Seasonal Change of Land-Use/Land-Cover (LULC) Detection Using MODIS Data in Rapid Urbanization Regions: A Case Study of the Pearl River Delta Region (China)

Seasonal changes were detected using multi-temporal MODIS images from March 2008 to December 2009 over the Pearl River Delta (PRD) region, China, to determine temporal and spatial changes of land-use/land-cover (LULC) classes in rapid urbanizing regions. The maximum likelihood method was applied to extract five types of LULC classes. A post-classification change detection technique was used to determine the types changed. Cropland, water and bare land had some seasonal changes, while urban and woodland had very little seasonal change. Results demonstrate that the urban area had a slight but continuous increase from early 2008 to late 2009, while the other land-use types had different increases or decreases at the same period. The study provides an example of seasonal change detection of LULC types using MODIS data in regions of rapid urbanization, such as the PRD in China.

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

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

[3]  Pieter Kempeneers,et al.  Increasing Robustness of Postclassification Change Detection Using Time Series of Land Cover Maps , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Xiuji Zhou,et al.  Long-term trend of visibility and its characterizations in the Pearl River Delta (PRD) region, China , 2008 .

[5]  Patrick Bogaert,et al.  Forest change detection by statistical object-based method , 2006 .

[6]  Robert H. Fraser,et al.  A method for detecting large-scale forest cover change using coarse spatial resolution imagery , 2005 .

[7]  Li Hui.  Review on landscape pattern change of the Pearl River Delta based on 3S technology , 2009 .

[8]  J. Carreiras,et al.  Assessing the extent of agriculture/pasture and secondary succession forest in the Brazilian Legal Amazon using SPOT VEGETATION data , 2006 .

[9]  A. Gaughan,et al.  Detecting tropical dry forest succession in a shifting cultivation mosaic of the Yucatán Peninsula, Mexico , 2008 .

[10]  G. Fischer,et al.  Land-use and land-cover change. Science/research plan , 1995 .

[11]  William B. Meyer,et al.  Modeling land use and cover as part of global environmental change , 1994 .

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

[13]  John A. Richards,et al.  Efficient maximum likelihood classification for imaging spectrometer data sets , 1994, IEEE Trans. Geosci. Remote. Sens..

[14]  Wang Liu.  An Analysis of Spatio-temporal Changes and Spatial Heterogeneity of Vegetation Coverage in Pearl River Delta , 2010 .

[15]  Yeh Anthony.  Short-interval Land Use Change Detection , 2011 .

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

[17]  Tao Liang.  A spatial autocorrelation analysis of land use change in Pearl River Delta , 2011 .

[18]  Patrick Bogaert,et al.  An object-based change detection method accounting for temporal dependences in time series with medium to coarse spatial resolution , 2008 .

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

[20]  Christopher O. Justice,et al.  Spatial variability of images and the monitoring of changes in the Normalized Difference Vegetation Index , 1995 .

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

[22]  L. Deren.  Change Detection from Remote Sensing Images , 2003 .

[23]  P. Curran,et al.  Mapping the regional extent of tropical forest regeneration stages in the Brazilian Legal Amazon using NOAA AVHRR data , 2000 .

[24]  Qihao Weng,et al.  Land Use and Land Cover Change in Guangzhou, China, from 1998 to 2003, Based on Landsat TM /ETM+ Imagery , 2007, Sensors (Basel, Switzerland).

[25]  C. Tucker,et al.  A comparative study of NOAA–AVHRR derived drought indices using change vector analysis , 2006 .

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