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Article

Analysis of the Coupling Trend Between the Urban Agglomeration Development and Land Surface Heat Island Effect: A Case Study of Guanzhong Plain Urban Agglomeration, China

by
Xiaogang Feng
1,*,
Fei Li
1,
Sekhar Somenahalli
2,
Yang Zhao
1,
Meng Li
1,
Zaihui Zhou
1 and
Fengxia Li
1
1
School of Architecture, Xi’an University of Architecture and Technology, Xi’an 710055, China
2
UniSA-STEM, University of South Australia, Mawson Lakes Campus, Adelaide 5095, Australia
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(12), 5239; https://doi.org/10.3390/su17125239
Submission received: 29 April 2025 / Revised: 22 May 2025 / Accepted: 29 May 2025 / Published: 6 June 2025
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Abstract

:
The exploration of the coupling trend between urban agglomeration development (UAD) and land surface temperature (LST) expansion is of great significance, and it is of scientific value for the regulation of the thermal environment of urban agglomerations, the optimization of urban spatial planning, and the achievement of sustainable urban development. This study employs an array of remote sensing datasets from multiple sources—employing a multi-faceted approach encompassing an overall coupling situation analysis model, a coordination and evaluation system, a geographically weighted spatial autocorrelation algorithm, and landscape pattern quantification indicators—to explore the mutual feedback mechanism and spatial coupling characterization of LST and UAD in the Guanzhong Plain Urban Agglomeration (GZPUA). The results of the study can provide data support for urban spatial planning and thermal environment regulation. The results indicate the following findings: (1) In the GZPUA, the nighttime light (NTL) and land surface temperature (LST) centroids show a significant tendency toward approaching one another, with a spatial offset decreasing from 45.0 km to 9.1 km at the end, indicating a strengthening trend in the photothermal system’s coupling synergy. (2) The coordination of light and heat in the study area exhibits significant non-equilibrium development, with a dynamic trend of urban development space shifting towards the southwest. It confirms the typical regional response law of rapid urbanization. (3) The Moran’s I index of the photothermal system in the study area increased from 0.289 to 0.335, an increase of 15.9%. The proportion of “high–high” (H-H)/“low–low” (L-L)-type regions with clustering distribution of cold and hot spots reaches 58.01%, and their spatial continuity characteristics are significantly enhanced, indicating a significant trend of spatial structural integration between urban heat island effect and construction land expansion.

Graphical Abstract

1. Introduction

As a result of rapid global urbanization, there are a number of significant phenomena such as population agglomeration, rapid changes in land use, increased energy consumption, and intensified urban heat island (UHI), to name a few [1]. In recent decades, China has experienced a significant increase in urbanization, which has adversely affected its cities’ ecological integrity. As a result of this phenomenon, urban water resources have become scarcer, air pollution has increased, and surface and atmospheric temperatures have risen [2]. Increasing levels of urbanization have led to significantly higher temperatures in urban areas than in suburban areas [3], causing the heat island effect, which poses a serious threat to urban residents’ thermal health. In its sixth Assessment Report, Climate Change: Impacts, Adaptation, and Vulnerability, the Intergovernmental Panel on Climate Change (IPCC) estimates that global surface temperatures over 2011–2020 are 1.1 °C higher than 1850–1900 levels, and that global warming will exceed 1.5 °C by 2050 [4]. Thus, the thermal ecological environment resulting from urbanization will continue to intensify due to global and regional warming [5]. Therefore, a clear understanding of how urban development impacts land surface temperature is vital to managing urban thermal environments [6,7].
Due to the complex interactions between urbanization and the thermal environment, understanding the mechanisms driving the urban heat island (UHI) requires not only an analysis of land use patterns [8], but also an examination of the broader socioeconomic activities that shape the urban landscape [9]. The level of urban development is an important indicator of urbanization [10]. It usually includes urbanization rate, population, economy, employment, etc. The urban thermal environment is usually measured by atmospheric and land surface temperatures [11,12]. In their research, researchers have mainly focused on the following two aspects of the relationship between urban development and land surface temperature: (1) Characterizing the level of urbanization by land use/cover type and analyzing the impact of different land use types on land surface temperature [13,14,15,16,17]. By developing mathematical models of impervious surface cover, normalized building index, impervious surface landscape pattern index, and surface temperature, many scholars have explored the spatial distribution of LST on the evolution of different land use types [18,19,20,21]. For example, Shen et al. (2020) examined the spatial response of relevant surface factors and heat island intensity in different local climate zones [22]. (2) Investigating the spatiotemporal correlation between human activities and LST [23,24,25], with relevant indicator types clustered around population density, regional GDP, and electricity consumption. Previous studies used land use/cover types to characterize the level of urban development and analyze the impact of different land use types on surface temperature [26]. A spatial mathematical model of surface coverage, building land use intensity index, and urban landscape fragmentation index was developed with the urban thermal environment to explain the dynamic mechanism for spatial differentiation of the thermal field based on land use [27,28]. The spatial and temporal coupling features between UD and LST were explored by Xia et al. (2025) using geographically weighted regression methods [29].
Although the relationship between urban development (UD) and LST has been recognized, further research is possible. In quantitative research on surface factors and thermal environment response, linear regression models or Pearson correlation tests are generally used [30,31]. These models fail to fully integrate the multidimensional characteristics of geographic spatial data, resulting in parameter bias and diminished explanatory power. Secondly, existing studies that analyze the driving mechanism of the heat island’s spatial pattern of surface elements tend to assume a single city-scale homogenization. A failure to systematically analyze the spatial heterogeneity of the UHI effect results in inadequate capability for fine-tuning heat mitigation strategies [32]. Thirdly, in the current research methodological framework, it is difficult to capture the threshold effect of spatial gradient differences in urban development levels on the nonlinear impact of heat island intensity and the interaction mechanism, which limits the study of urban parameter spatial differentiation on heat island effects [33,34].
In the current study, the NTL remote sensing index was used to determine the level of urbanization in GZPUA. NTL data offer distinct advantages in terms of measurement precision and objective representation, enabling effective characterization of urban temporal dynamics and spatial patterns. This dataset serves as a robust proxy for quantifying anthropogenic activities and regional economies while capturing their spatial heterogeneity across urban landscapes [35,36,37,38]. The scientific question addressed in this study is the following: how does urban sprawl affect the surface thermal environment? Accordingly, we explore the potential connection between sustainable urban development and Urban thermal environment. In order to investigate the correlation between UD level and LST, this study extracted urban-development-level data from DMSPOLS and MODIS, Landsat8, respectively. This study aims to achieve the following: (1) quantitatively analyze the spatiotemporal evolution characteristics of urban development and surface temperature data; (2) analyze spatiotemporal couplings between LST and UD; (3) determine whether UD and LST have spatial autocorrelation characteristics. The novelty of the study lies in the emphasis on the dynamic coupling at the scale of urban agglomerations, especially the superimposed effect of urbanization on heat islands in ecologically sensitive areas such as the Guanzhong Plain, and in the use of a multidimensional coupled analytical framework to explore the thermal response mechanisms of urban space and economic structure. A reference for urban scientific planning development and thermal environment creation could be provided by the findings of this study.

2. Methodology

2.1. Study Area

The GZPUA stretches across Gansu, Shaanxi, and Shanxi Provinces, with the Qinling Mountains to the south and the Yellow River to the east [39]. Figure 1a,b show its location at the intersection of Northwest and Central–Eastern China. Located in Northwest China, this area is the main ecological security barrier of the arid–semi-arid transition zone, and its ecological carrying capacity thresholds and biodiversity conservation efficiencies play crucial roles at the regional level. Urbanization in the GZPUA is characterized by high city population concentrations, continuous land expansion, anthropogenic heat emissions, and simultaneous thermal environmental problems. Based on the LandScan dataset, the distribution of population density (PD) in the GZPUA during the study period is shown in Figure 2. The average temperature of urban areas in Guanzhong increased by 0.05 °C·a−1 from 1981 to 2015 [40], and the suburban temperature increased by 0.396 °C during the same period, and Xi’an city’s maximum temperature reached over 40 °C. As a result, the urban thermal environment poses a particularly serious problem.

2.2. Data Sources and Processing

Moderate-Resolution Imaging Spectroradiometer (MODIS) MOD11A2 V6 remote sensing data for 2005, 2010, 2015, and 2020 were used in this study, courtesy of the Resource and Environmental Science and Data Center of the Chinese Academy of Sciences (RESDCCAS) (https://www.resdc.cn/), accessed on 1 November 2024; meanwhile, DMSP-OLS data were obtained from the Harvard Dataverse (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/GIYGJU), accessed on 2 November 2024. Land use/cover data were obtained from the RESDCCAS (https://www.resdc.cn/), accessed on 3 November 2024. and DEM data were obtained from the Geospatial Data Cloud (https://www.gscloud.cn/), accessed on 4 November 2024 with 90 m spatial resolution. In order to project and transform the coordinates of all the data, ArcGIS Pro 3.0 software was used, and the methodology and process used in this study is shown in Figure 3.

2.3. Methods

2.3.1. Overall Coupled Potential Model

Based on the center of gravity distance of different spatial variables, the overall coupled potential model (OCPM) analyzes the degree to which different system variables are coupled. In order to better understand the dynamic relationship between different spatial variables, this method can be used to examine the spatial relationship between urban development and environmental factors like surface temperature. The smaller the weighted center, L, of the spatial variables in the system, the greater the coupling; the OCPM can be calculated [41] as follows:
C t X t , Y t = ∑ i = 1 n m t i X t i ∑ i = 1 n m t i , ∑ i = 1 n m t i Y t i ∑ i = 1 n m t i
L = ( X L S T , t − X N T L , t ) 2 + ( Y L S T , t − Y N T L , t ) 2
α = a r c c o s ∆ X L S T × ∆ X N T L + ∆ Y L S T × ∆ Y N T L ( ∆ X L S T 2 + ∆ Y L S T 2 ) × ( ∆ X N T L 2 + ∆ Y N T L 2 ) × π
where C t is the weighted center of NTL or LST; X t and Y t are the coordinates of the weighted center during period t. X t i and Y t i are the center coordinates of pixel i during period t, respectively. L is the spatial distance between the weighted centers of NTL and LST during period, X L S T , Y L S T , X N T L , and Y N T L are the coordinates of the weighted centers of LST and NTL during period t. α is the angle between the movement trajectories of the weighted centers of LST and NTL during a certain period. ∆ X L S T , ∆ Y L S T , ∆ X N T L , and ∆ Y N T L represent the changes in the weighted center coordinates of LST and NTL compared to the previous period.

2.3.2. Spatially Coupled Coordination Models

The spatial coupled coordination model (SCCM) was used to analyze the degree of interaction and influence between multiple systems, and this study quantitatively evaluates the degree of coordination between UD and LST through the SCCM, achieving an overall optimization model of the system. The calculation is shown in Equation (4).
o = ( U R + T R ) / 2 U R 2 + T R 2
where o is the coupling coordination coefficient of LST and NTL. UR and TR are the average annual growth rates of NTL and LST, respectively. According to the formula, the value of the coordination coefficient ranges from 0 to 1. The coupling coordination type in this study refers to the categorization of the degree of coordination and dynamic relationship exhibited by NTL and LST during the interaction process, which is used to quantitatively assess the level of interdependence and synergistic development between the systems. The division of the different values of o and the comparison of the values of UR and TR are shown in Table 1.

2.3.3. Bivariate Spatial Autocorrelation Analysis

Spatial autocorrelation is a statistical method that studies the potential interdependence or similarity between data at different locations within geospatial space; it is divided into global spatial autocorrelation and local spatial autocorrelation. The spatially varying association and coupling mechanism of two variables, NTL and LST, was examined using bivariate spatial autocorrelation (BSA). BSA was performed using Open GeoDa software. Equation (5) can be used to calculate the spatial response pattern of LST to NTL [42].
I = ∑ i = 1 n ∑ j = 1 n W i j   ( x i − x ¯ ) ( y i − y ¯ ) S 2 ∑ i = 1 n ∑ j = 1 n W i j
where I is the global BSA index, i.e., the correlation between the spatial distributions of spatial variables x and y on the whole. n is the total number of spatial units. W i j is the spatial weight matrix established by the K-neighborhood model. x i and y i are the observed values of the independent variable and the dependent variable in spatial units i and j, respectively. S 2 is the variance of all samples.

2.3.4. Landscape Pattern Index

Landscape indexes are used to quantify the spatial configuration and structural composition of landscapes. To explore the spatial distribution characteristics of NTL and LST, Fragstats software was used to calculate the landscape index. The geometric features of landscape patches were presented using the largest path index (LPI) and the Aggregation Index (AI); the formula is shown below [43]:
L   P   I = m a x ⁡ ( a i j ) A × 100
A I = g i i m a x g i i × 100
where aij is the area of patch ij; A is the total landscape area; gii is the number of adjacent patches of the same landscape type i.

3. Results

3.1. Spatial and Temporal Patterns of UD and LST Evolution

The results indicate that the high-value NTL area of the GZPUA exhibits centralization, expansion, and connectivity characteristics. According to Figure 4, the distribution pattern follows a one-core and multiple-point model, with the core representing the Xi’an Xianyang development core and the multiple-point model representing the node urban areas of counties and cities. The scope of high-value NTL areas was relatively small in 2005 and showed a trend of isolation and scattering. By 2020, however, the distribution range of NTL clusters in core cities had expanded, and node cities relied on corridors for island connectivity, greatly improving centralization. There has been an increase in the average brightness of NTL in various cities within GZPUA over the last 15 years, with a growth value of 2.1544 × 105 nW·cm−2·sr−1 per year. Among these cities, Xi’an has demonstrated the most substantial increase in NTL, reaching 7.036 × 104 nW·cm−2·sr−1 per year, while Tianshui City has exhibited the highest NTL growth rate at 143.08%. The growth rate of the high-value NTL area in the GZPUA from 2005 to 2010 was 45.6%, which is higher than that from 2010 to 2015 and from 2015 to 2020, accounting for 25.86% and 35.77%, respectively. This indicates that the urbanization process was rapidly developing during this period.
The results indicate that the annual average daytime surface temperature within the GZPUA is 18.34 °C, with a maximum of 27.6 °C, a minimum of 5.8 °C, and a standard deviation of 3.26 °C. Overall, the spatial distribution pattern shows a peak at the regional center followed by gradual decreases layer by layer. Figure 3 shows the distribution of HII in the GZPUA. It can be observed that the extremely strong heat island area is primarily concentrated in the center of the GZPUA, where there is a high population density, rapid warming of construction and arable land, and a basin terrain that does not dissipate heat, causing large areas of high temperature. In contrast, the extremely cold islands are mostly located in the southern Qinling Mountains, where there are dense forests and low sensible heat flux, resulting in contiguous low temperatures. This study calculated the area proportion of different levels of heat island conditions in the GZPUA to investigate its spatiotemporal evolution further. There is a pattern of high HII in the middle and low HII on either side of the GZPUA at different levels. At the different levels within the region, heat island intensity peaks at the 6th, 7th, 8th, and 9th levels, with an average annual total area proportion of 60.23 percent. Between 2005 and 2010, the peak proportion of HII area was mostly distributed in the 8th and 9th levels. In 2010, the proportion of HII area in the 8th level was the highest during the study period, reaching 19.10%. GZPUA’s 5th, 6th, 7th, and 8th level heat islands showed a balanced distribution in 2015, with their area proportions ranging from 11% to 16%. The proportions of 6th, 7th, 9th, and 10th level areas in the research area decreased in 2020, with the 9th level having the highest proportion. Additionally, the proportion of the first-, second-, and third-level heat islands gradually increased during the study period, but their proportions did not exceed 8% (Figure 5). Outside the thermonuclear region, surface temperatures continued to decrease gradually.

3.2. Spatial-Temporal Coupling Trend Between UD and LST

During the research period, the built-up area of the GZPUA shifted southwest, then northeast, and then southwest again (Figure 6). From 2005 to 2010, the built-up area of the GZPUA expanded towards the southwest, causing the center of gravity of NTL and LST to move westward. From 2010 to 2015, the built-up area expanded towards the northeast and the center of gravity of NTL and LST shifted accordingly. Accordingly, the NTL and LST centers of gravity moved towards the northeast and southwest, respectively, from 2015 to 2020, as the built-up area of the GZPUA expanded toward the southwest. As a result of the development of the GZPUA during the research period, both NTL and LST shifted their centers of gravity to the southwest. The distance between the centers of gravity for NTL and LST was 45.0 km, 10.0 km, 3.9 km, and 9.1 km, respectively, with a reduction of 35.9 km in the past 15 years. This evidence clearly demonstrates a coupling relationship between the two factors (Figure 7).

3.3. Spatial Coupling Dynamics Between UD and LST

Combining the growth levels of LST, NTL, and built-up land in each city (Figure 8), the following experimental results can be obtained.
From 2005 to 2010, the NTL and LST of the cities in the GZPUA were dominated by the coupled potential of coordinated-lagging type (Figure 9), with the bonding-lagging type and coordinated-lagging type as secondary types (Figure 10). The overall performance of the lagging type of development potential shows that TR lags behind UR development, indicating that LST lags behind the NTL level. In terms of specific cities, Shangluo City and Yuncheng City show obvious bonding-lagging type. In terms of industry, Shangluo City implemented the policy of “shutting down small metallurgy and building materials” in 2006, while Yuncheng City implemented the transformation of Shanxi’s resource-based economy in 2006 and “mandatory energy-saving and emission reduction” measures in 2007. In terms of large-scale transportation, the Shanghai–Shaanxi Expressway through Shangluo City was opened to traffic in 2008, while the expansion of Yuncheng Airport began in 2005. These policy decisions may significantly affect the type of local coupling in the city; the elimination of high-energy-consuming firms may reduce a large amount of thermal emissions from industrial areas [44], while the construction of highways and large airports may enhance the NTL level of service facilities along or around the route [45,46]. This can be used to explain why LST lags behind NTL development. The Qinling Mountains and the northeastern part of the region have high elevations and lush vegetation, which have not allowed human activities to be fully developed. The areas represented by Tianshui City and Pingliang City show an obvious bonding-prior type, which may be related to the implementation of the equipment manufacturing revitalization policy in Tianshui City (2006) and the construction of the Longdong coal and power base in Pingliang City (2006), and the cement industry expansion policy (2007). The antagonistic types appearing in the urban built-up areas, on the other hand, showed a point-like distribution pattern, indicating that the urban areas with a very low degree of NTL and LST coupling were not connected. During this period, the urban centers of the cities were mostly of bonding-prior type, showing belt and piece development patterns, indicating that the NTL and LST levels of these areas were highly coupled and connected; the appearance of the belt pattern indicated that the integrated development of the city was initially visible, especially in Xi’an City, where the performance was the most obvious.
From 2010 to 2015, the coupling potential of development and surface temperature in the GZPUA generally continued to show development potential of the bonding type. The most obvious change is in the northwestern region, from the grinding ahead type to the grinding lag type, indicating that the NTL growth level is higher than the LST growth level in this period, especially in Tianshui City and Pingliang City. This may be related to the phenomenon of rapid development of transportation infrastructure in that period, such as “Bao Lan Passenger Dedicated (2012)”, “Ten-Tian Expressway (2015)”, “Xiping Railway (2013)”, and “Pengda Expressway (2015)”. The antagonistic types appearing in the urban built-up areas still show a point-like distribution pattern, and the coordinated type with highly coupled NTL and LST levels exhibited in the urban center city still shows a pattern of patchy development.
From 2015 to 2020, the coupling level of NTL and LST in the GZPUA is still the bonding type from an overall perspective. Compared with the previous study period, the significant change in the coupling type is in the southern and central regions, from the original bonding-lagging type to bonding-prior type. Baoji, Xianyang, Shangluo, and the surrounding areas of Xi’an are representative of this period, where the LST development level is higher than the NTL development level. During this period, the “Qinling ecological protection special rectification”, “Weihe River ecological management”, “South Shaanxi migrant relocation”, “precise poverty alleviation relocation”, and other ecological and demographic policies have been implemented; ecological and demographic policies may lead to a decrease in NTL levels. Large-scale factory and road construction has increased the large amount of impervious surfaces, and a large amount of transportation infrastructure has likewise led to heat accumulation. It is worth noting that the above areas (except for the Xi’an metropolitan area) are geographically close to each other, indicating that the type of coupling in this time period has begun to break through topographic and geomorphic constraints, and that synergistic development of the urban agglomerations is evident.

3.4. Spatial Autocorrelation Between UD and LST

(1)
Global spatial autocorrelation between UD and LST in GZPUA
According to Table 2, there was a positive bivariate Moran’s I value in the GZA throughout the study period, which was significant at the 0.001 level. This suggests a significant spatial autocorrelation between NTL and LST levels within the area. The Moran’s I value in the Guanzhong region exhibited a consistent upward trend, increasing from 0.289 in 2005 to 0.335 in 2020, and reaching its peak during the study period.
(2)
Local Spatial autocorrelation between UD and LST in GZPUA
The results indicate that, during the study period, there was a “high–high” (H-H) agglomeration pattern of NTL and LST in the main built-up areas of the Xi-xian region and various cities, with a gradual diffusion trend. The “low–low” (L-L) clustering type was mostly observed in the southern Qinling region and remained relatively stable. In the later stages, the “low–high” (L-H) agglomeration type emerged in the central and eastern regions in the latter stage and exhibited an expanding trend. The “high–low” (H-L) clustering pattern manifested initially in the northeastern and southern regions, as well as in urban fringe and scattered urban areas, but declined significantly in subsequent stages. The research period also revealed that the L-L agglomeration types were the most prevalent in the GZPUA, accounting for 41.55%, followed by the H-H agglomeration types, which accounted for 25.78%. The L-H agglomeration types had the lowest prevalence at 15.94%.
The results of the analysis demonstrate that the percentage of LPI values for H-H agglomeration types in the GZPUA has increased from 13.99% to 40.25%, indicating a consistent upward trend (Figure 11). As the study progressed, the LPI value for LH agglomeration types increased significantly, reaching 83.18% in 2015, making it the most prevalent agglomeration type. A total of 98.81% of LPI values for H-L clustering types showed significant differences between pre-period and post-period proportions. LPI values for L-L aggregation types fluctuated less, with a stable range of 22% to 30%. From the minimum AI proportion of 23.74% in 2005 to the maximum AI proportion of 26.89% in 2020, the AI for L-H aggregation types in the GZPUA exhibited a substantial increase during the research period. H-L aggregation types, however, showed a significant decline from their maximum AI proportion of 26.74% in 2005 to their minimum AI proportion of 23.78% in 2020. The proportion of AI with H-H and L-L aggregation types remained relatively stable during the study period, with fluctuating values of +0.513% and +0.66%, respectively.
The results show that the H-H agglomeration types of LST and NTL levels are mainly concentrated in the main urban areas of various cities (Figure 12). During the rapid urbanization of this region, agglomeration types have expanded in geographical space, and the proportion of LPI values has also increased, while AI values remain stable. The L-L agglomeration types of LST and NTL levels are stably distributed in the southern Qinling region, where the development of building land under ecological protection functions is limited and human activities are less frequent. LST and NTL levels are both low, and LPI and AI values remain stable, indicating that the region is well protected during rapid development. The H-L agglomeration type is studied in the northwestern and southern regions. In the early stage, building land expanded rapidly (Figure 13), but TR lagged behind UR development. In the later stage, population and industry continued to agglomerate, and LST levels gradually increased. Due to this, the LPI value of the HL agglomeration first increased and then decreased significantly, while the AI value continued to decrease. In the later stage, L-H clustering appeared in non-urban areas near H-H clustering. Due to the urban heat island effect in the surrounding areas, the LST level and the LPI value gradually increased, while the AI value slightly decreased.

4. Discussion

This study examines the coupling relationship between NTL and LST in the GZPUA, revealing how development in GZA impacts LST. As urbanization continues, the percentage of impervious surface coverage increases significantly, which corresponds to the total amount of nighttime lighting. The spatial centers of gravity of the above two indicators show a developmental trend of shifting first to the southwest, then to the northeast, and then to the southwest, which is consistent with the trajectory of the surface temperature high-value area. Combining the overlapping trajectories and consistency of the spatial patterns of the centers of gravity of the three types of indicators, it can be inferred that the surface temperature is directly driven by the impervious surface coverage and the total amount of nighttime lighting, which is most obvious in the city of Xi’an, consistent with previous research [47,48]. Additionally, there were noticeable regional differences in the impact of different levels of development in the GZPUA on the LST during the study period, as shown in the following.
According to the natural environment’s impact on LST, the spatial heterogeneity of LST in cities in the GZPUA is significantly correlated with their geographical location. Generally speaking, the high north–south and low middle terrain structures in the GZPUA have resulted in poor ventilation in the central area, mainly in the central parts of Baoji City, Xixian area, Weinan City, Yuncheng City, and Linfen City. In the early stages of urbanization, these regions encountered challenges dissipating accumulated heat from urban areas to rural areas in a timely manner. The result was an increase in LST within the urban built-up area, which outpaced the rate of UD. UD and LST have a relatively low degree of coupling and coordination. This finding is largely consistent with the research outcomes reported by Zhang Xueling et al. [42]. As urbanization progressed, regional transportation infrastructure, such as highways and railways, developed rapidly. Increasing urban infrastructure and the strategic design of spatial corridors led to a gradual shift in the type of coupling between urban development and surface temperature, from the bonding type to the coordinated type. As a result, the coordination between urban development and surface temperature in the region has steadily improved. Yang Zhiwei et al.’s research on the Guangdong–Hong Kong–Macao Greater Bay Area supports this finding [49]. The emergence of this phenomenon can be speculated to be due to the continuous promotion of urbanization, which has changed the land use type of the land, and a large amount of construction land has begun to appear [50].
The experimental results found that the NTL and LST of the cities in the GZPUA during the study period were dominated by the bonding-prior type and the bonding-lagging type, while the overall view was dominated by the bonding type; the coupling coefficient O was at the level of 0.5–0.9, which indicated that there was a certain coupling relationship between the NTL and the LST in this region. In the early part of the study (2005–2015), the results of the coupling type are influenced by the special geomorphology of Guanzhong region, which produces a clear boundary at the Qinling Mountains. In the later part of the study (2015–2020), the geographic boundaries are broken through, and bonding-prior type coupling types are gradually connected. In addition, although the proportion of bonding types is the highest, most of them are distributed in rural areas outside the urban built-up areas. Rural areas are less populated and relatively economically backward, so their NTL and LST levels are at a double-low level, which can be used to explain why the two are in moderate coupling. The coupling levels of NTL and LST indicators in urban built-up areas are relatively richer, indicating that urban built-up areas are significantly influenced by human construction interventions, and the distribution of impervious surfaces may have a direct impact on the type of coupling.
We examine the relationship between urbanization and the demand for urban build-up land, which is a key driver of LST changes. We find that changes in surface physical properties and energy balance directly affect LST. Furthermore, different types of land use contribute to variations in the thermal environment, resulting in an increase in impermeable areas in urban areas. Artificial heat sources and suspended particulate matter in the atmosphere exacerbate the warming effect. Furthermore, the decrease in surface albedo allows urban built-up areas to absorb more solar energy. Using land use data from 2005 to 2020 in the GZPUA, we conducted a transition matrix analysis to analyze the impact of land use changes on surface temperature in the GZPUA (Figure 13). The arrows in the graph represent the proportion of that land use type increasing or decreasing over the study period.
During the early stages of urban development, the GZPUA’s primary land uses were cultivated land and forest land, which together accounted for 83.60 percent of the total area. However, as urbanization has progressed, the area of cultivated land has decreased by 6.58%, with 2.9 × 105 km2 being converted into urban build-up land. This land is primarily located in peripheral areas of urban built-up land and has experienced persistent thermal increase, resulting in notable UHI effects. In the northern part of the study area and the Qinling Mountains region, vegetation is abundant, development is relatively stagnant, and LST is relatively low. This land use type has a predominantly L-L spatial aggregation pattern, which is consistent with Zhang Mingmin et al.’s findings [50]. According to all research results, NTL intensity has a significant driving effect on LST, which is consistent with the findings of Hu Yuchen et al. [51]. Also, the results indicate that the geological center, the LST, and the NTL of gravity used for construction in the GZPUA all moved to the southwest during the study period, consistent with Yang Jingyi et al.’s findings [52]. In the GZPUA, the center of gravity of human activities and high-quality environmental areas is moving westward.
We calculated the landscape indices of NTL and LST of GZPUA for HH, LL, LH, and HL aggregates. The H-H agglomeration types are primarily concentrated in the cities of Xi’an, Weinan, Xianyang, Baoji, and Tongchuan are predominantly present in the primary urban areas of various cities within the GZPUA. High population density, concentrated economic activities, and relatively high surface temperatures characterize these areas. Thus, they exhibit positive agglomeration characteristics in terms of NTL intensity and LST. By contrast, L-L clusters are mostly found in rural areas of various cities, with a notable presence in the Qinling Mountains. These areas have a relatively sparse population distribution and show low NTL intensity and LST, demonstrating an L-L clustering feature in both NTL and LST. This finding is consistent with the distribution patterns reported by Ji Wangdi et al. [53] and Shen Zhongjian et al. [54], who identified H-H clustering areas primarily concentrated in high-temperature construction land and L-L clustering areas concentrated in low-temperature cultivated land.
However, there are a few deficiencies in this article. To examine the intrinsic coupling mechanism between UD and LST, multiple data sources, such as human activities and socioeconomic factors, must be considered. In this article, however, the level of UD is only viewed from the perspective of NTL; this is a perspective that is not sufficiently comprehensive and does not include substantial indicators of human economic activity, production, and living. The combined effects of multiple influencing factors should therefore be considered in subsequent research.
Based on the above conclusions, the following recommendations for sustainable urban development are proposed:
(1)
It is important to emphasize the differences in regional status quo and formulate development plans according to local conditions. Preventive spatial planning should be implemented for lagging urban areas where LST lags behind NTL development, which are usually in the transition period of urban expansion. Its sustainable development suggestions are to delineate thermal environment sensitive areas, prioritize the layout of low-heat industries, regulate the development intensity in real time, and reserve resilient land to block the spread of heat islands in the future [55]. For the over-advanced urban areas where LST is ahead of NTL development, they usually show significant heat island effect but a relatively lagging urbanization level. In the short term, temporary cooling islands can be implanted or structures can be removed to clear emergency air ducts for emergency cooling. From the perspective of sustainable development, it is necessary to carry out urban spatial reconstruction, identify spatial heat sources, and repair industrial legacy areas, bare ground heat sources, transportation hubs, and other legacy problems that are caused by to geographic features or historical development [56,57].
(2)
It is important to rationalize land use planning and mitigate the heat island effect according to urban policies. For the high value of the heat island area (mainly for construction land and arable land agglomeration area), an integrated “control–repair–optimization” strategy should be taken; this involves the strict control of high-intensity development of the regional expansion of the development zone, through the delineation of the ecological red line to curb the spread of the city [58]. At the same time, we recommend that a body of water is built as a cold core, with a green corridor as the skeleton of the ecological network system; this would enhance the connectivity of the landscape in order to isolate the heat source agglomeration. This approach focuses on implementing measures such as ecological restoration of bare land and decentralized transformation of construction land, giving full play to the synergistic effect of vegetation transpiration and temperature difference regulation of water bodies. This would help in realizing the systematic improvement of the urban thermal environment [59], promoting the sustainable development of the city.
(3)
We recommend the construction of a full-scale governance system to promote healthy and sustainable urban development. A full-scale governance system taking a “macro-pattern regulation–meso-pattern optimization–micro-technology intervention” approach should be implemented. At the macro level, three blue–green composite corridors should be built along the Wei River, Jing River, and Ba River, and four ventilation corridors should be built based on the northern slope of the Qinling Mountains, the Feng River, the Chan River, and the Haliyang Lake, so as to construct a “three horizontal and four vertical” ecological cooling skeleton. At the meso level, the city form should be regulated, high-rise buildings should adopt a staggered layout of “high north and low south” to ensure the penetration of the southeast monsoon, and the north–south road should adopt the “narrow and dense network” to promote a composite cross-section in roads [60,61]. At the micro level, the local thermal environment can be improved through measures such as building skin improvement, sub-surface renewal, and pocket greening [62].

5. Conclusions

In this study, the paper takes the GZPUA as the research scope; LST, NTL, and construction land are taken as the research objects. The goal is to study the coupling trend of urban development and urban heat island expansion in the GZPUA, which reveals the coupling situation of the expansion of construction land and the expansion of the urban heat island in the GZPUA in different periods to a certain extent. Thus, we come to the original conclusion that the urban heat island effect is directly affected by the indicators of construction land and nighttime lighting. The characteristics of urban spatial continuity are significantly enhanced from a thermal environment perspective, and there is a significant trend of spatial structure integration between the urban heat island effect and the expansion of construction land. The conclusions of this study are presented in more detail here:
(1)
The spatial distribution of NTL and LST in the GZPUA exhibits a clear coupling relationship with the natural environment, such as vegetation and elevation. In the northwest and south are the Qinling Mountains, which have higher elevations, lush vegetation, and a lower surface temperature. The central basin area is not conducive to effective heat dissipation, resulting in relatively elevated surface temperatures.
(2)
During the research period, there was a noticeable increase in the correlation between NTL and LST in the built-up areas of different cities in the GZPUA. The spatial centers of gravity of LST, NTL, and built-up land produce highly overlapping trajectories toward the southwest, and urban development produces the same dynamic trend.
(3)
The coupling type between NTL and LST in GZPUA is influenced by the geographic factors dominated by the Qinling Mountains in the early stage of the study, gradually breaking through in the later stage. The level of coupling coordination is manifested as a base of bonding type in the non-built-up area; the coordinated type appears in the built-up area of the city in a contiguous form, and the antagonistic type becomes a point-like disjointed state.
(4)
The correlation coefficient and bivariate Moran’s I between NTL and LST in the GZPUA are both positive and significant, indicating a significant positive correlation between the two variables. Therefore, the UD in the GZPUA will increase LST in the area and surrounding areas, and NTL will gradually strengthen its positive effects on LST. With a clear trend of continuous coverage, the HH concentration of NTL and LST gradually increases.

Author Contributions

Conceptualization, X.F.; methodology, F.L. (Fei Li); formal analysis, X.F., F.L. (Fei Li) and S.S.; investigation, Z.Z. and F.L. (Fei Li); resources, M.L. and F.L. (Fengxia Li); writing—original draft preparation, X.F.; writing—review and editing, X.F., S.S., F.L. (Fei Li), M.L. and Z.Z.; visualization, Y.Z. and Z.Z.; supervision, X.F.; project administration, X.F. and F.L. (Fei Li); funding acquisition, X.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research was founded by Social Science Foundation of Shaanxi Province, grant number 2023F013, and Natural Science Foundation of Shaanxi Province of China, grant number 2025JC-YBMS-316.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on per request to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
UADurban agglomeration development
LSTland surface temperature
GZPUAGuanzhong Plain Urban Agglomeration
NTLnighttime light
UHIurban heat island
IPCCIntergovernmental Panel on Climate Change
UDurban development
PDpopulation density
MODISModerate-Resolution Imaging Spectroradiometer
RESDCCASResource and Environmental Science and Data Center of the Chinese Academy of Sciences
OCPMoverall coupled potential model
SCCMspatial coupled coordination model
SDEstandard deviational ellipse
BSAbivariate spatial autocorrelation
LPIlargest patch index
AIAggregation Index
H-Hhigh–high
L-Llow–low
H-Lhigh–low
L-Hlow–high

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Figure 1. (a) Location map of the GZPUA in China; (b) topography of the GZPUA.
Figure 1. (a) Location map of the GZPUA in China; (b) topography of the GZPUA.
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Figure 2. Population density distribution in GZPUA: (a) 2005, (b) 2010, (c) 2015, and (d) 2020.
Figure 2. Population density distribution in GZPUA: (a) 2005, (b) 2010, (c) 2015, and (d) 2020.
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Figure 3. Methodology flowchart.
Figure 3. Methodology flowchart.
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Figure 4. Spatial and temporal distribution map of LST level (a) and NTL level (b) in GZPUA (2005–2020).
Figure 4. Spatial and temporal distribution map of LST level (a) and NTL level (b) in GZPUA (2005–2020).
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Figure 5. Proportion of area with different intensity levels of UHI in GZPUA (2005–2020).
Figure 5. Proportion of area with different intensity levels of UHI in GZPUA (2005–2020).
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Figure 6. (a) Trajectories of the center of gravity in construction land areas. (b) The NTL and LST centers of gravity movement trajectory.
Figure 6. (a) Trajectories of the center of gravity in construction land areas. (b) The NTL and LST centers of gravity movement trajectory.
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Figure 7. The SDE and center of gravity of LST (a), NTL (b), and construction land (c).
Figure 7. The SDE and center of gravity of LST (a), NTL (b), and construction land (c).
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Figure 8. Level of coupled harmonization between the level of urban sprawl and the average annual growth rates of NTL and LST for each city.
Figure 8. Level of coupled harmonization between the level of urban sprawl and the average annual growth rates of NTL and LST for each city.
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Figure 9. Evolution of NTL and LST coupling coordination types (a) 2005–2010, (b) 2010–2015, and (c) 2015–2020.
Figure 9. Evolution of NTL and LST coupling coordination types (a) 2005–2010, (b) 2010–2015, and (c) 2015–2020.
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Figure 10. (a) Proportion of coupling areas: antagonistic type, bonding type, and coordinated type; (b) proportion of six types of coupling coordination in the GZPUA.
Figure 10. (a) Proportion of coupling areas: antagonistic type, bonding type, and coordinated type; (b) proportion of six types of coupling coordination in the GZPUA.
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Figure 11. Landscape index (LPI and AI) of NTL and LST in GZPUA from 2005 to 2020.
Figure 11. Landscape index (LPI and AI) of NTL and LST in GZPUA from 2005 to 2020.
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Figure 12. Bivariate LISA distribution of NTL and LST in the GZPUA: (a) 2005, (b) 2010, (c) 2015, and (d) 2020.
Figure 12. Bivariate LISA distribution of NTL and LST in the GZPUA: (a) 2005, (b) 2010, (c) 2015, and (d) 2020.
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Figure 13. The land transfer trajectory in the GZPUA: (a) 2005, (b) 2010, (c) 2015, and (d) 2020; (e) Land use transfer matrices from 2005 to 2020.
Figure 13. The land transfer trajectory in the GZPUA: (a) 2005, (b) 2010, (c) 2015, and (d) 2020; (e) Land use transfer matrices from 2005 to 2020.
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Table 1. Coupling type between urban development and surface temperature.
Table 1. Coupling type between urban development and surface temperature.
ConditionTypeMeaning
0.9 ˂ O ≤ 1, UR ˂ TRCoordinated-prior typeTR ahead of UR development
0.9 ˂ O ≤ 1, UR > TRCoordinated-lagging typeUR ahead of TR development
0.5 ˂ O ≤ 0.9, UR ˂ TRBonding-prior typeTR ahead of UR development
0.5 ˂ O ≤ 0.9, UR > TRBonding-lagging typeUR ahead of TR development
0 ≤ O ≤ 0.5, UR ˂ TRAntagonistic-prior typeTR ahead of UR development
0 ≤ O ≤ 0.5, UR > TRAntagonistic-lagging typeUR ahead of TR development
Table 2. Bivariate Moran’s I values in the GZPUA.
Table 2. Bivariate Moran’s I values in the GZPUA.
YearMoran’s IZ-Value
20050.28962.9797 ***
20100.29462.1902 ***
20150.27457.3977 ***
20200.33570.4616 ***
Note: Z-value is the Z-test value; *** represents p ˂ 0.001.
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Feng, X.; Li, F.; Somenahalli, S.; Zhao, Y.; Li, M.; Zhou, Z.; Li, F. Analysis of the Coupling Trend Between the Urban Agglomeration Development and Land Surface Heat Island Effect: A Case Study of Guanzhong Plain Urban Agglomeration, China. Sustainability 2025, 17, 5239. https://doi.org/10.3390/su17125239

AMA Style

Feng X, Li F, Somenahalli S, Zhao Y, Li M, Zhou Z, Li F. Analysis of the Coupling Trend Between the Urban Agglomeration Development and Land Surface Heat Island Effect: A Case Study of Guanzhong Plain Urban Agglomeration, China. Sustainability. 2025; 17(12):5239. https://doi.org/10.3390/su17125239

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Feng, Xiaogang, Fei Li, Sekhar Somenahalli, Yang Zhao, Meng Li, Zaihui Zhou, and Fengxia Li. 2025. "Analysis of the Coupling Trend Between the Urban Agglomeration Development and Land Surface Heat Island Effect: A Case Study of Guanzhong Plain Urban Agglomeration, China" Sustainability 17, no. 12: 5239. https://doi.org/10.3390/su17125239

APA Style

Feng, X., Li, F., Somenahalli, S., Zhao, Y., Li, M., Zhou, Z., & Li, F. (2025). Analysis of the Coupling Trend Between the Urban Agglomeration Development and Land Surface Heat Island Effect: A Case Study of Guanzhong Plain Urban Agglomeration, China. Sustainability, 17(12), 5239. https://doi.org/10.3390/su17125239

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