# Dominant Factors and Spatial Heterogeneity of Land Surface Temperatures in Urban Areas: A Case Study in Fuzhou, China

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methods

#### 2.1. Study Area

^{2}. Fuzhou is located in a typical estuarine basin and has a typical subtropical monsoon climate, with an annual average precipitation of 900–2100 mm and an annual average temperature of 20–25 °C. According to the data published by the Fuzhou City Bureau of Statistics in 2020, the resident population of Fuzhou is 8.29 million, with an urbanization rate of 72.5%. Fuzhou is currently in a rapid development phase, and rapid urbanization has led to an increase in impervious surfaces in urban areas and a decrease in wetlands and green spaces, resulting in a severe heat island effect [10,42]. According to the results of the Fuzhou Meteorological Bureau, the number of high temperature (above 35 °C) days in Fuzhou City in 2020 is 61–76 days, with some areas breaking the highest temperature in history for the same period. The main urban area of Fuzhou, which is the most populated area in the city [42], was selected as the study area, the central and southern parts of which are crossed by the Min and Wulong rivers, respectively, with a total area of approximately 248 km

^{2}(Figure 1).

#### 2.2. Multi-Source Data

#### 2.3. LST Retrieval

_{s}can be obtained using a function of Planck’s formula:

_{1}= 774.89 W/(m

^{2}·µm·sr) and K

_{2}= 1321.08 K.

#### 2.4. Determination of Influencing Factors

_{1}, RED, and TIR are the near-infrared, mid-infrared1, red band, and thermal-infrared bands of the Landsat8 images, respectively. The MNDWI uses the improved normalized water body index proposed by Xu [48] and can be calculated using.

#### 2.5. Spatial Autocorrelation Analysis (Moran’s I)

#### 2.6. Ordinary Least Squares Regression (OLS)

#### 2.7. Local Regression Modeling

#### 2.7.1. Geographically Weighted Regression (GWR)

_{i}is the random error term at point i. n is the number of independent variables. To estimate the parameters in Equation (15), an observation is weighted according to its proximity to a specific point i. Larger weights are assigned to observations closer to point i. When correcting the GWR Model, the weight is usually calculated by the Gaussian function, and the weight matrix for each location observation value k is established. The Gaussian function is:

_{i,j}is the geographical weight of the j-th geographical unit with respect to geographical unit i, ${d}_{ij}$ is the Euclidean distance between geographical units i and j, and b is the bandwidth. The optimal bandwidth used in this study was based on the golden section search under the A-corrected Akaike information criterion (AICc); when AICc is the smallest in the model, bandwidth b is the best.

#### 2.7.2. Multi-Scale Geographically Weighted Regression (MGWR)

^{2}), A-corrected Akaike information criterion (AICc), and the residual sum of squares (RSS) were used to describe the strengths and weaknesses of the model results [55,56]. In general, if the difference in AICc values between the two models is less than 3, the difference in the fit between the two models is considered to be insignificant [38]. Otherwise, the model with the smaller of the two AICc values should be selected [57]. For RSS, a smaller RSS value represents a better fit of the model to the empirical data [6].

## 3. Results

#### 3.1. Spatiotemporal Patterns of LST

#### 3.2. Dominant Impact Factors

#### 3.3. Optimal Regression Model

^{2}of the GWR model was better than the OLS model, and the improvement was significant, while the AICc and RSS were significantly decreased, which indicates that the parameters of the GWR model were better than those of the OLS model. The R

^{2}of the MGWR model further exceeded that of the GWR model, while the R

^{2}of the MGWR model was higher, and the values of AICc and RSS were smaller, indicating a better model fit. The MGWR model can more accurately reflect the relationship between the LST and the explanatory variables and the local spatial non-stationarity characteristics.

^{2}values of the GWR (Figure 3a) and MGWR (Figure 3b) models. Unlike the OLS model, the spatial patterns of R

^{2}for both the GWR and MGWR models exhibited regional divergence, with the strongest indicator being explanatory power for LST in the southern and eastern parts of the study area. The models had less explanatory power in the central part of the city, but the MGWR model had a better local fit than that of the GWR model.

#### 3.4. Spatial Pattern Analysis of Regression Coefficients

## 4. Discussion

#### 4.1. Spatial LST Differences

#### 4.2. Spatial Heterogeneity of Factors Affecting LST

#### 4.3. Suggestions for Managing Sustainable Development

#### 4.4. Limitations and Future Research

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## References

- Oke, T.R. City size and the urban heat island. Atmos. Environ.
**1973**, 7, 769–779. [Google Scholar] [CrossRef] - Ceplová, N.; Kalusová, V.; Lososová, Z. Effects of settlement size, urban heat island and habitat type on urban plant biodiversity. Landsc. Urban Plan.
**2017**, 159, 15–22. [Google Scholar] [CrossRef] - Guattari, C.; Evangelisti, L.; Balaras, C.A. On the assessment of urban heat island phenomenon and its effects on building energy performance: A case study of Rome (Italy). Energy Build.
**2018**, 158, 605–615. [Google Scholar] [CrossRef] - Sarrat, C.; Lemonsu, A.; Masson, V.; Guédalia, D. Impact of urban heat island on regional atmospheric pollution. Atmos. Environ.
**2006**, 40, 1743–1758. [Google Scholar] [CrossRef] - Grimm, N.B.; Faeth, S.H.; Golubiewski, N.E.; Redman, C.L.; Wu, J.G.; Bai, X.; Briggs, J.M. Global change and the ecology of cities. Science
**2008**, 319, 756–760. [Google Scholar] [CrossRef] [Green Version] - Song, J.; Yu, H.; Lu, Y. Spatial-scale dependent risk factors of heat-related mortality: A multiscale geographically weighted regression analysis. Sustain. Cities Soc.
**2021**, 74, 103159. [Google Scholar] [CrossRef] - Guo, Y.; Li, S.; Liu, D.L.; Chen, D.; Williams, G.; Tong, S. Projecting future temperature-related mortality in three largest Australian cities. Environ. Pollut.
**2016**, 208, 66–73. [Google Scholar] [CrossRef] [PubMed] - Gasparrini, A.; Guo, Y.; Sera, F.; Cabrera, A.M.V.; Huber, V.; Tong, S.; Coelho, M.D.S.Z.S.; Saldiva, P.H.N.; Lavigne, E.; Correa, P.M.; et al. Projections of temperature-related excess mortality under climate change scenarios. Lancet Planet. Health
**2017**, 1, e360–e367. [Google Scholar] [CrossRef] - Voogt, J.A.; Oke, T.R. Thermal remote sensing of urban climates. Remote Sens. Environ.
**2003**, 86, 370–384. [Google Scholar] [CrossRef] - Du, J.; Xiang, X.; Zhao, B.; Zhou, H. Impact of urban expansion on land surface temperature in Fuzhou, China using Landsat imagery. Sustain. Cities Soc.
**2020**, 61, 102346. [Google Scholar] [CrossRef] - Jiang, Y.; Fu, P.; Weng, Q. Assessing the Impacts of Urbanization-Associated Land Use/Cover Change on Land Surface Temperature and Surface Moisture: A Case Study in the Midwestern United States. Remote Sens.
**2015**, 7, 4880–4898. [Google Scholar] [CrossRef] [Green Version] - Guo, G.; Wu, Z.; Xiao, R.; Chen, Y.; Liu, X.; Zhang, X. Impacts of urban biophysical composition on land surface temperature in urban heat island clusters. Landsc. Urban Plan.
**2015**, 135, 1–10. [Google Scholar] [CrossRef] - Firozjaei, M.K.; Fathololoumi, S.; Kiavarz, M.; Arsanjani, J.J.; Alavipanah, S.K. Modelling surface heat island intensity according to differences of biophysical characteristics: A case study of Amol city, Iran. Ecol. Indic.
**2020**, 109, 105816. [Google Scholar] [CrossRef] - Deng, C.; Wu, C. Examining the impacts of urban biophysical compositions on surface urban heat island: A spectral unmixing and thermal mixing approach. Remote Sens. Environ.
**2013**, 131, 262–274. [Google Scholar] [CrossRef] - Xiao, R.-B.; Ouyang, Z.-Y.; Zheng, H.; Li, W.-F.; Schienke, E.W.; Wang, X.-K. Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing, China. J. Environ. Sci.
**2007**, 19, 250–256. [Google Scholar] [CrossRef] - Yu, X.J.; Ng, C.N. Spatial and temporal dynamics of urban sprawl along two urban–rural transects: A case study of Guangzhou, China. Landsc. Urban Plan.
**2007**, 79, 96–109. [Google Scholar] [CrossRef] - Chen, A.; Yao, L.; Sun, R.; Chen, L. How many metrics are required to identify the effects of the landscape pattern on land surface temperature? Ecol. Indic.
**2014**, 45, 424–433. [Google Scholar] [CrossRef] - Zhou, W.; Huang, G.; Cadenasso, M.L. Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landsc. Urban Plan.
**2011**, 102, 54–63. [Google Scholar] [CrossRef] - Estoque, R.C.; Murayama, Y.; Myint, S.W. Effects of landscape composition and pattern on land surface temperature: An urban heat island study in the megacities of Southeast Asia. Sci. Total Environ.
**2017**, 577, 349–359. [Google Scholar] [CrossRef] - Li, W.; Cao, Q.; Lang, K.; Wu, J. Linking potential heat source and sink to urban heat island: Heterogeneous effects of landscape pattern on land surface temperature. Sci. Total Environ.
**2017**, 586, 457–465. [Google Scholar] [CrossRef] - Yao, L.; Li, T.; Xu, M.; Xu, Y. How the landscape features of urban green space impact seasonal land surface temperatures at a city-block-scale: An urban heat island study in Beijing, China. Urban For. Urban Green.
**2020**, 52, 126704. [Google Scholar] [CrossRef] - Du, S.; Xiong, Z.; Wang, Y.-C.; Guo, L. Quantifying the multilevel effects of landscape composition and configuration on land surface temperature. Remote Sens. Environ.
**2016**, 178, 84–92. [Google Scholar] [CrossRef] - Du, H.; Wang, D.; Wang, Y.; Zhao, X.; Qin, F.; Jiang, H.; Cai, Y. Influences of land cover types, meteorological conditions, anthropogenic heat and urban area on surface urban heat island in the Yangtze River Delta Urban Agglomeration. Sci. Total Environ.
**2016**, 571, 461–470. [Google Scholar] [CrossRef] [PubMed] - Huang, G.; Zhou, W.; Cadenasso, M. Is everyone hot in the city? Spatial pattern of land surface temperatures, land cover and neighborhood socioeconomic characteristics in Baltimore, MD. J. Environ. Manag.
**2011**, 92, 1753–1759. [Google Scholar] [CrossRef] - Li, J.; Wang, F.; Fu, Y.; Guo, B.; Yu, H. A Novel SUHI Referenced Estimation Method in Multi-centers Urban Agglomeration with DMSP/OLS Nighttime Light Data. IEEE J. Stars
**2020**, 13, 1416–1425. [Google Scholar] - Liang, Z.; Huang, J.; Wang, Y.; Wei, F.; Wu, S.; Jiang, H.; Zhang, X.; Li, S. The mediating effect of air pollution in the impacts of urban form on nighttime urban heat island intensity. Sustain. Cities Soc.
**2021**, 74, 102985. [Google Scholar] [CrossRef] - Zhang, X.; Estoque, R.C.; Murayama, Y. An urban heat island study in Nanchang City, China based on land surface temper-ature and social-ecological variables. Sustain. Cities Soc.
**2017**, 32, 557–568. [Google Scholar] [CrossRef] - Huang, X.; Schneider, A.; Friedl, M.A. Mapping sub-pixel urban expansion in China using MODIS and DMSP/OLS nighttime lights. Remote Sens. Environ.
**2016**, 175, 92–108. [Google Scholar] [CrossRef] - Dutta, D.; Rahman, A.; Paul, S.; Kundu, A. Impervious surface growth and its inter-relationship with vegetation cover and land surface temperature in peri-urban areas of Delhi. Urban Clim.
**2021**, 37, 100799. [Google Scholar] [CrossRef] - Yin, C.; Yuan, M.; Lu, Y.; Huang, Y.; Liu, Y. Effects of urban form on the urban heat island effect based on spatial regression model. Sci. Total Environ.
**2018**, 634, 696–704. [Google Scholar] [CrossRef] - Oke, T.R. Canyon geometry and the nocturnal urban heat island: Comparison of scale model and field observations. J. Clim.
**1981**, 1, 237–254. [Google Scholar] [CrossRef] - Oke, T.R. Street design and urban canopy layer climate. Energy Build.
**1988**, 11, 103–113. [Google Scholar] [CrossRef] - Unger, J. Connection between urban heat island and sky view factor approximated by a software tool on a 3D urban database. Int. J. Environ. Pollut.
**2009**, 36, 59. [Google Scholar] [CrossRef] [Green Version] - Giridharan, R.; Ganesan, S.; Lau, S. Daytime urban heat island effect in high-rise and high-density residential developments in Hong Kong. Energy Build.
**2004**, 36, 525–534. [Google Scholar] [CrossRef] - Yu, X.; Liu, Y.; Zhang, Z.; Xiao, R. Influences of buildings on urban heat island based on 3D landscape metrics: An investigation of China’s 30 megacities at micro grid-cell scale and macro city scale. Landsc. Ecol.
**2021**, 36, 2743–2762. [Google Scholar] [CrossRef] - Deilami, K.; Kamruzzaman, M.; Liu, Y. Urban heat island effect: A systematic review of spatio-temporal factors, data, methods, and mitigation measures. Int. J. Appl. Earth Obs. Geoinf.
**2018**, 67, 30–42. [Google Scholar] [CrossRef] - Li, S.; Zhao, Z.; Miaomiao, X.; Wang, Y. Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression. Environ. Model. Softw.
**2010**, 25, 1789–1800. [Google Scholar] [CrossRef] - Gao, Y.; Zhao, J.; Han, L. Exploring the spatial heterogeneity of urban heat island effect and its relationship to block morphology with the geographically weighted regression model. Sustain. Cities Soc.
**2021**, 76, 103431. [Google Scholar] [CrossRef] - Chen, X.-L.; Zhao, H.-M.; Li, P.-X.; Yin, Z.-Y. Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sens. Environ.
**2006**, 104, 133–146. [Google Scholar] [CrossRef] - Zhao, X.; Zhao, Y.; Kuang, D. Seasonal dynamics of the relationship between landscape pattern and land surface temperature in a coastal city. In Proceedings of the 2014 Third International Workshop on Earth Observation and Remote Sensing Applications, Changsha, China, 11–14 June 2014; pp. 418–422. [Google Scholar] [CrossRef]
- Lu, Y.; Yue, W.; Liu, Y.; Huang, Y. Investigating the spatiotemporal non-stationary relationships between urban spatial form and land surface temperature: A case study of Wuhan, China. Sustain. Cities Soc.
**2021**, 72, 103070. [Google Scholar] [CrossRef] - Cai, Y.; Chen, Y.; Tong, C. Spatiotemporal evolution of urban green space and its impact on the urban thermal environment based on remote sensing data: A case study of Fuzhou City, China. Urban For. Urban Green.
**2019**, 41, 333–343. [Google Scholar] [CrossRef] - Yang, C.; Yan, F.; Zhang, S. Comparison of land surface and air temperatures for quantifying summer and winter urban heat island in a snow climate city. J. Environ. Manag.
**2020**, 265, 110563. [Google Scholar] [CrossRef] [PubMed] - Li, H.; Li, Y.; Wang, T.; Wang, Z.; Gao, M.; Shen, H. Quantifying 3D building form effects on urban land surface temperature and modeling seasonal correlation patterns. Build. Environ.
**2021**, 204, 108132. [Google Scholar] [CrossRef] - Tatem, A.J. WorldPop, open data for spatial demography. Sci. Data
**2017**, 4, 170004. [Google Scholar] [CrossRef] - Zhang, Q.; Seto, K.C. Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data. Remote Sens. Environ.
**2011**, 115, 2320–2329. [Google Scholar] [CrossRef] - Xu, H. A New Remote Sensing Index for Fastly Extracting Impervious Surface Information. Geomat. Inf. Sci. Wuhan Univ.
**2008**, 33, 1150–1153. [Google Scholar] - Xu, H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens.
**2006**, 27, 3025–3033. [Google Scholar] [CrossRef] - Connors, J.P.; Galletti, C.S.; Chow, W. Landscape configuration and urban heat island effects: Assessing the relationship be-tween landscape characteristics and land surface temperature in Phoenix, Arizona. Landsc. Ecol.
**2013**, 28, 271–283. [Google Scholar] [CrossRef] - Mcgarigal, K.S.; Cushman, S.A.; Neel, M.C.; Ene, E. FRAGSTATS: Spatial Pattern Analysis Program for Categorical Maps. 2002. Available online: www.umass.edu/landeco/research/fragstats/fragstats.html (accessed on 15 September 2020).
- Li, J.; Song, C.; Cao, L.; Zhu, F.; Meng, X.; Wu, J. Impacts of landscape structure on surface urban heat islands: A case study of Shanghai, China. Remote Sens. Environ.
**2011**, 115, 3249–3263. [Google Scholar] [CrossRef] - Peng, J.; Jia, J.; Liu, Y.; Li, H.; Wu, J. Seasonal contrast of the dominant factors for spatial distribution of land surface temper-ature in urban areas. Remote Sens. Environ.
**2018**, 215, 255–267. [Google Scholar] [CrossRef] - Jiang, W.; Wang, Y.; Dou, M.; Liu, S.; Liu, H. Solving Competitive Location Problems with Social Media Data Based on Cus-tomers’ Local Sensitivities. Int. J. Geoinf.
**2019**, 8, 202. [Google Scholar] - Oshan, T.M.; Li, Z.; Kang, W.; Wolf, L.J.; Fotheringham, A.S. mgwr: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale. ISPRS Int. J. Geoinf.
**2019**, 8, 269. [Google Scholar] [CrossRef] [Green Version] - Zhao, C.; Jennifer, J.; Weng, Q.; Russell, W. A Geographically Weighted Regression Analysis of the Underlying Factors Related to the Surface Urban Heat Island Phenomenon. Remote Sens.
**2018**, 10, 1428. [Google Scholar] [CrossRef] [Green Version] - Guo, A.; Yang, J.; Sun, W.; Xiao, X.; Cecilia, J.X.; Jin, C.; Li, X. Impact of urban morphology and landscape characteristics on spatiotemporal heterogeneity of land surface temperature. Sustain. Cities Soc.
**2020**, 63, 102443. [Google Scholar] [CrossRef] - Burnham, K.; Anderson, D. Multimodel inference: Understanding AIC and BIC in Model Selection. Sociol. Methods Res.
**2004**, 33, 261–304. [Google Scholar] [CrossRef] - Zhang, N.; Zhang, J.; Chen, W.; Su, J. Block-based variations in the impact of characteristics of urban functional zones on the urban heat island effect: A case study of Beijing. Sustain. Cities Soc.
**2021**, 76, 103529. [Google Scholar] [CrossRef] - Perini, K.; Magliocco, A. Effects of vegetation, urban density, building height, and atmospheric conditions on local temperatures and thermal comfort. Urban For. Urban Green.
**2014**, 13, 495–506. [Google Scholar] [CrossRef] - Alavipanah, S.; Schreyer, J.; Haase, D.; Lakes, T.; Qureshi, S. The effect of multi-dimensional indicators on urban thermal conditions. J. Clean. Prod.
**2017**, 177, 115–123. [Google Scholar] [CrossRef] - Yang, X.; Li, Y. The impact of building density and building height heterogeneity on average urban albedo and street surface temperature. Build. Environ.
**2015**, 90, 146–156. [Google Scholar] [CrossRef] - Chun, B.; Guldmann, J.M. Impact of greening on the urban heat island: Seasonal variations and mitigation strategies. Comput. Environ. Urban Syst.
**2018**, 71, 165–176. [Google Scholar] [CrossRef] - Masoudi, M.; Tan, P.Y.; Fadaei, M. The effects of land use on spatial pattern of urban green spaces and their cooling ability. Urban Clim.
**2020**, 35, 100743. [Google Scholar] [CrossRef] - Xiang, Y.; Huang, C.; Huang, X.; Zhou, Z.; Wang, X. Seasonal variations of the dominant factors for spatial heterogeneity and time inconsistency of land surface temperature in an urban agglomeration of central China. Sustain. Cities Soc.
**2021**, 75, 103285. [Google Scholar] [CrossRef] - Azhdari, A.; Soltani, A.; Alidadi, M. Urban morphology and landscape structure effect on land surface temperature: Evidence from Shiraz, a semi-arid city. Sustain. Cities Soc.
**2018**, 41, 853–864. [Google Scholar] [CrossRef] - Chen, M.; Dai, F.; Yang, B.; Zhu, S. Effects of neighborhood green space on PM2.5 mitigation: Evidence from five megacities in China. Build. Environ.
**2019**, 156, 33–45. [Google Scholar] [CrossRef] - Yao, X.; Yu, K.; Zeng, X.; Lin, Y.; Ye, B.; Shen, X.; Liu, J. How can urban parks be planned to mitigate urban heat island effect in “Furnace cities”? An accumulation perspective. J. Clean. Prod.
**2021**, 330, 129852. [Google Scholar] [CrossRef]

**Figure 1.**Study area: (

**a**) Fujian Province, China; (

**b**) Fuzhou City, Fujian Province; (

**c**) land cover map of the study area based on satellite imagery from GF-1 (2018).

**Figure 2.**(

**a**) Spatial distribution of land surface temperature (LST); (

**b**) local indicators of spatial associations (LISA); (

**c**) Global Moran’s I of LST.

**Figure 4.**(

**a**) Proportions of positive and negative effects of the different variables; (

**b**) box plots of coefficients for the different variables.

**Figure 5.**(

**a**–

**n**) Spatial distribution of the coefficients of each variable. Red indicates a positive correlation with LST, blue indicates a negative correlation, and gray indicates no spatial correlation.

Data Source | Date | Spatial Resolution (m) | Description |
---|---|---|---|

Landsat8 OLI | 6 September 2019 | Multispectral: 30 m; Thermal: 100 m | The cloud covers were 15.07, 11.77, and 0.88, respectively. Data for retrieving LST |

22 July 2020 | |||

27 September 2021 | |||

Night light image | 2020 | 130 m | Study area night light data extraction |

Population density | 2020 | 100 m | Study area population extraction |

Building data | 2020 | Vector | Building height, density, SVF calculation |

Gaofen-1 satellite | 2018 | Panchromatic resolution: 2 m; multi-spectral: 8 m | Land use mapping and calculation of landscape metrics |

Google Earth | 2018, 2019 | 1 m | Reference of sample selection for mapping and accuracy assessment |

Variable Category | Variable | Description | Unit | Min | Max | Mean | Std. Dev. |
---|---|---|---|---|---|---|---|

Urban form factors | BH | Average height of total buildings | m | 0 | 180 | 19.957 | 16.615 |

BD | Building area to unit ratio | % | 0 | 0.608 | 0.184 | 0.110 | |

SVF | Average sky viewing angle within the unit | 0.571 | 1 | 0.846 | 0.009 | ||

Biophysical parameters | NDISI | Normalized difference impervious surface index | 0.040 | 0.592 | 0.435 | 0.080 | |

NDVI | Normalized difference vegetation index | −0.002 | 0.409 | 0.155 | 0.005 | ||

Land use type | Forest_per | Percentage of forest land in the spatial unit | % | 0 | 0.932 | 0.176 | 0.118 |

Built_per | Percentage of built-up land in the spatial unit | % | 0 | 0.997 | 0.621 | 0.190 | |

Water_per | Percentage of water body in the spatial unit | % | 0 | 0.669 | 0.019 | 0.050 | |

Socioeconomics | Pop_Den | Population density in the spatial unit | people/km^{2} | 0 | 784.164 | 146.358 | 132.789 |

NL | Mean value of nighttime light in spatial unit | 0 | 0.0036 | 0.0003 | 0.00023 | ||

Landscape metrics | PD | Land use patch density in the spatial unit | n/km^{2} | 15.570 | 2732.394 | 884.262 | 416.714 |

LPI | Largest patch index of land use in the spatial unit | 10.719 | 100 | 56.376 | 18.094 | ||

LSI | Landscape shape index of the landscape in the spatial unit | 1.107 | 25.539 | 6.192 | 2.945 | ||

SHDI | Shannon diversity index of the landscape in the spatial unit | 0 | 1.651 | 0.862 | 0.240 | ||

MESH | Effective mesh size of the landscape in the spatial unit | Hectares | 0.178 | 155.391 | 3.491 | 6.532 |

Variable Category | Variable | Standardized Coefficient | VIF |
---|---|---|---|

Urban form factors | BH | −0.211 *** | 1.129 |

BD | 0.334 *** | 2.629 | |

SVF | 0.268 *** | 3.468 | |

Biophysical parameters | NDISI | 0.136 *** | 2.821 |

NDVI | −0.271 *** | 2.334 | |

Land use type | Forest_per | −0.104 ** | 1.787 |

Water_per | −0.109 *** | 1.932 | |

Built_per | 0.098 *** | 1.614 | |

Socioeconomics | Pop_Den | −0.167 *** | 1.510 |

NL | −0.083 * | 1.274 | |

Landscape metrics | PD | 0.089 *** | 1.584 |

LPI | −0.030 | 2.853 | |

LSI | 0.035 | 2.096 | |

SHDI | −0.087 ** | 1.550 | |

MESH | −0.016 | 3.400 | |

Moran’s I (error) | 0.493 *** |

Model Index | OLS | GWR | MGWR |
---|---|---|---|

R^{2} | 0.440 | 0.773 | 0.852 |

AICc | 4509.120 | 3473.876 | 3023.696 |

RSS | 1109.548 | 448.940 | 293.295 |

Variable | Bandwidth | |
---|---|---|

GWR | GWR | |

Constant | 219 | 44 |

BH | 219 | 179 |

BD | 219 | 1032 |

SVF | 219 | 311 |

NDISI | 219 | 1201 |

NDVI | 219 | 44 |

Forest_per | 219 | 1980 |

Water_per | 219 | 1980 |

Built_per | 219 | 44 |

Pop_Den | 219 | 44 |

NL | 219 | 166 |

PD | 219 | 117 |

LPI | - | - |

LSI | 219 | 221 |

SHDI | 219 | 1970 |

MESH | 219 | 1980 |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Yang, L.; Yu, K.; Ai, J.; Liu, Y.; Yang, W.; Liu, J.
Dominant Factors and Spatial Heterogeneity of Land Surface Temperatures in Urban Areas: A Case Study in Fuzhou, China. *Remote Sens.* **2022**, *14*, 1266.
https://doi.org/10.3390/rs14051266

**AMA Style**

Yang L, Yu K, Ai J, Liu Y, Yang W, Liu J.
Dominant Factors and Spatial Heterogeneity of Land Surface Temperatures in Urban Areas: A Case Study in Fuzhou, China. *Remote Sensing*. 2022; 14(5):1266.
https://doi.org/10.3390/rs14051266

**Chicago/Turabian Style**

Yang, Liuqing, Kunyong Yu, Jingwen Ai, Yanfen Liu, Wufa Yang, and Jian Liu.
2022. "Dominant Factors and Spatial Heterogeneity of Land Surface Temperatures in Urban Areas: A Case Study in Fuzhou, China" *Remote Sensing* 14, no. 5: 1266.
https://doi.org/10.3390/rs14051266