Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes

Chen, Xiaoling; Zhao, Hongmei; Li, Pingxiang; Yin, Zhiyong

doi:10.1016/j.rse.2005.11.016

Cited by 1,376 publications

(773 citation statements)

References 27 publications

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“…These studies have mainly used satellite images to derive land cover indices (e.g., normalized difference built-up index (NDBI), normalized difference water index (NDWI), normalized difference bareness index (NDBaI), impervious surface areas (ISA), and normalized difference vegetation index (NDVI)) to establish a relationship between UHI and land cover types. A well-known example is a study in Pearl River Delta (PRD) in China [20]. Using OLS regression, this study found a positive association between NDBI and UHI.…”

Section: Links Between Land Cover Type and The Uhi Effectmentioning

confidence: 94%

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

2016

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Numerous studies have identified associations between the surface urban heat island (SUHI) effect (i.e., SUHI, hereinafter is referred to as UHI) and urban growth, particularly changes in land cover patterns. This research questions their causal links to answer a key policy question: If cities restrict urban expansion and encourage people to live within existing urban areas, will that help in controlling UHI? The question has been answered by estimating four models using data from Brisbane, Australia: Model 1-cross-sectional ordinary least square (OLS) regression-to examine the association between the UHI effect and land cover patterns in 2013; Model 2-cross-sectional geographically weighted regression (GWR)-to examine whether the outputs generated from Model 1 possess significant spatial variations; Model 3-longitudinal OLS-to examine whether changes in land cover patterns led to changes in UHI effects between 2004 and 2013; and Model 4-longitudinal GWR-to examine whether the outputs generated from Model 3 vary significantly over space. All estimations were controlled for potential confounding effects (e.g., population, employment and dwelling densities). Results from the cross-sectional OLS and GWR models were consistent with previous findings and showed that porosity is negatively associated with the UHI effect in 2013. In contrast, population density has a positive association. Results from the longitudinal OLS and GWR models confirm their causal linkages and showed that an increase in porosity level reduced the UHI effect, whereas an increase in population density increased the UHI effect. The findings suggest that even a containment of population growth within existing urban areas will lead to the UHI effect. However, this can be significantly minimized through proper land use planning, by creating a balance between urban and non-urban uses of existing urban areas.

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Section: Links Between Land Cover Type and The Uhi Effectmentioning

confidence: 94%

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

2016

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“…To reduce the effect of bare soils and sparsely vegetated grids on the NDVI trends, grid cells with an annual mean NDVI smaller than 0.1 during the 11 years were excluded from the analysis, as in Zhou et al (2001Zhou et al ( , 2003. Many studies have validated these kinds of data for vegetation growing conditions, biomass estimation, environment monitoring, and global change (Li et al 2011;Jeganathan et al 2014;de Jong et al 2013;Chen et al 2006;Fang et al 2007;Xiao et al 2002).…”

Section: Datasetmentioning

confidence: 99%

Driving force and changing trends of vegetation phenology in the Loess Plateau of China from 2000 to 2010

Wang

et al. 2016

J. Mt. Sci.

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“…Meanwhile, their respective standard errors are 0.02353 and 0.01635, which are smaller than those obtained with coterminous or random cluster sampling. These standard errors are still substantially larger than their counterpart values furnished by Equation (4).…”

Section: Increasing Domain Subregionsmentioning

confidence: 67%

“…Regression is extensively used to model various phenomena such as land use and land cover [4,11], NDVI [12,13], urban heat island [4], and landslide susceptibility [14], but spatial autocorrelation has been barely accommodated in modeling remotely-sensed data. Remotely-sensed data has a strong positive spatial autocorrelation in most cases: even one with a fragmented (e.g., land use) pattern with a coarse resolution (e.g., 250 m of MODIS).…”

Section: Discussionmentioning

confidence: 99%

“…One well known impact of positive spatial autocorrelation is variance inflation (VIF; e.g., [1]) which, in turn, impacts uncertainty quantification and assessment of remotely sensed data. However, although popular versions of spatial regression techniques have existed since 1972 [2] as spatial statistical tools, and since 1988 [3] as spatial econometric tools, remote sensing researchers continue to shy away from them and use non-spatial regression techniques (e.g., [4]; as of 21 January 2016, this article had 447 Google Scholar citations, and 216 Web of Science citations). Principal drawbacks of these implementations vis-à-vis massively large georeferenced datasets, such as remotely-sensed images, include that they: (1) involve nonlinear regression, which requires multiple iterations, each essentially executing a linear regression, to calculate parameter estimates; and (2) require calculating the eigenvalues of an n-by-n spatial weights matrix in order to compute the normalizing constant for an auto-normal probability model.…”

Section: Introductionmentioning

confidence: 99%

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Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data

Griffith

Chun

2016

Remote Sensing

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Virtually all remotely sensed data contain spatial autocorrelation, which impacts upon their statistical features of uncertainty through variance inflation, and the compounding of duplicate information. Estimating the nature and degree of this spatial autocorrelation, which is usually positive and very strong, has been hindered by computational intensity associated with the massive number of pixels in realistically-sized remotely-sensed images, a situation that more recently has changed. Recent advances in spatial statistical estimation theory support the extraction of information and the distilling of knowledge from remotely-sensed images in a way that accounts for latent spatial autocorrelation. This paper summarizes an effective methodological approach to achieve this end, illustrating results with a 2002 remotely sensed-image of the Florida Everglades, and simulation experiments. Specifically, uncertainty of spatial autocorrelation parameter in a spatial autoregressive model is modeled with a beta-beta mixture approach and is further investigated with three different sampling strategies: coterminous sampling, random sub-region sampling, and increasing domain sub-regions. The results suggest that uncertainty associated with remotely-sensed data should be cast in consideration of spatial autocorrelation. It emphasizes that one remaining challenge is to better quantify the spatial variability of spatial autocorrelation estimates across geographic landscapes.

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Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes

Cited by 1,376 publications

References 27 publications

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

Correlation or Causality between Land Cover Patterns and the Urban Heat Island Effect? Evidence from Brisbane, Australia

Driving force and changing trends of vegetation phenology in the Loess Plateau of China from 2000 to 2010

Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data

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