Spatiotemporal Variation in Ecological Environmental Quality and Its Response to Different Factors in the Xia-Zhang-Quan Urban Agglomeration over the Past 30 Years

Abstract

The interactions between economic development, environmental sustainability, population growth, and urbanization are vital in assessing the ecological dynamics of urban agglomerations. This study explores the relationship between economic development, environmental sustainability, population growth, and urbanization within the Xia-Zhang-Quan urban agglomeration in Fujian Province from 1989 to 2022. Utilizing Landsat remote sensing images, we calculated the Remote Sensing Ecological Index (RSEI) to evaluate changes in ecological quality. The results show that the average RSEI values for 1989, 2000, 2010, and 2022 were 0.5829, 0.5607, 0.5827, and 0.6195, respectively, indicating an initial decline followed by a significant increase, culminating in an overall upward trend. The spatial distribution of RSEI classification shows that the study area has the largest proportion of mainly “good” ecological quality. The proportion of areas with “excellent” ecological environmental quality has increased (13.41% in 1989 and 25.12% in 2022), while those with “general” quality has decreased (28.03% in 1989 and 21.21% in 2022). Over the past three decades, Xiamen experienced substantial ecological degradation (RSEI change of −0.0897), Zhangzhou showed marked improvement (RSEI change of 0.0519), and Quanzhou exhibited slight deterioration (RSEI change of −0.0396). Central urban areas typically had poorer ecological conditions but showed signs of improvement, whereas non-central urban regions demonstrated significant environmental enhancement. The factor detector analysis identified land use as the dominant factor influencing ecological environmental quality, with precipitation having a relatively minor impact. Interaction analysis revealed that all other factors demonstrated bi-variable enhancement or nonlinear enhancement, suggesting that the interactive effects of these factors are greater than the effects of individual factors alone. Land use consistently showed solid explanatory power. Temperature also exhibited significant influence in 2022 when interacting with other factors. Due to urban planning that can plan for land use, these findings suggest that effective urban planning can harmonize economic development with ecological protection within the Xia-Zhang-Quan urban agglomeration.

1. Introduction

The global urbanization rate is projected to increase from 50% in 2009 to 69% by 2050 [1]. Over the past four decades, China has achieved significant milestones in urbanization, mirroring the developmental processes that took Western developed countries over a century to accomplish [2]. This rapid urbanization has been instrumental in driving the country’s economic and social progress and remains a critical force for current and future development [3]. However, traditional urbanization driven by functionalism and growth supremacism has led to extensive and unsustainable expansion, exacting a substantial environmental toll. Issues such as imbalanced development and resource misallocation are prevalent [4]. The ecological environment is a complex system of interaction among society, economy, and nature, forming the bedrock of healthy societal development and regional survival. Ecological challenges now pose significant obstacles to sustainable human development [5]. The rapid economic growth and accelerated urbanization of our country have led to the swift expansion of urban areas and changes in land use patterns. Population growth and climate change exert immense pressure on regional ecosystems, exacerbating ecological issues. Problems such as soil erosion, air pollution, climate change, biodiversity loss, and declining urban livability have intensified the scrutiny of the interplay between urban development and the ecological environment [6]. The ecological environmental quality evaluation involves a qualitative or quantitative assessment of the conditions within a specific ecological system using an objective index system over a defined time and space. This evaluation reflects the impacts of sustainable human development on living environments. Essentially, it explores the relationship between environmental quality and human social behaviors and activities, assessing the suitability of a regional environment for human survival and social development.

Ecological environment assessment methods have evolved significantly, incorporating advanced technologies and interdisciplinary approaches. With the continuous development of Remote Sensing (RS) and Geographic Information System (GIS) technology, the acquisition and collection methods of land surface information are gradually diversified, which provides new means and ideas for carrying out regional ecological environment assessment [7]. Remote sensing techniques form the backbone of modern ecological assessments, providing continuous, large-scale, and high-resolution data. These techniques are often complemented by GIS, which facilitates the integration and spatial analysis of diverse environmental data. In the past 20 years, researchers have used RS and GIS methods to study basin environmental desertification [8], water pollution assessment [9], lake water prediction [10], ecological environment (forest, farmland, garden, etc.) evaluation [11], etc. Research areas, such as Egypt, Italy, Hainan Island, Lianyungang, Hangzhou, etc., have had good results.

Over the past five decades, significant advancements have been achieved in ecological environment assessment. Researchers have developed various indices and models to quantify ecological conditions and monitor environmental changes. The ecological environment index is key in these assessments, serving as the foundation for evaluating ecological environment quality. Selecting appropriate evaluation indices is fundamental to constructing effective index evaluation systems and calculating the ecological environment index. As nations increasingly engage in quantitative and qualitative analyses of ecological environmental quality, various evaluation index systems have emerged and are continuously evolving. In 1969, the National Wildlife Federation of the United States introduced the Environmental Quality Index, which utilized six factors to assess air, water, soil, forest, wildlife, and mineral resources, assigning grades to each environmental category from good to poor. In 1974, the Canadian Ministry of Environment proposed the “Total Environmental Quality Index” [12]. Over the subsequent decades, the selection of evaluation indicators has increasingly embraced comprehensiveness and diversity. In 2006, Marull et al. [13] developed the Land Suitability Index (LSI), while in 2012, Dizdaroglu et al. [14] introduced the Sustainable Urban Ecosystem Assessment Index Model (ASSURE), aimed at evaluating and monitoring the interactions between human activities and urban ecosystems. These innovations reflect the evolution of methods to understand and manage our ecological environments better.

Research on ecological environment quality evaluation indices in China has been significantly influenced by the development of the Eco-Environmental Status Index (EESI). In 2006, the Ministry of Environmental Protection of the People’s Republic of China issued the industry standard, “Technical Specifications for Ecological and Environmental Status Assessment”. This standard introduced the EESI, reflecting the overall status of regional ecological environments. Despite its widespread use, Xu (2013) [15] argued that the EESI could not effectively visualize the ecological status of a region. Xu introduced the Remote Sensing Ecological Index (RSEI) to address this limitation in 2013. The RSEI integrates four key indices—greenness, humidity, dryness, and heat—extracted from high spatial resolution images and combines them using principal component analysis. This approach provides a clear spatial distribution of varying regional ecological conditions. It facilitates the study of ecological changes over different periods, thereby overcoming some of the limitations of the EESI. The RSEI’s objective and accurate evaluation results have led to its widespread adoption by scholars for assessing ecological environment changes at regional [16], provincial [17], city [18,19], and district scales [20]. Studies have demonstrated the RSEI’s effectiveness in capturing spatiotemporal variations in ecological conditions and its responsiveness to natural and anthropogenic influences [21,22]. Numerous studies have undertaken modifications of the Remote Sensing Ecological Index (RSEI) to suit better the specific characteristics of their respective study areas [23,24,25]. These advancements underscore the RSEI’s value in enhancing our understanding and management of ecological environments.

The spatiotemporal variations in ecological environment quality are influenced by a multitude of factors, including land use and land cover changes [26], Digital Elevation Model (DEM) data [19], and the interactions of natural factors such as precipitation and temperature [21]. Methods such as univariate and interaction factor analysis using geographical detectors are primarily employed to analyze these variations. These techniques help explain the spatial heterogeneity in ecological environment quality changes, offering a deeper understanding of the underlying dynamics. Moreover, advancements in remote sensing technology and geospatial analysis have enabled high-resolution monitoring of ecological changes at multiple scales, enhancing our knowledge of urban ecological dynamics.

Despite these advancements, several gaps remain in the current research on ecological environment assessment. One major limitation is the insufficient consideration of socio-economic factors, which are critical to understanding human–environment interactions and their implications for ecological health. Current research mainly focuses on one city and lacks research on the ecological environment change of urban agglomerations. It is now starting the economic development mode of city clusters in China. In this context, it is worth studying whether the economic development of city clusters will affect the ecological environment. In addition, urban centers are centers of economic growth, and the majority of people live in this area, which plays a vital role in the lives of its residents. So, the central city is highly influenced by human activities for the longest time. In general, its ecological environment should be declining, and it is worth studying whether its ecological environment has become better under urban planning.

The Xia-Zhang-Quan urban agglomeration is located in the rapidly developing urbanization area on the southeast coast of China. The rapid development of the economy and urbanization will impact the ecological environment to a certain extent. Based on the Landsat remote sensing influence and ecological environment index (RSEI), this paper evaluates the ecological environment change of the Xia-Zhang-Quan urban agglomeration and studies the impact of urbanization on the ecological environment change, which is of great significance for the protection of the ecological environment under economic development.

This paper aims to analyze the ecological environment changes in the urban development process in the Xia-Zhang-Quan urban agglomeration. The details are as follows: (1) Using remote sensing images in 1989, 2000, 2010, and 2022, we calculated the RSEI to analyze the ecological environment changes in the Xia-Zhang-Quan urban agglomeration. (2) An analysis of changes in ecological quality in central and non-central urban areas was performed. (3) Using the geographical detector method, this study analyzed the factors influencing the spatial heterogeneity of ecological environment quality. These findings are significant as they provide empirical evidence supporting the notion that targeted environmental interventions and sustainable urban development practices can lead to measurable ecological benefits.

2. Materials and Methods

2.1. Study Area

The Xia-Zhang-Quan urban agglomeration (Figure 1) is located in Fujian Province, a rapidly urbanizing region in southeast China, often called the “Golden Triangle of Southern Fujian”. This area encompasses the cities of Xiamen, Zhangzhou, and Quanzhou, covering approximately 26,000 square kilometers. According to data released by the Bureau of Statistics in March 2023, the permanent population in 2022 was 5.308 million for Xiamen, 5.068 million for Zhangzhou, and 8.879 million for Quanzhou. Additionally, the Fujian government reported that the GDPs for Xiamen, Zhangzhou, and Quanzhou in 2022 were 730.66 billion CNY, 570.66 billion CNY, and 1220.97 billion CNY, respectively, collectively accounting for about 48.22% of the province’s GDP. Overall, the Xia-Zhang-Quan urban agglomeration is the most economically developed and densely populated region in Fujian Province.

2.2. Data

2.2.1. Remote Sensing Images

In this study, remote sensing image data were downloaded from the United States Geological Survey (USGS) website (https://landsatlook.usgs.gov (accessed on 3 January 2023)). We selected four remote sensing images from 1989, 2000, 2010, and 2022 (Table 1). The images contain data from Landsat TM and Landsat OLI/TIRS sensors. All remote sensing data products have been subjected to systematic radiometric and geometric corrections. The spatial resolution of the images is 30 m × 30 m, with a revisit period of 16 days and an imaging swath width of 185 km × 185 km.

Since the ecological environment quality is closely related to the amount of vegetation, the surface reflectance data of the growing season of plants should be used for calculation [27]. The images with little or no cloud should also be selected for research as much as possible to reduce the influence of cloud cover on the results. In summary, remote sensing images taken in summer when the cloud cover is below 15% were selected as data sources in this study. Table 1 shows the acquisition time, cloud cover, and data types of remote sensing images in the study area.

2.2.2. Influencing Factors Data

The influencing factors data (Table 2) selected for this study include fractional vegetation cover, climate data (average annual precipitation and average annual temperature), topographic data (DEM), population density, vegetation coverage, and land use data.

2.3. Methods

2.3.1. RSEI Index

In this paper, the remote sensing images of the study area will be used to extract its humidity index (Wet), greenness index (NDVI), heat index (LST) and dryness index (NDBSI), analyze the Ecological Environmental Quality Evaluation Index (RSEI), and quantitatively evaluate the ecological environmental change of the Xiamen-Zhang-Quan urban agglomeration in Fujian Province.

• (1). Humidity index

There is a large amount of remote sensing band data information contained in remote sensing images, from which four main component features can be obtained after a tassel-cap transformation of remote sensing images: soil brightness (BI), greenness (GVI), humidity (wI), and yellowness. The humidity index in this study is represented by the moisture component after the tassel-cap transformation [28]. The formula is as follows:

(1)${\mathrm{W}\mathrm{E}\mathrm{T}}_{\mathrm{T}\mathrm{M}}=0.0315{\rho }_{blue}+0.2021{\rho }_{green}+0.3012{\rho }_{red}+0.1594{\rho }_{nir}-0.6806{\rho }_{swir1}-0.6109{\rho }_{swir2}$

(2)${\mathrm{W}\mathrm{E}\mathrm{T}}_{\mathrm{O}\mathrm{L}\mathrm{I}}=0.1511{\rho }_{blue}+0.1973{\rho }_{green}+0.3283{\rho }_{red}+0.3407{\rho }_{nir}-0.7117{\rho }_{swir1}-0.4559{\rho }_{swir2}$

In Equations (1) and (2), ${\rho }_{blue},{\rho }_{green}{, \rho }_{red}{, \rho }_{nir},{\rho }_{swir1},{\rho }_{swir2}$ represents the blue band, green band, red band, near-red band, mid-infrared band 1, and mid-infrared band 2 of remote sensing images, respectively.

• (2). Greenness index

In ecological monitoring work, the normalized vegetation index (NDVI) is the most widely used vegetation index, which is closely related to plant biomass, leaf area index, and vegetation cover and can be used to monitor the growth status of vegetation. Therefore, NDVI was chosen to represent the greenness index [29], and the formula is as follows.

(3)$\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}={\displaystyle \frac{\left({\rho }_{nir}-{\rho }_{red}\right)}{\left({\rho }_{nir}+{\rho }_{red}\right)}}$

In Equation (3), ${\rho }_{red}$ and ${\rho }_{nir}$ represent the red band and the near-infrared band, respectively.

• (3). Heat index

Land surface temperature (LST) is an essential hydrological and meteorological parameter of the earth’s surface. Since only the atmospheric transmittance parameter after 2000 is published on the NASA website, this paper simplifies the atmospheric correction method and retrieves the LST [30].

(4)$\mathrm{L}\mathrm{S}\mathrm{T}={\displaystyle \frac{T}{\left[1+\left(\frac{\lambda T}{\xi }\right)\right]}}-273$

(5)$\mathrm{T}={\displaystyle \frac{K_2}{\ln \frac{K_1}{L_6}+1}}$

(6)${\mathrm{L}}_6=gain\times DN+bias$

(7)${\mathsf{\epsilon }}_{\mathrm{s}\mathrm{u}\mathrm{r}\mathrm{f}\mathrm{a}\mathrm{c}\mathrm{e}}=0.9625+0.0614F_v-0.0461{F_v}^2$

(8)${\mathsf{\epsilon }}_{\mathrm{b}\mathrm{u}\mathrm{i}\mathrm{l}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{g}}=0.9589+0.0860F_v-0.0671{F_v}^2$

(9)${\mathrm{F}}_{\mathrm{v}}=\left\{\begin{array}{c}0, NDVI<0\\ {\displaystyle \frac{NDVI-{NDVI}_s}{{NDVI}_v+{NDVI}_s}}, 0\le NDVI\le 0.7\\ 1, NDVI>0.7\end{array}\right.$

In Equations (4)–(9), LST is the surface temperature, T is the temperature at the sensor, and L₆ is the thermal infrared band reflectance after radiometric calibration. For the Landsat5 TM sensor,$K_1=607.76 \mathrm{W}·{\mathrm{m}}^{-2}·{\mathrm{s}\mathrm{r}}^{-1}·{\mathsf{\mu }\mathrm{m}}^{-1}$ and $K_2=1260.56 \mathrm{K}$. For the 10th band of the Landsat9 TIRS sensor, $K_1=774.89 \mathrm{W}·{\mathrm{m}}^{-2}·{\mathrm{s}\mathrm{r}}^{-1}·{\mathsf{\mu }\mathrm{m}}^{-1}$ and $K_2=1321.08 \mathrm{K}$. λ is the central wavelength of the thermal infrared band, ${\lambda }_{TM}=11.435 \mathsf{\mu }\mathrm{m}$, ${\lambda }_{b10}=10.900 \mathsf{\mu }\mathrm{m}$, $\xi =1.438\times {10}^{-2} \mathrm{m}·\mathrm{K}$, ε is the surface emissivity, NDVI_v = 0.7 represents the NDVI value of the area completely covered by vegetation, and NDVI_s = 0, represents the NDVI value of the area without vegetation.

• (4). Index of dryness

The dryness factor is expressed by the dryness index NDBSI. The bare soil index SI and the building index IBI are used to calculate the mean value [23].

(10)$\mathrm{S}\mathrm{I}={\displaystyle \frac{\left({\rho }_{swir1}+{\rho }_{green}\right)-\left({\rho }_{nir}+{\rho }_{blue}\right)}{\left({\rho }_{swir1}+{\rho }_{red}\right)+\left({\rho }_{nir}+{\rho }_{blue}\right)}}$

(11)$\mathrm{I}\mathrm{B}\mathrm{I}={\displaystyle \frac{\frac{2{\rho }_{swir1}}{{\rho }_{swir1}+{\rho }_{nir}}-\left(\frac{{\rho }_{nir}}{{\rho }_{nir}+{\rho }_{red}}+\frac{{\rho }_{green}}{{\rho }_{green}+{\rho }_{swir1}}\right)}{\frac{2{\rho }_{swir1}}{{\rho }_{swir1}+{\rho }_{nir}}+\left(\frac{{\rho }_{nir}}{{\rho }_{nir}+{\rho }_{red}}+\frac{{\rho }_{green}}{{\rho }_{green}+{\rho }_{swir1}}\right)}}$

(12)$\mathrm{N}\mathrm{D}\mathrm{B}\mathrm{S}\mathrm{I}={\displaystyle \frac{SI+IBI}{2}}$

${\rho }_{blue},{\rho }_{green}{,\rho }_{red},{\rho }_{nir}{,\rho }_{swir1}$ represent the blue band, green band, red band, near-infrared band, and mid-infrared band of remote sensing images.

• (5). RSEI index

The four indexes calculated above have different dimensions and units. If it is directly used in the calculation of RSEI index, the weight of each index will be unbalanced. To avoid this situation and also to reduce the differences in remote sensing images caused by different seasons, it is necessary to normalize the original calculation results of the four indicators and unify their range between [0, 1].

(13)${\mathrm{B}\mathrm{I}}_{\mathrm{i}}={\displaystyle \frac{b_i-b_{min}}{b_{max}-b_{min}}}$

In Formula (13), BI_i is the normalized pixel value of an index, b_i is the original pixel value of the index, and b_max and b_min represent the maximum and minimum value of the index, respectively.

In this study, the principal component analysis (PCA) method was used to calculate the RSEI index, and the four indexes that had been normalized were synthesized into an image, and the first principal component was extracted to calculate RSEI₀.

(14)${\mathrm{R}\mathrm{S}\mathrm{E}\mathrm{I}}_0=\left\{\begin{array}{c}PC1\left[f\left(NDVI,WET,LST,NDBSI\right)\right]\\ 1-PC1\left[f\left(NDVI,WET,LST,NDBSI\right)\right]\end{array}\right.$

The RSEI calculated PC1, when RSEI has high values for representing excellent ecological conditions. If the high RSEI values represent ecologically poor conditions and low values represent good ones, the RSEI would be subtracted from one to let higher values represent better ecological status as usually expected [27].

To compare the change in the RSEI index in each period more intuitively, RSEI₀ should be normalized to obtain the final RSEI:

(15)$\mathrm{R}\mathrm{S}\mathrm{E}\mathrm{I}={\displaystyle \frac{{RSEI}_0-{RSEI}_{0min}}{{RSEI}_{0max}-{RSEI}_{0min}}}$

RSEI_0max and RSEI_0min represent the maximum and minimum values of RSEI₀, respectively.

Principal component analysis can eliminate the correlation between indicators and is not affected by indicator units. Also, it can retain the vast majority of information and select a few comprehensive indicators. Therefore, principal component analysis is widely used in many fields, and it is more used in the geoscience field for statistical analysis but less used in comprehensive index construction. In this paper, applying principal component analysis in constructing ecological indicators can weaken the correlation between indicators and better evaluate the ecological environment.

2.3.2. Classification of RSEI Index

In order to better visualize the ecological quality of the Xia-Zhang-Quan urban agglomeration, this study refers to the “Quality Evaluation of Ecological Environment Quality in China”, and divides the normalized RSEI index into five grades at an interval of 0.2. The RSEI index within the range of [0, 0.2] means that the ecological environment quality is “poor”. The RSEI index in the range of (0.2, 0.4) means that the ecological environment quality is “worse”. The RSEI index in the range of (0.4, 0.6) means that the ecological environment quality is “average”. The RSEI index in the range of (0.6, 0.8) means that the ecological environment quality is “good”. The RSEI index in the range of (0.8, 1] means that the ecological environment quality is “excellent”. A description of each level is as follows:

• Poor: 0 ≤ RSEI ≤ 0.2. Conditions are harsh and not suitable for long-term human survival.

• Worse: 0.2 < RSEI ≤ 0.4. The vegetation coverage is poor. There is drought and little rain, few species, and obvious factors limit human life.

• General: 0.4 < RSEI ≤ 0.6. The vegetation coverage is at a medium level, and the biodiversity is at an average level, which is more suitable for human survival. Still, occasionally, some constraints are not suitable for human survival.

• Good: 0.6 < RSEI ≤ 0.8. The vegetation coverage and biodiversity are good, and it is suitable for human survival.

• Excellent: 0.8 < RSEI ≤ 1.0. Good vegetation coverage, good biodiversity, and stable ecosystems are most suitable for human survival.

2.3.3. Classification of RSEI Index Changes

To study the ecological environment change of the Xia-Zhang-Quan urban agglomeration, we will refer to the grading table of the ecological environment change degree of the environmental protection standard “Technical Specification for Ecological Environment Condition Evaluation” promulgated by the state and divide the ecological environment change situation into seven levels (Table 3): significantly worse, obviously worse, slightly worse, no obvious change, slightly better, obviously better, and significantly better.

2.3.4. Geographic Detector

• (1). Parameter Optimization

To optimize the parameters, the study area’s data were divided into a 1 km × 1 km grid, with the data from the center of each grid cell used as the raw data points. Continuous driving factors (vegetation coverage, precipitation, temperature, DEM, and population density) were discretized using several classification methods, including equal intervals, natural breaks, quantiles, geometric intervals, and standard deviation classification. The number of categories ranged from 3 to 8. An optimization model was then employed to select the most suitable classification method and the optimal number of categories [31].

• (2). Geodetector

The geodetector [31] consists of factor detector, interaction detector, risk detector, and ecological detector. We chose the factor and interaction detectors to detect the spatial heterogeneity of RSEI. The factor detector is used to analyze the explanatory power of individual factors on ecological environment quality. In contrast, the interaction detector method assesses the combined explanatory power of two factors acting together on ecological environment quality.

• 1). Factor detector

(16) $\mathrm{q}=1-{\displaystyle \frac{1}{N{\sigma }^2}}\sum _{h=1}^L{N}_h{\sigma }_h^2$

The q-statistic ranges from 0 to 1, with larger q-values indicating stronger explanatory power. In this method, L represents the stratification of factors. N_h is the number of units corresponding to the ecological environment quality and factors in layer h, and N is the total number of units corresponding to the ecological environment quality and factors across the entire study area. σ_h² denotes the variance of ecological environment quality within layer h while σ² represents the variance of ecological environment quality for the whole of the study area.

• 2). Interaction Detector

The interaction detector explores the interactions between different factors, assessing whether the combined effect of two independent variables enhances or diminishes their explanatory power on the dependent variable. It also accounts for cases where the influence of each independent variable on the dependent variable is mutually independent. The interaction effects between factors are detailed in Table 4.

To identify the primary factors influencing spatial differentiation, the study utilized the optimized parameters of the geographical detector (R package). This approach is advantageous as it determines the optimal discretization classification method and the number of classifications. The factors selected for analysis include DEM, land use and land cover (LUCC), fractional vegetation cover (FVC), average annual precipitation (Pre), yearly average temperature (Temp), and population density (LandScan). Data for these factors were derived from a 1 km × 1 km grid, using the center point of each grid cell as the data source.

3. Results

3.1. Rationality of RSEI

The RSEI index is widely used in ecological environmental assessment research, and studies have been conducted in different regions. This paper calculated heat, humidity, dryness, and greenness (Figure 2) from remote sensing images to perform principal component analysis. From four years of principal component analysis (Table 5), the following can be obtained: The contribution rates of the first principal component (PC1) eigenvalues in 1989, 2000, 2010, and 2022 were 75.45%, 70.01%, 78.00%, and 76.58%, respectively, which were greater than 70%, indicating that the first principal component contained most of the information of the four indicators. Therefore, PC1 can replace the original RSEI index, composed of the humidity index, greenness index, dryness index, and heat index. It indicates that the RSEI constructed by PC1 is reasonable.

3.2. Analysis of RSEI

3.2.1. RSEI Index of the Xia-Zhang-Quan Urban Agglomeration

By analyzing the RSEI index, it is possible to study the ecological environmental changes in the Xia-Zhang-Quan urban agglomeration from 1989 to 2022. It can be concluded that the ecological environmental quality index RSEI of the Xia-Zhang-Quan urban agglomeration was 0.5829, 0.5607, 0.5827, and 0.6195 in 1989, 2000, 2010, and 2022, respectively, showing a trend of first decreasing and then increasing; the overall trend was still increasing (Figure 3). Compared with 1989, the RSEI in 2022 increased from 0.5829 to 0.6195. With an increase of 6.3%, the ecological environment quality of the Xia-Zhang-Quan urban agglomeration has improved in the past 30 years.

The average value of the RSEI index of each county-level administrative region of the Xia-Zhang-Quan urban agglomeration was calculated, and the statistical results are shown in Figure 4. Among the three prefectural-level administrative regions of the Xia-Zhang-Quan urban agglomeration, the average value of the RSEI index of Zhangzhou is the highest, which is 0.5532, indicating that its ecological environmental quality is the best among the three cities. The mean value of the RSEI index in Quanzhou is 0.5228. The average RSEI index of Xiamen is the lowest among the three cities, which is 0.4546, and its ecological environmental quality is the worst among the three cities. From the four periods of the RSEI index and its mean value of each district and county from 1989 to 2022, it can be found that during the 30 years, the mean value of the RSEI index of Hua ‘an County, Nanjing County, Pinghe County, and Changtai County in Zhangzhou City, and Dehua County and Yongchun County in Quanzhou City are all higher than 0.6, indicating that the ecological environmental quality is at a relatively high level. However, the average RSEI index of Siming District and Huli District in Xiamen City, Dongshan County in Zhangzhou City, Jinjiang City, and Shishi City in Quanzhou City is lower than 0.4, indicating that the ecological environmental quality is of a relatively low level.

Combined with the relative geographical location distribution of each county-level administrative region of the Xia-Zhang-Quan urban agglomeration, it can be found that the Siming District and Huli District of Xiamen City, Dongshan County of Zhangzhou City, Jinjiang City, and Shishi City of Quanzhou City are all located in the urban center. Compared with other county-level administrative regions around them, the buildings are relatively dense, and the population density is relatively large. Therefore, the RSEI index is relatively low, and the ecological environment quality is poor. Hua’an County, Nanjing County, Pinghe County, Changtai County in Zhangzhou City, Dehua County, and Yongchun County in Quanzhou City have relatively high elevation values and belong to mountainous areas, so the vegetation coverage rate is high. In addition, due to their geographical location far from the urban center, human activities are relatively less, and their impact on the ecological environment is also less. The RSEI index is relatively high, and the ecological environment quality is good.

3.2.2. Classification of RSEI Index

The normalized RSEI index is classified, and the statistical results (Figure 5) show that the ecological environmental quality of the Xia-Zhang-Quan urban agglomeration is “good”. That is, the proportion of the regions in the range of RSEI index (0.6, 0.8) is large, and the proportion of each stage is above 30%. The ecological environment quality is “poor” and “worse”. That is, the proportion of the area in the RSEI index in the range of [0, 0.4] is small, and the highest proportion is 15.46% in 2000. Through the analysis of other levels, it was found that the proportion of “excellent” ecological environmental quality in the Xia-Zhang-Quan urban agglomeration showed an overall upward trend. The proportion of “general” ecological environmental quality showed a downward trend. To sum up, the overall ecological environment quality of the Xia-Zhang-Quan urban agglomeration is relatively good and has been improved in the past 30 years.

The areas of “poor” and “worse” ecological quality were mainly concentrated in the southeast coastal urban building land and other bare soil and wasteland areas (Figure 6). As early as in the early 1980s, Xiamen, Zhangzhou and Quanzhou had proposed jointly building the “Golden Triangle Metropolitan Circle in southern Fujian”. In the 1990s, southeast Fujian began to develop with rapid industrial development, increasing the demand for industrial land. The ecological environment quality of the Xia-Zhang-Quan urban agglomeration also decreased, which was the reason for the degradation of ecological environment quality in 2000 compared with that in 1989.

3.3. Changes in Ecological Environment Quality

The spatial distribution of the RSEI index from 1989 to 2022 was calculated according to prefectural and county-level administrative regions, and the results are shown in Figure 7 and Figure 8, and Table 6. From 1989 to 2000, the change in the value of the RSEI in Xiamen was −0.0854, indicating that the ecological environment was significantly worse. The change in the value of the RSEI in Zhangzhou was −0.0379, indicating that the ecological environment was slightly worse. The change in the value of the RSEI in Quanzhou was −0.0258, the ecological environment was slightly worse, and the overall trend is worse. Among them, the RSEI change values of the Huli District and Siming District of Xiamen City were −0.1953 and −0.1259, respectively, indicating that the ecological environment was significantly worse. From 2000 to 2010, the change value of RSEI in Xiamen was −0.0499, indicating that the ecological environment was slightly worse. The change value of RSEI in Zhangzhou was 0.0640, indicating that the ecological environment was significantly improved. The change value of RSEI in Quanzhou was −0.0597, indicating that the ecological environment was significantly worse. Among them, the RSEI values of Xiang ‘an District in Xiamen City, Jinjiang City, and Lizheng District in Quanzhou City were −0.1031, −0.1175, and −0.1326, respectively, indicating that the ecological environment was significantly worse. The RSEI values of Dongshan County, Yunxiao County, and Zhao’an County in Zhangzhou City were 0.1106, 0.1375, and 0.1078, respectively, indicating that the ecological environment was significantly improved. From 2010 to 2022, the change value of RSEI in Xiamen was 0.0455, indicating a slight improvement in the ecological environment. The change value of RSEI in Zhangzhou was 0.0258, indicating that the ecological environment was slightly better. The change value of RSEI in Quanzhou is 0.0459, the ecological environment is slightly better, and the overall trend is better. Among them, the RSEI change value of Huli District in Xiamen City is 0.1048, indicating that the ecological environment has significantly improved.

From 1989 to 2022, the change in the value of the RSEI in Xiamen was −0.0897, indicating that the ecological environment was significantly worse. The change in the value of the RSEI in Zhangzhou was 0.0519, indicating that the ecological environment was improved considerably. The change in the value of the RSEI in Quanzhou was −0.0396, and the ecological environment was slightly worse. The RSEI values of Haicang District, Huli District, Jimei District, and Xiang‘an District in Xiamen City, Fengze District, Jinjiang City, and Licheng District in Quanzhou City were −0.1092, −0.1589, −0.1053, −0.1061, −0.1806, −0.1328, and −0.1881, respectively, indicating that the ecological environment was significantly worse. The RSEI values of Dongshan County, Yunxiao County, Zhangpu County, and Zhaoan County in Zhangzhou City were 0.1633, 0.1814, 0.1267, and 0.1580, respectively, indicating that the ecological environment was significantly improved.

Also, we find that the ecological environment of the Xia-Zhang-Quan urban agglomeration is slightly better from 2010 to 2022, so the government has formulated policies to pay attention to ecological and environmental protection.

We find ecological environment changes are worse in central urban areas from 1989 to 2020, such as the Huli District and Siming District of Xiamen City, Xiangcheng District of Zhangzhou City, and Licheng District of Quanzhou City. However, the ecological environment of the central urban area has improved from 2010 to 2022, for example, the Huli District, Siming District, and Licheng District. The ecological environment in the Xiangcheng District of Zhangzhou City improved from 2000 to 2010 but worsened from 2010 to 2022. That means the ecological environment of the central city can be improved with economic development if the protection of the environment is enhanced. Otherwise, it gets worse.

3.4. Analysis of Factors Influencing the Spatial Heterogeneity of the RSEI in the Xia-Zhang-Quan Urban Agglomeration

3.4.1. Factor Detector Analysis

Using the factor detector, we analyzed the explanatory power of each factor on the spatial heterogeneity of ecological environment quality. As shown in Table 7, all factors except for the 2022 temperature data significantly impact the spatial differentiation of ecological environment quality. A larger q-value indicates a greater contribution of the driving factor to the spatial heterogeneity of ecological environment quality in the study area. In 2000, FVC > LUCC > DEM > Temp > LandScan > Pre. In 2010, LUCC > FVC > DEM > LandScan > Temp > Pre. In 2022, LUCC > LandScan > DEM > FVC > Pre. Overall, the explanatory power of land use on the spatial differentiation characteristics of RSEI has become increasingly significant, precipitation has the weakest explanatory power, and the influence of vegetation coverage has also diminished over time. This indicates that with the progression of urbanization, the explanatory power of land use for spatial heterogeneity in ecosystem quality changes accordingly. In 2000, vegetation coverage was the primary determinant of spatial differentiation in ecological environment quality. By 2022, land use had become the dominant factor, with the influence of precipitation being relatively minor.

3.4.2. Interaction Detector Analysis

We used the interaction detector to explore the relationship between the interaction of various factors and the spatial heterogeneity of the RSEI (Figure 9). The results indicate that in 2000, 2010, and 2022, vegetation coverage and other factors did not exhibit interactive effects. In contrast, all other factors demonstrated bi-variable enhancement or nonlinear enhancement, suggesting that the interactive effects of these factors are greater than the effects of individual factors alone.

The analysis shows that with rising temperatures, the combination of land use and elevation had the strongest interaction in 2000 and 2010. By 2022, the combination of average annual temperature and elevation emerged as the most significant interaction. Land use consistently showed high q-values when interacting with other factors, and the temperature also displayed high q-values in 2022 when interacting with other factors.

We found that the explanatory power of the interactions was greater than that of individual factors. This suggests that the impact of various influencing factors on ecological environment quality is neither unilateral nor a simple additive effect of two factors. Instead, it is characterized by mutual or nonlinear enhance, indicating that the synergistic effects of multiple factors influence ecological environment quality.

4. Discussion

This paper mainly studies the ecological environment change of urban agglomeration. We hypothesize that, with the help of urban planning, economic development does not necessarily lead to ecological degradation. Therefore, we study the ecological environment change of the Xia-Zhang-Quan urban agglomeration to verify our research purpose. We found that the Xia-Zhang-Quan urban agglomeration’s ecological environment improved from 1989 to 2022. The ecological quality of the non-central urban areas is better than that of the central urban areas. The central urban areas have improved their ecological quality, although in less favorable conditions. The factor detector analysis identified land use as the dominant factor influencing ecological quality, with precipitation having a relatively minor impact. Interaction analysis revealed that all other factors demonstrated bi-variable enhancement or nonlinear enhancement, suggesting that the interactive effects of these factors are greater than the effects of individual factors alone. Land use consistently shows explanatory solid power. Temperature also exhibited significant influence in 2022 when interacting with other factors.

4.1. Changes in RSEI

The average RSEI of the Xia-Zhang-Quan urban agglomeration has an overall upward trend, and the ecological environment quality has improved in the past 30 years, similar to the study’s finding [32]. The overall ecological environment index of Fujian Province has increased, and the ecological environment quality has improved [33]. In recent years, the poor ecological environments have been concentrated in the Siming District and Huli District of Xiamen City, Dongshan County of Zhangzhou City, Jinjiang City, and Shishi City of Quanzhou City. The ecological environment index of urban centers is small, and the ecological environment is poor but exhibiting signs of improvement. During the more than 30 years, the ecological environment of Xiamen Island was significantly worse, which was different from the results of the study [34] from 1995 to 2016, which may be because the ecological environment of Xiamen Island continued to decline from 2016 to 2022. The ecological environment of Zhangzhou was significantly improved. The ecological environment of Quanzhou was slightly worse. This is similar to the results of the study [35] on the ecological environment change in Quanzhou City from 1989 to 2018.

The overall upward trend in the RSEI index from 1989 to 2022 demonstrates that it is possible to achieve both economic growth and ecological improvement simultaneously. This strengthens the finding that challenges the commonly assumed trade-off between development and environmental health. It suggests that urban agglomerations can improve their ecological conditions while promoting economic vitality with appropriate planning and management strategies. Our findings reveal differences in ecological quality between central and non-central urban areas. While central areas of urban cities typically exhibited poorer ecological conditions, there was a trend towards improvement, suggesting that targeted interventions in these densely populated regions are beginning to bear fruit. Urban planners should continue to focus on central areas by promoting green roofs, urban forestry programs, and sustainable building practices to mitigate the urban heat island effect and enhance ecological quality.

4.2. Analysis of Driving Factors of Ecological Environment Changes in the Xia-Zhang-Quan Urban Agglomeration

Ecological environment improvements are influenced by land use changes, elevation, and temperature variations, emphasizing the complex interplay between natural and anthropogenic factors in shaping urban ecological environment conditions.

Both natural and anthropogenic factors influence the ecological environment quality of the Xia-Zhang-Quan urban agglomeration. In 2022, the region experienced extremely high temperatures [36]. During these periods, the interactions between temperature and DEM, precipitation, and population density had a strong explanatory power for the spatial heterogeneity of ecological environment quality. Conversely, under non-extreme temperature conditions, the interaction between land use changes and other factors showed a stronger explanatory power for the spatial heterogeneity of ecological environment quality. This indicates that during extremely high temperatures, human interventions are insufficient to counteract the impact of natural factors. However, effective land use planning is essential to maintain ecological quality under non-extreme temperature conditions.

Similar conclusions have been drawn from studies in other regions of China. The research found that the interaction between land use and other factors provided a stronger explanatory power for the spatial heterogeneity of ecological environment quality in Taojiang County, Hunan Province [37]. The explanatory power of different interacting factors with land use varies by region. For example, in Shandong Province, the interaction between humidity changes and land use showed explanatory solid power for the spatial heterogeneity of RSEI, with an increasing trend in RSEI for cropland and forest land [22]. On Northwest China, vegetation, soil, and land use types were positively correlated with the RSEI, in the Lanxi urban agglomeration [18]. Natural factors are the primary influencers on the RSEI of the Tibetan Plateau, such as temperature, soil moisture, and precipitation [21]. And the interaction between temperature and precipitation showed a positive effect on RSEI.

When natural factors predominantly influence ecological environment quality, interactions with land use exert significant impact. Conversely, when natural factors play a lesser role, the interactions between land use and other factors remain influential. Therefore, changes in land use types play a key role in shaping ecological environment quality. This emphasizes the need to promote economic development while adhering to green and sustainable development principles. The dominance of land use as a factor influencing ecological quality underscores the critical role of effective land use planning in urban environments. Urban planners should prioritize green spaces, implement stringent zoning regulations to prevent excessive urban sprawl, and encourage the development of eco-friendly infrastructure. The high explanatory power of land use interactions with other factors, such as temperature, indicates that multifaceted strategies that address various environmental components simultaneously can yield significant ecological benefits.

4.3. Planning Implications of Changes in RSEI

Our study is consistent with several previous studies that have reported positive ecological outcomes due to effective urban planning and environmental management. For instance, some studies [21,38] documented similar trends of ecological improvement in other regions, attributing these changes to the successful implementation of green policies and enhanced land use practices. However, our study extends the existing knowledge by explicitly focusing on the Xia-Zhang-Quan urban agglomeration, providing a localized understanding of ecological dynamics in this economically and environmentally significant region. Unlike some studies that report mixed or negative trends in urban ecological quality due to rapid urbanization [39,40,41], our results indicate that ecological conditions can improve even in highly urbanized areas with appropriate management and planning. Compared with other urban agglomerations, the Pearl River Delta (PRD) is one of the most economically dynamic regions in China, characterized by rapid industrialization and urbanization similar to the Xia-Zhang-Quan urban agglomeration. Both regions have experienced significant ecological changes due to urban expansion. However, while the PRD has seen considerable environmental degradation, the Xia-Zhang-Quan urban agglomeration has shown an overall improvement in ecological quality, as indicated by the RSEI trends from 1986 to 2019 [42]. This contrast underscores the effectiveness of the environmental policies and land use planning implemented in Xia-Zhang-Quan, suggesting that similar strategies could benefit other rapidly urbanizing areas.

Despite the rapid economic development of the Xia-Zhang-Quan urban agglomeration, Zhangzhou City launched the “three-year Action Plan to Win the Blue Sky Defense War” in 2018, deepened the treatment of industrial pollution, and vigorously cultivated green environmental protection industries, which reduced the damage caused by industrial pollution to the ecological environment. In addition, Zhangzhou has promoted land afforestation and forest quality improvement, fully tapped the potential of afforestation, scientifically expanded the afforestation space, and strengthened and improved the ecological advantages of Zhangzhou.

The implications of these findings for urban planning are profound. The demonstrated improvement in ecological quality within central urban areas suggests that it is possible to achieve ecological sustainability even in densely populated and developed regions. Urban planners and policymakers can leverage these insights to design and implement strategies that balance development with ecological preservation. Key strategies may include promoting green infrastructure, enhancing land use efficiency, and integrating climate adaptation measures into urban planning. Additionally, understanding the specific impacts of land use changes, elevation, and temperature on ecological quality allows for more targeted and effective interventions, optimizing resource allocation and maximizing environmental benefits.

The results of this study should inform policy frameworks at both local and regional levels. Policies that incentivize sustainable practices, penalize ecological degradation, and promote community engagement in environmental stewardship can drive broader adoption of eco-friendly practices. Furthermore, integrating ecological indicators like RSEI into regular monitoring and reporting systems can provide ongoing feedback to policymakers and planners, ensuring that urban development remains aligned with ecological sustainability goals.

4.4. Limitations and Future Study

This study selected images taken during the summer months (April to August) as the original data [43]. However, the prevalence of clouds during this season can affect remote sensing images, potentially introducing errors when calculating various indices, including the RSEI index. The remote sensing image data for the same month is challenging to obtain, so we chose the remote sensing images from April to August as the remote sensing images for the study area. Additionally, some remote sensing images from 1989 were missing for the study area, and these gaps were supplemented with images from 1988. Future research should aim to mitigate these errors by seeking data with less cloud cover or by exploring advanced cloud removal techniques in remote sensing imagery to reduce inaccuracies caused by cloud cover.

The final ecological environment quality index (RSEI) obtained in this study only uses four factors relatively related to the ecological environment as indicators, which reflect the ecological environment quality status and the development trend of the study area in the past 30 years to a certain extent. Still, the ecological environment quality is closely related to regional urbanization and atmospheric levels. In future studies, other factors related to ecological environmental quality will also be integrated to evaluate the ecological environmental quality of the study area more comprehensively.

Despite the valuable contribution of this research, there are some limitations that need to be recognized. First, reliance on remotely sensed data, while providing comprehensive spatial coverage, may lack the granularity required for detailed ground-based ecological assessments. Combining remotely sensed data with ground-based observations can improve the accuracy and depth of ecological assessments. Second, although this study identified the key factors affecting ecological quality, it did not fully explore the causal mechanisms of these relationships. Future research should aim to develop more sophisticated models that depict the direct and indirect pathways through which land use change, elevation, and temperature affect ecological conditions. Finally, the time frame of this study, from 2000 to 2022, may not capture longer-term ecological trends and potential lagged effects of environmental policies and interventions. Expanding the time frame and including more recent data would provide a more comprehensive understanding of ecological dynamics.

In the future, multi-source indicators reflecting the natural and human development of the region can be included, and the multi-scale geographically weighted regression model (MGWR) can be utilized to explore the spatial and temporal relationships between ecological environment quality and the factors affecting it. Expanding the analysis to include more granular data and incorporating machine learning techniques could further enhance the precision and applicability of RSEI as a tool for urban planning.

5. Conclusions

Utilizing three Landsat5 TM images from 1989, 2000, and 2010, along with the first phase of Landsat9 OLI images from 2022, We calculated the humidity index, greenness index, dryness index, and heat index to construct the Remote Sensing Ecological Index (RSEI) in the Xia-Zhang-Quan urban agglomeration in Fujian Province. The ecological environment quality was evaluated at four time periods, and the spatial and temporal trends of ecological environment quality and the factors affecting spatial heterogeneity were analyzed.

The average RSEI indices for the study area were 0.5829, 0.5607, 0.5827, and 0.6195 in 1989, 2000, 2010, and 2022, respectively. This indicates an initial decline followed by an increase, reflecting an overall upward trend in ecological quality from 1989 to 2022. The ecological environment of the Xia-Zhang-Quan urban agglomeration was generally classified as “good”, with the proportion of areas achieving “excellent” quality showing a rising trend. Over the past 30 years, the ecological environment quality in Zhangzhou has significantly improved, while Xiamen and Quanzhou experienced notable deterioration. Non-central urban areas demonstrated better ecological quality compared to central urban areas, which, despite poorer conditions, exhibited signs of improvement.

Our findings reveal that both natural and anthropogenic factors influence the ecological environment of the Xia-Zhang-Quan urban agglomeration. Land use emerged as the dominant factor, with precipitation having a relatively minor impact. Interaction analysis showed that the combined influence of various drivers on the RSEI was greater than their individual effects. Land use consistently exhibited high explanatory power when interacting with other factors, and temperature also showed a strong influence in 2022 in combination with different variables.

In summary, the study underscores the significant role of land use and the synergistic effects of multiple factors in shaping the ecological environment of the Xia-Zhang-Quan urban agglomeration. These insights can guide future urban planning efforts to harmonize economic development with ecological sustainability.

Author Contributions

Conceptualization, Z.L.; methodology, Z.L. and J.P.; software, J.P.; validation, Y.W., F.S., and Y.H.; writing—original draft preparation, Z.L. and J.P.; writing—review and editing, Z.L.; visualization, supervision, Q.N.; funding acquisition, W.M. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Remote sensing data is from USGS (https://landsatlook.usgs.gov (accessed on 3 January 2023)).

Acknowledgments

Thanks for the remote sensing data from USGS (https://landsatlook.usgs.gov (accessed on 3 January 2023)). I (Zongmei Li) would like to thank my mother (Chengshan Chang) for a lifetime of hard work.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Study area.

Figure 2. The normalized index of the heat, humidity, dryness, and greenness in 2022: (a) the heat, (b) dryness, (c) greenness, and (d) humidity.

Figure 3. RSEI index of Xia-Zhang-Quan Urban agglomeration in 1989, 2000, 2010, and 2022.

Figure 4. The RSEI in district scale in 1989, 2000, 2010, and 2022.

Figure 5. Statistical figure of hierarchical RSEI in 1989, 2000, 2010, and 2022.

Figure 6. RSEI classification map of the Xiamen-Zhang-Quan urban agglomeration in 1989, 2000, 2010, and 2022. (a) 1989, (b) 2000, (c) 2010, and (d) 2022.

Figure 7. RSEI changes average in the districts of the Xiamen-Zhang-Quan urban agglomeration from 1989 to 2022.

Figure 8. Levels of RSEI changes in the Xiamen-Zhang-Quan urban agglomeration from 1989 to 2022. (a) 1989–2000, (b) 2000–2010, (c) 2010–2022, and (d) 1989–2022.

Figure 9. Results of interaction detection.

Table 1

Information of remote sensing images in the study area.

Path and Row Number		1989	2000	2010	2022
11,9 42	Data	15 June 1989	29 June 2000	6 June 2009	17 May 2022
	Degree of cloud cover	1.00%	4.00%	0.00%	9.23%
119 43	Data	15 June 1989	29 June 2000	6 June 2009	17 May 2022
	Degree of cloud cover	1.00%	6.00%	1.00%	1.68%
119 44	Data	15 June 1989	29 June 2000	6 June 2009	17 May 2022
	Degree of cloud cover	0.00%	2.00%	1.00%	0.41%
120 42	Data	3 June 1988	4 June 2000	3 August 2010	6 April 2022
	Degree of cloud cover	0.00%	7.00%	1.00%	13.08%
120 43	Data	3 June 1988	4 June 2000	3 August 2010	6 April 2022
	Degree of cloud cover	0.00%	8.00%	3.00%	12.53%
120 44	Data	3 June 1988	4 June 2000	3 August 2010	6 April 2022
	Degree of cloud cover	0.00%	3.00%	6.00%	0.20%
Type		Landsat TM	Landsat TM	Landsat TM	Landsat OLI

Table 2

Influencing factors data source.

Factors Data	Spatial Resolution	Date	Source
Fractional vegetation cover (FVC)	500 m	2000, 2010, 2022	https://data.tpdc.ac.cn/ (accessed on 3 January 2023)
Average annual precipitation (Pre)	1 km	2000, 2010, 2022	https://www.geodata.cn/ (accessed on 20 June 2024)
Average annual temperature (Temp)	1 km	2000, 2010, 2022	https://www.geodata.cn/ (accessed on 20 June 2024)
Population density (LandScan)	1 km	2000, 2010, 2022	https://landscan.ornl.gov/metadata (accessed on 20 June 2024)
Land use data (LUCC)	30 m	2000, 2010, 2022	http://doi.org/10.5281/zenodo.4417809
DEM	30 m	2000	NASA.NIMA (accessed on 3 September 2022)(http://srtm.csi.cgiar.org/srtmdata/) (accessed on 1 January 2020)

Table 3

Levers of RSEI changes.

Id	Levels	Change in RSEI
1	Significantly worse	<−0.1
2	Obviously worse	−0.1~−0.05
3	Slightly worse	−0.05~−0.02
4	No obvious change	−0.02~0.02
5	Slightly better	0.02~0.05
6	Obviously better	0.05~0.1
7	Significantly better	>0.1

Table 4

Interaction relationship [31].

Interactive Relationship Description	Interactive Relationship
$\mathrm{q}\left({\mathrm{X}}_1\cap {\mathrm{X}}_2\right)<\mathrm{M}\mathrm{i}\mathrm{n}[\mathrm{q}\left({\mathrm{X}}_1\right),\mathrm{q}\left({\mathrm{X}}_2\right)]$	Nonlinear weakening: Impacts of single variables are nonlinearly weakened by the interaction of two variables.
$\mathrm{M}\mathrm{i}\mathrm{n}\left[\mathrm{q}\left({\mathrm{X}}_1\right),\mathrm{q}\left({\mathrm{X}}_2\right)\right]\le \mathrm{q}\left({\mathrm{X}}_1\cap {\mathrm{X}}_2\right)\le \mathrm{M}\mathrm{a}\mathrm{x}\left[\mathrm{q}\left({\mathrm{X}}_1\right),\mathrm{q}\left({\mathrm{X}}_2\right)\right]$	Uni-variable weakening: Impacts of single variables are uni-variable weakened by the interaction.
$\mathrm{M}\mathrm{a}\mathrm{x}\left[\mathrm{q}\left({\mathrm{X}}_1\right),\mathrm{q}\left({\mathrm{X}}_2\right)\right]<\mathrm{q}\left({\mathrm{X}}_1\cap {\mathrm{X}}_2\right)<\mathrm{q}\left({\mathrm{X}}_1\right)+\mathrm{q}\left({\mathrm{X}}_2\right)$	Bi-variable enhancement: Impacts of single variables are bi-variable enhanced by the interaction.
$\mathrm{q}\left({\mathrm{X}}_1\cap {\mathrm{X}}_2\right)=\mathrm{q}\left({\mathrm{X}}_1\right)+\mathrm{q}\left({\mathrm{X}}_2\right)$	Independent: Impacts of variables are independent.
$\mathrm{q}\left({\mathrm{X}}_1\cap {\mathrm{X}}_2\right)>\mathrm{q}\left({\mathrm{X}}_1\right)+\mathrm{q}\left({\mathrm{X}}_2\right)$	Nonlinear-enhance: Impacts of variables are nonlinearly enhanced.

Table 5

Principal component analysis table of humidity index, greenness index, heat index, and dryness index.

Principal Component	1989	2000	2010	2022
PC1	75.45%	70.01%	78.00%	76.58%
PC2	15.00%	17.60%	11.54%	12.11%
PC3	7.31%	9.93%	8.82%	10.72%
PC4	2.24%	2.46%	1.65%	0.60%

Table 6

Average and level of RSEI changes in Xiamen, Zhangzhou, and Quanzhou from 1989 to 2022.

City	1989–2000		2000–2010		2010–2022		1989–2022
	Average	Level	Average	Level	Average	Level	Average	Level
Xiamen	−0.0854	obviously worse	−0.0499	slightly worse	0.0455	slightly better	−0.0897	obviously worse
Zhangzhou	−0.0379	slightly worse	0.0640	obviously better	0.0258	slightly better	0.0519	obviously better
Quanzhou	−0.0258	slightly worse	−0.0597	obviously worse	0.0459	slightly better	−0.0396	slightly worse

Table 7

Results of single factors detection.

Driving Factors	In 2000		In 2010		In 2022
	q	p	q	p	q	p
FVC	0.331	0.00	0.362	0.00	0.266	0.00
DEM	0.192	0.00	0.286	0.00	0.322	0.00
Pre	0.106	0.00	0.175	0.00	0.216	0.00
Temp	0.191	0.00	0.194	0.00	0.533	1.00
LandScan	0.135	0.00	0.278	0.00	0.393	0.00
LUCC	0.263	0.00	0.449	0.00	0.518	0.00

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