A quantitative analysis of urban growth and associated thermal characteristics using Landsat satellite data

Urbanization transforms the natural landscape to anthropogenic urban land use and changes surface physical characteristics. Accurate information on the extent of urban growth and its impacts on environment are of great interest for diverse purposes. As a result, increased research interest is being directed to the mapping and monitoring of urban land use using remote sensing techniques. However, there are many challenges in deriving urban extent and development densities quantitatively. This study utilized remote sensing data of Landsat TM/ETM+ to assess urban sprawl and its thermal characteristics in Changsha of central China. A new approach was proposed for quantitatively determining urban land use extents and development densities. Firstly, impervious surface areas were mapped by integrating spectral index derived from remotely sensed data. Then, the urban land extents and development densities were identified by using moving window calculation and selecting certain threshold values. The urban surface thermal patterns were investigated using Landsat thermal band. Analysis results suggest that urban extent and development density and surface thermal characteristics and patterns can be identified through qualitatively based remotely sensed index and land surface temperature. Results show the built-up area and urban development densities have increased significantly in Changsha city since 1990s. The differences of urban development densities correspond to thermal effects where higher percent imperviousness is usually associated with higher surface temperature. Remotely sensed index and land surface temperature are demonstrated to be very useful sources in quantifying urban land use extent, development intensity, and urban thermal patterns.

[1]  Ago Yeh,et al.  Urban growth management in the Pearl river delta: An integrated remote sensing and GIS approach , 1996 .

[2]  B. Engel,et al.  MINIMIZING THE IMPACT OF URBANIZATION ON LONG TERM RUNOFF 1 , 2005 .

[3]  Alan T. Murray,et al.  Estimating impervious surface distribution by spectral mixture analysis , 2003 .

[4]  D. Maktav,et al.  Remote sensing of urban areas , 2005 .

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

[6]  Alan T. Murray,et al.  Monitoring Growth in Rapidly Urbanizing Areas Using Remotely Sensed Data , 2000 .

[7]  Changshan Wu,et al.  Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery , 2004 .

[8]  E. Kalnay,et al.  Impact of urbanization and land-use change on climate , 2003, Nature.

[9]  D. Lu,et al.  Use of impervious surface in urban land-use classification , 2006 .

[10]  Matthias Roth,et al.  Effects of Cities on Local Climates , 2002 .

[11]  M. Bauer,et al.  Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery , 2007 .

[12]  A. Karnieli,et al.  A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region , 2001 .

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

[14]  Hanqiu Xu.  Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[15]  Toby N. Carlson,et al.  The impact of land use — land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective , 2000 .

[16]  Qihao Weng.  A remote sensing?GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China , 2001 .

[17]  John R. Weeks,et al.  Measuring the Physical Composition of Urban Morphology Using Multiple Endmember Spectral Mixture Models , 2003 .

[18]  C. Tucker,et al.  Evidence for a significant urbanization effect on climate in China. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[19]  Victor Mesev,et al.  Remote sensing of urban systems: Hierarchical integration with GIS , 1997 .

[20]  Xiaoling Chen,et al.  Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+ , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[21]  Xiaojun Yang.  Remote sensing for urban analysis: An introduction , 2005, Comput. Environ. Urban Syst..

[22]  Jay Gao,et al.  Use of normalized difference built-up index in automatically mapping urban areas from TM imagery , 2003 .