Limits...
Neighborhood Landscape Spatial Patterns and Land Surface Temperature: An Empirical Study on Single-Family Residential Areas in Austin, Texas

View Article: PubMed Central - PubMed

ABSTRACT

Rapid urbanization has accelerated land use and land cover changes, and generated the urban heat island effect (UHI). Previous studies have reported positive effects of neighborhood landscapes on mitigating urban surface temperatures. However, the influence of neighborhood landscape spatial patterns on enhancing cooling effects has not yet been fully investigated. The main objective of this study was to assess the relationships between neighborhood landscape spatial patterns and land surface temperatures (LST) by using multi-regression models considering spatial autocorrelation issues. To measure the influence of neighborhood landscape spatial patterns on LST, this study analyzed neighborhood environments of 15,862 single-family houses in Austin, Texas, USA. Using aerial photos, geographic information systems (GIS), and remote sensing, FRAGSTATS was employed to calculate values of several landscape indices used to measure neighborhood landscape spatial patterns. After controlling for the spatial autocorrelation effect, results showed that larger and better-connected landscape spatial patterns were positively correlated with lower LST values in neighborhoods, while more fragmented and isolated neighborhood landscape patterns were negatively related to the reduction of LST.

No MeSH data available.


Examples of two buffers measuring LST and neighborhood landscape spatial patterns. (a) Example 1; (b) Example 2.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5036713&req=5

ijerph-13-00880-f002: Examples of two buffers measuring LST and neighborhood landscape spatial patterns. (a) Example 1; (b) Example 2.

Mentions: To measure landscape spatial patterns in the various neighborhoods based on the final samples, this research used an 800 m Euclidian buffer around each home. The 800 m distance was based on the reported distance of an approximate perceptual and behavioral boundary for a neighborhood. This number has been widely adopted in previous studies measuring neighborhood built environment conditions as a distance that residents are willing to walk in their neighborhoods [7,71,72,73,74] (see Figure 2).


Neighborhood Landscape Spatial Patterns and Land Surface Temperature: An Empirical Study on Single-Family Residential Areas in Austin, Texas
Examples of two buffers measuring LST and neighborhood landscape spatial patterns. (a) Example 1; (b) Example 2.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036713&req=5

ijerph-13-00880-f002: Examples of two buffers measuring LST and neighborhood landscape spatial patterns. (a) Example 1; (b) Example 2.
Mentions: To measure landscape spatial patterns in the various neighborhoods based on the final samples, this research used an 800 m Euclidian buffer around each home. The 800 m distance was based on the reported distance of an approximate perceptual and behavioral boundary for a neighborhood. This number has been widely adopted in previous studies measuring neighborhood built environment conditions as a distance that residents are willing to walk in their neighborhoods [7,71,72,73,74] (see Figure 2).

View Article: PubMed Central - PubMed

ABSTRACT

Rapid urbanization has accelerated land use and land cover changes, and generated the urban heat island effect (UHI). Previous studies have reported positive effects of neighborhood landscapes on mitigating urban surface temperatures. However, the influence of neighborhood landscape spatial patterns on enhancing cooling effects has not yet been fully investigated. The main objective of this study was to assess the relationships between neighborhood landscape spatial patterns and land surface temperatures (LST) by using multi-regression models considering spatial autocorrelation issues. To measure the influence of neighborhood landscape spatial patterns on LST, this study analyzed neighborhood environments of 15,862 single-family houses in Austin, Texas, USA. Using aerial photos, geographic information systems (GIS), and remote sensing, FRAGSTATS was employed to calculate values of several landscape indices used to measure neighborhood landscape spatial patterns. After controlling for the spatial autocorrelation effect, results showed that larger and better-connected landscape spatial patterns were positively correlated with lower LST values in neighborhoods, while more fragmented and isolated neighborhood landscape patterns were negatively related to the reduction of LST.

No MeSH data available.