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学者姓名：陈佐旗
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Abstract ：
Understanding high spatiotemporal resolution monthly industrial production is crucial for detailed economic analysis and effective policy intervention. However, the lack of high-resolution spatial data and validation samples at the regional scales makes this estimation challenging. To address this, we propose a Spatial Analysis-based Convolutional Neural Network (SA-CNN) model that estimates the grid-level Gross Value of Industrial Output (GVIO) in Shanghai's monthly time series by accounting for heterogeneous nighttime light (NTL) patterns across different land uses. Firstly, we locate each factory based on the corresponding category in Point of Interest (POI) data and propose a new sampling strategy using buffer zones around each factory to capture local NTL patterns. Secondly, the SA-CNN extracts feature from monthly NTL patterns, incorporating urban and rural NTL statistical characteristics to address the few-shot problem. Finally, we map the monthly estimated GVIO grid data from 2014 to 2021 by establishing a linear correlation between NTL in factory areas and the estimated results. Experimental results indicate that SA-CNN outperforms the random forest and baseline models, with R2 values of 0.94 for estimation results and 0.90 for the mapping grid. The spatial distribution of monthly GVIO in northern Shanghai was more balanced and developed faster than in the south and the overall GVIO increased oscillation. The SA-CNN method proposed a new sampling strategy to overcome the problem of the need for large samples in estimating regional industrial activities and offers a fast update and labor-free methodology for monthly GVIO estimation. © 2025
Keyword ：
CNN CNN Monthly gross value of industrial outputs Monthly gross value of industrial outputs NPP-VIIRS monthly composite data NPP-VIIRS monthly composite data Regional economics Regional economics
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| GB/T 7714 | Gong, W. , Chen, Z. , Wang, C. et al. Estimating monthly gross value of industrial outputs at the pixel level with nighttime lights image products and a spatial-analysis-based CNN model [J]. | International Journal of Applied Earth Observation and Geoinformation , 2025 , 143 . |
| MLA | Gong, W. et al. "Estimating monthly gross value of industrial outputs at the pixel level with nighttime lights image products and a spatial-analysis-based CNN model" . | International Journal of Applied Earth Observation and Geoinformation 143 (2025) . |
| APA | Gong, W. , Chen, Z. , Wang, C. , Zhang, L. , Yu, B. . Estimating monthly gross value of industrial outputs at the pixel level with nighttime lights image products and a spatial-analysis-based CNN model . | International Journal of Applied Earth Observation and Geoinformation , 2025 , 143 . |
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Scientific and technological innovation (STI) index refers to the level of STI and has become a powerful indicator of national competitiveness, since STI can help revolutionize traditional products and generate new products, and lead to rapid economic growth, and obvious energy conservation. Traditionally, assessing the STI index has mainly relied on statistical data, which has a coarse spatio-temporal resolution. Thus, this study proposes a novel method to estimate the annual and monthly STI index at county level by fusing multi-modal data: NTL data and POI data. This method demonstrates robust performance with an R2 value of 0.81 and has a strong generalization capability. By analyzing the driving factors under different innovation development patterns, we conclude strategies to improve the STI level in counties following various patterns of STI. Due to its accuracy and effectiveness, our method has potential for application across a wide range of regions and over extended time periods. © 2025 Wuhan University. Published by Informa UK Limited, trading as Taylor & Francis Group.
Keyword ：
multi-modal data fusion multi-modal data fusion Nighttime light data (NTL) Nighttime light data (NTL) point of interest (POI) point of interest (POI) scientific and technological innovation (STI) scientific and technological innovation (STI)
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| GB/T 7714 | Qiu, Y. , Chen, Z. , Zou, C. et al. A novel estimation of scientific and technological innovation index using multi-modal data [J]. | Geo-Spatial Information Science , 2025 . |
| MLA | Qiu, Y. et al. "A novel estimation of scientific and technological innovation index using multi-modal data" . | Geo-Spatial Information Science (2025) . |
| APA | Qiu, Y. , Chen, Z. , Zou, C. , You, X. . A novel estimation of scientific and technological innovation index using multi-modal data . | Geo-Spatial Information Science , 2025 . |
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The urban scaling theory (UST) strives for a universal taxonomy that depicts relationships among urban indicators (e.g. energy consumption, economic output) with city size. However, the lack of international agreement on city definitions and statistics complicates cross-country comparisons of urban scaling performance. Remote sensing provides a uniform standard for measuring cities around the world. To scrutinize the consistency of UST, we quantified changes in remotely sensed urban built-up areas (UBA) and nighttime lights (NTL) distributions from 11,581 cities in 61 countries spanning 2000-2020, representing urban physical elements and socioeconomic activities, respectively. We find that UBA is well described by UST in all analyzed countries, while NTL aligns with 98% of them. UST quantified by remote sensing shows greater robustness than country-dependent aggregate statistics. We also observed disparities of scaling exponents (beta) among countries, with UBA all being sublinear (beta < 1), and NTL ranging from 0.46 to 1.22 with a median of 0.94. Both UST and rank-size distributions of urban area and population show stronger scaling relationships for countries with larger networks of built environments, (Brazil, China, Germany, Japan, Russia, United States) suggesting that both size and evolution of urban systems impact the underlying scaling processes. Comparison of scaling properties of remotely sensed UBA and NTL captures complementary physical characteristics of built environments while minimizing the Modifiable Area Unit Problem introduced using spatially aggregated metrics within administrative units. Our findings highlight the consistency of urban growth patterns while confirming the systematic socioeconomic disparities among urban systems of varying size and growth trajectory.
Keyword ：
complex systems complex systems remote sensing remote sensing scaling laws scaling laws urban scaling urban scaling urban science urban science
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| GB/T 7714 | Xu, Zhibang , Xu, Gang , Lan, Ting et al. Global consistency of urban scaling evidenced by remote sensing [J]. | PNAS NEXUS , 2025 , 4 (2) . |
| MLA | Xu, Zhibang et al. "Global consistency of urban scaling evidenced by remote sensing" . | PNAS NEXUS 4 . 2 (2025) . |
| APA | Xu, Zhibang , Xu, Gang , Lan, Ting , Li, Xi , Chen, Zuoqi , Cui, Hao et al. Global consistency of urban scaling evidenced by remote sensing . | PNAS NEXUS , 2025 , 4 (2) . |
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Accurate and timely estimation of gross domestic product (GDP) is essential for evaluating economic development. Nighttime light (NTL) data effectively estimate subindustry GDP, yet previous studies relied on single panchromatic bands. Whether multispectral nighttime remote sensing data, detecting spectral differences from economic activities, improves subindustry GDP estimates remains unverified. This article leverages multispectral NTL and thermal infrared data from the SDGSAT-1 satellite, combined with land cover data, to estimate subindustry GDP using machine learning models. We compare support vector machines, neural networks, and random forest (RF), identifying RF as the optimal model due to its lowest RMSE values (9.16, 171.06, and 180.51 for primary, secondary, and tertiary industries, respectively). Empirical results demonstrate that multispectral SDGSAT-1 data significantly outperforms its single panchromatic band counterpart, improving R-2 values for secondary and tertiary industries from 0.58 to 0.88 and 0.68 to 0.90, respectively. Compared to VIIRS NTL data, SDGSAT-1 further reduces spatial misdistribution over farmland and industrial zones, achieving a 7.7% R-2 improvement at smaller scale (industrial parks level). Key factors driving GDP estimation vary across industries: cropland area dominates for the primary industry; thermal infrared and red light intensity for the secondary industry; and blue light intensity for the tertiary industry. These findings validate the superiority of multispectral NTL data in subindustry GDP estimation and offer actionable insights for enhancing urban economic monitoring and policy formulation.
Keyword ：
Accuracy Accuracy Economic indicators Economic indicators Estimation Estimation Feature extraction Feature extraction Industries Industries Land surface Land surface Nighttime light (NL) remote sensing Nighttime light (NL) remote sensing nighttime thermal infrared nighttime thermal infrared Remote sensing Remote sensing Satellite broadcasting Satellite broadcasting SDGSAT-1 imagery SDGSAT-1 imagery Socioeconomics Socioeconomics subindustry gross domestic product (GDP) estimation subindustry gross domestic product (GDP) estimation Urban areas Urban areas
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| GB/T 7714 | Zhang, Lingxian , Chen, Zuoqi , Gong, Wenkang et al. Improving Sub-Industry GDP Estimation With SDGSAT-1 Multispectral Nighttime Light and Thermal Infrared Data: Effectiveness and Potential [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2025 , 18 : 20279-20293 . |
| MLA | Zhang, Lingxian et al. "Improving Sub-Industry GDP Estimation With SDGSAT-1 Multispectral Nighttime Light and Thermal Infrared Data: Effectiveness and Potential" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 18 (2025) : 20279-20293 . |
| APA | Zhang, Lingxian , Chen, Zuoqi , Gong, Wenkang , Wang, Congxiao , Xiong, Jing , Dong, Linxin et al. Improving Sub-Industry GDP Estimation With SDGSAT-1 Multispectral Nighttime Light and Thermal Infrared Data: Effectiveness and Potential . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2025 , 18 , 20279-20293 . |
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The nighttime urban environment is increasingly affected by various forms of artificial light at night. Color temperature, a critical characteristic of light, has significant effects on numerous fields and industries. The widespread adoption of light-emitting diode (LED) light, a low-carbon technology, has resulted in extensive use of lights with varying color temperatures in diverse settings. However, it is crucial to recognize that different color temperatures have distinct impacts on human health and ecological systems. Therefore, understanding the spatial distribution and composition of nighttime light (NTL) with different color temperatures is essential for developing sustainable strategies that balance public safety, energy consumption, and ecosystem conservation. In response to this need, we propose a color temperature-based lighting source classification system utilizing autonomous aerial vehicle (AAV)-captured NTL images, rather than the traditional satellite-based NTL images, due to the superiority of spatial resolution (SR). We employ an object-oriented classification method to categorize lights into high-pressure sodium (HPS), warm LEDs, cool LEDs, and colored LEDs. Moreover, to evaluate the effect of flight altitude on classification accuracy, we classify lights at seven different altitudes and compare their accuracy at each level. Our results indicate that the random forest (RF) algorithm can accurately identify the four types of lights, with the highest classification accuracy achieved at a flight altitude of 350 m, where the overall accuracy (OA) and kappa coefficient were 0.957 and 0.947, respectively. Moreover, at this altitude, the highest producer's accuracy (PA) for warm LEDs and colored LEDs was 0.971 and 0.942, respectively, while the user's accuracy (UA) for each light type exceeded 0.9. In addition, the methodology also demonstrated strong performance in more complicated regions, as evidenced by an off-site application accuracy of 0.847 and a kappa coefficient of 0.808. This study is the first to identify NTL types based on color temperature, offering a new perspective for urban lighting planning and light pollution management.
Keyword ：
Accuracy Accuracy Artificial light Artificial light autonomous aerial vehicle aerial vehicle (AAV) autonomous aerial vehicle aerial vehicle (AAV) Autonomous aerial vehicles Autonomous aerial vehicles Color temperature Color temperature Data mining Data mining Image color analysis Image color analysis Light emitting diodes Light emitting diodes Light sources Light sources light source type light source type nighttime light (NTL) nighttime light (NTL) Phantoms Phantoms random forest (RF) random forest (RF) Spatial resolution Spatial resolution
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| GB/T 7714 | Zou, Chenru , Chen, Zuoqi , Yu, Bailang et al. An Object-Oriented Nighttime Light Classification Based on Light Color Temperature: A New Perspective From AAV Nighttime Images [J]. | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2025 , 63 . |
| MLA | Zou, Chenru et al. "An Object-Oriented Nighttime Light Classification Based on Light Color Temperature: A New Perspective From AAV Nighttime Images" . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63 (2025) . |
| APA | Zou, Chenru , Chen, Zuoqi , Yu, Bailang , Zheng, Qiming , Wang, Congxiao . An Object-Oriented Nighttime Light Classification Based on Light Color Temperature: A New Perspective From AAV Nighttime Images . | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING , 2025 , 63 . |
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The Sustainable Development Goals Satellite 1 (SDGSAT-1), equipped with the Glimmer Imager (GLI), provides high-resolution nighttime light (NTL) data across multiple spectral bands. Thus, it can notably monitor human dynamics and light pollution with enhanced spectral and spatial resolution. However, cloud cover and lowquality observations often contaminate the SDGSAT-1 GLI NTL data, limiting its effectiveness. This challenge is addressed by the development of a novel method, namely the SpatioTemporal And spectRal gap-filling method for Sdgsat-1 (STARS) GLI NTL images, which combines spatiotemporal and spectral information to generate cloud-free NTL images with satisfactory pixel brightness and continuity. STARS is the first method to effectively address gap-filling in multiband NTL data using RGB spectral information, even with irregular time intervals and limited image inputs. Compared with traditional methods such as the temporal gap-filling method and the meanweighted gap-filling method, the Cloud Removing bY Synergizing spatioTemporAL information (CRYSTAL) method, and the spatial and temporal adaptive reflectance fusion model (STARFM), which do not specifically account for the differences in light source variations in multi-band NTL data, STARS demonstrates superior performance (higher R-squared (R2) and lower root-mean-square error (RMSE)) in simulations across seven global cities, demonstrating its effectiveness in filling cloud-induced gaps in multi-band NTL data. On average, STARS achieves R2 values for the gap-filling results compared to the actual values of 0.79, 0.78, and 0.70 in the RGB bands, respectively. The cloud-free images produced by STARS extend the time series of the SDGSAT-1 GLI NTL data, supporting multitemporal quantitative analysis. In cloudy regions like Tianjin, China, STARS effectively captures dynamic changes in NTL before and after the Spring Festival, closely matching human activity patterns from Baidu Maps, both spatially and temporally. Overall, STARS offers an innovative and effective approach for gap-filling multiband NTL data, with potential applications in similar datasets.
Keyword ：
Cloud removal Cloud removal Gap-filling Gap-filling Glimmer imager Glimmer imager Human dynamics monitoring Human dynamics monitoring Image reconstruction Image reconstruction SDGSAT-1 SDGSAT-1
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| GB/T 7714 | Wang, Congxiao , Xu, Wei , Chen, Zuoqi et al. STARS: A novel gap-filling method for SDGSAT-1 nighttime light imagery using spatiotemporal and spectral synergy [J]. | REMOTE SENSING OF ENVIRONMENT , 2025 , 322 . |
| MLA | Wang, Congxiao et al. "STARS: A novel gap-filling method for SDGSAT-1 nighttime light imagery using spatiotemporal and spectral synergy" . | REMOTE SENSING OF ENVIRONMENT 322 (2025) . |
| APA | Wang, Congxiao , Xu, Wei , Chen, Zuoqi , Liu, Shaoyang , Li, Wei , Zhang, Lingxian et al. STARS: A novel gap-filling method for SDGSAT-1 nighttime light imagery using spatiotemporal and spectral synergy . | REMOTE SENSING OF ENVIRONMENT , 2025 , 322 . |
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Green roof installations and photovoltaic (PV) systems are widely employed roof retrofits that aid cities in mitigating climate change impacts, while avoiding the need for increased land utilization. By integrating PV systems with vegetation on urban roofs, a photovoltaic-green (PV-Green) system can be achieved for multifunctional use of the roof space, thereby simultaneously achieving PV and greening benefits. However, current research solely based on one retrofit type cannot meet the requirement for assessing the multifunctional retrofit potential of urban roofs. In this study, an assessment method is proposed to identify and quantify the multiple retrofit potential of urban roofs by integrating roof attributes (slope, orientation, and area), roof type (gable or flat), solar attributes (radiation and irradiation duration), and biogeochemical simulation. Moreover, three roof retrofit scenarios, Scenario 1 (S1): maximization of PV-Green roofs, Scenario 2 (S2): maximization of PV economic benefits, and Scenario 3 (S3): maximization of public subjective well-being through roof greening, were designed to allocate the use of urban roof spaces and evaluate their respective potential power and carbon benefits at the city scale. Using Shanghai's downtown as an example, the results showed that 85,722 roofs (or 7310.86 ha) were available for multifunctional use. S1 revealed that applying PV-Green roofs can increase the additional green biomass by 0.74 x 107 kg C/yr compared to only installing PV roofs. Moreover, S1 produced the highest power output of 2.31 x 1010 kWh/yr to meet 15.4% of Shanghai's electricity demand. S2 identified 609 flat roofs and 70,527 gable roofs that were uneconomical for PV system installation. This indicated that the solar radiation received by most gable roofs was insufficient to cover the installation cost. S3 offered a biomass production of 1.48 x 107 kg C/yr and increased carbon stocks in Shanghai by 0.87%. This assessment method provides urban planners and policymakers with an analytical tool to optimize the use of urban roof spaces, thereby enhancing urban livability and sustainability.
Keyword ：
GIS GIS Green roof Green roof Photovoltaic-green roof Photovoltaic-green roof Potential area Potential area Retrofit scenario Retrofit scenario Roof retrofitting Roof retrofitting
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| GB/T 7714 | Pan, Zhan , Wang, Congxiao , Yu, Bailang et al. Assessing multifunctional retrofit potential of urban roof areas and evaluating the power and carbon benefits under efficient retrofit scenarios [J]. | JOURNAL OF CLEANER PRODUCTION , 2024 , 444 . |
| MLA | Pan, Zhan et al. "Assessing multifunctional retrofit potential of urban roof areas and evaluating the power and carbon benefits under efficient retrofit scenarios" . | JOURNAL OF CLEANER PRODUCTION 444 (2024) . |
| APA | Pan, Zhan , Wang, Congxiao , Yu, Bailang , Chen, Zuoqi , Yuan, Yuan , Li, Guorong et al. Assessing multifunctional retrofit potential of urban roof areas and evaluating the power and carbon benefits under efficient retrofit scenarios . | JOURNAL OF CLEANER PRODUCTION , 2024 , 444 . |
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Nighttime Light (NTL) is highly concentrated in China ' s coastal zone, leading to negative health impacts on both humans and wildlife. Particularly, in recent years, the widespread adoption of broad-spectrum Light -Emitting Diode (LED) light, a low -carbon technology providing substantial increases in luminosity, has led to certain ecological consequences. Thus, information regarding spatial distribution and composition of different NTL types is essential for formulating sustainable strategies that balance nighttime public security, energy consumption, and ecosystem conservation. However, the availability of such information remains limited. To address this challenge and meet the demand, we developed two new light indices, namely the Ratio Red Light Index (RRLI) and Ratio Blue Light Index (RBLI), based on SDGSAT-1 Glimmer Imager (GLI) multispectral NTL data. We then proposed a threshold method and applied it to the entire coastal zone of China to identify White LED (WLED), Red LED (RLED), and Other lights (Other). Results showed the following. (1) In the coastal zone of China, the total lighting area was 20,517 km 2 , including 20,257 km 2 of terrestrial lights and 260 km 2 of offshore lights; (2) WLED light covered 67% (13,727 km 2 ) of all lighting areas, while RLED lights accounted for only 1% (220 km 2 ); (3) Guangdong had the largest lighting area (5221 km 2 ), with the proportion of WLEDs being the highest among all coastal provinces (almost 90%); (4) The proportions of lighting areas were relatively low in Guangxi, Liaoning, and Hebei. This study represents the first attempt to identify NTL types over large regions at a finer spatial resolution. The approach proposed, including the light indices of RRLI and RBLI, as well as the defined thresholds, is universal and robust for use in NTL type classification. The developed lighting type map, containing comprehensive information on the spatial distribution and composition of NTL, could facilitate the sustainable management of China ' s coastal zones.
Keyword ：
China 's coastal zone China 's coastal zone Light -emitting diode (LED) Light -emitting diode (LED) Light index Light index Nighttime light (NTL) Nighttime light (NTL) SDGSAT-1 GLI SDGSAT-1 GLI
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| GB/T 7714 | Jia, Mingming , Zeng, Haihang , Chen, Zuoqi et al. Nighttime light in China's coastal zone: The type classification approach using SDGSAT-1 Glimmer Imager [J]. | REMOTE SENSING OF ENVIRONMENT , 2024 , 305 . |
| MLA | Jia, Mingming et al. "Nighttime light in China's coastal zone: The type classification approach using SDGSAT-1 Glimmer Imager" . | REMOTE SENSING OF ENVIRONMENT 305 (2024) . |
| APA | Jia, Mingming , Zeng, Haihang , Chen, Zuoqi , Wang, Zongming , Ren, Chunying , Mao, Dehua et al. Nighttime light in China's coastal zone: The type classification approach using SDGSAT-1 Glimmer Imager . | REMOTE SENSING OF ENVIRONMENT , 2024 , 305 . |
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Poverty is a major problem faced by developing countries. As the world’s largest developing country, China has been committed to poverty eradication. 2020 is the final year of China’s comprehensive victory in the war against poverty. At present, China has entered the post-poverty era, and the reasonable assessment of the poverty reduction effect is the focus of the acceptance work at this stage, which is of great significance to explore a long-term mechanism for solving relative poverty. The county-level geographical unit is the basic unit for China to formulate and implement the macro and micro policies and strategies for regional poverty reduction. Concentrated contiguous poverty-stricken areas concentrated in mountainous areas, old revolutionary base areas, and areas with poor natural resource endowment, with large internal development differences, belong to the most disaster-hit areas of poverty in China. After synthesizing an annual dataset of NPP-VIIRS nighttime light (NTL) data from 2014 to 2020, we developed a county-level NTL index to investigate the poverty reduction effects of 831 national level poverty-stricken counties and 14 concentrated contiguous poverty-stricken areas in China. The economic level of most poverty-stricken counties in China improved significantly during the study period, and the poverty reduction effect was prominent. However, 108 poverty-stricken counties still suffer from negative growth in terms of NTL intensity; these counties are located mainly at the junction of concentrated and contiguous poverty-stricken areas in the western region. The border area, mainly inhabited by ethnic minorities, has a poor ecological environment, a low level of economic development, and a relatively poor self-development ability, which may lead to a relatively poor poverty reduction effect. In addition, the NTL intensity development between the northern and southern parts of the western region is unbalanced. The growth rate of NTL in poor counties decreased from the east to the middle and western regions. In terms of the overall poverty alleviation trend, there was a period of rapid development in poor counties in the year before the declaration of poverty alleviation. However, after the declaration of poverty alleviation, the intensity of NTL decreased, the speed of poverty reduction slowed down, and there may be a risk of returning to poverty in some poor counties. Four NTL development modes, i.e., a small NTL base with a rapid growth rate (mode I), a large NTL base with a rapid growth rate (mode II), a large NTL base with a slow growth rate (mode III), and a small NTL base with a slow growth rate (mode IV), were identified in the 14 concentrated contiguous poverty-stricken areas. The high- and low-restriction modes were distributed at the junction areas of the different provincial administrative boundaries. In addition, poor counties along the border are vulnerable to marginalization. Further analysis indicated that significant NTL changes are apparent in the poverty-stricken counties, as demonstrated by their four poverty alleviation paths including infrastructure poverty alleviation, characteristic industry poverty alleviation, asset income poverty alleviation (photovoltaic poverty alleviation), and relocation poverty alleviation. However, the poverty reduction effect of poverty-stricken counties that take ecological compensation poverty alleviation, social guarantee poverty alleviation, and agricultural industry poverty alleviation as the leading poverty reduction methods are difficult to reflect in the NTL. © 2024 Science Press. All rights reserved.
Keyword ：
Concentration (process) Concentration (process) Developing countries Developing countries Disasters Disasters
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| GB/T 7714 | Hua, Jing , Wu, Bin , Chen, Zuoqi et al. Spatiotemporal variations in nighttime lights in poverty-stricken counties in China [J]. | National Remote Sensing Bulletin , 2024 , 28 (4) : 940-955 . |
| MLA | Hua, Jing et al. "Spatiotemporal variations in nighttime lights in poverty-stricken counties in China" . | National Remote Sensing Bulletin 28 . 4 (2024) : 940-955 . |
| APA | Hua, Jing , Wu, Bin , Chen, Zuoqi , Yang, Chengshu , Tang, Xi , Sun, Feiran et al. Spatiotemporal variations in nighttime lights in poverty-stricken counties in China . | National Remote Sensing Bulletin , 2024 , 28 (4) , 940-955 . |
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Given that Xinjiang Uygur Autonomous Region is a strategic barrier and an important platform of opening up to the western region, assessing its urbanization is critical to promote the national reform strategy and the Belt and Road Initiative. Compared with traditional method, nighttime light (NTL) remote sensing has been proved to be able to monitor human activity intensity and regional comprehensive development level in a more objective, flexible spatial scale and wider coverage. NTL remote sensing data has been able to analyze the urbanization evolution process and the level of social and economic development, but it is still necessary to expand and enrich the breadth and depth of research, especially to explore its spatial pattern and long-term evolution process, so as to more comprehensively understand the urbanization process and social development balance in Xinjiang. This paper comprehensively analyzes and discusses the evolution process of NTL in Xinjiang since the 21st century from three dimensions: time change trend, spatial distribution pattern and social development equilibrium, using NTL remote sensing data of long time series from 2000 to 2020, time series decomposition, spatial standard deviation ellipse and Night Light Development Index (NLDI). (1) From 2000 to 2020, the total amount of NTL in all regions of Xinjiang Uygur Autonomous Region increased to varying degrees. In terms of spatial pattern, urbanization in northern and eastern Xinjiang developed steadily, while rapid development in southern Xinjiang. The planning and construction of transportation lines is one of the important driving forces for the spatial expansion of urbanization in Xinjiang. (2) In the past 20 years, the total amount of NTL in Xinjiang has increased by 5.30 times, and the growth trend is accelerating. The NTL intensity in rural areas of Xinjiang increased by 7.60 times, which was larger than that in urban areas (4.10 times). The process of urbanization in Xinjiang can be divided into three stages: slow development (before 2007), volatile growth (from 2008 to 2014), and rapid development (after 2015). Policy support and the transformation of industrial and agricultural development make Xinjiang’s urbanization transition from the slow development period to the volatile growth period. The growth rate of the volatile growth period is nearly three times that of the slow development period, but at the same time, it is also disturbed by various extreme events. (3) From 2000 to 2019, the NLDI in most areas of Xinjiang decreased, the distribution of population and infrastructure construction in the whole region and most cities in Xinjiang became more reasonable, and the social development showed a trend of balanced development. However, compared with urban areas, the balance in rural areas of Xinjiang is weaker. This is due to the low starting point of urbanization development in rural areas of Xinjiang, which is still in the stage of rapid development, and urbanization is undergoing a process of 'from point to surface', so the current social development balance in rural areas shows a trend of decline. In general, the urbanization process of Xinjiang has been developing rapidly and evenly since the 21st century. © 2024 Science Press. All rights reserved.
Keyword ：
Economic and social effects Economic and social effects Long Term Evolution (LTE) Long Term Evolution (LTE) Remote sensing Remote sensing Time series Time series Urban growth Urban growth
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| GB/T 7714 | Liu, Shaoyang , Chen, Zuoqi , Shi, Kaifang et al. Nighttime light remote sensing reveals the pattern and process of urbanization evolution in northwest China since the 21st century [J]. | National Remote Sensing Bulletin , 2024 , 28 (6) : 1497-1514 . |
| MLA | Liu, Shaoyang et al. "Nighttime light remote sensing reveals the pattern and process of urbanization evolution in northwest China since the 21st century" . | National Remote Sensing Bulletin 28 . 6 (2024) : 1497-1514 . |
| APA | Liu, Shaoyang , Chen, Zuoqi , Shi, Kaifang , Wu, Bin , Wei, Ye , Wang, Congxiao et al. Nighttime light remote sensing reveals the pattern and process of urbanization evolution in northwest China since the 21st century . | National Remote Sensing Bulletin , 2024 , 28 (6) , 1497-1514 . |
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