Using Remote Sensing and Geographic Information Systems to Study Urban Quality of Life and Urban Forest Amenities

Abstract

This study examines urban quality of life by assessing the relationship between observed socioeconomic conditions and urban forest amenities in Terre Haute, Indiana, USA. Using remote-sensing methods and techniques, and ordinary least squares regression, the paper determines the relationship between urban leaf area and a population density parameter with median income and median housing value. Results demonstrate positive correlations between urban leaf area, population density, and their interaction with median income and median housing value. Furthermore, leaf area, density, and their interaction statistically account for observed variance in median income and median housing value, indicating that these variables may be used to study observed quality-of-life metrics. The methods used in this study may be useful to city managers, planners, and foresters who are concerned with urban quality-of-life issues, and who are interested in developing and implementing alternative policy assessment regimes