Based on remote sensing images from 1987, 1992, 1997, 2002, and 2007, the present study used the weighted Ripley's K-function multi-scale analysis method to calculate temporal changes and trends in the spatial distribution (i.e., heterogeneity) of coastal wetland landscape patterns over 20 years in Yancheng, Jiangsu. To analyze changes in the spatial clustering characteristics of different wetland landscape types, we divided the area into small belt transects and created a point pattern database for changing wetland landscapes from 1987 to 2007. The results obtained based on weighted Ripley's K-function analyses demonstrate that, over different spatial and temporal scales, all wetland landscapes presented an aggregated distribution. Moreover, for different wetland types, most patch radius and crowding indices indicate an obvious increase or decrease since 1987. Except for Spartina alterniflora, the aggregated patch radius and crowding of all other natural wetlands dropped sharply or even became undetectable, whereas both the aggregated patch radius and crowding indices in the constructed wetland increased rapidly, at higher rates over time. Our analyses revealed that the weighted Ripley's K-function method, which considers both the location and attributes of point samples, can clearly reveal spatial variations of landscapes at multiple scales. These novel results also agree with those obtained using other conventional analysis methods, such as landscape metrics. Additionally, in future, a deeper analysis of the mechanisms controlling such landscapes at various spatial scales will be conducted to provide quantitative implications for wetland management planning. Specifically, after the wetland polygon data were processed in ArcGIS, we transferred and calculated the spatial indices (patch radius and crowding) in the Matlab R2011a. It was found that, to some extent, the weighted Ripley's K-function could mitigate the occurrence of two false results that were caused by the traditional Ripley's K-function. First, in conventional analysis, large wetland areas are more likely to produce limited numbers of point samples when they are exported directly into a dimensionless point from a polygon; this has resulted in the spatial distribution of some wetlands being interpreted as random or dispersed, even when they exhibit obvious aggregations in the GIS layers. Conversely, the weighted Ripley's K-function takes the dimensions of such points into consideration; accordingly, it is able to reflect changes in the area of study objects in continuous space, thus allowing characterization of changes in the spatial clustering of wetlands at multiple scales. In this study, the extraction and spatial distribution of wetlands distributed in a large area proved the validity of the improved method. Second, traditional methods have tended to produce "false" conclusions or overestimate aggregation when applied to the most seriously fragmented wetlands, which typically generate numerous point samples. In contrast, sample areas in the improved method are weighted, allowing spatial distributions to be represented more accurately. For example, in the present study, we were able to avoid the "over-aggregation" of the spatial distribution of Aeluropus littoralis that may have been produced by traditional methods. Thus, we were able to reveal actual changes in the distribution of Aeluropus littoralis, demonstrating that it shrunk gradually (and almost disappeared) at various scales owing to crowding.