Monitoring the change of urban wetland using high spatial resolution remote sensing data

Abstract

Accurate and timely information describing wetland resources and their changes over time, especially in coastal urban areas, is becoming more important. In this study, we mapped and monitored land-cover change in an urban wetland using high spatial resolution IKONOS images acquired in June 2003 and January 2006. An optimal iterative unsupervised classification (OIUC) method was used to overcome the limitations of unsupervised classification. The images were categorized into six classes, and an accuracy assessment was conducted using error matrices and the Kappa coefficient. The overall accuracies were 83.2% and 86.3% for the 2003 and 2006 images, respectively. A post-classification comparison method was used to detect the wetland change by calculating a detailed land-cover type transformation matrix. The results indicated a decrease in the area of water bodies and an increase in the area of vegetation in the wetland. This paper shows that high spatial resolution remote sensing data is advanced in studying an urban wetland at a local scale. An OIUC method, combined with visual interpretation, could yield high classification accuracy. A post-classification comparison method is also efficient in wetland change detection.

Acknowledgements

Funding support was partially from The State Key Fundamental Science Funds of China (No. 2002CB111504, 2002CB410811, 2005CB422208), the NSF-China Project (40671132) and the State Data Synthesis and Analysis Funds of China (No. 2005DKA32300). We thank Mr Yueping Jiang of Zhejiang University for his assistance with image acquisition and the ground survey.