Quantifying correlation between the spatial patterns of natural wetland plants and environmental gradient gives better understanding of wetland habitats, which is the fundamental for the strategy making on the protection and restoration of natural wetlands. In this study, the spatial patterns of wetland plants and the environmental gradient of wetland habitats were assessed in the Honghe National Nature Reserve (HNNR) in Northeast China, a wetland of international importance on the Ramsar list. Biophysical parameters' values of wetland plants were obtained by field sampling methods, and wetland mapping at the community scale was completed using remote sensing techniques. Digital delineation of the surface water system, hydrological zoning and wetness index were produced by spatial analysis methods in Geographic Information System. An ecological ordination method and two clustering methods were used to quantify the relationship between the spatial distribution patterns of wetland plants and the corresponding environmental gradients. Such quantitative analyses also present the specific diversity of different types of wetland plants based on the environmental attributes of their habitats. With the support from modern geo-information techniques, the experimental results indicate how four ecotypes of wetland plants spatially transit from forest swamp, shrub wetland and meadow into marsh wetland with increasing wetness index and water table. And they also show how wetland spatial distribution patterns are controlled by an environmental gradient of wetness. Another key finding of this research work is that our results present the exact fundamental differences between marsh and non-marsh plants of 11 wetland plant communities within the core study area. Hence, this case study gives a good sample for better understanding of the complex correlation between the spatial patterns of wetland plants and their environmental attributes using advanced digital analysis methods. It is also useful to show how to integrate geoinformatic techniques with statistical analysis methods based on the field data base.