Urban Land Use Change Detection Using Multisensor Satellite Images

Abstract Due to inappropriate planning and management, accelerated urban growth and tremendous loss in land, especially cropland, have become a great challenge for sustainable urban development in China, especially in developed urban area in the coastal regions; therefore, there is an urgent need to effectively detect and monitor the land use changes and provide accurate and timely information for planning and management. In this study a method combining principal component analysis (PCA) of multisensor satellite images from SPOT (systeme pour l'observation de la terre or earth observation satellite)-5 multispectral (XS) and Landsat-7 enhanced thematic mapper (ETM) panchromatic (PAN) data, and supervised classification was used to detect and analyze the dynamics of land use changes in the city proper of Hangzhou. The overall accuracy of the land use change detection was 90.67% and Kappa index was 0.89. The results indicated that there was a considerable land use change (10.03% of the total area) in the study area from 2001 to 2003, with three major types of land use conversions: from cropland into built-up land, construction site, and water area (fish pond). Changes from orchard land into built-up land were also detected. The method described in this study is feasible and useful for detecting rapid land use change in the urban area.

[1]  E. LeDrew,et al.  Application of principal components analysis to change detection , 1987 .

[2]  Limin Yang,et al.  Urban Land-Cover Change Detection through Sub-Pixel Imperviousness Mapping Using Remotely Sensed Data , 2003 .

[3]  A. Yeh,et al.  Principal component analysis of stacked multi-temporal images for the monitoring of rapid urban expansion in the Pearl River Delta , 1998 .

[4]  D. Lu,et al.  Change detection techniques , 2004 .

[5]  B. Haack,et al.  Multisensor remote sensing data for land use/cover mapping. , 1999 .

[6]  C. Lo,et al.  Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area , 2002 .

[7]  A. R. Harrison,et al.  Standardized principal components , 1985 .

[8]  T. Warner,et al.  Using Thematic Mapper data for change detection and sustainable use of cultivated land: a case study in the Yellow River delta, China , 2004 .

[9]  A. Yeh,et al.  An integrated remote sensing and GIS approach in the monitoring and evaluation of rapid urban growth for sustainable development in the Pearl River Delta, China , 1997 .

[10]  Danfeng Sun,et al.  Monitoring urban expansion with remote sensing in China , 2001 .

[11]  J. R. Jensen,et al.  An evaluation of the CoastWatch change detection protocol in South Carolina , 1993 .

[12]  J. Rogan,et al.  Remote sensing technology for mapping and monitoring land-cover and land-use change , 2004 .

[13]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[14]  A. Yeh,et al.  Economic Development and Agricultural Land Loss in the Pearl River Delta, China , 1999 .

[15]  Christiane Weber,et al.  Urban development in the Athens metropolitan area using remote sensing data with supervised analysis and GIS , 2005 .

[16]  James R. Anderson,et al.  A land use and land cover classification system for use with remote sensor data , 1976 .