Long-term effects of land use/land cover change on surface runoff in urban areas of Beijing, China

Abstract The objective of this paper is to present a case study to derive land use/land cover (LULC) maps and investigate the long-term effects of LULC change on surface runoff in the fast urbanizing Beijing city. The LULC maps were derived from Landsat TM/ETM+ imagery (acquired in 1992, 1999, 2006, and 2009) using support vector machine method. A long-term hydrologic impact assessment model was applied to assess the impact of LULC change on surface runoff. Results indicated that the selected study area experienced rapid urbanization from 1992 to 2009. Because of urbanization, from 1992 to 2009, modeled runoff increased 30% for the whole area and 35% for the urban portion. Our results also indicated that the runoff increase was highly correlated with urban expansion. A strong relationship ( R 2 = 0.849 ) was observed between the impervious surface percent and the modeled runoff depth in the study area. In addition, a strong positive relationship was observed between runoff increase and percentage of urban areas ( R 2 = 0.997 for the whole area and R 2 = 0.930 for the urban portion). This research can provide a simple method for policy makers to assess potential hydrological impacts of future urban planning and development activities.

[1]  Chih-Jen Lin,et al.  Probability Estimates for Multi-class Classification by Pairwise Coupling , 2003, J. Mach. Learn. Res..

[2]  W. Shuster,et al.  Impacts of impervious surface on watershed hydrology: A review , 2005 .

[3]  Francisco Olivera,et al.  Urbanization and Its Effect On Runoff in the Whiteoak Bayou Watershed, Texas 1 , 2007 .

[4]  Peter P. Pratt,et al.  Archaeological Survey of the Flint Creek Project for the United States Department of Agriculture Soil Conservation Service , 1974 .

[5]  T. Esch,et al.  Large-area assessment of impervious surface based on integrated analysis of single-date Landsat-7 images and geospatial vector data , 2009 .

[6]  Chien-Ju Lin,et al.  Modeling and assessing land-use and hydrological processes to future land-use and climate change scenarios in watershed land-use planning , 2007 .

[7]  Vijay P. Singh,et al.  Effect of urbanization on runoff characteristics of the On-Cheon Stream watershed in Pusan, Korea , 1998 .

[8]  Paul M. Mather,et al.  Support vector machines for classification in remote sensing , 2005 .

[9]  Xinwu Li,et al.  Estimating urban impervious surfaces from Landsat-5 TM imagery using multilayer perceptron neural network and support vector machine , 2011 .

[10]  A. Kalra,et al.  Estimating soil moisture using remote sensing data: A machine learning approach , 2010 .

[11]  Sheng-Hsun Hsu,et al.  Application of SVM and ANN for image retrieval , 2006, Eur. J. Oper. Res..

[12]  B. Markham,et al.  Revised Landsat-5 TM radiometric calibration procedures and postcalibration dynamic ranges , 2003, IEEE Trans. Geosci. Remote. Sens..

[13]  Heejun Chang,et al.  The effects of climate change and urbanization on the runoff of the Rock Creek basin in the Portland metropolitan area, Oregon, USA , 2009 .

[14]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[15]  Jon Harbor,et al.  IMPACTS OF URBANIZATION ON SURFACE HYDROLOGY, LITTLE EAGLE CREEK, INDIANA, AND ANALYSIS OF LTHIA MODEL SENSITIVITY TO DATA RESOLUTION , 2001 .

[16]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[17]  J. Choi,et al.  Effects of calibration on L-THIA GIS runoff and pollutant estimation. , 2006, Journal of environmental management.

[18]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[19]  John R. Williams,et al.  LARGE AREA HYDROLOGIC MODELING AND ASSESSMENT PART I: MODEL DEVELOPMENT 1 , 1998 .

[20]  Lawrence E. Band,et al.  Simulating runoff behavior in an urbanizing watershed , 2000 .

[21]  J. Gill.  PREQUALIFYING AND PARTNERING FOR MICROTUNNELING , 1998 .

[22]  Qihao Weng,et al.  Assessing Surface Water Quality and Its Relation with Urban Land Cover Changes in the Lake Calumet Area, Greater Chicago , 2010, Environmental management.

[23]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[24]  Vinay Bhardwaj,et al.  Urbanization impact on hydrogeological regime in Jaipur Urban Block: a rapidly growing urban center in NW India , 2006 .

[25]  L. Duponchel,et al.  Support vector machines (SVM) in near infrared (NIR) spectroscopy: Focus on parameters optimization and model interpretation , 2009 .

[26]  Giles M. Foody,et al.  A relative evaluation of multiclass image classification by support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[27]  J. Harbor.  A Practical Method for Estimating the Impact of Land-Use Change on Surface Runoff, Groundwater Recharge and Wetland Hydrology , 1994 .

[28]  B. Deal,et al.  Long-Term Impacts of Land-Use Change on Non-Point Source Pollutant Loads for the St. Louis Metropolitan Area, USA , 2005, Environmental management.

[29]  Cuizhen Wang,et al.  Impacts of Urbanization on Surface Runoff of the Dardenne Creek Watershed, St. Charles County, Missouri , 2009 .

[30]  P. Termonia,et al.  Effects of urbanization and climate change on surface runoff of the Brussels Capital Region: a case study using an urban soil–vegetation–atmosphere‐transfer model , 2011 .

[31]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[32]  Ying Zhang,et al.  Multi-resolution integration of land cover for sub-pixel estimation of urban impervious surface and forest cover , 2009, Int. J. Digit. Earth.

[33]  Peijun Shi,et al.  The effect of land use/cover change on surface runoff in Shenzhen region, China , 2007 .

[34]  Qihao Weng,et al.  Modeling Urban Growth Effects on Surface Runoff with the Integration of Remote Sensing and GIS , 2001, Environmental management.

[35]  J. Marsalek,et al.  Simulation of the Effects of Urbanization on Basin Streamflow , 1989 .

[36]  Guobin Zhu,et al.  Classification using ASTER data and SVM algorithms;: The case study of Beer Sheva, Israel , 2002 .

[37]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .