The spatiotemporal patterns of urban expansion have attracted worldwide attention and have been generalized into several prevalent hypotheses, such as the diffusion–coalescence hypothesis and the three-growth-type hypothesis. Although many studies have examined the applicability of these hypotheses, long-term research and evidence are still lacking. This study incorporated a compiled dataset of multisource remote sensing images and historical maps covering nine snapshots of the urban built-up area from 1914 to 2018 to monitor the urban expansion process in Hangzhou, China. A fractal analysis of the area–radius relationship was employed for areal demarcation to explore the heterogenous patterns across different intra-city spatial extents. The results show that (1) Hangzhou has experienced a turbulent period of primitive urbanization in the pre-reform era and a consecutive period of market-oriented urbanization after the economic reform; (2) the urban expansion pattern characterized by landscape metrics demonstrates the existence of multiple alternations between diffusion and coalescence phases with peculiarities across different intra-city spatial extents; (3) the analysis of urban growth types documents a consistent predominance of edge-expansion with wax and wane between infilling and leapfrogging; and (4) institutional reform, industrial development, and administrative division adjustment are the main drivers of urban expansion in Hangzhou. Our findings suggest that effective planning policies need to be raised to curb urban sprawl. Differentiated planning strategies should be proposed to accommodate unique conditions in different urban subregions. The integrated-analysis approach based on multisource remote sensing images and historical maps establishes a feasible pathway for long-term urban research.
CITATION STYLE
Lu, H., Wang, R., Ye, R., & Fan, J. (2023). Monitoring Long-Term Spatiotemporal Dynamics of Urban Expansion Using Multisource Remote Sensing Images and Historical Maps: A Case Study of Hangzhou, China. Land, 12(1). https://doi.org/10.3390/land12010144
Mendeley helps you to discover research relevant for your work.