Urban land use change detection through spatial statistical analysis using multi-temporal remote sensing data

Feixue Li; Manchun Li; Jian Liang; Yongxue Liu; Zhenjie Chen; Dong Chen

doi:10.1117/12.812699

3 November 2008 Urban land use change detection through spatial statistical analysis using multi-temporal remote sensing data

Feixue Li, Manchun Li, Jian Liang, Yongxue Liu, Zhenjie Chen, Dong Chen

Author Affiliations +

Proceedings Volume 7144, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics; 714409 (2008) https://doi.org/10.1117/12.812699
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China

Abstract

Numerous remote sensing change detection methods have been used in urban land use change identification and analysis, in which image regression is regarded as effective as other approaches. Traditional image regression approaches for change detection often produce unsatisfactory results by assuming the relationships in study data in a consistent manner in place, and spatial correlation between pixels inherent in remote sensing images is usually ignored in the analysis. Geographically Weighted Regression (GWR) addresses this weakness by obtaining local parameter estimates for each observation. This paper reports preliminary results from a study applying GWR to the land use change detection in urban center and urban fringe of Nanjing city, China, using satellite images of 2000 and 2004. The results show that the use of GWR can identify the land use change, the global patterns, the local patterns, as well as the points not consistent with local patterns in the urban environment; and the under-development and over-development points are also detected by GWR model.

Citation Download Citation

Feixue Li, Manchun Li, Jian Liang, Yongxue Liu, Zhenjie Chen, and Dong Chen "Urban land use change detection through spatial statistical analysis using multi-temporal remote sensing data", Proc. SPIE 7144, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics, 714409 (3 November 2008); https://doi.org/10.1117/12.812699

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KEYWORDS

Remote sensing

Data modeling

Statistical analysis

Earth observing sensors

Statistical modeling

Image analysis

Landsat

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