基于加权Ripley's K-function的多尺度景观格局分析——以江苏盐城滨海湿地为例

以1987,1992,1997,2002,2007年的遥感影像为例,首次尝试使用加权Ripley's K-function的多尺度格局分析方法,计算了20年来景观异质性在江苏盐城滨海湿地的时间变化和空间分布趋势。通过对研究区的样带划分以及景观类型的点状化处理,建立滨海湿地样带图层和1987—2007年间各类型景观的点格局数据库,从而分析滨海湿地不同类型景观的空间聚集特征变化。基于加权Ripley's K-function的计算表明,在各级空间尺度和时间变化上,各类型湿地的斑块都呈现出空间聚集分布状态,且1987年以来,不同湿地类型的聚集空间特征尺度和空间分布强度均出现了大幅的增减变化,除互花米草滩之外的自然湿地的聚集空间特征尺度和强度都有明显下降甚至少到无法被检测到,而人工湿地却呈现聚集特征尺度和强度的双增长,且该聚集程度有逐渐增强的趋势。分析表明,既考虑样点的空间位置信息又考虑样点分布范围的加权Ripley's K-function方法能很好地表征湿地景观在多尺度上的变异,且与传统空间景观指数等分析方法的结论在一定程度上保持一致。

Multi-scale analysis of landscape characteristics in coastal wetlands in Yancheng, Jiangsu, based on the weighted Ripley's K-function method

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.