Assessment of land surface temperature in relation to landscape metrics and fractional vegetation cover in an urban/peri-urban region using Landsat data

Zhang, Youshui; Odeh, I. A.; Ramadan, Elnazir

doi:10.1080/01431161.2012.712227

Cited by 87 publications

(45 citation statements)

References 46 publications

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“…Finally, the land architecture of urban areas, from the parcel to larger levels of assessment, has been hypothesized to amplify or ameliorate ecosystem services, such as those related to SUHI effects (Turner et al, 2013). Nascent research suggests that, controlling for land composition, edge and patch densities, landscape shape index, and fractal dimensions (FRAGSTAT metrics) of land covers hold significant consequences for land surface temperatures (Buyantuyev & Wu, 2010;Connors et al, 2013;Li et al, 2011;Li et al, 2012;Middel, Brazel, Kaplan, & Myint, 2012;Middel et al, 2014;Stone & Rodgers, 2001;Zhang, Odeh, & Ramadan, 2013;Zhang, Zhong, Feng, & Wang, 2009;Zhou et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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

199

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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: Introductionmentioning

confidence: 99%

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

199

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“…The relationship between NDVI (Normalized Difference Vegetation Index) [12][13][14][15][16] and LST indicates that vegetation is important in urban heat reduction [9]. FVC (Fractional Vegetation Coverage) is also a key parameter in thermal remote-sensing analysis from which surface emissivity can be estimated [17][18][19], and it is an indicator of the LST, with which it has a negative linear relationship [19,20]. In addition, SAVI (Soil Adjusted Vegetation Index) [21] and GVI (Green Vegetation Index) [22] have also been shown to have a negative relationship with LST.…”

Section: Introductionmentioning

confidence: 99%

Research on the Contribution of Urban Land Surface Moisture to the Alleviation Effect of Urban Land Surface Heat Based on Landsat 8 Data

Chen

Wang

et al. 2015

Remote Sensing

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This paper presents a new assessment method for alleviating urban heat island (UHI) effects by using an urban land surface moisture (ULSM) index. With the aid of Landsat 8 OLI/TIRS data, the land surface temperature (LST) was retrieved by a mono-window algorithm, and ULSM was extracted by tasselled cap transformation. Polynomial regression and buffer analysis were used to analyze the effects of ULSM on the LST, and the alleviation effect of ULSM was compared with three vegetation indices, GVI, SAVI, and FVC, by using the methods of grey relational analysis and Taylor skill calculation. The results indicate that when the ULSM value is greater than the value of an extreme point, the LST declines with the increasing ULSM value. Areas with a high ULSM value have an obvious reducing effect on the temperature of their surrounding areas within 150 m. Grey relational degrees and Taylor skill scores between ULSM and the LST are OPEN ACCESSRemote Sens. 2015, 7 10738 0.8765 and 0.9378, respectively, which are higher than the results for the three vegetation indices GVI, SAVI, and FVC. The reducing effect of the ULSM index on environmental temperatures is significant, and ULSM can be considered to be a new and more effective index to estimate UHI alleviation effects for urban areas.

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“…The GCF is closely related to crop growth (Boissard et al, 1992;Klassen et al, 2003;Ritchie et al, 2010;Vierling et al, 2006) and radiation capture (Asrar et al, 1992;Gonias et al, 2012). In addition, surface temperature and emissivity of a crop are dependent on the relative contributions from bare soil and plant cover (Amiri et al, 2009;Zhang et al, 2013). Ground cover fraction can be used to estimate crop coefficients, which are used to predict evapotranspiration relative to a standardized grass surface.…”

Section: Introductionmentioning

confidence: 99%

Near‐Remote Green: Red Perpendicular Vegetation Index Ground Cover Fraction Estimation in Cotton

Sharma

Ritchie

Rajan³

2015

Crop Science

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Assessment of land surface temperature in relation to landscape metrics and fractional vegetation cover in an urban/peri-urban region using Landsat data

Cited by 87 publications

References 46 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

Research on the Contribution of Urban Land Surface Moisture to the Alleviation Effect of Urban Land Surface Heat Based on Landsat 8 Data

Near‐Remote Green: Red Perpendicular Vegetation Index Ground Cover Fraction Estimation in Cotton

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