高分辨率影像支持的群落尺度沼泽湿地分类制图

湿地作为众多野生动物和植物的栖息地,具有稳定环境及物种基因保护等重要功能.但是,湿地复杂的水陆交界生境特征及难以进入等客观条件限制给湿地研究造成了很大的困难.因此,遥感技术作为地表生态环境过程参量获取的重要工具,在当今湿地科学领域发挥着重要作用,特别是,当前高空间分辨率影像的性能与应用水平不断得到提高.以自然状态下的黑龙江三江平原洪河国家级自然保护区为研究对象,应用飞艇搭载的空间高分辨率摄像系统获取影像地面分辨率为0.13m的影像数据,主要结合面向对象分类方法,开展了基于湿地植物群落尺度的分类制图研究.结果表明:①因飞艇影像对植物形态、纹理等细致特征的刻画非常充分,沼泽植被型、草甸植被型和各种乔木、灌木植被型,都可以在合适的遥感分类方法下提取出来,总体分类精度能达到91.77％；②通过采用针对高分辨率影像面向对象的分类方法与传统的最大似然比遥感分类方法对比,前者达到很高的精度,而后者效果不理想,说明遥感分类方法的选择对于群落尺度湿地植物分类制图结果非常重要；③遥感分类制图的结果显示出研究区湿地植物群落分布格局受到水分环境梯度和微地貌的共同控制,呈现交替环带状分布规律.

Marshclassification mapping at a community scale using high-resolution imagery

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.