Wetland is a valuable natural resource, with irreplaceable ecological functions. With the development of industrialization, urbanization and population growth, wetland area is decreasing. And ecological functions gradually weakened. Based on TM remote sensing image from 2000 to 2011, this paper built decision tree model of wetland classification to extract wetland by classify. The paper has an research on the dynamic change of wetland of Xiamen city. The results show a growing trend between 2000 and2011, wetland area of Xiamen city increased 583.16 hm 2. About 448.93 hm 2 area is changed from wetland to non-wetland, which 440.73 hm 2 area of waters into non-wetland. About 583.16 hm 2 area is changed from non-wetland to wetland. The total area of natural wetlands is increasing 239.39 hm 2 ; including 477.15 hm 2 non-wetland turn into natural wetlands and 237.76 hm 2 natural wetlands turn into artificial wetlands. This shows that human activities lead to a large of natural wetlands of the study area had transformed into wetlands. According to the wetland area and the average annual rate of change, it is easily seen that tidal wetlands, mangroves, farms and salt wetland are large impacted by human activities or other factors.

Coastlines change with urbanization, and methods to extract coastlines have been previously reported. However, comparisons of these methods are rare. Based on remote sensing image, methods of coastline extraction, namely, the visual interpretation method, the threshold segmentation method, improved normalized water indexes and edge detection algorithms and were studied in Xiamen City, China. The best method to extract coastlines was then determined. The results show that the visual interpretation method for coastline extraction was inefficient. The threshold segmentation method was suitable for small-scale, but not large-scale, coastline extraction, based on coastline area. Improved normalized water indexes were insensitive to sediment shadows. The Sobel method (edge detection algorithms) was suitable for large-scale coastline extraction but could yield false edges. Finally, the block classification method, which combines the advantages of different extraction methods, specifically the threshold segmentation method and improved normalized water indexes, was studied. The results of this study show that coastline extraction by the block classification method is easier and produces better results than coastline extraction by other methods. Therefore, block classification is recommended for the study of coastlines and coastal ecology in large areas.