Wetlands are valuable as one type of the most important ecosystems on earth. As the natural habitats of many wild animals and plants, wetlands play a key role for protection of wild genes and environmental stability. However, it exists the complex characteristics of wetland habitats due to the wetland locating between land and water bodies, and it is well known that inaccessibility to wetlands often causes a large difficulty to do wetland research. Hence remote sensing plays an important role in the wetland scientific research as a useful tool to generate parameters of ecological and environmental process of wetlands. Especially, it has achieved so much onthe ability of high resolution imagery and its application methods recently. In this study, the Honghe National Nature Reserve (HNNR) was selected as the study area, which locates in the Northeast portion of the Sanjiang Plain in China. And HNNR has been listed as a key international wetland within the Ramsar list in 2002. A camera system equipped on unmanned airship was used to obtain multiple high-resolution imagery with a very high spatial resolution of 0.13m for our wetland classification mapping purpose. And a very detailed classification system of wetland plants was made for the 9 types of plant communities. Object-based classification method, the approach for classification based on subjects (groups of pixels) rather than each single pixel, was used to delineate and map the different wetland communities as a new methods. For detecting the efficiency of the different classification methods of remote sensing, the authors also attempted another method of supervised maximum likelihood classification for this wetland mapping. The result indicates that: (1) Airship-imagery can fully characterize the detailed plant features such as plant shape and structure,the different vegetation types such as marsh, meadow, various arbors, and shrub, can all be derived from our images at plant community scale with an overall accuracy of 91.77%; (2) By comparison between the object-oriented classification method especially for the high-resolution imagery and the traditional maximum likelihood classification method, authors can conclude that the former classification method has a higher accuracy, while the latter result is not so satisfactory. Hence, one conclusion from this research indicates that the selection of classification method is very important for wetland mapping at a community scale by using remote sensing technique; (3) Our wetland mapping result shows that the spatial distribution pattern of wetland plant communities are controlled by both the environmental gradient of wetness and micro-topographies of wetlands, showing a mutual alternative zonal distribution pattern within the HNNR.