in other parts of the world.

  18. Plans to Build a Better Product • NGDC recently completed an improved radiance calibrated nighttime lights image for 2006. A new collection for radiance calibrated nighttime lights is planned for 2009-10. • U.S. Department of Energy made substantial improvements in the Landscan 2006 and 2007 products. • Use reference data on the density of constructed surfaces collected from high resolution imagery from a wide range of locations and settings. • High spatial resolution satellite imagery is available for many urban areas around the world from systems such as IKONOS and Quickbird. • It would be expensive and time consuming to select, purchase and analyze large numbers of high resolution satellite images. • Could we collect reference data using the high spatial resolution imagery available for view in Google Earth?

  19. Components of Gridded Point Count Collection System For Use of Google Earth Base Imagery • Google Earth Plug-in. • Google Earth Application Programming Interface (API) . • MySQL database used to record the data. • KMZ for DMSP nighttime lights image. • Interactive Data Language program for generating corner points of 30 arc second grid cells. • Scripting done in javascript and php. • Current version is at: http://www.ngdc.noaa.gov/dmsp/imaps/isamapper/

  20. Google Earth Tool

  21. Google Earth Tool

  22. Google Earth Tool

  23. Google Earth Tool

  24. Google Earth Tool

  25. Google Earth Tool

  26. Google Earth Tool

  27. Google Earth Tool

  28. Google Earth Tool

  29. Google Earth Tool

  30. Preliminary Results

  31. Summary • A system for visual collection of gridded point counts of constructed surface density using high spatial resolution imagery available in Google Earth has been developed. • The system is designed to allow contributions from a widely distributed open set of analysts. • The system will make it possible to produce a substantially improved global constructed surface density grid. • Accessing the high resolution imagery in GE saves the project from the costs of selecting, purchasing, storing, organizing and accessing high resolution color imagery from around the world. • Improvements to the system are being discussed (tutorial, randomization of grid point selections, additional cover type categories). • The system could be modified for other types of gridded point count collections (e.g. tree cover).

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