Detecting horizontal and vertical urban growth from medium resolution imagery and its relationships with major socioeconomic factors

ABSTRACT

Urban growth consists of horizontal and vertical expansions. An integrative framework for estimating horizontal and vertical expansions of city urban areas using Landsat images was presented. It includes following steps: (1) a spectrum-based classifier (here Support Vector Machine) is first used to preclassify Landsat images; (2) the spectral similarity-enhanced Markov chain random field cosimulation model is then applied to postclassify the preclassified images and detect building shadows; and (3) a morphological operator based on spatial logic reasoning is used to estimate mid-rise or taller buildings (MTBs) from detected shadows. Both horizontal urban growth and vertical urban growth in the main city area of Guangzhou for the time period of 1993–2013 were detected. The accuracy of identified MTBs by shadows was validated to be 78.1% on average for 2013. The case study indicates that Guangzhou had undergone both horizontal and vertical urban growth from 1993 to 2013, and vertical urban growth followed horizontal urban growth successively. The relationships between the horizontal and vertical urban growth and three major socioeconomic factors during the studied period were analysed. Results indicate that both the total area of built-up areas and the total area of detected MTBs are significantly correlated with population density, real gross domestic product, and fixed investment (i.e. investment in fixed assets such as land, buildings), respectively. While population density is the major driving force of horizontal urban expansion, fixed investment is the major driving force of vertical urban expansion for the city as a whole. Although the method is not perfect currently in detecting MTBs in various situations and the case study is mainly exploratory, the proposed framework and the case study can be helpful in quantitatively exploring the horizontal urban growth and vertical urban growth of a city and their causes.

Acknowledgements

Constructive revision suggestions from anonymous reviewers and the Editor-in-Chief Timothy A. Warner are greatly appreciated. We would like to thank Weiduan Zhang and Weiwen Zhang for helping collecting socioeconomic data of 1993 of Guangzhou, and thank Krista Rogers for her helpful reviews of this manuscript. We acknowledge the Guangzhou Bureau of Statistics for sharing with us the city’s socioeconomic data of 2001 and 2013.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported in part by the USA NSF [grant number 1414108].