Date of Award

2008

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Natural Resources

First Advisor

Troy, Austin R.

Abstract

Humans have been dramatically changing the Earth’s ecosystems through urbanization since the past century. It is crucial to characterize and understand the heterogeneous structure of urban landscapes and their changes, and understand how these relate to ecological and social processes. Remote sensing and Geographic Information Systems (GIS) provide effective tools for analyzing the spatial patterns of landscapes, and their interactions with social and ecological processes. In particular, recent availability of high-resolution satellite and aerial imagery and advances in digital image processing have greatly improved our ability to characterize and model urban ecosystems. This dissertation presents research on the development and application of new methods and techniques for characterizing and analyzing urban landscape structure using high-resolution remote sensing and socioeconomic data. Further, it investigates the interactions between urban landscape structure and social and ecolo gical processes. Specifically, the issues addressed in this text are: (1) Development of an object-oriented approach for analyzing and characterizing urban landscape at the parcel level using high-resolution remote sensing data: The object-oriented classification approach proved to be effective for urban land cover classification. The object-oriented approach using parcels as predefined patches provided a framework to spatially explicitly incorporate social and biophysical factors for integrated research in urban ecosystems, especially the research on relationships between household and neighborhood characteristics and structures of urban landscapes. (2) Modeling household lawn fertilization practices by integrating high-resolution remote sensing and socioeconomic data: Remotely sensed lawn greenness and lawn area data combined with household characteristics data serves as useful predictors of household lawn fertilization practices. Particularly, a combination of parcel lawn area, lawn greenness, and housing value is the best predictor of household annual fertilizer nitrogen application rate, whereas a combination of parcel lawn greenness and lot size best predicts variation in household annual fertilizer nitrogen application rate per unit lawn area. (3) The use of household and neighborhood characteristics in predicting lawncare expenditures and lawn greenness on private residential lands: Indicators of lifestyle behavior theory are the best predictors of lawn greenness and lawncare expenditure on private residential lands. (4) Development of an object-oriented framework for classifying and inventorying human-dominated forest ecosystems: The patch-based, multiscale classification and inventory framework provides an effective and flexible way of reflecting different mixes of human development and forest cover in a hierarchical fashion for human-dominated forest ecosystems.

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