Investigation and comparison of land-cover change patterns in Xuzhou city, China, and Dortmund city region, Germany, using multitemporal Landsat images

Abstract Analyzing spatiotemporal characteristics of land-cover (LC) change is important for assessing environmental consequences of urban growth and supporting land management and planning. Less attention, however, has been given to the comparison between land-cover change patterns in developing and developed countries. In this study, Xuzhou city and Dortmund city region were selected as study areas. Multitemporal Landsat images were classified by using the integration method of maximum likelihood classifier, subpixel classifier, and multiple normalized difference vegetation index values based on Vegetation-Impervious Surface-Soil model. Urban growth patterns and processes of the two study areas were investigated and compared through land-cover change detection, buffer analysis, and jaggedness degree. The results indicated that the urban area in Xuzhou city increased more than threefold dramatically from 128.5 to 418.3     km 2 , and the increased sprawling development trend was observed during the study period, while Dortmund city region showed a slight increase from 498 to 715.5     km 2 in urban areas with an increasingly compact development trend. The results revealed a notable difference of spatiotemporal land-cover pattern dynamics between the two study areas as well as confirmed the effectiveness of the combined method of remote sensing and spatial analysis that can be used to support land management and policy decisions.

[1]  Tiziana Simoniello,et al.  Mapping badland areas using LANDSAT TM/ETM satellite imagery and morphological data , 2009 .

[2]  Daniel L. Civco,et al.  Quantifying and Describing Urbanizing Landscapes in the Northeast United States , 2002 .

[3]  Y. Yamaguchi,et al.  A case study on the relation between city planning and urban growth using remote sensing and spatial metrics , 2011 .

[4]  Hao Zhang,et al.  Land use dynamics, built-up land expansion patterns, and driving forces analysis of the fast-growing Hangzhou metropolitan area, eastern China (1978-2008) , 2012 .

[5]  Xia Li,et al.  Modelling sustainable urban development by the integration of constrained cellular automata and GIS , 2000, Int. J. Geogr. Inf. Sci..

[6]  A. Dewan,et al.  Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization , 2009 .

[7]  Xiaodong Zhu,et al.  Characterizing growth types and analyzing growth density distribution in response to urban growth patterns in peri-urban areas of Lianyungang City , 2012 .

[8]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[9]  J. Mas.  Monitoring land-cover changes: A comparison of change detection techniques , 1999 .

[10]  Martin Herold,et al.  The spatiotemporal form of urban growth: measurement, analysis and modeling , 2003 .

[11]  W. Cohen,et al.  Land cover mapping in an agricultural setting using multiseasonal Thematic Mapper data , 2001 .

[12]  Linli Cui,et al.  Urbanization and its environmental effects in Shanghai, China , 2012 .

[13]  J. Qi,et al.  Spatio-temporal dynamics and evolution of land use change and landscape pattern in response to rapid urbanization , 2009 .

[14]  Saro Lee,et al.  Probabilistic landslide susceptibility and factor effect analysis , 2005 .

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

[16]  D. Lu,et al.  Spectral Mixture Analysis of the Urban Landscape in Indianapolis with Landsat ETM+ Imagery , 2004 .

[17]  Martin Herold,et al.  Mapping imperviousness using NDVI and linear spectral unmixing of ASTER data in the Cologne-Bonn region (Germany) , 2004, SPIE Remote Sensing.

[18]  Ibrahim Baz,et al.  Monitoring urban growth and detecting land-cover changes on the Istanbul metropolitan area , 2007, Environmental monitoring and assessment.

[19]  Chen Liang,et al.  A GIS-based buffer gradient analysis on spatiotemporal dynamics of urban expansion in Shanghai and its major satellite cities , 2010 .

[20]  R. Lunetta,et al.  Land-cover change detection using multi-temporal MODIS NDVI data , 2006 .

[21]  Alexander Siegmund,et al.  Change Detection Analysis for Assessing the Vulnerability and Protective Effect of Beach Forests in Case of the Tsunami 2004 in Thailand , 2011 .

[22]  Ton C M de Nijs,et al.  Determinants of Land-Use Change Patterns in the Netherlands , 2004 .

[23]  J. R. Jensen,et al.  Effectiveness of Subpixel Analysis in Detecting and Quantifying Urban Imperviousness from Landsat Thematic Mapper Imagery , 1999 .

[24]  Ian Masser,et al.  Understanding Spatial and Temporal Processes of Urban Growth: Cellular Automata Modelling , 2004 .

[25]  Xiubing Li,et al.  Urban land expansion and arable land loss in China - a case study of Beijing-Tianjin-Hebei region , 2005 .

[26]  P. Chavez.  An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data , 1988 .

[27]  Hery Setiawan,et al.  Assessing the applicability of the V-I-S model to map urban land use in the developing world: Case study of Yogyakarta, Indonesia , 2006, Comput. Environ. Urban Syst..

[28]  Jianguo Wu,et al.  A gradient analysis of urban landscape pattern: a case study from the Phoenix metropolitan region, Arizona, USA , 2004, Landscape Ecology.

[29]  R. Tateishi,et al.  Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing , 2006 .

[30]  D. Marceau.  The Scale Issue in the Social and Natural Sciences , 1999 .

[31]  Ashbindu Singh,et al.  Review Article Digital change detection techniques using remotely-sensed data , 1989 .

[32]  M. Bauer,et al.  Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing , 2005 .

[33]  Xia Li,et al.  Discovering and evaluating urban signatures for simulating compact development using cellular automata , 2008 .

[34]  Antonio Leone,et al.  Land cover and land use change in the Italian central Apennines: A comparison of assessment methods , 2009 .

[35]  S. Ventura,et al.  THE INTEGRATION OF GEOGRAPHIC DATA WITH REMOTELY SENSED IMAGERY TO IMPROVE CLASSIFICATION IN AN URBAN AREA , 1995 .

[36]  Dengsheng Lu,et al.  Regional mapping of human settlements in southeastern China with multisensor remotely sensed data , 2008 .

[37]  Yuji Murayama,et al.  Landscape pattern and ecosystem service value changes: Implications for environmental sustainability planning for the rapidly urbanizing summer capital of the Philippines , 2013 .

[38]  Soyoung Park,et al.  Prediction and comparison of urban growth by land suitability index mapping using GIS and RS in South Korea , 2011 .

[39]  Pol Coppin,et al.  Review ArticleDigital change detection methods in ecosystem monitoring: a review , 2004 .

[40]  W. Tobler.  A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .

[41]  Y. Hayashi,et al.  Application of an integrated system dynamics and cellular automata model for urban growth assessment: A case study of Shanghai, China , 2009 .

[42]  Xiaoting Wang,et al.  Analysis of land use and cover change in Sichuan province, China , 2012 .

[43]  C. Woodcock,et al.  Compact, Dispersed, Fragmented, Extensive? A Comparison of Urban Growth in Twenty-five Global Cities using Remotely Sensed Data, Pattern Metrics and Census Information , 2008 .

[44]  Shuisen Chen,et al.  Remote sensing and GIS-based integrated analysis of coastal changes and their environmental impacts in Lingding Bay, Pearl River Estuary, South China , 2005 .

[45]  Feng Li,et al.  Comprehensive concept planning of urban greening based on ecological principles: a case study in Beijing, China , 2005 .

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

[47]  G. S. Dwarakish,et al.  Land use/land cover change and urban expansion during 1983–2008 in the coastal area of Dakshina Kannada district, South India , 2012 .

[48]  P. Zhao.  Sustainable urban expansion and transportation in a growing megacity: Consequences of urban sprawl for mobility on the urban fringe of Beijing , 2010 .

[49]  M. Ridd.  Exploring a V-I-S (vegetation-impervious surface-soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities , 1995 .

[50]  Brian J. L. Berry,et al.  City Size Distributions and Economic Development , 1961, Economic Development and Cultural Change.