Landscape configuration and urban heat island effects: assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona

Connors, John P.; Galletti, Christopher S.; Chow, Winston

doi:10.1007/s10980-012-9833-1

Cited by 429 publications

(222 citation statements)

References 36 publications

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“…Urban warming is caused by hard surfaces that absorb radiation from the sun and emit it as heat [20]. Because these hard surfaces are patchily distributed, cities are thermal mosaics [21,22]. These mosaics provide ready-made experiments, in which trees have been planted and then subjected to a variety of thermal environments.…”

Section: Introductionmentioning

confidence: 99%

Urban warming reduces aboveground carbon storage

et al. 2016

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A substantial amount of global carbon is stored in mature trees. However, no experiments to date test how warming affects mature tree carbon storage. Using a unique, citywide, factorial experiment, we investigated how warming and insect herbivory affected physiological function and carbon sequestration (carbon stored per year) of mature trees. Urban warming increased herbivorous arthropod abundance on trees, but these herbivores had negligible effects on tree carbon sequestration. Instead, urban warming was associated with an estimated 12% loss of carbon sequestration, in part because photosynthesis was reduced at hotter sites. Ecosystem service assessments that do not consider urban conditions may overestimate urban tree carbon storage. Because urban and global warming are becoming more intense, our results suggest that urban trees will sequester even less carbon in the future.

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Section: Introductionmentioning

confidence: 99%

Urban warming reduces aboveground carbon storage

et al. 2016

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“…For this purpose, a large body of research (Chen et al, 2014;Connors et al, 2013;Li et al, 2011;Weng et al, 2007;Zhou et al, 2011) has focused on the relationship between surface UHIs and landscape patterns using landscape indices based on the concept of landscape ecology; that is, the theory that landscape patterns strongly affect and are affected by ecological processes (Turner, 1989). Composition and configuration, as two aspects of landscape patterns, were both tested (Chen et al, 2014;Connors et al, 2013;Zhou et al, 2011), with the conclusion that composition is more effective than configuration in influencing LSTs or surface urban heat islands (SUHIs) (Chen et al, 2014;Li et al, 2012;Zhou et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Composition and configuration, as two aspects of landscape patterns, were both tested (Chen et al, 2014;Connors et al, 2013;Zhou et al, 2011), with the conclusion that composition is more effective than configuration in influencing LSTs or surface urban heat islands (SUHIs) (Chen et al, 2014;Li et al, 2012;Zhou et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Usually, a set of metrics rather than an integrated metric was needed to describe the landscape pattern. The percentage of a certain class in the whole landscape (PLAND) as a composition metric is a prerequisite and is the most used (Chen et al, 2014;Connors et al, 2013;Zhou et al, 2011), whereas several other configuration metrics were also needed, including shape metrics, edge metrics and isolation metrics. For instance, the perimeter-area ratio (PARA), shape index (SI) and fraction index (FRAC) are used to describe shape while the gyration index (GYRATE), total edge (TE), edge density (ED) and so on are used to describe the edge (McGarigal et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Application of a new integrated landscape index to predict potential urban heat islands

Chen

Zhao

Yao

et al. 2016

Ecological Indicators

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a b s t r a c tStudies on the interactions between urban landscape patterns and land surface temperatures are the key to urban heat island (UHI) mitigation. However, the landscape pattern indices used in previous studies were inconsistent in type and number. Furthermore, few studies combined the composition and configuration indices into one integrated indicator. The description of landscape pattern is thus a great yet rewarding challenge. In this study, we used the integrated location weighted landscape index (LWLI) proposed by Chen et al. (2009) and revised it to indicate potential UHIs. Fifty-six circular landscape samples along four transects were created and the LWLI was derived based on the theory of the GINI index. The LWLI considers the type, composition and configuration of different land covers inside a landscape, where configuration is treated as location weights. The potential UHI was represented by the land surface temperature (LST) of each circle landscape center. The correlation analysis results showed that the LWLI was significantly positively correlated with summer potential UHIs, with a Pearson R equaling 0.736, and barely correlated with winter potential UHI. Moreover, the LWLI was slightly more strongly correlated with potential UHI than composition alone. These correlations weakened as the landscapes grew larger. The linear regression results further revealed that the LWLI explained about 53% of the summer potential UHI, which was slightly better than composition alone (49%). This indicated that the LWLI was as effective in predicting the potential UHI as the combination of several pattern indicators, echoing Tobler's first law of geography, which states that "all things are related, but nearby things are more related than distant things." This study also leaves room for improvement of the index by integrating more environmental/ecological parameters as weights, and for further application of the index in other fields.

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“…Configuration disagreement quantifies the degree to which the simulated spatial configurations of categories matches the observed map irrespective of the specific location of those categories. Numerous ecosystem functions, including biodiversity [27,28], nutrient cycles [29], pollination [30], as well as water quality [31] and urban heat islands [32] are influenced by landscape configuration. All three measurements of accuracy are important, and should be considered when assessing the validity of LULC models.…”

Section: Introductionmentioning

confidence: 99%

Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models

Pickard

Gray

Meentemeyer

2017

Land

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Abstract:The growing numbers of land change models makes it difficult to select a model at the beginning of an analysis, and is often arbitrary and at the researcher's discretion. How to select a model at the beginning of an analysis, when multiple are suitable, represents a critical research gap currently understudied, where trade-offs of choosing one model over another are often unknown. Repeatable methods are needed to conduct cross-model comparisons to understand the trade-offs among models when the same calibration and validation data are used. Several methods to assess accuracy have been proposed that emphasize quantity and allocation, while overlooking the accuracy with which a model simulates the spatial configuration (e.g., size and shape) of map categories across landscapes. We compared the quantity, allocation, and configuration accuracy of four inductive pattern-based spatial allocation land change models (SLEUTH, GEOMOD, Land Change Modeler (LCM), and FUTURES). We simulated urban development with each model using identical input data from ten counties surrounding the growing region of Charlotte, North Carolina. Maintaining the same input data, such as land cover, drivers of change, and projected quantity of change, reduces differences in model inputs and allows for focus on trade-offs in different types of model accuracy. Results suggest that these four land change models produce representations of urban development with substantial variance, where some models may better simulate quantity and allocation at the trade-off of configuration accuracy, and vice versa. Trade-offs in accuracy exist with respect to the amount, spatial allocation, and landscape configuration of each model. This comparison exercise illustrates the range of accuracies for these models, and demonstrates the need to consider all three types of accuracy when assessing land change model's projections.

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Landscape configuration and urban heat island effects: assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona

Cited by 429 publications

References 36 publications

Urban warming reduces aboveground carbon storage

Urban warming reduces aboveground carbon storage

Application of a new integrated landscape index to predict potential urban heat islands

Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models

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