The structure of human environment interactions in an urbanizing watershed
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The Structure of Human-Environment Interactions in an Urbanizing Watershed. N.L. Law and L.E. Band University of North Carolina at Chapel Hill Department of Geography AGU Meeting Spring 1999 Boston, MA. Problem Statement.

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The structure of human environment interactions in an urbanizing watershed l.jpg

The Structure of Human-Environment Interactions in an Urbanizing Watershed

N.L. Law and L.E. Band

University of North Carolina at Chapel Hill

Department of Geography

AGU Meeting Spring 1999

Boston, MA


Problem statement l.jpg
Problem Statement Urbanizing Watershed

  • Establish link between socio-economic and biophysical characteristics of urban watersheds

  • Study relationship with vegetation

  • Exploratory statistical analysis to uncover associations to help understand system


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Gwynns Falls Urbanizing Watershed

Watershed

Baltimore


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Population density (persons/ha) in the Urbanizing Watershed

Gwynns Falls Watershed



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Observed vegetation cover in the Urbanizing Watershed

Gwynns Falls watershed averaged

per census block.


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Methods Urbanizing Watershed

Data

  • Summer TM image to calculate NDVI

  • Averaged NDVI per census block

  • 1990 census block data

    • 9 independent variables, n =303

      • 3 socio-economic indices

      • 4 periods of development

      • population density (pers/ha)

      • distance to nearest stream


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Demographic variables Urbanizing Watershed

  • Socio-economic index (socio)

    • occupation

    • household income

    • education

  • Household index (house)

    • marital status

    • one-family household

    • owner occupied dwelling

  • Ethnicity

    • ‘black’ or ‘other races’

    • foreign born


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Methods Urbanizing Watershed

Model Development

  • regression model using 9 independent variables

  • multi-variate model

    • tree regression



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Predicted vs observed NDVI Urbanizing WatershedNLS regression model based on population density


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Regression Tree-based Models Urbanizing Watershed

  • recursive partitioning

  • decision rules to split data into homogeneous subsets


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< 1.2 km Urbanizing Watershed

> 1.2 km


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Predicted NDVI based on tree regression model averaged Urbanizing Watershed

per census block.



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Tree Regression Model: Part 1 Urbanizing Watershed

Predicted NDVI


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Tree Regression Model: Part II Urbanizing Watershed


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Conclusions Urbanizing Watershed

  • Multiple, integrative factors affect spatial distribution of vegetation cover

  • Tree regression model suggests:

    • lower NDVI

      • city

      • mixed aged of development

      • urban parks

    • higher NDVI

      • county

      • newer development


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