Spatial configuration of anthropogenic land cover impacts on urban warming

Zheng, Boyuan; Myint, Soe W.; Fan, Chao

doi:10.1016/j.landurbplan.2014.07.001

Cited by 126 publications

(88 citation statements)

References 24 publications

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“…Surprisingly, the area of impervious surfaces is significant only in the same sample and model as building, but increases LST as expected. The absence of this variable in our other models requires further investigation, given its prominence in other studies (e.g., Zheng et al, 2014).…”

Section: The Implications Of Land Architecture On Lstmentioning

confidence: 81%

“…Area and NMI #s refer to 1: building 2: impervious surfaces 3: soil 4: vegetation 5: swimming pool resolution land-cover data and effective shape/configuration measures of individual land covers (but see Zheng et al, 2014). Our results provide insights into testable hypotheses about land architecture, point to characteristics of that architecture that may serve to reduce the SUHI effect during the summer day, and offer insights to studies of neighborhood inequalities with respect to SUHI intensity.…”

Section: Tablementioning

confidence: 87%

“…Chow & Brazel, 2012;Li et al, 2011;Maimaitiyiming et al, 2014;Weng, Lu, & Schubring, 2004;Xian & Crane, 2006;Zheng et al, 2014;Zhou, Qian, Li, Li, & Han, 2014). These relationships tend to hold across different urban areas and environments, but vary in magnitude diurnally and seasonally.…”

Section: Introductionmentioning

confidence: 97%

“…To date, this research has focused on the relationship between land surface temperature (LST) and particular land-cover types (or land composition) (e.g. Chow & Brazel, 2012;Li, Song, Cao, Meng, & Wu, 2011;Middel, Häb, Brazel, Martin, & Guhathakurta, 2014;Stone & Rodgers, 2001;Zheng, Myint, & Fan, 2014;Zhou et al, 2011), and between spatial thermal patterns and social economic factors (Buyantuyev & Wu, 2010;Harlan et al, 2006;Hondula et al, 2013;Huang, Zhou, & Cadenasso, 2011;Jenerette, Harlan, Stefanov, & Martin, 2011). Recently, however, attention to land system architecture (Turner, Janetos, Verburg, & Murray, 2013)-the composition and configuration (e.g., size, shape, patterns, and connectivity) of the urban land cover-on SUHI has been examined in regard to possible UHI mitigation strategies (Chen, Zhao, Li, & Yin, 2006;Connors, Galletti, & Chow, 2013;Li, Zhou, Quyang, & Zheng, 2012;Li et al, 2013b;Zhou et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Remote sensing of the surface urban heat island and land architecture in Phoenix, Arizona: Combined effects of land composition and configuration and cadastral–demographic–economic factors

Middel

et al. 2016

Remote Sensing of Environment

202

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This study seeks to determine the role of land architecture-the composition and configuration of land cover-as well as cadastral-demographic-economic factors on land surface temperature (LST) and the surface urban heat island effect of Phoenix, Arizona. It employs 1 m National Agricultural Imagery Program data of land-cover with 120 m Landsat-derived land surface temperature, decomposed to 30 m, a new measure of configuration, the normalized moment of inertia, and U.S. Census data to address the question for two randomly selected samples comprising 523 and 545 residential neighborhoods (census blocks) in the city. The results indicate that, contrary to most other studies, land configuration has a stronger influence on LST than land composition. In addition, both land configuration and architecture combined with cadastral, demographic, and economic variables, capture a significant amount of explained variance in LST. The results indicate that attention to land architecture in the development of or reshaping of neighborhoods may ameliorate the summer extremes in LST.

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Section: The Implications Of Land Architecture On Lstmentioning

confidence: 81%

Section: Tablementioning

confidence: 87%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Remote sensing of the surface urban heat island and land architecture in Phoenix, Arizona: Combined effects of land composition and configuration and cadastral–demographic–economic factors

Middel

et al. 2016

Remote Sensing of Environment

202

View full text Add to dashboard Cite

show abstract

“…Landscape configuration has been demonstrated to be an important factor in many research foci including biodiversity [27,28,[65][66][67], nutrient cycles [29], pollination [30], water quality [31], climate change [68], and mapping ecosystem services [69,70]. To account for this, a landscape similarity index (LSI) [71] was calculated for each simulation run using FRAGSTATS 4.1 [72].…”

Section: Configuration Disagreementmentioning

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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Albedo, Land Cover, and Daytime Surface Temperature Variation Across an Urbanized Landscape

et al. 2017

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Land surface albedo is a key parameter controlling the local energy budget, and altering the albedo of built surfaces has been proposed as a tool to mitigate high near‐surface temperatures in the urban heat island. However, most research on albedo in urban landscapes has used coarse‐resolution data, and few studies have attempted to relate albedo to other urban land cover characteristics. This study provides an empirical description of urban summertime albedo using 30 m remote sensing measurements in the metropolitan area around Boston, Massachusetts, relating albedo to metrics of impervious cover fraction, tree canopy coverage, population density, and land surface temperature (LST). At 30 m spatial resolution, median albedo over the study area (excluding open water) was 0.152 (0.112–0.187). Trends of lower albedo with increasing urbanization metrics and temperature emerged only after aggregating data to 500 m or the boundaries of individual towns, at which scale a −0.01 change in albedo was associated with a 29 (25–35)% decrease in canopy cover, a 27 (24–30)% increase in impervious cover, and an increase in population from 11 to 386 km−2. The most intensively urbanized towns in the region showed albedo up to 0.035 lower than the least urbanized towns, and mean mid‐morning LST 12.6°C higher. Trends in albedo derived from 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) measurements were comparable, but indicated a strong contribution of open water at this coarser resolution. These results reveal linkages between albedo and urban land cover character, and offer empirical context for climate resilient planning and future landscape functional changes with urbanization.

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Spatial configuration of anthropogenic land cover impacts on urban warming

Cited by 126 publications

References 24 publications

Remote sensing of the surface urban heat island and land architecture in Phoenix, Arizona: Combined effects of land composition and configuration and cadastral–demographic–economic factors

Remote sensing of the surface urban heat island and land architecture in Phoenix, Arizona: Combined effects of land composition and configuration and cadastral–demographic–economic factors

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

Albedo, Land Cover, and Daytime Surface Temperature Variation Across an Urbanized Landscape

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