城市化对城市森林组成和结构的影响——以上海“城-郊”样带为例

由于城市森林可改善人居环境并提高居民的生活环境质量而备受关注,而城市化进程的加剧使生态系统的结构也发生了很大变化。论文以快速城市化进程中上海“城-郊”样带为例,基于地理信息技术,采用景观格局分析和数理统计方法,有效量化“城-郊”样带;并借助植被功能分类的相关理论,以乔木组成和结构变化为载体,探讨城市化程度对城市森林的影响。结果表明：1）城区乔木丰富度指数（S）、Shannon-Wiener指数（H）和Simpson指数（D）高于郊区,二者存在显著性差异（P<0.05）;但城区和郊区乔木物种均匀度分别为0.448±0.043和0.394±0.038,不存在显著差异（P>0.05）。2）城区乔木的密度小于郊区,分别为248±25和472±57株/hm²;且二者间存在显著性差异（P<0.05）。3）城区和郊区的城市森林群落的生长潜能存在一定的差异,郊区具有较多数量的高生长潜能乔木,而城区中低生长潜能的常绿乔木密度比郊区高;4）城区、郊区中各胸径量级的乔木数量分布均呈倒J型曲线。通过该研究以期促进城市森林生态系统服务功能发挥的同时也为进行城市森林的管理提供科学依据。

The Impact of Urbanization on the Composition and Structure of Urban Forest

Ecosystems adjacent to and in cities are experiencing increasing disturbance from urbanizations and alteration of vegetation, soils, and biogeochemical cycles. Indeed the structure, function and subsequent ecosystem services of urban forests are highly influenced by land use change and human activities. And the fast urbanization process affected the composition and structure of urban ecosystem. The major data sources included in this study are remote sensing images and field inventory data of urban forests. Also, GIS tools and statistical analysis methods were used in this study. The study tried to explore the impacts of urbanization process on urban forest by taking Shanghai “urban-to-suburban” transect as an example. Based on remote sensing images, geographic information system (ArcGIS 9.3), landscape pattern analysis (Fragstates 3.3) and statistics (SPSS 17) were used to calculate six commonly used indicators, including physical variables and landscape metrics. Principal components analysis and multiple linear regression were used to assess the urbanization level of Shanghai. The results showed that: 1) The species richness (S), Simpson diversity (D), Shannon-Wiener diversity (H) of tree species in urban area were higher than those in suburban area, and there were significant differences (P < 0.05). While the species evenness of trees in urban area and suburban area were 0.448 ± 0.043 and 0.394 ± 0.038, with no significant difference. 2) The number of trees per unit area (hectare) in urban area was in the urban area, less than 472 ± 57 in the suburban area. It showed significant difference between them (P <0.05). 3) There were differences in the tree size potential in urban and suburban areas. The suburban area had more trees with high size potential, while the urban area had more large evergreens trees per hectare. 4) DBH (diameter at breast height) distribution presented an inverse “J-curve” in both urban and suburban areas. The study might be useful in scientific managing urban forest ecosystem function and provide a baseline for improving urban forest function.