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2006 National Monitoring Conference, San Jose, CA, May 2006

The use of remotely sensed and GIS-derived variables to characterize urbanization in the National Water-Quality Assessment Program. James Falcone. jfalcone@usgs.gov. 2006 National Monitoring Conference, San Jose, CA, May 2006. Presentation Objective.

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2006 National Monitoring Conference, San Jose, CA, May 2006

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  1. The use of remotely sensed and GIS-derived variables to characterize urbanization in the National Water-Quality Assessment Program James Falcone jfalcone@usgs.gov 2006 National Monitoring Conference, San Jose, CA, May 2006

  2. Presentation Objective • Overview of some data sources, tips, and techniques used in NAWQA urban studies • Perspective generally at the national/regional level

  3. Urbanization • Continually increasing stressor • Trend in last decades towards landscape fragmentation (‘sprawl’) • Fragmentation argues for understanding spatial pattern as well as intensity The “Mixing Bowl”, Washington, D.C. Beltway

  4. Spatial pattern A B % impervious surface low high C

  5. How to measure ‘urbanization’? • We have tried to use traditional methods: • Basin or riparian-scale statistics • And ‘value-added’ methods: • Landscape pattern metrics • Proximity-based metrics • Data-source merges • Multimetric indexes

  6. Major Sources of Urban Ancillary Data for Ecological Studies • Land cover/imperviousness • Roads/transportation • Census-derived • EPA-regulated sites • e.g. NPDES

  7. Update on NLCD01 • USGS National Land Cover Data (NLCD) for ’00-01 time frame • Completed zones as of April 18, ‘06 (below) • Includes sub-pixel imperviousness and forest canopy layers, in addition to traditional categorical land cover data • Urban classes differ from NLCD92 (“land cover”, not “land use”) www.mrlc.gov

  8. NOAA 1-km Imperviousness NOAA has mapped impervious surface for entire US at 1-km resolution http://dmsp.ngdc.noaa.gov/html/download_isa2000_2001.html

  9. 0.3-m orthoimagery – USGS 133 cities http://seamless.usgs.gov

  10. Roads • CENSUS TIGER 2000 roads • Entire US; free; consistent • Positional accuracy not great • But representational accuracy reasonable • Better accuracy roads available for localized areas • TIGER 1990 roads not easily available and inconsistent with 2000 Reston, VA

  11. Many Census statistics under-examined • e.g. percent homes > 50 years old • % homes with incomplete plumbing, etc. Census-derived data • Vast panoply of urban statistics from Census for various geographies (county, tract, block group).

  12. “Value-added” metrics/techniques: Example 1 merging residential classes from GIRAS • NAWQA “enhanced” land cover – “improving” NLCD92 by merging with high-confidence classes from other data sources (Hitt, Nakagaki, Price) NLCD92-”enhanced” GIRAS

  13. Example 2: Distance-weighting by proximity to sampling site or stream Wolf Creek, Ohio 14% urban “standard” 43% urban “distance-weighted” Lamington River, NJ 14% urban “standard” 12% urban “distance-weighted”

  14. Urban Index (entire session on this method Tuesday PM) • Used by NAWQA Effects of Urbanization on Stream Ecosystems (EUSE) Program (and others) • Combining multiple urban variables in a single “urban intensity” metric • Most consistent correlation to population density across geographic settings: • Road density • % urban land cover • Housing unit density

  15. Vexing Issues Often Overlooked or Under-considered • Scale – what is the appropriate scale of ancillary data for your project? Is a 1-km pixel “too coarse” for a 5-km2 basin? • Accuracy – what is “good enough” accuracy? Should you use land cover classes that are only 60% accurate? How about 40%? • Currency – how old is “too old”? Loudoun County, VA exploded in population by 100% between 1990 and 2000 – should you use 1990 land cover to evaluate stream sampling done in 2004?

  16. Summary • Both “traditional” and “non-traditional” metrics may add explanatory information • Roads powerful driver in process and generally consistent over broad areas • Some data/metrics may be better representations of “process” vs “structure” • Acquiring consistent geospatial time-series data a significant issue • Important considerations of scale, accuracy, and currency should drive data collection.

  17. Portland by night

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