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Thomas J. Cova Department of Geography, University of Utah Paul C. Sutton

Light My Fire Proneness: Residential Change Detection in The Urban-Wildland Interface with Nighttime Satellite Imagery. Thomas J. Cova Department of Geography, University of Utah Paul C. Sutton Department of Geography, University of Denver David M. Theobald

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Thomas J. Cova Department of Geography, University of Utah Paul C. Sutton

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  1. Light My Fire Proneness: Residential Change Detection in The Urban-Wildland Interface with Nighttime Satellite Imagery Thomas J. Cova Department of Geography, University of Utah Paul C. Sutton Department of Geography, University of Denver David M. Theobald Department of Natural Resource Recreation & Tourism and The Natural Resource Ecology Lab (NREL), CSUApril 2004

  2. The Urban-Wildland Interface

  3. Tracking Interface Development • Where is interface development occurring and at what densities? • How many people live in the interface? • What is the historic rate of development? • Are emergency services keeping pace with the change? • Where might development occur in the future?

  4. Existing Change-Detection Approaches • The Census? • Really big tracts in exurban areas. • Updated once every 10 years on the ’00. • Existing RS Change Detection Methods? • Expensive for large, forested areas. • City, County or State Agencies? • Difficult to acquire and tile for large areas and regions that cross jurisdictions.

  5. Wildlands? or Residential?

  6. DMSP-OLS Data (1 km)

  7. Exploring DMSP Nighttime Lights Data • 30m National Land Cover Data (NLCD) classified areas as ‘forest’ that Ikonos revealed had many homes. • DMSP saw low levels of light in the same areas.

  8. Classifying DMSP Change (’92-’00) • 6-bit data (64 values) • 0 – “no light” (NL) • 1-62 – “low light” (LL) • 63 – “saturated” (S) NL LL S NL LL S

  9. DMSP Change Map for Colorado

  10. “Extreme” Fire Risk Mapping Method • Composite score based on fire frequency and fire severity of seven forest types found in Colorado: Lower montane, montane, and upper montane, mixed conifer, aspen, pinyon-juniper, and shrubland. • Fire frequency: (normalized 1-10) based on the inverse of historical frequency and mapped using species level NLCD data set. • Fire severity: (normalized 1-10) relies on DEM to distinguish moist north-facing from dry south-facing slopes and very dry ridges. • Multiply the two together and average with a ¼ mile filter.

  11. Light on Fire Risk For 2000

  12. Results Highlights (’92-’00) • Grand County had the greatest absolute increase in ambient sprawl into fire-prone areas (215 km2). • Teller County had the greatest percentage increase (7.3%). • In 2000, La Plata County had the most ambient development in fire-prone areas (909 km2) • In 2000, Jefferson County had the greatest percentage (42%) of ambient development in fire-prone areas.

  13. Conclusions • DMSP with Fire Mapping shows promise for detecting residential change in fire prone areas. • It can get into problems with snow reflectance, changes in vegetation, ephemeral light, etc. • New processing of DMSP data will improve this method (low gain images available from ‘98).

  14. Paper / Questions? Cova, T.J., Sutton, P.C., and Theobald, D.M. (2004) Exurban change detection in fire-prone areas with nighttime satellite imagery, Photogrammetric Engineering and Remote Sensing, in press. http://www.geog.utah.edu cova@geog.utah.edu

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