Vegetation and Population Density in Urban and Suburban Areas in the U.S.A. Francesca Pozzi Center for International Earth Science Information Network.
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Vegetation and Population Density in Urban and Suburban Areas in the
U.S.A.
Francesca PozziCenter for International Earth Science Information Network
Columbia UniversityNew York, USA
Christopher SmallLamont-Doherty Earth Observatory
Columbia UniversityNew York, USA
Istanbul, 11-13 June 2002
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?
Case Study: The USA
• 6 cities with different geographic location, physical environment and urban growth dynamics
AtlantaChicagoLos AngelesNew YorkPhoenixSeattle
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
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)
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)
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
Population Density Distribution in the U.S.
• Multimodal Distribution• Modes are:
– Rural: pop. dens. <100– Suburban: 100 <pop.dens.
< 10,000– Urban: pop. dens >10,000
people/km2
• Grey line: Western US (west of the 90 ° W)
• Black line: Eastern US
Demographic Classification
Population Density
Demographic Classification
Vegetation Fraction
3 Classes of population density Demographic ClassificationOverlay with vegetation fraction
Blue: RuralGreen: SuburbanRed: Urban
Example: portion of Chicago
Bivariate Distributions
Distributions of people as functions of Population Density and Vegetation FractionLegend: warmer colors = higher numbers of people on Log scale
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
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
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
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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