独流减河流域绿色基础设施空间格局与景观连通性分析的尺度效应

尺度效应是景观生态学研究的核心问题之一，是正确理解和感知景观格局与生态过程相互作用及其动态变化特征的基础.针对GI（绿色基础设施）进行空间格局与景观连通性分析具有强烈的尺度依赖性的问题，以天津市独流减河流域为例，利用ArcGIS 10.1、ENVI 5.3和eCognition 8.9软件进行面向对象土地利用分类，基于MSPA（形态学空间格局分析）方法和景观连通性分析方法，借助GuidosToolbox 2.6和Conefor 2.6软件，通过设置不同的粒度、边缘宽度和扩散距离，对2016年研究区GI的粒度效应、边缘效应和距离效应进行定量分析和评价.结果表明:①MSP（形态学空间格局）类型具有明显的粒度效应和边缘效应，较小的粒度和边缘宽度会得到更为详细的空间格局信息.②粒度效应改变GI像元的空间分布，会导致GI空间信息的丢失或增加，直接影响MSP类型的空间配置和量化关系；而边缘效应不会改变GI像元的空间分布，使得GI空间信息不发生改变，但对MSP类型的影响更为显著.③景观连通性与扩散距离具有直接关系，扩散距离越大，GI核心区景观连通性越高，当扩散距离增至一定程度时，所有核心区会连接成一个网络整体，景观连通性达到最大，研究区GI核心区的扩散距离关键值在2.5~4.5 km之间.研究显示，独流减河流域GI要素尺度效应明显但机理不同，基于MSPA和景观连通性分析方法能够更加直观地反映各景观类型和网络组分的空间变化特征和数值变化规律. 

Scale Effect of the Spatial Pattern and Connectivity Analysis for the Green Infrastructure in Duliujian River Basin

Scale effect is one of the core issues in the study of landscape ecology, which is the basis for correctly understanding and perceiving the interaction between landscape pattern and ecological process and its dynamic change characteristics. For the problem of strong scale dependence in the spatial pattern and connectivity analysis of the green infrastructure (GI), land-use classification of the Duliujian River Basin in Tianjin was extracted using ArcGIS 10.1, ENVI 5.3 and eCognition 8.9 software. Then the methods of morphological spatial pattern analysis (MSPA) and landscape connectivity analysis were used, and the GuidosToolbox 2.6 and Conefor 2.6 were used as software tools. Finally, the grain effect, edge effect and distance effect of GI in 2016 were analyzed quantitatively and evaluated by setting different grain size, edge width and diffusion distance. The results show that:(1) Morphological spatial pattern (MSP) types had obvious grain effect and edge effect, and smaller grain size and edge width would obtain more detailed spatial pattern information. (2) The grain effect changed the spatial distribution of GI pixel, which lead to the loss or increase of GI space information, and finally affected the spatial configuration and quantization relationship of MSP types directly. However, the edge effect did not change the spatial distribution of GI pixel, but the impact on MSP types was even more pronounced. (3) Landscape connectivity had a direct relationship with the diffusion distance. The greater the distance was set, the higher the landscape connectivity of the GI core area was. When the diffusion distance increased to a certain extent, all core areas would be connected into a network as a whole and the maximum connectivity of the landscape would be achieved. The pivotal diffusion distance of the GI core area in the study area was 2.5-4.5 km. The research result shows that the scale effect of GI elements in the Duliujian River Basin is obvious, but the mechanism is different. MSPA and landscape connectivity analysis method can reflect the spatial change characteristics and numerical change rules of various landscape types and network components more intuitively.