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Article

Remote Sensing Insights into Urban–Rural Imbalance and Sustainable Development: A Case Study in Guangdong, China

by
Fushan Zhang
,
Qingling Zhang
* and
Minduan Xu
School of Aeronautics and Astronautics, Sun Yat-Sen University, Shenzhen Campus, Guangming District, Shenzhen 518107, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(5), 2247; https://doi.org/10.3390/su17052247
Submission received: 26 December 2024 / Revised: 3 March 2025 / Accepted: 3 March 2025 / Published: 5 March 2025
(This article belongs to the Section Sustainable Urban and Rural Development)
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Abstract

:
Urbanization challenges city sustainability by aggravating uneven population migration and land exploitation. Understanding the characteristics and dynamics of this imbalance is crucial for promoting sustainable development. With a focus on population-related land change, this study analyzes the urban–rural imbalance characterized by settlement expansion from 1985 to 2019, using nighttime light (NTL) remote sensing imagery and global settlement distribution data, with Guangdong province, China, as a case study. The key findings reveal significant spatiotemporal differences in settlement expansion between the urban and rural regions. The urban settlements experienced faster expansion from 1985 to 2005, which slowed post-2005, while the rural settlements maintained a stable growth rate throughout the study period. The economic and environmental conditions were identified as major drivers of expansion diversity, with economic factors playing a dominant role in the urban regions and both factors influencing the rural regions. A linear regression analysis highlighted the diverse quantity relationships between the urban and rural settlements across different spatial extents; the urban settlements dominated in quantity at the provincial level, primarily due to the contributions of the core Pearl River Delta (PRD) region. In contrast, the rural settlements outnumbered the urban ones in most of the other prefectures, a trend that continued to deepen across Guangdong province. The findings of this study provide deeper insights into the characteristics and evolvement of the urban–rural imbalance, policy implications and actionable strategies are offered for equitable and sustainable city development.

1. Introduction

Urbanization has been a global trend for decades, serving as a driving force for national economies and social advancement [1,2]. However, the changes brought about by urbanization have raised significant concerns regarding their adverse impacts on sustainability. From an environmental perspective, unregulated urban sprawl encroaches on natural and arable lands, undermining ecological and agricultural stability [3,4]. Urbanites’ demand for new natural resources is now among the most important drivers of environmental threats [5,6]. From a social perspective, uneven urban growth exacerbates inequalities between urban and rural areas, posing a major challenge to sustainable development and social equity [7,8]. Consequently, investigating the patterns and characteristics of the urban–rural imbalance in terms of physical expansion and demographic change is critical for understanding the processes and current state of these imbalances. Such knowledge is essential for formulating strategies to achieve sustainable development.
It is generally accepted that economic growth promotes industrial flourish and urban expansion [9]. However, the unregulated and uneven allocation of development resources can lead to significant regional inequalities. For instance, China’s early strategy of prioritizing manufacturing hubs, particularly in coastal regions, accelerated economic growth but failed to ensure balanced development across all areas [10]; Guangdong province, a key industrial center and economic powerhouse in China, exemplifies this trend. The majority of its development has been concentrated in the core area of the Pearl River Delta (PRD), resulting in pronounced regional disparities within the province [11,12].
Sustainability is a broad and multidimensional concept [8], and various measurement approaches have been proposed to better understand the impact of urbanization on sustainability. Existing research primarily examines urbanization’s effects through three perspectives. The first perspective focuses on changes in the quality of natural resources—such as air, water, and soil [13,14,15]—resulting from urban construction, aiming to assess the degradation of resource sustainability. The second perspective evaluates spatial attributes of urban areas, such as population density [16,17] and urban form compactness [18,19], to infer energy consumption and greenhouse gas emissions that are influenced by factors like land-saving and commuting distances. The third perspective compares social disparities between urban and rural regions, including employment, infrastructure, and healthcare access [20,21]. However, the first two perspectives primarily quantify sustainability impacts within urban areas, often overlooking the joint effects of rural areas. Similarly, the third perspective tends to neglect the natural sustainability dimension. Therefore, an approach that considers the environmental and social impacts of urbanization on sustainability is essential.
Many proxies and indices are employed to analyze urbanization’s impact on sustainability. The most extensively used socio-economic indicators, however, face some limitations. Temporally, the infrequent updates of census data result in a coarse temporal resolution, making it unsuitable for analyzing rapidly developing regions. Spatially, socio-economic data are often aggregated at administrative levels, e.g., prefectures or counties, neglecting minor changes and obscuring detailed expansion patterns. To avoid these limitations, some studies have turned to extracting the impervious surface area (ISA) from remote sensing images. Since the V-I-S (vegetation, impervious surface, and soil) model was proposed [22] to explain the composition of urban landscapes in multi-spectral images, much progress has been made. However, the pixel-by-pixel-level estimation of the ISA means that low-resolution satellite images struggle to accurately capture expansion in small-scale urban and rural areas, and often underestimate minor developments. Moreover, the ISA cannot differentiate between land-use types, e.g., residential, commercial and industrial land, without support from auxiliary data. As a result, the estimated ISA expansion cannot be accurately correlated with changes in the population. Recent studies such as Microsoft’s Global ML Building dataset [23] and GLAMOUR [24] present the urban morphology using high-precision building footprints. However, these datasets either lack coverage in rapidly urbanizing regions, e.g., China and parts of Africa, or exclude unurbanized areas critical for inequality studies. Furthermore, they often rely on single-timepoint images, missing temporal variations essential for dynamic analysis. Settlements inherently link built-up land and the population, each significantly influencing sustainability—built-up land environmentally, and population socially [25,26]. Therefore, the dynamics of settlement expansion are a suitable proxy to reflect the urban–rural imbalance and its subsequent impact on sustainability.
The criterion for delineating urban and rural regions is critical, as the dynamics of settlement expansion are measured within these defined areas. However, urban–rural classification lacks a unified standard due to the widespread urban–rural disparities. Previous studies have used indicators such as the building density, population, income differences, or energy consumption to classify urban and rural areas [27,28,29,30]. However, these descriptors often develop from a single or limited angle, leading to misclassification in complex circumstances. The urban–rural fringe (URF), conceptualized as a transitional zone between urban and rural areas, presents additional challenges. Its identification is highly sensitive to land-use changes and socio-economic shifts, which vary widely across different spatial configurations and urbanization patterns [31]. These complexities hinder its reliability and transferability for urban–rural classification. Nighttime light (NTL) data, captured by satellite sensors, have been widely used to study human activity dynamics due to their global coverage and sensitivity to faint light sources [32,33]. The urban extent can be distinguished from its rural background by the disparity in the NTL intensity [34,35]. Nevertheless, in practice, NTL data cannot accurately identify residential lighting in urban and rural areas. In urban areas, the bright lights emitted by commercial districts and industrial activities can lead to overestimations of the economic or residential distribution, distorting the actual ground conditions. In rural areas, disparities in electrification can obscure villages without access to electricity and underestimate agricultural or economic activities that do not rely on electric lighting. Combining stable NTL data with pixel-level global settlement distribution data improves the accuracy and reliability of classifying settlement distribution in urban and rural areas.
In summary, this paper investigates the spatiotemporal characteristics and dynamics of settlement expansion in urban and rural regions of Guangdong province by integrating global settlement distribution data with NTL remote sensing imagery. The findings can enhance our understanding of the characteristics and patterns of the urban–rural imbalance, which pose significant challenges to sustainable development environmentally and socially.

2. Data

2.1. Study Area

Guangdong is a coastal province located in South China known for its advancements in manufacturing and industry. Guangdong province consists of 21 municipal prefectures and can be divided into three functional zones according to the “one Core, one Belt and one District” regional coordinated development strategy proposed by the provincial government in 2019, based on the current development status and regional characteristics of each prefecture [36]. The ‘one Core’ functions as the core area for the economic and technological development of Guangdong province; it includes Guangzhou, Shenzhen, Dongguan, Foshan, Zhongshan, Zhuhai, Huizhou, Jiangmen, and Zhaoqing. This subzone forms the PRD, a world-famous processing and manufacturing hub with a high economy level that relies heavily on exogenous investment [37]. The ‘one Belt’ acts as the coastal district with advantages in commercial and industrial activity; it consists of Shanwei, Jieyang, Shantou, Chaozhou, and Zhanjiang, and Maoming, and Yangjiang, which are located in the eastern and western wings of Guangdong, respectively. The ‘one District’ is mainly responsible for the ecological remediation and natural resources preservation of the province; it includes Yunfu, Qingyuan, Shaoguan, Heyuan, and Meizhou in northern Guangdong, with a relatively under-developed condition. The heterogeneity in its development level makes Guangdong a suitable case for imbalanced development exploration. The geographic location and subzone division are presented in Figure 1.

2.2. Data Source

2.2.1. World Settlement Footprint

WSF-Evolution and WSF2019 are global settlement distribution layers developed by the German Aerospace Center, the European Space Agency, and Google in 2021 as part of the WSF suite. WSF-Evolution is built on the earlier WSF2015 dataset [38], which assumes that settlement dynamics remain relatively stable over time. By applying adaptive thresholding and morphological filtering to WSF2015, WSF-Evolution delineates global settlement distribution annually from 1985 to 2015 at a 30 m resolution. Note that the method used by WSF-Evolution cannot address the case where settlement shrinkage occurs, although this phenomenon is minor compared to the global trend of urbanization. Similarly, WSF2019 is based on the same assumptions as WSF2015 but incorporates temporal statistics derived from Sentinel-1 and Sentinel-2 data. It has a 10 m resolution for settlement distribution in the year 2019. In view of the temporal length of WSF data, a nearly 35-year span composed of eight evaluation years, including seven from 1985 to 2015 with a 5-year interval and the year 2019, is set as the study period.

2.2.2. DMSP-OLS

The NTL data used in this paper are derived from the Defense Meteorological Satellite Program (DMSP)-Operational Linescan System (OLS). The DMSP-OLS dataset is a composite of annual cloud-free observations with six satellites, including F10, F12, F14, F15, F16, and F18, spanning from 1992 to 2013 [39]. Through a series of processing steps, the stable light product of Version 4 DMSP-OLS has ephemeral light removed and only includes stable radiance from cities, villages, and other sites with stable lighting. The spatial resolution of stable light product is a 30 arc second (equals 1 km in equator) and digital number (DN) of each pixel ranging from 0 to 63 [39], representing the range from darkness to the highest NTL intensity. The stable NTL image is able to reflect the variation in anthropogenic activities at night, and it has been widely adopted in many research fields, such as urban expansion, economic evaluation, energy consumption, population density, and disaster assessment [40,41,42,43,44].

3. Methods

The identification of spatiotemporal dynamics and quantity relationships of settlements in urban and rural regions requires knowledge of settlement distribution within defined spatial ranges at each temporal stage. Therefore, it is critical to determine the extent of these regions and the presence of settlements over time. Our framework consists of three steps: (1) Temporal Settlement Extraction: Each pixel in the WSF dataset is assigned a four-digit value during production, indicating the earliest year when the corresponding settlement is detected. Settlements for each temporal stage can be extracted by filtering pixels with values less than or equal to a given year. (2) Urban–Rural Division: Urban and rural regions are delineated by thresholding the DN values of NTL pixels. A pre-defined threshold serves as the classification standard, with pixels above or below the threshold categorized as urban or rural, respectively. The extents of NTL pixels in each group are extracted as masks, which are then applied to the WSF data to classify settlements into urban and rural regions. (3) Quantitative Analysis: The quantity, expansion rate, and linear relationships of settlements are calculated and modeled based on the number of urban and rural settlements in each evaluation year. All the above processing steps are conducted in WGS84 projection on Google Earth Engine [45], where WSF and DMSP-OLS data are available. Particularly, the WFS-Evolution dataset is forced to compute at 10 m resolution to align with WSF2019 and enhance pixel precision. DMSP-OLS images are preprocessed by National Oceanic and Atmospheric Administration’s National Geophysical Data Center, with denoising and necessary correction performed.

3.1. NTL-Based Urban–Rural Classification

Despite several measures having been proposed to identify ideal thresholds for urban–rural classification [40,46,47], they cannot be directly applied to other cases due to differences in spatial scale, socio-economic structure, and other factors. In this study, we adopt a simple method by using the median value of the full digital number (DN) range as the threshold for separating urban and rural areas. Although the absence of on-board calibration makes raw DMSP-OLS images discontinuous and incomparable [48,49,50], mono-temporal pixels from the same satellite share identical influencing factors, ensuring that the captured radiance undergoes the same transformation and maintains consistent relativity with ground lighting. Except for limited core areas exhibiting saturation phenomena [50], the median value of the full DN range serves as a threshold that equally divides nighttime light (NTL) intensity into higher and lower halves based on the original radiance. Consequently, areas with NTL intensity above the median threshold are highly likely to belong to urban regions, while the remaining areas are predominantly rural. Although the classification results may deviate from the actual extent of urban and rural regions, the settlement number in misclassified regions has minimal impact compared to the demarcation of the entire study area.
The NTL distribution of the 2003 annual image taken by satellite F15 is chosen as the reference for the following reasons: (1) The year 2003 lies in the middle of the WSF time series, minimizing the cumulative variation in settlement distribution over the study period, as demonstrated in previous studies [50,51]. (2) Compared to other annual images, the 2003 image records a wider DN range across the study region, making it a common choice in related research [49,52,53]. (3) The cloud-free observation data used to composite the annual image can be examined and compared between satellites. In this case, image F152003 exhibits superior quality compared to image F142003.

3.2. Indicator for Imbalanced Expansion

Long-term population shifts significantly influence settlement expansion. To examine the rate of settlement expansion in urban and rural regions over different temporal stages, we employ a metric termed the periodic settlement expansion rate (PER), calculated as follows:
P E R r = S t + 5 − S t S t
where P E R r refers to periodic expansion rate for settlements in region r during each 5-year span, r can be urban or rural region of respective prefecture, S t and S t + 5 individually represent settlement number in current evaluation year and next evaluation year. Using PER, dynamics and trend of settlement expansion can be identified.

3.3. Indicator for Imbalanced Degree

Urbanization leads to imbalanced population migration and land exploitation between urban and rural regions, resulting in disparities in their respective settlement quantities. To quantify this relationship as an indicator of imbalance, a linear regression model is developed to describe the relationship, as follows:
S r u r = b + a × S u b
where S r u r and S u b are, respectively, number of rural settlements and urban settlements of same year, and b and a are the intercept and slope of the linear regression model. The slope describes the quantity ratio of rural settlements to urban settlements. A ratio of 1 indicates equal urban–rural development in terms of settlement quantity, a smaller ratio indicates quantity dominance of urban settlements, and vice versa.

4. Results

4.1. Pattern of Settlement Distribution on NTL Intensity

The distribution of settlements based on the NTL intensity in each temporal stage at the municipal and provincial level are obtained using the framework described in Section 3, presented in a line-style histogram with different colors for clarity, with abscissa and ordinates representing the DN range of the NTL image and settlement number in each DN value, respectively. The settlement number in DN = 2 consists of all the settlements in the regions with a lower NTL intensity, as 2 is the minimal radiance response of image F152003 over Guangdong province.
The settlement distribution based on the NTL intensity at the provincial level is positioned at the top of Figure 2, marked as Figure 2a. The peaks at both ends of Figure 2a indicate that the settlements in Guangdong province are predominantly concentrated in areas with DN = 2~10 and DN = 55~63. The increment between the annual lines of the two peaks show that settlement expansion occurred differently in the two regions. For the right peak, where the urban region is largely located, the fastest growth occurred between 1990 and 1995 (yellow area); then, the area with the above colors gradually shrinks, indicating a transformation from a slower pace for urban settlements after 1995 to near-stagnation after 2005. In contrast, for the left peak, where the rural region is largely located, a gradual increase persists even after 2005. The most significant gap in the left peak appears between 2015 and 2019. This phenomenon also occurs among prefectures, and we attribute the gap to the improved resolution of WSF2019, which enables the detection of previously undetected rural settlements, rather than providing evidence of rural revitalization post-2015.
At the municipal level, prefectures are classified into three categories based on their histogram form: left-peaking, right-peaking, and fluctuation. The representative prefecture of each category is demonstrated in Figure 2, below subplot (a). Figure 2b demonstrates the form of right-peaking, characteristic of economically advanced prefectures in the core PRD, including Guangzhou, Shenzhen, Dongguan, Foshan, Zhongshan, and Zhuhai. Right-peaking is a reasonable structure for prefectures with a high urbanization level, where most settlements concentrated in the urban areas emit high-intensity light, reflecting imbalanced expansion favoring the urban region. Figure 2c illustrates the form of fluctuation, the category that includes Jiangmen, Huizhou, Chaozhou, and Shantou. Here, settlements cluster at multiple DN values without dominant aggregation, indicating a relatively balanced distribution between the urban and rural settlements. Figure 2d shows the form of left-peaking, which comprises Zhanjiang, Maoming, Yangjiang, Yunfu, Zhaoqing, Qingyuan, Shaoguan, Heyuan, Meizhou, Jiayang, and Shanwei. The settlements in these prefectures are primarily located in areas with a low NTL intensity, with minimal presence in the high-intensity range. This pattern reflects inadequate high-intensity lighting around settlements, driving settlement expansion predominantly in the rural region. The location of the prefectures in each category are mapped in Figure 3.

4.2. Characteristics and Evolving Pattern of Expansion Rate

First, the spatiotemporal characteristics of the imbalanced settlement expansion in Guangdong province are analyzed by comparing the cumulative PER throughout the study period. The PER is evaluated using Equation (1) and visualized in Figure 4 using a stacked bar chart. Each colored section of the stacked bar represents the PER during a specific temporal period. A taller bar indicates that the cumulative PER for the corresponding region is greater than that of the other region. The comparison of the bar heights reveals that, for most prefectures, the cumulative PER of the urban region exceeds that of the rural region, reflecting the dominance of the expansion rate of urban settlements in the spatial dimension. The prefectures with higher bars for rural regions include Guangzhou, Shenzhen, Foshan, Zhanjiang, Maoming, Shaoguan, and Shantou. The first three prefectures are highly urbanized with limited space for further settlement expansion, explaining the faster expansion of rural settlements. Notably, Zhanjiang, Maoming, Shaoguan, and Shantou are located in the western, northern, and eastern peripheries of Guangdong province, respectively. We believe that this spatial pattern suggests a shift in settlement expansion away from the provincial economic center.
The color proportions of the stacked bars reveal another trend: for most prefectures, the blue and green segments (1985–1995) dominate the urban bars, while the colors representing the post-2005 periods are nearly indiscernible. This temporal imbalance highlights the rapid urban expansion from 1985 to 1995, followed by a slowdown after 2005. In contrast, the rural bars show a more balanced color distribution, indicating steady rural expansion throughout the study period. It is worth noting that, as settlement quantities increase over time, this stable rural expansion may lead to a numerical advantage of rural settlements over urban settlements in some prefectures by the later stages of the study period.
Second, the dynamics patterns of urban and rural settlement expansion are individually categorized by differentiating the variation in the PER throughout the study period. Based on the spatial distribution of the prefectures with diverse expansion patterns, the main driving factors for expansion in different regions are proposed.
The PER dynamics for rural settlements at the municipal level are illustrated in Figure 5, from which three distinct patterns are identified: The first pattern features a declining trend with noticeable fluctuations, including at least one significant PER rise before 2000. The prefectures exhibiting this pattern—Guangzhou, Foshan, Dongguan, Zhongshan, Zhuhai, Huizhou, and Jiangmen—are all located in the PRD and have advanced urbanization levels. Despite their right-peaking settlement distribution, these prefectures experienced periodic rural settlement expansion, particularly from 1995 to 2000, suggesting that rural settlements can experience expansion through the influence of urbanization. The second pattern shows a steady decline with minimal fluctuations. The prefectures in this category include Shenzhen, Maoming, Zhanjiang, Yangjiang, Yunfu, Shantou, Jieyang, and Shanwei. Their PER typically starts around 0.4, with the exception of Shenzhen, the PER of which starts at 1.6, due to its special role in economic reforms [54]. This pattern indicates the gradual decrease in the PER for rural settlements in coastal prefectures. The last pattern is characterized by a PER fluctuating around 0.2 throughout the study period, with at least one significant rise. Prefectures such as Zhaoqing, Qingyuan, Shaoguan, Heyuan, Meizhou, and Chaozhou exhibit this trend. Although Chaozhou’s PER dynamics resemble the second pattern, it is categorized here due to its consistently low PER. The last pattern suggests that the PER of rural settlements has always been low in prefectures with relatively poor economic conditions.
The spatial distribution of these PER patterns is mapped in Figure 6. The similarity between the pattern distribution and municipal functional zoning suggests local economic and environmental conditions are key criteria for rural settlement expansion.
The PER dynamics for urban settlements at the municipal level are illustrated in Figure 7. For the urban region, two distinct patterns are identified based on the dynamics of the PER. The fist pattern features a sharp decline before 1995, followed by a gradual decrease. Prefectures exhibiting this trend include Shenzhen, Dongguan, Heyuan, Huizhou, Yangjiang, Foshan, Zhongshan, and Zhuhai. An initial PER greater than 0.8 is the classification criterion. The rest of the prefectures are categorized into the second pattern, characterized by a relatively steady decline in the PER throughout the study period. A high PER during the first temporal stage does not necessarily equate to a high settlement number, as the initial quantity also matters. For example, Heyuan’s high PER in the first temporal stage is attributed to the extremely low urban settlement number in that period.
The expansion patterns for the urban settlements are mapped in Figure 8. The prefectures with a greater expansion rate before 1995 are mainly concentrated around the PRD region, indicating urban settlement expansion is strongly influenced by regional economic conditions.

4.3. Relationship Between Settlement Expansion

Equation (2) is modeled using linear regression at both the municipal and provincial levels. All of the parameters are listed in Table 1, with column (b) showing the intercepts and column (a) showing the slopes. Notably, the intercepts have high magnitudes due to the large population sizes. The slope, a key feature of the linear relationship, indicates, for each settlement built in the urban region, how many settlements are built in the rural region.
To validate the results, we compare them with real-world data from the China Urban-Rural Construction Statistical Yearbook 2019, which provides urban-specific statistics for prefectures. We calculate the ratio of the non-urban population (PDP-UP) to the urban population (UP), as settlements are a reliable predictor of the population distribution [55]. These population ratios are listed in the last column of Table 1. For slopes below 1, the values align closely with the population ratios. However, for slopes above 1, the values exceed the population ratios. This discrepancy can be explained by the large proportion of rural laborers who maintain settlements in their hometowns while temporarily migrating to economically advanced prefectures, resulting in lower population ratios but higher settlement ratios.
To comprehensively analyze urban–rural imbalance across Guangdong province, Figure 9 illustrates the expansion trajectories of urban and rural settlements for each prefecture over the study period. The red dots represent the eight evaluation years, with coordinates indicating the number of settlements in the urban and rural regions for each year. The blue trend lines represent the linear relationships. At the provincial level (last subplot of Figure 9), the slope indicates an initial advantage in the urban settlement number. However, the upward turn of the dots after 2005 suggests faster rural settlement growth, which is consistent with the findings in Section 4.2. At the municipal level, prefectures within the same functional zone and spatial proximity exhibit similar trends. The trend lines for most of the PRD prefectures incline horizontally, reflecting the quantity dominance of urban settlements. Shenzhen and Dongguan, with extremely low ratios, indicate that nearly all residents reside in high-intensity urban regions. The trend lines for the other prefectures incline vertically, indicating the quantity dominance of rural settlements. Compared to the finding in Section 4.2, the cumulative PER is greater in the urban region of most of the prefectures, revealing a contrast between the expansion speed and quantity. Most prefectures follow the provincial-level pattern, with the diminishing distances between dots indicating a slowdown in settlement expansion. Exceptions include Huizhou and Jiangmen (the second row), which exhibit relatively stable expansion over time. The slope values are mapped in Figure 10, revealing a radial pattern with the smallest values in the core PRD and increasing outward toward the peripheries.
Figure 11 presents all lines in a single plane, with an additional dashed line representing an even urban–rural settlement expansion. The prefectures are grouped based on their settlement distribution pattern based on the NTL intensity: on the left side, right side, and parallel to the dashed line. Prefectures with trend lines parallel to the dashed line exhibit balanced urban–rural expansion. On the left side of the dashed line, Meizhou and Jieyang exhibit smaller slope values, indicating a faster settlement growth in urban regions in the left-peaking category. On the right side of the dashed line, Guangdong and Foshan show contrasting trends of faster settlement growth in rural regions among the right-peaking category.

5. Discussion

As urbanization continues to widen the urban–rural disparity, knowledge regarding the dynamics and patterns of the urban–rural imbalance is critical to city sustainability from environmental and social perspectives.
With a specific focus on population-related change, the urban–rural imbalance characterized by settlement expansion from 1985 to 2019 is analyzed, using nighttime light (NTL) remote sensing imagery and the global settlement distribution layer, with Guangdong province, China, as a case study. Different distribution patterns, expansion dynamics, and quantity linear relationships are identified for settlements in urban and rural regions, classified by their difference in NTL intensity. The findings show a significant temporal distinction for settlement expansion in urban and rural regions. The urban settlements experienced a faster expansion rate from 1985 to 2005 and slowed post-2005, while the rural settlements kept expanding at a stable pace. Economic and environmental conditions are attributed as major influencing factors of expansion diversity, both for rural settlements and the former for urban settlements. The linear regression analysis reveals diverse quantity relationships for urban and rural settlements on different spatial scales. Urban settlements dominate in terms of quantity at the provincial level, mainly due to the contribution of the core Pearl River Delta (PRD) region; rural settlements outnumbered urban settlements in the rest of the prefectures, and the trajectory of the scatter plots shows that the trend continues to deepen. Spatially, the slope values of the linear regression at the municipal level show a radial pattern over Guangdong province, with an increasing trend from the core PRD to the provincial peripheries.
The findings of this study have important policy implications for promoting balanced urban–rural development and sustainable growth. The observed shift in settlement expansion post-2005 in most prefectures suggests that policies should focus on promoting rural economic development to cope with the steady growth of rural settlements, which demands matching living standards and development levels reflected by the coverage of high-intensity NTL. This can be achieved through targeted investments in rural infrastructure, such as education, healthcare, especially transportation, to improve the quality of rural life and provide higher accessibility to public service. In addition, the development of rural industries and the diversification of the rural economy should be encouraged to reduce the reliance on PRD for employment and economic growth. Meanwhile, for urban regions, policies should focus on compact city planning and expansion management to mitigate sustainability impairment from unregulated urbanization. Additionally, as most Guangdong prefectures with high urbanization levels are manufacturing-intensive, the use of renewable energy should be encouraged to reduce carbon emissions, since the manufacturing industry consumes large amounts of energy, negatively impacting sustainability.
The implementation of the above policy recommendations could potentially boost economic and social development in rural areas and decrease the urban–rural disparity across Guangdong province. Optimized urban planning in terms of land expansion and energy consumption could aid in the promotion of city sustainability. By adopting respective planning methods to urban–rural regions, policymakers can address the challenges of regional imbalance and take a closer step toward sustainable development.

6. Future Works

The objective of this study is to enhance the understanding of imbalanced urban–rural expansion, a phenomenon detrimental to environmental and social sustainability due to uneven population migration and land exploitation. By integrating NTL remote sensing imagery and global settlement data, with Guangdong province, China, as a case study, we demonstrate the characteristics and patterns of settlement expansion and model the linear relationship between urban and rural settlement quantities to quantitatively assess the degree of imbalance. Our proposed framework advances previous research by systematically considering both related land-use dynamics and demographic shifts, given settlements’ inherent connection to built-up land and population. The use of remote sensing data enhances the timeliness and accuracy of our findings compared to studies relying solely on socio-economic indices.
Despite its advantages, our study still contains several limitations that warrant further exploration. First, calibrating NTL images and determining optimal thresholds for urban–rural classification should be prioritized, as the spatial extents of these regions evolve over time. Second, integrating auxiliary data sources, such as high-resolution satellite imagery, e.g., QuickBird and Sentinel-2, as well as socio-economic statistics, could improve the classification accuracy. Diurnal satellite imagery can provide detailed ground configurations, facilitating urban–rural discrimination. Derivative remote sensing datasets, such as GlobalLand30 and CORINE Land Cover, can offer comprehensive land-use information with multiple categories. Spectral indices like NDVI and EVI can further enhance classification by incorporating vegetation coverage, adding an ecological dimension to the analysis. Additionally, socio-economic data can help differentiate regions with varying economic intensities.
Urbanization, as a global phenomenon, continues to draw significant attention for its impact on environment and sustainability. Aligned with the United Nations’ Sustainable Development Goals (SDGs), our methodology can be applied to other rapidly urbanizing regions, particularly in Africa and Southeast Asia, to provide timely and reliable analyses of urban–rural development imbalances. The effectiveness of our model can be validated by comparing the results with demographic census data or land-use survey information, ensuring alignment with historical records and current trends in respective regions.

7. Conclusions

The imbalanced development of urbanization poses significant environmental and social challenges to sustainability. This study analyzes the dynamics and patterns of urban–rural expansion in Guangdong, China, from 1985 to 2019, using nighttime light (NTL) remote sensing imagery and global settlement distribution data. By classifying urban and rural regions based on disparities in NTL intensity, we identify distinct settlement distribution patterns that reflect varying urbanization levels. Urban settlements, predominantly concentrated in the Pearl River Delta (PRD) region, initially experienced rapid expansion from 1985 to 2005 but slowed significantly after 2005. In contrast, the rural settlements expanded at a relatively stable pace throughout the study period. Economic and environmental conditions are identified as major drivers of expansion diversity, with economic factors playing a dominant role in urban areas and both factors influencing rural areas. The linear regression analysis reveals diverse quantity relationships between urban and rural settlements across different spatial scales. At the provincial level, urban settlements dominate in quantity, primarily due to the contributions of the core PRD region. In contrast, the rural settlements outnumber urban ones in most other prefectures—a trend that continues to deepen over time. Spatially, the slope values of linear regression at the municipal level exhibit a radial pattern across Guangdong province, increasing from the core PRD region toward the provincial peripheries.
The findings of this study provide valuable insights for researchers and policymakers into the characteristics and evolution of the urban–rural imbalance in Guangdong. The identified patterns and trends can inform policies and planning strategies to promote equitable and sustainable development. Future research could enhance urban–rural classification methods to improve the analytical accuracy, while the transferability of the proposed methodology opens opportunities for global-scale applications.

Author Contributions

Conceptualization, F.Z. and Q.Z.; methodology, F.Z.; software, F.Z. and M.X.; validation, F.Z. and Q.Z.; formal analysis, F.Z.; investigation, F.Z. and Q.Z.; resources, F.Z. and M.X.; data curation, F.Z.; writing—original draft preparation, F.Z.; writing—review and editing, Q.Z.; visualization, F.Z. and M.X.; supervision, Q.Z.; project administration, Q.Z.; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program of China (No. 2022YFE0209300). The funder is the corresponding author, Qingling Zhang. The funding period for this project is from 1 November 2022 to 31 October 2025.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

WSF-Evolution and WSF-2019 are publicly available at https://gee-community-catalog.org/projects/wsf (accessed on 4 March 2025). The DMSP-OLS dataset can be found on the Google Earth Engine platform.

Acknowledgments

We are grateful for all the experts who provided us with project insights and information.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Functional zoning and geographical location of Guangdong province.
Figure 1. Functional zoning and geographical location of Guangdong province.
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Figure 2. Representative forms of settlement distribution based on NTL intensity. Notes: The abscissa and ordinates in (a–d) represent the DN range of the NTL image and settlement number in each DN value, respectively. Area of different colors represents settlement quantity growth during corresponding temporal stage.
Figure 2. Representative forms of settlement distribution based on NTL intensity. Notes: The abscissa and ordinates in (a–d) represent the DN range of the NTL image and settlement number in each DN value, respectively. Area of different colors represents settlement quantity growth during corresponding temporal stage.
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Figure 3. Prefecture with different forms of settlement distribution based on NTL intensity.
Figure 3. Prefecture with different forms of settlement distribution based on NTL intensity.
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Figure 4. Cumulative PER at municipal level. Notes: The abscissa labels ‘rur’ and ‘ub’ represent rural and urban, respectively. The ordinate represents cumulative PER of settlements in corresponding regions, PER in each temporal stage is displayed in different color.
Figure 4. Cumulative PER at municipal level. Notes: The abscissa labels ‘rur’ and ‘ub’ represent rural and urban, respectively. The ordinate represents cumulative PER of settlements in corresponding regions, PER in each temporal stage is displayed in different color.
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Figure 5. Expansion patterns of rural settlements at municipal level. Notes: The ordinates represent settlement expansion rate in each corresponding temporal stage. Different colors are used to differentiate the subplots as many prefectures exhibit similar patterns.
Figure 5. Expansion patterns of rural settlements at municipal level. Notes: The ordinates represent settlement expansion rate in each corresponding temporal stage. Different colors are used to differentiate the subplots as many prefectures exhibit similar patterns.
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Figure 6. Expansion patterns for rural settlements at municipal level.
Figure 6. Expansion patterns for rural settlements at municipal level.
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Figure 7. Expansion patterns of urban settlements at municipal level. Notes: The ordinates represent settlement expansion rate in each corresponding temporal stage. Different colors are used to differentiate the subplots as many prefectures exhibit similar patterns.
Figure 7. Expansion patterns of urban settlements at municipal level. Notes: The ordinates represent settlement expansion rate in each corresponding temporal stage. Different colors are used to differentiate the subplots as many prefectures exhibit similar patterns.
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Figure 8. Expansion patterns for urban settlements at municipal level.
Figure 8. Expansion patterns for urban settlements at municipal level.
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Figure 9. Expansion trajectories and trend lines of settlements in different scales.
Figure 9. Expansion trajectories and trend lines of settlements in different scales.
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Figure 10. Spatial distribution of slope values from linear regression analysis.
Figure 10. Spatial distribution of slope values from linear regression analysis.
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Figure 11. Comparison of settlement expansion at municipal level.
Figure 11. Comparison of settlement expansion at municipal level.
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Table 1. Parameters of linear regression model and ratio of non-urban to urban population for each prefecture. Column (a) shows the slope of regression line and column (b) shows the intercept, which is represented in conventional units.
Table 1. Parameters of linear regression model and ratio of non-urban to urban population for each prefecture. Column (a) shows the slope of regression line and column (b) shows the intercept, which is represented in conventional units.
Study RegionLinear Regression Model Parameter(PDP-UP)/UP
ba
Guangdong province52.597 × 1050.7110.919
Guangzhou−3.012 × 1050.3280.395
Shenzhen−6.657 × 1030.0161.5 × 10−4
Dongguan−1.265 × 1040.0171.8 × 10−3
Foshan−2.869 × 1050.2910.168
Zhongshan−1.205 × 1040.1182.45
Zhuhai−2.401 × 1040.5820.22
Jiangmen2.017 × 1051.4010.49
Huizhou−1.595 × 1051.5030.38
Zhaoqing−5.702 × 1043.6481.19
Jieyang3.810 × 1052.3571.50
Chaozhou9.999 × 1041.0681.03
Shantou−2.061 × 1051.0201.30
Shanwei5.394 × 1043.5020.71
Yangjiang2.165 × 1051.3871.19
Zhanjiang−4.711 × 1055.9340.98
Maoming−3.170 × 1055.7742.23
Meizhou7.895 × 1052.8931.2
Qingyuan1.369 × 1053.7091.95
Shaoguan−3.822 × 1054.7490.44
Heyuan2.805 × 1054.3590.22
Yunfu5.361 × 1045.1491.5
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Zhang, F.; Zhang, Q.; Xu, M. Remote Sensing Insights into Urban–Rural Imbalance and Sustainable Development: A Case Study in Guangdong, China. Sustainability 2025, 17, 2247. https://doi.org/10.3390/su17052247

AMA Style

Zhang F, Zhang Q, Xu M. Remote Sensing Insights into Urban–Rural Imbalance and Sustainable Development: A Case Study in Guangdong, China. Sustainability. 2025; 17(5):2247. https://doi.org/10.3390/su17052247

Chicago/Turabian Style

Zhang, Fushan, Qingling Zhang, and Minduan Xu. 2025. "Remote Sensing Insights into Urban–Rural Imbalance and Sustainable Development: A Case Study in Guangdong, China" Sustainability 17, no. 5: 2247. https://doi.org/10.3390/su17052247

APA Style

Zhang, F., Zhang, Q., & Xu, M. (2025). Remote Sensing Insights into Urban–Rural Imbalance and Sustainable Development: A Case Study in Guangdong, China. Sustainability, 17(5), 2247. https://doi.org/10.3390/su17052247

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