Rapid urbanization threatens urban green spaces and vegetation, demonstrated by a decrease in connectivity and higher levels of fragmentation. Understanding historic spatial and temporal patterns of such fragmentation is important for habitat and biological conservation, ecosystem management and urban planning. Despite their potential value, Local Indicators of Spatial Autocorrelation (LISA) measures have not been sufficiently exploited in monitoring the spatial and temporal variability in clustering and fragmentation of vegetation patterns in urban areas. LISA statistics are an important structural measure that indicates the presence of outliers, zones of similarity (hot spots) and of dissimilarity (cold spots) at proximate locations, hence they could be used to explicitly capture spatial patterns that are clustered, dispersed or random. In this study, we applied landscape metrics, LISA indices to analyse the temporal variability in clustering and fragmentation patterns of vegetation patches in Harare metropolitan city, Zimbabwe using Landsat series data for 1994, 2001 and 2017. Analysis of landscape metrics showed an increase in the fragmentation of vegetation patches between 1994–2017 as shown by the decrease in mean patch size, an increase in number of patches, edge density and shape complexity of vegetation patches. The study further demonstrates the utility of LISA indices in identifying key hot spot and cold spots. Comparatively, the highly vegetated northern parts of the city were characterised by significantly high positive spatial autocorrelation (p < 0.05) of vegetation patches. Conversely, more dispersed vegetation patches were found in the highly and densely urbanized western, eastern and southern parts of the city. This suggest that with increasing vegetation fragmentation, small and isolated vegetation patches do not spatially cluster but are dispersed geographically. The findings of the study underline the potential of LISA measures as a valuable spatially explicit method for the assessment of spatial clustering and fragmentation of urban vegetation patterns.

快速的城市化威胁着城市的绿地和植被，其表现为连通性下降和碎片化程度更高。了解这种碎片化的历史时空格局对于栖息地和生物保护，生态系统管理和城市规划至关重要。尽管具有潜在价值，但尚未充分利用空间自相关局部指标（LISA）来监测城市地区植被格局的聚类和碎片化的时空变化。LISA统计数据是一种重要的结构度量，它指示在邻近位置是否存在异常点，相似区域（热点）和不相似区域（冷点），因此它们可以用于显式捕获聚集，分散或随机的空间模式。在这项研究中，我们使用1994、2001和2017年的Landsat系列数据，运用景观指标，LISA指数分析了津巴布韦哈拉雷都会城市植被斑块的聚类和破碎模式的时间变化。景观指标分析表明， 1994-2017年间植被斑块的碎片化表现为斑块平均尺寸的减小，斑块数量的增加，边缘密度和植被斑块的形状复杂性。该研究进一步证明了LISA指数在识别关键热点和冷点方面的实用性。相比之下，该市北部植被茂密的地区的植被斑块的空间自相关性极高（p <0.05）。反过来，在城市高度密集的西部，东部和南部地区发现了更加分散的植被斑块。这表明，随着植被破碎化程度的增加，小而孤立的植被斑块不会在空间上聚集，而会在地理上分散。这项研究的结果强调了LISA措施作为评估城市植被格局的空间聚集和碎片化的有价值的空间明确方法的潜力。