Simulating urban growth processes incorporating a potential model with spatial metrics

Kong, Fanhua; Yin, Haiwei; Nakagoshi, Nobukazu; James, Philip

Abstract

Urbanization is one phenomena that drives land use pattern change. Persistent rapid urbanization is associated with depletion of natural resources and worsening conditions in the urban environment. Monitoring urban development is, therefore, an absolute necessity in order to assure sustainable cities in the future. The main objective of this paper is to develop and apply an urban growth potential
model incorporating spatial metrics. The model has been tested in Jinan City, China. Firstly, two satellite images (1989 and 2004 SPOT) were used to extract the land-cover. A general land use spatial pattern analysis, based on landscape metrics and a transformation matrix analysis, was
conducted. Secondly, a moving window method was used to identify and capture the urbanization process through the PLAND landscape metric. The remote satellite data have been further processed:first to produce an initial state of the land-cover surface, and second to perform a time-series
analysis and to assess the potential accuracy of the model application. In the second step, the calibrated model was used to predict the location of the urban growth over 16 years (2004–2020). The results indicated there will be a significant land use change until 2020. However, the spatial
distribution of the potential growth areas is not homogenous. The study has confirmed the usefulness of a growth potential model incorporating the moving window method to predict urban growth trends and examining the impacts of urban development on natural resources. The results can provide decision support documents for urban planners and stakeholders with spatially explicit information for future planning and monitoring plans.

Citation

Kong, F., Yin, H., Nakagoshi, N., & James, P. (2012). Simulating urban growth processes incorporating a potential model with spatial metrics. Ecological Indicators, 20, 82-91. https://doi.org/10.1016/j.ecolind.2012.02.003

• Journal Article Type: Article
• Publication Date: Jan 1, 2012
• Deposit Date: Mar 7, 2012
• Journal: Ecological Indicators
• Print ISSN: 1470-160X
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 20
• Pages: 82-91
• DOI: https://doi.org/10.1016/j.ecolind.2012.02.003
• Publisher URL: http://dx.doi.org/10.1016/j.ecolind.2012.02.003
• Related Public URLs: http://www.sciencedirect.com/science/article/pii/S1470160X12000490