Spatio-temporal analysis of the relationship between 2D/3D urban site characteristics and land surface temperature

Berger, Christian; Rosentreter, Johannes; Voltersen, M.; Baumgart, Caroline; Schmullius, C.; Hese, S.

doi:10.1016/j.rse.2017.02.020

Cited by 215 publications

(92 citation statements)

References 108 publications

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“…Studies characterizing the spatial and temporal patterns of LST are well documented, however, the effective mitigation of excessive urban heat demands further knowledge about how LST is impacted by potential land surface drivers [13,14]. As a result, numerous studies have examined the associations between landscape indicators and LST [15][16][17][18][19][20], so as to generate useful knowledge about how the urban physical form interacts with the climatic context to support further climate-sensitive planning. On the whole, the analysis of the urban landscape and LST association has demonstrated three significant tendencies in consideration of the seasonal variation and spatial non-stationarity of the association, and a prominent growth in sophistication regarding the explanatory variables used for regression.…”

Section: Introductionmentioning

confidence: 99%

Seasonal Variation of the Spatially Non-Stationary Association Between Land Surface Temperature and Urban Landscape

et al. 2019

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There has been a growing concern for the urbanization induced local warming, and the underlying mechanism between urban thermal environment and the driving landscape factors. However, relatively little research has simultaneously considered issues of spatial non-stationarity and seasonal variability, which are both intrinsic properties of the environmental system. In this study, the newly proposed multi-scale geographically weighted regression (MGWR) is employed to investigate the seasonal variations of the spatial non-stationary associations between land surface temperature (LST) and urban landscape indicators under different operating scales. Specifically, by taking Wuhan as a case study, Landsat-8 images were used to achieve the LSTs in summer, winter and the transitional season, respectively. Landscape composition indicators including fractional vegetation cover (FVC), albedo and water percentage (WP) and urban morphology indicators covering building density (BD), building height (BH) and building volume density (BVD) were employed as potential landscape drivers of LST. For reference, the conventional geographically weighted regression (GWR) and ordinary least squares (OLS) regression were also employed. Results revealed that MGWR outperformed GWR and OLS in terms of goodness-of-fit for all seasons. For the specific associations with LST, all six indicators exhibited evident seasonal variations, especially from the transition season to winter. FVC, albedo and BD were observed to possess great spatial non-stationarity for all seasons, while WP, BH and BD tended to influence LST globally. Overall, FVC exhibited certain positive effect in winter. The negative effect of WP was the greatest among all indicators, although it became the weakest in winter. Albedo tended to influence LST more complicatedly than simple cooling. BD, with a consistent heating effect, was testified to have a greater influence on LST than BH for all seasons. The BH-LST association tended to transfer into positive in winter, while the BVD-LST association remained negative for all seasons. The results could support the establishment of season- and site-specific mitigation strategies. Generally, this study facilitates our understanding of human-environment interaction and narrows the gap between climate research and city management.

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Section: Introductionmentioning

confidence: 99%

Seasonal Variation of the Spatially Non-Stationary Association Between Land Surface Temperature and Urban Landscape

et al. 2019

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“…Yue et al (2007) and Zhang et al (2009) examined the relationship between satellite-derived land surface temperature (LST) and normalized difference vegetation index (NDVI), a measure of vegetation abundance. Studies by Zhou et al (2011), Lu et al (2015, Gage and Cooper (2017), Odindi et al (2017) and Berger et al (2017) highlighted the relationship between LST and land use/land cover. Gage and Cooper (2017) and Berger et al (2017) included vertical structure in their analysis to study the effect of the height of structures on surface temperature.…”

Section: Introductionmentioning

confidence: 99%

“…Studies by Zhou et al (2011), Lu et al (2015, Gage and Cooper (2017), Odindi et al (2017) and Berger et al (2017) highlighted the relationship between LST and land use/land cover. Gage and Cooper (2017) and Berger et al (2017) included vertical structure in their analysis to study the effect of the height of structures on surface temperature. Bechtel (2012) and Jiang and Weng (2017) estimated UHI parameters from land surface temperature.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variations in the Impacts of Urban Land Use Types on Urban Heat Island Effects: The Case of Riyadh, Saudi Arabia

Aina

Adam

Ahmed

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Urban heat island (UHI) effect is considered to be one of the key indicators of the impacts of urbanization and the climate changes on the environment. Thus, the growing interest in studying the impacts of urbanization on changes in land surface temperature (LST). The literature on LST indicates the need for more studies on the relationship between changes in LST and land use types, especially in the arid environment. This paper examines the spatial and temporal changes in land surface temperature influenced by land use/land cover types in Riyadh, Saudi Arabia. Multi-temporal Landsat images of the study area, 1985, 1995, 2002 and 2015, were processed to derive land surface temperatures. UHI index was computed for the different land use/land cover types (highdensity residential, medium-density residential, low-density residential, industrial, vegetation, and desert) in the study area. The results indicate a trend of rising temperatures in all the land use types in the study area. This is probably due to climate change. The industrial area has the highest temperatures among the land use types. The lowest temperatures are found in the vegetation area as expected. There is a need to implement mitigating measures to reduce the effects of rising temperatures in the study area.

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“…The most prominent growth of ISA in areas undergoing rapid urbanization is due to a sharp increase in land areas used for building. Building density (BD) is the most effective parameter reflecting the expansion of building land, and it undoubtedly impacts LST [46]. Based on the building vector map data for the study area in 2013, several square buffer zones with a spacing of 0.5 km were selected around the main urban center.…”

Section: Driving Factors Of Lstmentioning

confidence: 99%

Measuring the Urban Land Surface Temperature Variations in Zhengzhou City Using the Landsat-Like Data

Wang¹,

P²,

Yang³

et al. 2018

Preprint

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Satellite-based remote sensing technologies are utilized extensively to investigate urban thermal environments under rapid urban expansion. Current MODIS data is, however, unable to adequately represent the spatially detailed information because of its relatively coarser spatial resolution, while Landsat data can’t explore temporally the refined analysis due to the low temporal resolution. In order to resolve this situation, we used MODIS and Landsat data to generate “Landsat-like” data by using the flexible spatiotemporal data fusion method (FSDAF), and then studied spatiotemporal variation of land surface temperature (LST) and its driving factors. The results showed that 1) The estimated “Landsat-like” data have high precision; 2) By comparing 2013 and 2016 datasets, LST increases ranging from 1.8°C to 4°C were measurable in areas where the impervious surface area (ISA) increased, while LST decreases ranging from -3.52°C to -0.70°C were detected in areas where ISA decreased; 3) LST has a strongly negative relationship with the Normalized Difference Vegetation Index (NDVI), and a strongly positive relationship with Normalized Difference Built Index (NDBI) in summer; and 4) LST is well correlated with Building density (BD), in a complex conic mode, and LST may increase by 0.460°C to 0.786°C when BD increases by 0.1. Our findings can provide information useful for mitigating undesirable thermal conditions and for long-term urban thermal environmental management.

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Spatio-temporal analysis of the relationship between 2D/3D urban site characteristics and land surface temperature

Cited by 215 publications

References 108 publications

Seasonal Variation of the Spatially Non-Stationary Association Between Land Surface Temperature and Urban Landscape

Seasonal Variation of the Spatially Non-Stationary Association Between Land Surface Temperature and Urban Landscape

Spatiotemporal Variations in the Impacts of Urban Land Use Types on Urban Heat Island Effects: The Case of Riyadh, Saudi Arabia

Measuring the Urban Land Surface Temperature Variations in Zhengzhou City Using the Landsat-Like Data

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