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Defining landscape structure and key relationships between landscape structure and function is challenging in urban areas characterized by density and patchy spatial patterns. In order to trace the spatial and temporal patterns of urban landscape structures, compare patterns across cities, or inform urban design principles, we need to classify the landscape in a way that captures context and landscape heterogeneity, but can be broadly applied across different cities or landscape variations within a city. In this study, we introduce a simple and reproducible approach for classifying the structure of urban landscapes (STURLA) that utilizes heterogeneous, composite classes which represent combinations of built and natural features, and examine the response of a landscape function – surface temperature. This classification approach is unique in that it develops composite (as opposed to homogeneous) classes, which are defined a posteriori, based on compositions of adjacent structural elements that emerge in the urban landscape, using a cellular grid to define units of analysis. We test the separability of classes that emerge from this approach, and find that it is possible to discern classes – comprised of the mix of land and building covers common in urban areas – which have meaningfully distinct temperature signatures. This classification approach may be extended to multiple cities and ecological indicators in order to offer insight into the relationship between urban landscape structure and ecosystem response, in a way that accounts for interactions among different types of urban landscape surfaces. We suggest that this approach can support spatial prioritization of landscape function needs in urban development and design approaches for improving particular types of functioning, such as reductions in urban heat. [ABSTRACT FROM AUTHOR]

This study introduces a novel approach to classifying urban structure using land cover and building height. The goal of the study was to improve comparability of urban structure–function relationships across cities through development of a novel classification framework that can facilitate urban studies of ecological patterns and processes. We tested the suitability of the classification in two very different urban settings – continental Berlin and coastal New York City. Using Landsat temperature data as an ecological function variable, we compared how urban structures in both cities relate to temperature. Results show that in both cities a large range of urban structure classes show similar trends with respect to land surface temperature, despite differences in climate and structure of the two cities. We found that approximately 68% of the area of Berlin and 79% of the area of New York City can be represented with the same fifteen urban structure classes. Results indicate that these common classes share very similar temperature patterns and may indicate broader utility of the classification framework. Among the classes which have the most dissimilar temperature trends between the two cities, we find large differences in inner-class composition and neighboring classes. Findings also show that the presence of water has a strong influence on temperature regulation, as classes containing water have the lowest surface temperatures, indicating a need for prioritizing aquatic ecosystems in urban planning and management. [ABSTRACT FROM AUTHOR]