Vegetation and Population Density in Urban and Suburban Areas in the U.S.A.

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Vegetation and Population Density in Urban and
Suburban Areas in the U.S.A.

• Francesca Pozzi
• Center for International Earth Science
  Information Network
• Columbia University
• New York, USA
• Christopher Small
• Lamont-Doherty Earth Observatory
• Columbia University
• New York, USA

Istanbul, 11-13 June 2002
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Objectives

• Characterize urban areas based on demographic and
  physical characteristics
• Population Density
• Vegetation Abundance
• Examine consistency of relationship between the
  two variables in the USA
• Compare with existing land cover classification
  (USGS)
• Can this help us find alternative classification
  systems for urban areas?

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Case Study The USA

• 6 cities with different geographic location,
  physical environment and urban growth dynamics

Atlanta Chicago Los Angeles New
York Phoenix Seattle
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Data Population Density

• 1990 US Census Bureau population counts at the
  block level (Spatial and tabular data)
• Density in people/km2
• Data reprojected to UTM,
• Rasterized to 30 m,
• Co-registred to Landsat

New York City
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Data Vegetation Abundance

• Landsat TM data, circa 1990
• Spectral reflectance of many urban areas can be
  described as linear mixing of
• Low albedo
• High albedo
• Vegetation
• Linear un-mixing
• Fraction images showing areal of each endmember
  within each pixel (0 to 1)
• Validation with IKONOS, accuracy within 10

Vegetation Fraction (White 0, Dark Green 1)
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Data USGS National Land Cover Dataset

• Based on Landsat TM data
• Nominal-1992 acquisitions
• Modified Anderson LULC Classification System
• Selected 3 Developed classes
• Low Intensity Residential
• High Intensity Residential
• Commercial/Industrial/Transportation

USGS NLCD Developed classes (Light orange
LIR, Orange HIR, Red CIT)
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Analysis

• Analysis of population distributions across the
  entire U.S.
• Demographic Classification
• Quantification of the relationship between
  population density and vegetation fraction
• Bivariate distributions
• Marginal distributions
• Comparison with USGS NLCD Classes
• Distributions of areal extent of each USGS class
  as a function of population density and
  vegetation fraction

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Population Density Distribution in the U.S.

• Multimodal Distribution
• Modes are
• Rural pop. dens. lt100
• Suburban 100 ltpop.dens. lt 10,000
• Urban pop. dens gt10,000
• people/km2
• Grey line Western US (west of the 90 W)
• Black line Eastern US

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Geographic Distribution of U.S. Population
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Demographic Classification
Population Density
Example portion of Chicago
3 Classes of population density ? Demographic
Classification Overlay with vegetation
fraction Blue Rural Green Suburban Red Urban
Demographic Classification
Vegetation Fraction
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Bivariate Distributions
Distributions of people as functions of
Population Density and Vegetation
Fraction Legend warmer colors higher numbers
of people on Log scale
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Marginal Distributions
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Comparison with USGS NLCD Classes

• Distributions of areal extent of each USGS
  Developed class as functions of population
  density and vegetation fraction
• Red High Intensity Residential
• Green Low Intensity Residential
• Blue Commercial/Industrial/Transportation

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Comparison with USGS NLCD Classes

• Visual comparison between Demographic
  Classification and USGS NLCD Developed Classes
• Legend
• Blue Rural/CIT
• Green Suburban/LIR
• Red Urban/HIR
• Cities
• Top Chicago
• Middle New York
• Bottom Los Angeles

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Conclusions

• Population density distribution in the U.S.
  ?demographic classification (urban/suburban/rural)
  
• Vegetation cover is the most consistent spectral
  characteristics in suburban areas
• Spectral heterogeneity ? wide range of vegetation
  fractions in demographically urban and suburban
  areas
• Not possible to consistently characterize urban
  and suburban areas in the U.S. based on
  reflectance characteristics at Landsat
  resolutions

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What next?

• Emphasize results on quantitative
  characterization of vegetation abundance as means
  to provide physical basis for comparison of urban
  environments
• Explore classification schemes based on spectral
  heterogeneity at multiple pixel scales,
  supplemented by auxiliary data sources
• Demographic Classification for the year 2000 and
  urban sprawl analysis

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Thank you!

• fpozzi_at_ciesin.columbia.edu
• http//sedac.ciesin.columbia.edu/urban_rs