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The relationships between rapid urban development and vegetation in the pan Pacific region : spatio-temporal quantification using satellite images

University of British Columbia

Cities strive for economic strength while recognize the necessity of being environmentally sustainable. The balance between economic development and the environment has been challenging particularly for cities in the pan Pacific region, which is seeing some of the most rapid urban growth rates. Remotely sensed satellite images offer much larger and more consistent spatial and temporal coverages than conventional census data therefore are increasingly being utilized for regional and global urban studies. Two key remote sensing datasets, namely urban vegetation cover derived from Landsat time series, and brightness generated from NOAA’s nighttime lights datasets to represent urban development were the focus of this dissertation. I first extracted annual urban vegetation characteristics using spectral indices (e.g. EVI) as well as a spectral mixture analysis from 1984 to 2012. Nighttime lights brightness was used to assess urban expansion and its relationship with census-derived variables. Lastly, I examined the relationships between urban development and the environment using Environment Kuznets Curve (EKC) theory as a lens, addressing how urban vegetation responds to urban nighttime brightness in 25 cities across the pan Pacific region. I identified inter- and intra-city patterns of vegetation and brightness changes that were strongly related to social and economic contexts. Spectral indices demonstrated opposing trends between urban vegetation and built-up area both spatially and temporally. Spectral mixture analysis successfully extracted the urban vegetation fraction at a sub-pixel level, setting a robust base for cross-city comparisons. I found that urban vegetation changed linearly both positively and negatively with urban brightness, particularly in higher income cities in North America. Pixels with statistically strong quadratic relationships between vegetation and brightness were less prevalent but more spatially clustered in comparison to those that expressed a linear relationship. Overall, there are three key contribution of this dissertation. Firstly, the integration of gap-free satellite images and innovative processing techniques unlocked new ways of informing urban environmental and socio-economic dynamics. Secondly, a classic econometric model (i.e. Granger causality test) was used to examine the casual relationship between census and remote sensing nighttime lights data. Lastly, a pixel-based model fitting was use to confirm EKC at a sub-city scale.

Text

2018-06-18

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/66312

Forestry

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/