Examining Spatial Patterns of Urban Distribution and Impacts of Physical Conditions on Urbanization in Coastal and Inland Metropoles

Dengsheng Lu,Peilei Fan,Emilio Moran,Zutao Ouyang,Longwei Li,Guiying Li

doi:10.3390/rs10071101

Abstract

Urban expansion has long been a research hotspot and is often based on individual cities, but rarely has research conducted a comprehensive comparison between coastal and inland metropoles for understanding different spatial patterns of urban expansions and driving forces. We selected coastal metropoles (Shanghai and Shenzhen in China, and Ho Chi Minh City in Vietnam) and inland metropoles (Ulaanbaatar in Mongolia, Lanzhou in China, and Vientiane in Laos) with various developing stages and physical conditions for examining the spatiotemporal patterns of urban expansions in the past 25 years (1990–2015). Multitemporal Landsat images with 30 m spatial resolution were used to develop urban impervious surface area (ISA) distributions and examine their dynamic changes. The impacts of elevation, slope, and rivers on spatial patterns of urban expansion were examined. This research indicates that ISA is an important variable for examining urban expansion. Coastal metropoles had much faster urbanization rates than inland metropoles. The spatial patterns of urban ISA distribution and expansion are greatly influenced by physical conditions; that is, ISA is mainly distributed in the areas with slopes of less than 10 degrees. Rivers are important geographical factors constraining urban expansion, especially in developing stages, while bridges across the rivers promote urban expansion patterns and rates. The relationships of spatial patterns of urban ISA distribution and dynamics with physical conditions provide scientific data for urban planning, management, and sustainability.

Highlights

The migration of populations from rural to urban regions and improved economic conditions in developing countries have resulted in rapid urban expansion [1,2], requiring timely updates of urban spatial distribution and expansion datasets
No accuracy assessments for other years’ Impervious surface area (ISA) mapping results were conducted, previous research [43] had proved that this approach for ISA mapping is robust and reliable, and we are confident that the ISA mapping in this research is reliable
All the ISA results were used for analysis of urban dynamics

Summary

Introduction

The migration of populations from rural to urban regions and improved economic conditions in developing countries have resulted in rapid urban expansion [1,2], requiring timely updates of urban spatial distribution and expansion datasets. Urban distribution and dynamic change products are often obtained from remotely sensed data using classification-based approaches, such as maximum likelihood, decision tree classifier, support vector machine, and object-oriented classifier [3,4,5]. Directly extracting urban features from remotely sensed data is often difficult due to the complex composition of urban land covers and their spectral confusions, such as among buildings, roads, and bare soils, and between building-cast shadows and water [6]. Because ISA is an important parameter for urban environmental modeling, hydrological modeling, socioeconomic analysis, and urban climate [9,10,11,12,13], many studies have been conducted to explore approaches to accurately extract urban ISA using different sensor data, such as Quickbird, IKONOS, Landsat, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and nighttime light data [6]

Results

Discussion

Conclusion