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

Geographic Distribution of U.S. Population

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

Marginal Distributions

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

Thank you!

fpozzi@ciesin.columbia.edu

http://sedac.ciesin.columbia.edu/urban_rs