a b s t r a c tUrban heat island (UHI) is a global issue as a result of urbanization. Land surface temperature (LST) is closely related to the thermal environment and energy budget of the earth surface, and is an important parameter in identifying UHI effects. Previous studies have proved the effects of landscape pattern on LST by using landscape metrics. However, the metrics used were inconsistent in number and type. Further, fewer studies tried to select representative metrics from the numerous metrics for LST indication. In this study we tried to explore the effects of landscape pattern on LST in Beijing by using the representative class level metrics selected through cluster analysis, factor analysis and regression. The results showed a composition metric such as PLAND (e.g. percentage of impervious surface in a landscape) alone explained about 56% of the landscape mean LST, whereas adding a configuration metric such as LSI (landscape shape index) or Gyrate MN (mean gyration index) explained approximate another 6-12%. Adding more other configuration metrics does not improve the regression model performance more than 1%. The regression results also revealed that without targeted dependent variables, the factor analysis is of no use for choosing landscape metrics. These indicate that landscape composition and configuration both have effects on landscape mean LST, while composition is much more important than configuration, and that a combination of one composition metric with no more than four configuration metrics of impervious surface is sufficient for LST prediction. These results can help landscape ecologists in using landscape metrics and further help landscape planners to balance land cover in urban planning.

a b s t r a c tUrban green spaces often form urban cool islands (UCIs), which are important for human health and urban sustainability. Previous studies have emphasized the cooling effects of urban green spaces on their surrounding areas at landscape level. Less attention, however, has been directed to effects of urban green space patterns on their own UCIs at patch level. In this study, we focused on the effects of spatial patterns of urban green patches on their own surface UCIs. The urban green spaces of Beijing, China, were extracted from one QuickBird image and were classified as Trees, Shrubs, Grass, Crops, River and Lake. Land surface temperatures (LSTs) were derived from four Landsat images, each in one season. The UCI was represented by the minimum LST of each urban green patch. Results showed spatial patterns of urban green patches had significant effects on their UCIs in four seasons. In detail, the size, edge and connectivity of urban green spaces all affected the UCIs negatively, and the influence was stronger in warm seasons. Shape of urban green space also had effects on UCIs, but the effects were stronger in cool seasons. Great differences were found between predictive values of metrics for different green types. Shape metrics were more important for indicating UCIs of River, Trees and Crops than were patch size and connectivity. However, patch size and connectivity metrics were more effective in determining UCIs of Shrubs, Grass and Lake than were shape metrics. Further, among shape metrics, only shape index was a good indicator of UCIs. The results of this study suggest that a combination of specific urban green types and pattern metrics are a prerequisite for analyzing the influence of urban green patterns on UCIs and for urban green design.

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