Spatial Reconstruction and Economic Vitality Assessment of Historical Towns Using SDGSAT-1 Nighttime Light Imagery and Historical GIS: A Case Study of Suburban Shanghai

Abstract

Historical towns embody the origins and continuity of urban civilization, preserving distinctive spatial fabrics, cultural lineages, and latent economic value within contemporary metropolitan systems. Their integrated conservation directly aligns with SDG 11.4, and advances the holistic preservation objectives of historic urban landscapes (HULs). However, achieving these objectives cannot be solely dependent on modern remote sensing technologies; it necessitates the integration of historical geographic information system (HGIS) theoretical frameworks and methodological approaches. Leveraging HGIS and multisource data—including SDGSAT-1 nighttime light imagery, textual documents, and historical maps—this study reconstructed the spatial extent of historical towns in suburban Shanghai and assessed their present-day economic vitality through light-based spatial proxies. Key results comprised the following. (1) Most suburban historical towns are small, yet nighttime light intensity varies markedly. Jiading County, Songjiang Prefecture, and Jinshan Wei rank highest in both spatial extent and brightness. (2) Town area exhibits a strong positive relationship (R² > 0.80) with the total nighttime light index, indicating that larger settlements generally sustain higher economic activity. (3) Clusters of “low area–low light” towns showed pronounced intra-regional disparities in economic vitality, underscoring the need for targeted revitalization. (4) Natural setting, historical legacy, policy interventions, and transport accessibility jointly shape development trajectories, with policy emerging as the dominant driver. This work demonstrates a transferable framework for multidimensional assessment of historical towns, supports differentiated conservation strategies, and aids the synergistic integration of heritage preservation with regional sustainable development.

1. Introduction

Historical towns serve as vital carriers of living cultural heritage, embodying both tangible records and intangible memories of human civilization evolution. Their preservation and sustainable development have emerged as pivotal issues in global heritage science [1,2,3,4]. In 2015, the United Nations Educational, Scientific and Cultural Organization (UNESCO) released Transforming Our World: The 2030 Agenda for Sustainable Development, proposing 17 Sustainable Development Goals (SDGs) to be achieved by 2030 [5]. That same year, the General Assembly of the States Parties to the World Heritage Convention adopted a policy document integrating sustainable development perspectives into heritage management [6]. Subsequently, in 2017, the protection of cultural and natural heritage was formally incorporated into the global development agenda within the SDG framework and designated a Tier II indicator [7], highlighting the critical role of heritage conservation within the global sustainable development system. In particular, historical urban landscapes—valued for their cultural significance, natural context, and layered historical structures—are increasingly recognized as key assets in SDGs. While early conservation efforts focused on the static preservation of buildings [8,9,10,11], recent approaches emphasize dynamic, adaptive reuse that balances historical integrity with contemporary functionality. This shift reflects a growing consensus that heritage town protection should not only preserve spatial forms but also integrate social and economic considerations to achieve sustainable development [12,13,14].

In China, a long and continuous settlement history has given rise to diverse historical towns, many of which are now embedded in rapidly urbanizing regions. According to the China Historical Geographic Information System (CHGIS) project, over 8000 towns are documented as historical settlements (https://chgis.fas.harvard.edu/), each shaped by unique growth trajectories and socioecological patterns [15]. Under China’s new urbanization strategy, these cultural heritage sites face dual challenges: maintaining historical stratification diversity while responding to intensive land use and high-quality development demands [16,17]. As urbanization accelerates, historical town conservation has entered a critical phase of “connotative enhancement,” urgently requiring innovative pathways integrating holistic preservation with sustainable development.

Current studies predominantly concentrate on architectural conservation [18], cultural gene transmission [19,20], landscape value assessment [21], and cultural tourism development [22,23]. Most existing studies tend to adopt either a historical or contemporary perspective in isolation, lacking a systematic investigation into the internal linkages between the long-term evolution of historical towns and their current development. Although historical townscapes play a significant role in the sustainable development of cultural heritage, quantitative assessments of their economic vitality at the regional scale remain limited, primarily constrained by data availability and spatial resolution. The spatial extent and geographic characteristics of historical towns often differ from those defined by modern administrative boundaries, making it difficult to accurately capture their economic attributes through conventional statistical data. Meanwhile, their deeply embedded cultural identity and historical continuity suggest that development patterns are closely tied to their historical foundations. Therefore, adopting a diachronic perspective to examine the pace and mechanisms of urban development, alongside evaluations of contemporary economic vitality, may offer new insights for future planning and conservation strategies. Against this backdrop, this study proposes a methodological framework that enables the consistent delineation of historical town boundaries and supports multisource spatiotemporal integration, aiming to establish an innovative approach for assessing the vitality of town-based cultural heritage by linking historical value with current development dynamics.

The implementation of the United Nations 2030 Agenda for Sustainable Development faces persistent challenges related to data availability and methodological limitations. In this context, space observation has emerged as an effective means of acquiring large-scale, dynamic, and objective data for monitoring human activities and supporting the evaluation of Sustainable Development Goals (SDGs). Among these approaches, nighttime light (NTL) remote sensing data, characterized by broad spatial coverage and high temporal resolution, has been widely applied to the study of the spatial distribution of regional socioeconomic activities [24]. By capturing the radiative characteristics of human activities, nighttime light remote sensing has become an important tool for characterizing social contexts and generating key variables at meso- and micro-scales. Especially at the urban scale, nighttime light (NTL) data, with its high-precision geolocation capabilities, has been increasingly employed to estimate multidimensional socioeconomic parameters, typically covering domains such as population, economy, energy, and material conditions. NTL data have been used to support population estimation and spatial analysis [25,26,27,28], assess economic indicators and poverty assessment [29,30,31], estimate electricity consumption and carbon emissions [32,33,34], and approximate material stock and housing vacancy rates [35,36]. Notably, in the evaluation of economic indicators, existing studies have shown that the correlation R² value between NTL and GDP exceeds 0.8 and the R² value of poverty assessment reaches approximately 0.85, demonstrating the high applicability of NTL in quantifying regional economic development [37]. However, it should be noted that the strong correlations reported here apply primarily to cross-sectional analyses. Prior studies have shown that NTL data have much lower predictive power in time-series applications, especially at the local scale [38,39]. For historical towns—small-scale areas without clear administrative boundaries where GDP and population data are difficult to obtain through modern administrative statistics—NTL data offer a means to overcome the limitations of conventional surveys constrained by city- or province-level units, mitigating boundary effects that lead to abrupt statistical changes [40] and thereby making it possible to quantitatively assess the economic vitality of different historical towns at different times and provide data support for the evaluation of cultural heritage of the SDGS.

The Sustainable Development Science Satellite 1 (SDGSAT-1), launched on 5 November 2021, is the world’s first scientific satellite dedicated to supporting the implementation of the United Nations 2030 Agenda for Sustainable Development and meeting the demands of global scientific research [41]. SDGSAT-1 is designed to achieve fine-scale characterization of “human activity footprints” and to explore new approaches and techniques for detecting surface environmental elements under low-light nighttime conditions. Its mission supports the development of indicators for human–nature interactions in the context of the SDGs. SDGSAT-1 is equipped with three payloads: the Thermal Infrared Spectrometer (TIS), the Glimmer Imager (GI), and the Multispectral Imager (MI). Notably, the GI sensor offers high radiometric resolution (16-bit) and captures nighttime light data around 21:00 local time on each revisit day. It provides 10 m nighttime imagery with a single panchromatic (PAN) band and 40 m nighttime color imagery in red, green, and blue bands. These capabilities enable precise identification of lighting patterns along secondary urban roads and residential areas. In September 2022, data from SDGSAT-1 were released for global public access, making GI imagery one of the most advanced publicly available nighttime light (NTL) datasets. A previous study [42] utilized Luojia-1 nighttime imagery to conduct large-scale analyses of the economic vitality of historical towns in the Yellow River Basin. However, with a spatial resolution of approximately 130 m and no updates since 2018, Luojia-1 data are limited in both spatial precision and temporal coverage for fine-scale urban studies. In contrast, the GI sensor of SDGSAT-1 significantly surpasses existing products such as DMSP-OLS, NPP-VIIRS, and Luojia-1 in terms of detection sensitivity, light saturation capacity, spectral range, and spatial resolution [43], making it the highest-resolution nighttime imaging system currently available for civilian use [44]. This advancement provides essential technical support for fine-grained studies of historical towns.

In achieving the goal of evaluating the modern economic vitality and development rates of historical towns, in addition to the spatial observation data of NTL, historical maps and textual records are also needed to reconstruct their spatial extent and distribution so as to integrate multisource spatiotemporal data and combine the spatial perspective of geography with the temporal perspective of history. This inspired the use of the H-GIS method [45] as an innovative approach to explore the development of historical towns. This method can expand the analytical dimensions, establish an organic linkage between past and present, geography and culture, and enable deeper exploration of the complex interactions between natural environments and sociocultural factors. Now, a large number of historical data of different fields, scales, and types, such as old maps, images, and historical documents, have been spatialized and digitized [46], which has helped historical research gradually expand from traditional text records to dynamic analysis based in 2D or 3D space. Georeferencing tools in GIS enable precise alignment of Republican-era large-scale topographic maps with today’s digital maps, permitting accurate feature digitization and built-up area extraction through image characterization. These advancements provide methodological foundations for reconstructing historical urban morphology and analysis, particularly when combined with modern remote sensing for historical–contemporary cross-validation.

This study selected historical towns in suburban Shanghai as research samples and reconstructed their historical spatial forms and scales by integrating documentary archives, historical maps, and satellite remote sensing imagery. Based on SDGSAT-1 nighttime light data, nighttime light indices (TNLI/ANLI) are generated, and a classification model for evaluating modern economic vitality is developed through area–light correlation analysis. Through the H-GIS method, the study aimed to identify towns where modern economic vitality is inconsistent with historical scale, explore the underlying mechanisms of development disparities, and provide a quantitative basis for formulating differentiated conservation strategies. It also offers a new approach for dynamic monitoring of the economic vitality of urban cultural heritage and promotes the coordinated development of heritage preservation and regional economies.

2. Study Area and Data

2.1. Study Area

Shanghai is located in eastern China, between 120.9°E–122.2°E and 30.7°N–31.9°N, at the mouth of the Yangtze River facing the Pacific Ocean. As one of China’s pioneering cities in urbanization and the core of the Yangtze River Delta urban agglomeration, it forms the Yangtze River Delta region with neighboring Zhejiang, Jiangsu, and Anhui provinces, recognized as one of China’s most economically dynamic, open, and innovative regions [47]. Through its transformation from a peripheral area in the national political landscape to an international metropolis and national center, Shanghai’s administrative divisions have undergone multiple adjustments. The incorporation of ten counties from Jiangsu Province in 1958 established the fundamental administrative framework that persists today [48]. Since Chongming County was abolished and established as a district in 2016, Shanghai has entered an era without counties. By the end of 2024, Shanghai’s administrative area spanned 6340.5 km², comprising 16 districts.

This study focused on nine suburban districts in Shanghai: Minhang, Baoshan, Jiading, Pudong New Area, Jinshan, Songjiang, Qingpu, Fengxian, and Chongming (Figure 1), selecting historical administrative towns from the Ming and Qing dynasties within their territories as representative cases. These historical towns demonstrate three core features. (1) They retain multilayered cultural traits from ancient layouts to Republican-era relics, exemplifying the spatial transformation from traditional administrative towns to modern county-level cities. (2) Their development reflects shifts in national planning—from 1940s “satellite towns” to post-2000 “new towns”—capturing key stages of China’s suburban urbanization [49,50]. (3) Under new urbanization strategies, they face the dual challenge of preserving cultural authenticity while enhancing regional vitality, highlighting the need for a scientific framework to assess cultural–economic dynamics. As representative cases, Shanghai’s suburban historical towns offer ideal ground for examining endogenous development, cultural inheritance, and urban renewal theory, with both historical depth and real-world significance.

2.2. Data

The research data utilized in this study primarily originate from three categories: historical documents, old map materials, and satellite remote sensing imagery (

Table 1. Data.

Type	Data	Time
Historical documents	Official history, geography, local chronicles, etc.	Ming and Qing Dynasties
Old maps	Large-scale 1:25,000 map of Shanghai	1932
	Shanghai historical atlas	1999
	Complete atlas of Shanghai antiquated maps	2018
Remote sensing imagery	SDGSAT-1 Nighttime Light Data	2025
	Base map satellite imagery from Tianditu	2025

). The historical records from the Ming and Qing dynasties in China provide detailed accounts of the lengths and construction times of the city walls at the county level and above, offering reliable data for reconstructing the scale of these walls. Old maps are primarily based on the 1:25,000 topographic map of Shanghai (1932), and were cross-verified with map materials from the Historical Atlas of Shanghai [51] and the Compilation of Shanghai Urban Maps [52]. The base map imagery used for spatial alignment was sourced from the Tianditu API (National Geographic Information Public Service Platform Map, http://lbs.tianditu.gov.cn/server/MapService.html, accessed on 16 June 2025). The NTL data from the SDGSAT-1 satellite were obtained from the SDG Big Data Platform of the International Research Center of Big Data for Sustainable Development Goals (CBAS) (https://sdg.casearth.cn/datas/SDGSAT, accessed on 16 June 2025), with data collection taking place in January 2025.

3. Methods

Our procedure was mainly divided into four parts: historical urban spatial positioning and measurement, NTL index calculation, modern economic vitality assessment, and influencing factor analysis (Figure 2).

3.1. Restoration of Historical Towns and Spatial Calculation

The spatial extent of historical towns can to a certain degree serve as a reference indicator for exploring urban development during historical periods [53,54]. However, due to the predominance of traditional maps in ancient times, accurately reconstructing the spatial extent of towns from that era is challenging. Researchers commonly employ alternative methods to represent the spatial morphology of historical cities. Among these, city walls, one of the fundamental markers of ancient Chinese cities, have become the most frequently used representative indicator for delineating the spatial extent of historical towns [55]. By the Ming and Qing dynasties, the construction of large-scale city walls had become widespread in Chinese cities. As settlements expanded outward due to population growth, larger city walls were often built. Existing research indicates that 80% of Chinese cities had city walls in the 15th century, and this figure rose to 95% in the 16th century. Therefore, the city walls of the Ming and Qing periods can be regarded as boundaries for delineating the spatial extent of towns. Generally, the closer to the time of the wall’s construction, the more consistent the scale of the walls is with the extent of the town [53]. Although many city walls were demolished during the Republican era and the early years of the People’s Republic—primarily to accommodate road construction [56]—they often survive as morphological traces. Former walls were commonly replaced by ring roads, and the enclosed areas continue to exhibit coherent street networks and relatively continuous historical area. In most suburban counties of Shanghai, post-1950s urbanization has continued to expand outward from the historical walled towns.

To reconstruct the spatial extent of city walls in the towns of Shanghai’s suburbs during the Ming and Qing dynasties, the following steps were undertaken. First, data on the years of wall construction or reconstruction were collected from a vast array of local gazetteers and historical documents. This was supplemented by town data from CHGISto clarify the start and end time nodes of the walls. Second, remote sensing imagery of the Shanghai region from the Tianditu platform was used as a base map. Ground control points such as city gates, road crossings, and streets with consistent names across historical and modern times were selected for spatial alignment with the surveyed Shanghai topographic maps (Figure 3). Finally, based on spatial correction, urban elements were extracted to reconstruct the spatial extent of the towns, and spatial measurements such as area and perimeter were conducted.

3.2. Nighttime Light Index

In this study, nighttime light (NTL) data were used to construct the light-based indices as dependent variables to represent the economic vitality of historical towns. Compared with other commonly used urban vitality proxies—such as Tencent location big data or point-of-interest (POI) datasets [57,58]—NTL data were selected primarily for their accessibility, large-scale applicability, and strong cross-regional comparability [59]. Furthermore, NTL imagery has been widely validated and applied in existing research as a reliable indicator of socioeconomic activity. Among the available NTL datasets, SDGSAT-1 imagery was chosen based on the advantages outlined earlier in the Introduction. Specifically, compared with other nighttime light sources such as DMSP-OLS, NPP-VIIRS, and Luojia-1, the Glimmer Imager (GI) onboard SDGSAT-1 offers several notable benefits: (1) high radiometric resolution (16-bit), which helps reduce saturation effects in brightly lit urban areas; (2) 10 m spatial resolution, enabling detailed spatial delineation at the town scale; and (3) open-source access with ongoing data updates.

Before utilizing the SDGSAT-1 low-light imaging data, radiometric calibration preprocessing is required to convert the original digital number (DN) values into physically meaningful apparent radiance values. This process quantitatively reflects the radiation intensity of NTL data in urban areas. The calibration formula provided by the International Research Center of Big Data for Sustainable Development Goals (CBAS) is as follows

L = DN × Gain + Bias

(1)

In Equation (1), L represents the radiance at the sensor’s aperture, measured in W/m²/sr/μm. The Glimmer Imager (GI) onboard SDGSAT-1 includes one panchromatic and 3 color bands, each with distinct spectral response characteristics. P stands for the panchromatic band, including low gain (panchromatic low, PL) and high gain (panchromatic high, PH). In this study, NTL analysis was conducted using the high-gain panchromatic (PH) band, with the corresponding calibration coefficients (gain and bias) applied accordingly. The absolute radiometric calibration parameters for the GI payload were provided by the Handbook of SDGSAT-1 Satellite Products downloaded from CBAS and are listed in

Table 2. Absolute radiation calibration coefficients of SDGSAT-1 satellite low-light sensor.

Band	Gain	Bias
PL	0.00008832	0.0000167808
PH	0.00008757	0.0000183897
R	0.00001354	0.0000136754
G	0.00000507	0.000006084
B	0.0000099253	0.0000099253

.

To characterize regional NTL patterns and quantitatively analyze regional development, it is necessary to further construct the total nighttime light index (TNLI) and the average nighttime light index (ANLI). The equations are as follows:

$$\begin{gathered} TNLI=\sum _{i=1}^n{L}_i \\ ANLI={\displaystyle \frac{TNLI}{n}} \end{gathered}$$

(2)

In Equation (2), L represents the radiance value of each grid cell within the region, and n denotes the total number of grid cells in the region.

3.3. Identification and Classification of Economic Vitality Based on Nighttime Light Index

Identification of urban built-up area boundaries. NTL indices are generally effective in characterizing the extent of urban built-up areas. Before assessing modern economic vitality using NTL data, it is necessary to conduct spatial buffer analysis outside historical towns and calculate the TNLI to determine whether these towns are located within the urban built-up area.

Correlation analysis between area and TNLI. The size and hierarchical level of historical towns are closely related to their area, while modern NTL indices can indicate the economic vitality and development status of different regions within a city. By conducting an “area–light” correlation analysis, it is possible to assess the influence of historical foundations on the evolution of urban development to a certain extent and reflect differences in the rate of urban development.

This study employed the correlation coefficient R as a numerical feature to quantitatively reflect the relationship between two variables: the area of historical towns and the TNLI.

$$R={\displaystyle \frac{E\left(XY\right)-E\left(X\right)E\left(Y\right)}{Var\left[X\right]Var\left[Y\right]}}$$

(3)

In Equation (3), R is the correlation coefficient between variables x and y; E(XY), E(X), and E(Y) denote the mathematical expectations of XY, X, and Y; and Var[X] and Var[Y] are the variances of X, Y respectively.

Economic vitality classification. Based on the “area–light” correlation analysis, area and the total nighttime light index (TNLI) are introduced as classification indicators. By performing correlation calculations and interval divisions for both, four quadrants are formed: “high area–high light,” “low area–high light,” “low area–low light,” and “high area–low light.” This classification is used to evaluate the economic vitality of historical towns within the region. The specific classification method is as follows [60]:

$$\begin{gathered} R_i=\left\{\begin{array}{c}I, X_i\le 0.5M\\ II,0.5M<X_i<M\\ III,M<X_i\le 2M\\ IV,X_i>2M\end{array}\right. \\ M={\displaystyle \frac{{\sum }_{i=1}^n{X}_i}{n}} \end{gathered}$$

(4)

In Equation (4), Ri represents the type of the i-th town, Xi denotes the area or TNLI of the i-th town, M is the average area or TNLI of all towns, and n is the total number of towns. Thresholds are set using the median (M) to reduce outlier bias. A proportional segmentation (0.5M, M, 2M) ensures balanced classification with clear spatial implications, distinguishing between micro-scale, typical, and high-vitality towns.

4. Results and Analysis

4.1. Analysis of Town Distribution and Scale in Historical Periods

Based on the alignment of historical [61] and modern features in surveyed topographic maps of Shanghai and contemporary remote sensing imagery, the spatial extent of city walls corresponding to 18 historical towns in Shanghai’s suburbs was extracted. A list of these towns, along with their reconstructed area and perimeter, is provided in

Table 3. Eighteen historical towns in suburban Shanghai.

Historical Town	Reconstructed Area (km2)	Reconstructed Perimeter (km)
Jiading County	2.95	6.21
Songjiang Prefecture	2.21	5.76
Jinshan Wei	2.90	6.81
Nanhui County	0.68	3.29
Zhujing Town	0.14	2.67
Fengcheng Town	0.59	3.07
Nanqiao Town	0.11	1.36
Baoshan County	0.39	2.50
Wusongsuo Town	0.32	2.26
Baoshan Suo	0.14	1.49
Chuansha Ting	0.32	2.18
Tanghang Town	0.62	3.02
Qinglong Town	0.28	2.12
Chengqiao Town	0.66	3.24
Pingyangsha	1.07	4.14
Yaoliusha	0.81	3.59
Majiabang	0.65	3.23
Qinjiafu	0.56	2.99

. Their spatial distribution is illustrated in Figure 4.

By measuring the area and perimeter of each town, Figure 5a,b were generated. Among the 18 historical towns, Jiading County has the largest area within its city walls, covering 2.95 km², while Nanqiao Town is the smallest, with an area of only 0.11 km². The average area is 0.87 km², and the total area is 15.41 km². In terms of perimeter, Jinshan Wei has the longest city wall perimeter at 6.81 km, while Nanqiao Town has the shortest at 1.36 km, with an average perimeter of 3.33 km.

Combining the area and perimeter data and comparing them with the urban extent of Jiangsu and Shanghai during the Ming and Qing dynasties, which exceeded 100 km² (Figure 6) [53], it is evident that the historical towns in Shanghai’s suburbs were generally small in scale. Among them, Jiading County, Jinshan Wei, and Songjiang Prefecture (with areas greater than 2 km²) were relatively larger, which is related to their natural conditions, socioeconomic status, and political and military significance during historical periods.

4.2. Analysis of NLT Distribution and Indices in Historical Towns

Through a 1 km buffer analysis conducted on the periphery of the towns, it was clarified that, except for Qinjiafu and Baoshansuo, which are no longer within the modern administrative boundary of Shanghai due to changes in administrative divisions, the remaining 17 historical towns are located within the built-up areas of the modern city. After radiometric calibration correction of the SDGSAT-1 NTL data, an NTL image map of Shanghai’s suburban areas was obtained, as shown in Figure 7 and Figure 8.

Based on the calculation results of the TNLI and ANLI for each historical town (Figure 9a,b), the top three towns ranked by TNLI are Jiading County with a value of 820,662, Songjiang Prefecture with 452,109, and Jinshan Wei with 338,723. The bottom three towns are Majiabang with 6891, Qinglong Town with 3189, and Wusongsuo with 271. After averaging the number of covered pixels per region to obtain the ANLI, the top three towns are Nanqiao Town, Tanghang Town (Qingpu County), and Baoshan County. The bottom three towns are Qinglong Town, Majiabang, and Yaoliusha. These results reflect the intensity of nighttime economic activities per unit area in historical towns, with the ANLI mitigating the influence of town size compared to the TNLI.

4.3. Grading Evaluation of Economic Vitality of Modern Historical Towns

Building on the measurement of NTL data, a correlation analysis was conducted between the TNLI and the historical urban area of towns, yielding a correlation coefficient of R = 0.825. This indicates a strong linear positive correlation between the two variables, with a more significant relationship compared to the correlation coefficient of 0.738 calculated against the town perimeter. In other words, historically larger and more expansive towns generally exhibit higher TNLI values. The scatterplot in Figure 10 illustrates this trend, with the towns of Jiading, Jinshan Wei, and Songjiang standing out prominently, with low-value data points clustered in the plot.

To clarify developmental disparities among these towns, the relationship between historical town area and TNLI was further categorized into hierarchical tiers, generating a quadrant distribution diagram (Figure 11). Towns are dispersed across four quadrants: the first and third quadrants represent positive correlations (i.e., “high area–high light” or “low area–low light”), while the second and fourth quadrants reflect negative correlations (i.e., “low area–high light” or “high area–low light”). The map reveals that Jiading County, Songjiang Prefecture, and Jinshan Wei fall within the first quadrant, only Tanghang Town and Chengqiao Town occupy the second quadrant, the majority of historical towns cluster in the third quadrant, and Pingyangsha alone appears in the fourth quadrant. This distribution highlights that the average economic vitality of historical towns in Shanghai’s suburbs remains modest, with numerous towns exhibiting low-area and low-light levels. Overall, the development of these historical towns demonstrates significant disparities and imbalances.

4.4. Analysis of the Influence Mechanisms of Modern Economic Vitality in Historical Towns

4.4.1. Natural Geographical Conditions

Natural geographical conditions significantly shaped the development of historical towns in the Shanghai region. Located along the Yangtze River Delta, Shanghai’s waterways—primarily the Yangtze River, Huangpu River, and Suzhou Creek—facilitated agricultural irrigation and commercial growth. Jiading County and Songjiang Prefecture exemplify towns that flourished due to water networks. Jiading, with its intersecting Lianqi River and Hengli Rive forming a “cross-and-ring” pattern [62], became a cotton trade hub in northern Songjiang during the Ming dynasty, transitioning from an agrarian economy to commercial specialization. Songjiang Prefecture, situated in the Taihu Basin lowlands, developed as a grain and textile hub, reaching peak prosperity in the late Ming dynasty, described as the pinnacle of the beautiful Jiangnan [63]. Together with Jiading in the north, it formed a north–south connection and played a central role in the commercial network of the Jiangnan region. Conversely, environmental instability limited development in towns like Pingyangsha, Yaoliusha, and Majiabang, where shifting sandbanks in the Yangtze Estuary hindered sustainable growth [64]. Following administrative relocations, these settlements declined, leaving only historical remnants.

4.4.2. Historical Foundations and Continuity

Historical foundations and their continuity profoundly influence the development of historical towns. The positive correlation between “area and light” suggests that towns with larger historical scales, reflecting early population and economic prosperity, exhibit stronger continuity and modernization advantages. Towns like Jiading, Songjiang, and Jinshan Wei expanded outward from traditional administrative functions, forming stable spatial structures and autonomous influence circles. Songjiang, a cotton textile hub in the late Ming dynasty, evolved into the modern Songjiang Industrial Zone. Similarly, Chengqiao Town in Chongming County, benefiting from its historical role as Chongming’s political, economic, and cultural center, maintains high NTL index values due to stable functions and concentrated economic activities. Its spatial structure extends outward from core urban areas, preserving historical continuity. In contrast, towns in the third and fourth quadrants, with smaller populations, weaker economies, and discontinuous administrative functions, struggle with industrial transformation and regional influence expansion.

4.4.3. Policy Guidance

Policy has decisively shaped the spatial evolution and development trajectory of historical towns. Historically, imperial policies on defense, river management, and taxation directly influenced urban prosperity and industrial patterns, such as Jinshan Wei’s early emergence driven by coastal defense in the Ming dynasty [65]. In recent decades, government strategies, including administrative division adjustments, new town construction, and heritage conservation initiatives, have significantly guided the development and transformation of suburban historical towns within Shanghai’s modern urban system.

The ongoing urban–rural restructuring in Shanghai’s suburbs, driven primarily by new city development, has disrupted the traditional urban hierarchy centered on historically independent towns [66] (Figure 12). The 2017 Shanghai Urban Master Plan (2017–2035) (https://www.shanghai.gov.cn/nw42806/, accessed on 16 June 2025) specifically designated Jiading, Songjiang, Qingpu, Fengxian, and Nanhui as comprehensive nodes and Jinshan Binhai and Chongming Chengqiao as gateway cities, reinforcing their regional influence. This shift caused the decline and transformation of historical town centers due to the migration of administrative and public facilities, altering their traditional spatial character. Concurrently, rapid urban expansion and industrial diffusion bridged spatial gaps, integrating historical towns into modern urban networks. TNLI and ANLI results confirm that historical towns like Jiading and Songjiang, incorporated early into new city frameworks, maintain strong vitality. Nanjiao Town, located in the central area of Fengxian New City, performs especially well, ranking first in ANLI. In contrast, the NTL index of Nanhui Old Town is weaker, partly due to its weak economic foundation and outdated urban functions, which urgently need to be transformed through urban renewal. Furthermore, its distance from the Nanhui New City town circle limits its radiating effects. This NTL index result, which aligns with the current state of new city construction, significantly reflects the differentiated radiating effects of new city planning on historical towns within the region.

Since the introduction of the “historical and cultural cities” concept in 1982, regulations like the Urban Purple Line Management Measures and Protection Planning Standards have promoted comprehensive protection of historical towns and villages [67]. Recent policies emphasize integrating protection with revitalization through a people-centered approach, preserving historical texture, spatial scale, and landscape environment while enhancing economic and social development. Shanghai, a national historical and cultural city, has effectively balanced protection and development through practices in Jiading, Chuansha, and Songjiang. By incorporating cultural tourism, night markets, and cultural activities, these towns have preserved historical memory and enhanced local economic vitality. Such practices avoid traditional demolition-based urbanization, instead achieving dynamic balance through systematic protection, reflected in positive NTL indices.

4.4.4. Transportation

Transportation conditions constrain the functional positioning and economic connectivity of urban settlements. Transportation networks serve as critical infrastructure developments during urbanization, facilitating optimal resource allocation across broader spatial dimensions while enhancing external and intra-urban economic interactions [68]. Historically, Shanghai’s water networks and maritime transportation drove population growth and economic development. Following its port opening, advancements in railway, highway, and aviation infrastructure established a multitiered urban transportation system, promoting integration with peri-urban regions and supporting the Yangtze River Delta’s development. The historical trajectories of Qinglong Town and Qingpu County, both located in the present-day Qingpu District, exemplify transportation’s impact on urban prosperity and decline. Qingpu’s growth began with Tang Dynasty maritime trade, with Qinglong Port flourishing by the Song Dynasty [69]. However, Wusong River siltation and coastal migration led to decline. The administrative center shifted to Tanghang, strategically located at a waterways junction, promoting regional connectivity. Modern transportation systems have integrated Qingpu into Shanghai’s logistics networks, spurring industrial and demographic growth. NTL index variations highlight transportation’s profound impact on historical towns’ evolution.

5. Discussion

5.1. Integration of High-Resolution Nighttime Light Imagery and Historical Geographical Data

SDGSAT-1 imagery, with its 10 m panchromatic and 40 m multispectral nighttime resolution, allows for fine-scale detection of urban lighting, overcoming the limitations of earlier datasets such as DMSP-OLS and VIIRS. By aligning SDGSAT-1 data with reconstructed historical town boundaries based on historical maps and documents, we achieved a multitemporal spatial analysis that links past urban spatial form to present-day economic activity.

Historical geographical data add critical temporal depth to urban studies. While modern remote sensing captures the current built environment, historical maps and documents reveal the original settlement structures, administrative layouts, and early socioeconomic landscapes. Their integration with contemporary imagery enables the identification of both spatial continuities—such as persistent urban cores—and transformations such as functional decline or spatial reorganization. This framework, grounded in remote sensing science and HGIS, reconstructs historical towns through the integration of multimodal data sources—including historical maps, local gazetteers, and satellite imagery—and, in doing so, embodies a deeper innovation in cross-modal semantic alignment. Our analysis shows that some historical towns maintain strong spatial continuity, with nighttime light hotspots overlapping historical centers, while others exhibit weak or fragmented light patterns, reflecting functional decline over time. Historical towns are not static relics, but dynamic spaces where accumulated infrastructure, spatial memory, and modern development interact. By understanding this interaction, sustainable urban planning can better balance heritage preservation and economic growth.

5.2. Value of Economic Vitality in Urban Cultural Heritage

The economic vitality of historical towns is an essential indicator for assessing their resilience, sustainability, and functional relevance within contemporary urban systems. This study demonstrates that historical spatial form and scale continue to exert significant influence on modern economic vitality. The strong positive correlation (R = 0.825) between the historical areas of towns and their nighttime light indices confirms that the scale established in earlier historical periods remains a key determinant of current economic activity.

Furthermore, the quadrant-based classification system, developed through the relationship between town area and nighttime light indices (TNLI/ANLI), provides a nuanced understanding of spatial disparities in economic vitality among historical towns. Towns such as Jiading, Songjiang, and Jinshan Wei, which fall into the “high area–high light” quadrant, demonstrate strong continuity between their historical importance and present-day economic vitality. Their sustained prominence is reinforced by favorable policy interventions, historical foundations, and transportation connectivity. For towns in this quadrant, local governments should prioritize balanced development and refined heritage integration, ensuring that ongoing urban functions do not compromise cultural identity while stabilizing regional economic vitality through adaptive reuse of heritage buildings, upgrading of public services, and promotion of high-quality cultural tourism. In contrast, towns such as Pingyangsha, Majiabang, Yaoliusha, and Qinglongzhen, classified as “low area–low light,” reveal a trajectory of decline. These settlements, often former county seats abandoned due to administrative relocations or shifting river systems, have gradually lost their economic relevance. For such vulnerable towns, policy should emphasize conservation and protection. Initiatives may include documenting intangible heritage, supporting community-based cultural programs, paying attention to ecological functions, and designating protective zoning to prevent further erosion of the historical fabric. Drawing on the development experience of first-tier towns to promote low-impact eco-tourism or to identify a viable industrial base may offer a sustainable path forward. The existence of “low area–high light” towns indicates cases where small-scale settlements have leveraged modern development opportunities—such as proximity to transportation hubs or inclusion in new town construction initiatives—to achieve economic development. For this group, the key lies in continuously leveraging the spillover effects from adjacent suburban cores or new town centers, while strengthening land-use regulation to maintain economic vitality and enhance cultural heritage preservation. “High area–low light” towns suggest underutilization of historical spatial resources, possibly constrained by environmental protection policies, insufficient infrastructural investment, or limited governance capacity. For such towns, it is essential to first identify the underlying causes of the mismatch between nighttime economic vitality and their historical spatial scale and to assess whether further development of nighttime economic functions is appropriate. Based on this assessment, targeted measures should be taken to prevent further hollowing out or even abandonment. This may include improving infrastructure, offering incentives, activating historical spaces to attract cultural tourism, and supporting small-scale industries consistent with the town’s heritage.

This quadrant-based spatial differentiation highlights that historical foundations alone do not fully determine contemporary economic outcomes. Instead, the intersection of historical heritage with modern policy frameworks, infrastructural accessibility, and market integration critically shapes the vitality landscape of historical towns. Notably, the spatial distribution of nighttime light hotspots overlapping with historical town cores indicates that modern suburban expansion in Shanghai has radiated outward from these traditional centers. This shows that historical towns still play a dual role in culture and economy. From the particular to the universal, recognizing and quantifying the economic vitality of urban cultural heritage contributes not only to heritage conservation and management but also to sustainable regional planning.

6. Conclusions

Historical towns, repositories of cultural heritage and urban civilization, must revitalize their vitality through balanced preservation and development. By fusing archival texts, antique maps, and SDGSAT-1 high-resolution nighttime light imagery, this study reconstructed the walled footprints of 18 suburban Shanghai towns—averaging just 0.87 km² in area and 3.33 km in perimeter—and demonstrated that most still anchor the brightest NTL cores of the region. Nighttime light indices scale linearly with town area (R = 0.825), and a quadrant typology (“high area–high light,” “low area–high light,” etc.) exposes sharp disparities in economic vitality, with Jiading, Jinshan Wei, and Songjiang performing well, while several low-light settlements warrant targeted support. The resulting methodology is applicable to small-scale, long-term spatial and economic analyses and provides a decision-making framework for graded preservation and adaptive reuse of historical towns under cultural heritage conservation mandates. Under this research framework, historical towns dynamically narrate their unique spatial configurations, cultural lineages, and multidimensional value systems (encompassing latent economic vitality, ecological adaptability, and community identity). Their comprehensive conservation directly aligns with the mandate of Sustainable Development Goal 11.4, which is to “strengthen efforts to protect and safeguard the world’s cultural and natural heritage,” and at the same time, it advances the holistic conservation objectives of historic urban landscapes (HULs).

In summary, this study demonstrates the applicability of SDGSAT-1 remote sensing data in analyzing cultural heritage towns at a fine spatial scale, highlighting the growing potential of remote sensing in emerging research contexts. It contributes to the continued expansion of remote sensing applications in historical urban studies, localized economic vitality assessment, and heritage-led sustainable planning. Moreover, by integrating historical GISs with interdisciplinary methods, this research offers a novel geospatial and methodological perspective for the protection and revitalization of urban cultural heritage, thereby supporting both sustainable development goals and evidence-based decision-making. Future research should extend this methodology to other regions or national scales, incorporate functional classifications and richer socioeconomic datasets such as population and income data, and explore temporal dynamics through multi-period nighttime imagery to better understand how historical patterns influence modern urbanization processes.