scholarly journals Do Urban Functional Zones Affect Land Surface Temperature Differently? A Case Study of Beijing, China

2019 ◽  
Vol 11 (15) ◽  
pp. 1802 ◽  
Author(s):  
Yuning Feng ◽  
Shihong Du ◽  
Soe W. Myint ◽  
Mi Shu
Keyword(s):  
Surface Temperature ◽  
Spatial Heterogeneity ◽  
Land Surface ◽  
Urban Areas ◽  
Spatial Differentiation ◽  
Heat Emission ◽  
Landscape Composition ◽  
Anthropogenic Heat ◽  
Functional Zones ◽  
The Difference

The non-uniformity of the relationships between urban temperature and landscape has attracted board attention. The non-uniformity in urban areas is reflected in the spatial landscape’s heterogeneity and the difference of socio-economic functions. The former is shown as the spatial differentiation of land-cover, land-use, landscape composition, and configuration, while the latter leads to the difference of the intensity of human activities and population density, which are closely related with anthropogenic heat emission. Therefore, this study introduces urban functional zones (UFZs) to express urban spatial heterogeneity. This study also attempts to comprehend urban heat island (UHI) effects and discloses the variability of urban surface temperature (LST)–landscape relationships in different kinds of UFZs. There are two main technical difficulties—how to characterize the spatial heterogeneity of UFZs and how to quantify non-uniform LST effects. A three-level variable system is established from their attributes, inner structures, and interrelationships to characterize UFZs and their LST effects hierarchically. Considering the multi-collinearity among high-dimensional variables, the Elastic Net regression method is selected for quantitative analysis. The experimental results reveal the deficiency of uniform LST analysis for heterogeneous urban areas and verify the variable relationships of LST-landscaped with different kinds of UFZs.

scholarly journals Quantification of Urban Heat Island-Induced Contribution to Advance in Spring Phenology: A Case Study in Hangzhou, China

2021 ◽  
Vol 13 (18) ◽  
pp. 3684
Author(s):  
Yingying Ji ◽  
Jiaxin Jin ◽  
Wenfeng Zhan ◽  
Fengsheng Guo ◽  
Tao Yan
Keyword(s):  
Surface Temperature ◽  
Urban Heat Island ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Index ◽  
Heat Island ◽  
Spring Phenology ◽  
Urban Heat ◽  
The Difference

Plant phenology is one of the key regulators of ecosystem processes, which are sensitive to environmental change. The acceleration of urbanization in recent years has produced substantial impacts on vegetation phenology over urban areas, such as the local warming induced by the urban heat island effect. However, quantitative contributions of the difference of land surface temperature (LST) between urban and rural (ΔLST) and other factors to the difference of spring phenology (i.e., the start of growing season, SOS) between urban and rural (ΔSOS) were rarely reported. Therefore, the objective of this study is to explore impacts of urbanization on SOS and distinguish corresponding contributions. Using Hangzhou, a typical subtropical metropolis, as the study area, vegetation index-based phenology data (MCD12Q2 and MYD13Q1 EVI) and land surface temperature data (MYD11A2 LST) from 2006–2018 were adopted to analyze the urban–rural gradient in phenology characteristics through buffers. Furthermore, we exploratively quantified the contributions of the ΔLST to the ΔSOS based on a temperature contribution separation model. We found that there was a negative coupling between SOS and LST in over 90% of the vegetated areas in Hangzhou. At the sample-point scale, SOS was weakly, but significantly, negatively correlated with LST at the daytime (R2 = 0.2 and p < 0.01 in rural; R2 = 0.14 and p < 0.05 in urban) rather than that at nighttime. Besides, the ΔSOS dominated by the ΔLST contributed more than 70% of the total ΔSOS. We hope this study could help to deepen the understanding of responses of urban ecosystem to intensive human activities.

scholarly journals Impacts of Anthropogenic Heat on Summertime Rainfall in Beijing

2017 ◽  
Vol 18 (3) ◽  
pp. 693-712 ◽  
Author(s):  
Wanshu Nie ◽  
Benjamin F. Zaitchik ◽  
Guangheng Ni ◽  
Ting Sun
Keyword(s):  
Land Surface ◽  
Urban Areas ◽  
Energy Demand ◽  
Energy Transport ◽  
Sensible Heat Flux ◽  
Wrf Model ◽  
Turbulent Energy ◽  
Heat Emission ◽  
Anthropogenic Heat ◽  
Energy Budgets

Abstract Anthropogenic heat is an important component of the urban energy budgets that can affect land surface and atmospheric boundary layer processes. Representation of anthropogenic heat in numerical climate modeling systems is therefore important when simulating urban meteorology and climate and has the potential to improve weather forecasts, climate process studies, and energy demand analysis. Here, spatiotemporally dynamic anthropogenic heat data estimated by the Building Effects Parameterization and Building Energy Model (BEP-BEM) are incorporated into the Weather Research and Forecasting (WRF) Model system to investigate its impact on simulation of summertime rainfall in Beijing, China. Simulations of four local rainfall events with and without anthropogenic heat indicate that anthropogenic heat leads to increased rainfall over the urban area. For all four events, anthropogenic heat emission increases sensible heat flux, enhances mixing and turbulent energy transport, lifts PBL height, increases dry static energy, and destabilizes the atmosphere in urban areas through thermal perturbation and strong upward motion during the prestorm period, resulting in enhanced convergence during the major rainfall period. Intensified rainfall leads to greater atmospheric dry-down during the storm and a higher poststorm LCL.

scholarly journals Impact of Multitemporal Land Use and Land Cover Change on Land Surface Temperature Due to Urbanization in Hefei City, China

2021 ◽  
Vol 10 (12) ◽  
pp. 809
Author(s):  
Jing Sun ◽  
Suwit Ongsomwang
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Decomposition Analysis ◽  
Lulc Change ◽  
Main Components ◽  
The Impact

Land surface temperature (LST) is an essential parameter in the climate system whose dynamics indicate climate change. This study aimed to assess the impact of multitemporal land use and land cover (LULC) change on LST due to urbanization in Hefei City, Anhui Province, China. The research methodology consisted of four main components: Landsat data collection and preparation; multitemporal LULC classification; time-series LST dataset reconstruction; and impact of multitemporal LULC change on LST. The results revealed that urban and built-up land continuously increased from 2.05% in 2001 to 13.25% in 2020. Regarding the impact of LULC change on LST, the spatial analysis demonstrated that the LST difference between urban and non-urban areas had been 1.52 K, 3.38 K, 2.88 K and 3.57 K in 2001, 2006, 2014 and 2020, respectively. Meanwhile, according to decomposition analysis, regarding the influence of LULC change on LST, the urban and built-up land had an intra-annual amplitude of 20.42 K higher than other types. Thus, it can be reconfirmed that land use and land cover changes due to urbanization in Hefei City impact the land surface temperature.

scholarly journals Quantifying the Effects of Urban Form on Land Surface Temperature in Subtropical High-Density Urban Areas Using Machine Learning

2019 ◽  
Vol 11 (8) ◽  
pp. 959 ◽  
Author(s):  
Yanwei Sun ◽  
Chao Gao ◽  
Jialin Li ◽  
Run Wang ◽  
Jian Liu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Form ◽  
Urban Areas ◽  
Thermal Environment ◽  
High Density ◽  
Cooling Effects ◽  
Urban Thermal Environment ◽  
The Impact

It is widely acknowledged that urban form significantly affects urban thermal environment, which is a key element to adapt and mitigate extreme high temperature weather in high-density urban areas. However, few studies have discussed the impact of physical urban form features on the land surface temperature (LST) from a perspective of comprehensive urban spatial structures. This study used the ordinary least-squares regression (OLS) and random forest regression (RF) to distinguish the relative contributions of urban form metrics on LST at three observation scales. Results of this study indicate that more than 90% of the LST variations were explained by selected urban form metrics using RF. Effects of the magnitude and direction of urban form metrics on LST varied with the changes of seasons and observation scales. Overall, building morphology and urban ecological infrastructure had dominant effects on LST variations in high-density urban centers. Urban green space and water bodies demonstrated stronger cooling effects, especially in summer. Building density (BD) exhibited significant positive effects on LST, whereas the floor area ratio (FAR) showed a negative influence on LST. The results can be applied to investigate and implement urban thermal environment mitigation planning for city managers and planners.

Reconciling the Spatial Distribution of the Surface Temperature Trends in the Southeastern United States

2012 ◽  
Vol 25 (10) ◽  
pp. 3610-3618 ◽  
Author(s):  
V. Misra ◽  
J.-P. Michael ◽  
R. Boyles ◽  
E. P. Chassignet ◽  
M. Griffin ◽  
...  
Keyword(s):  
United States ◽  
South Carolina ◽  
Surface Temperature ◽  
Spatial Heterogeneity ◽  
Rural Areas ◽  
Urban Areas ◽  
Summer Season ◽  
Boreal Summer ◽  
Temperature Trends ◽  
Linear Trends

Abstract This study attempts to explain the considerable spatial heterogeneity in the observed linear trends of monthly mean maximum and minimum temperatures (Tmax and Tmin) from station observations in the southeastern (SE) United States (specifically Florida, Alabama, Georgia, South Carolina, and North Carolina). In a majority of these station sites, the warming trends in Tmin are stronger in urban areas relative to rural areas. The linear trends of Tmin in urban areas of the SE United States are approximately 7°F century−1 compared to about 5.5°F century−1 in rural areas. The trends in Tmax show weaker warming (or stronger cooling) trends with irrigation, while trends in Tmin show stronger warming trends. This functionality of the temperature trends with land features also shows seasonality, with the boreal summer season showing the most consistent relationship in the trends of both Tmax and Tmin. This study reveals that linear trends in Tmax in the boreal summer season show a cooling trend of about 0.5°F century−1 with irrigation, while the same observing stations on an average display warming trends in Tmin of about 3.5°F century−1. The seasonality and the physical consistency of these relationships with existing theories may suggest that urbanization and irrigation have a nonnegligible influence on the spatial heterogeneity of the surface temperature trends over the SE United States. The study also delineates the caveats and limitations of the conclusions reached herein due to the potential influence of perceived nonclimatic discontinuities (which incidentally could also have a seasonal cycle) that have not been taken into account.

Correlation of air temperature and land surface temperature of Landsat-8 in cities of South Korea

2020 ◽  
Author(s):  
Sungwon Choi ◽  
Donghyun Jin ◽  
Noh-hun Seong ◽  
Daeseong Jung ◽  
Kyung-soo Han
Keyword(s):  
Correlation Analysis ◽  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Urban Areas ◽  
Temperature Data ◽  
Landsat 8 ◽  
In Situ Data ◽  
Urban Greenery ◽  
Important Position

&lt;p&gt;Recently, there are many problems in urban area such as urban thermal island phenomenon, changes in urban green area, changes in urban weather and various urban types. And surface temperature data have been utilized in many areas to identify these phenomena. This means that surface temperatures is an important position in urban greenery and weather. High temporal and spatial resolution satellite data are needed to continuously observe the phenomenon in urban areas. In addition, the surface temperature varies from type of indicator, topography, and various factors, so there is a limit to the in-situ data for observing changes throughout the city. Therefore, various organizations around the world are currently conducting surface temperature measurements using satellites. However, the use of data in clear pixel is essential for accurate surface temperature calculations using satellites, but the accuracy of results will be reduced if the data from in the pixel which conclude clouds.&lt;/p&gt;&lt;p&gt;Therefore, we tried to solve these problems by analyzing the correlation between the air temperature data and the Landsat-8 LST data. The variables used in the correlation analysis are air temperature, Landsat-8 LST, NDVI and NDWI, and the study period is 2014 to 2016 and the study area is South Korea's five cities (Seoul, Busan, Daejeon, Daegu, Gwangju). For correlation analysis, the air temperature data points provided by the Korea Meteorological Administration and the Landsat-8 pixels were matched, and the correlation coefficient calculated by the correlation analysis was applied to the Landsat-8 satellite to calculate the LST. We validated by direct comparison the re-produced Landsat-8 LST with observed Landsat-8 LST. And the result of validation showed a high correlation of 0.9. It shows that compensation for the satellite's shortcomings from clouds by using the correlation between temperature and LST.&lt;/p&gt;

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