Search Results (179)

Urban sprawl refers to the undesirable expansion of cities and the irrational exploitation of land resources. This study takes the Yellow River Basin as the research domain and measures the urban sprawl index of 73 prefecture-level cities in the basin from 2000 to 2020. Utilizing DMSP/OLS, NPP/VIIRS nighttime light data, and LandScan population data, the research applies the Theil index to examine urban sprawl levels and spatial heterogeneity among the upper, middle and lower reaches of the basin, as well as within individual cities. The results show that: (1) between 2000 and 2020, urban sprawl levels in the 73 prefecture-level cities within the Yellow River Basin demonstrated a consistent downward trend, with a spatial decrease observed from west to east; (2) the overall Theil index revealed regional disparities that gradually lessened over the years, with differences within the basin being significantly greater than those between its upper, middle, and lower sections; and (3) in terms of spatial heterogeneity, multiple prefecture-level cities in Qinghai Province, at the source of the basin, are primarily located in the “high high cluster” region, whereas the “low low cluster” is largely concentrated in the eastern downstream areas of the Yellow River. Sanmenxia City, located in the middle reaches, was long term the “high low cluster” zone, while the “low high cluster” zone was concentrated in Xining, Lanzhou, and Baotou cities in the upper reaches. Investigating urban sprawl in the Yellow River Basin contributes to advancing the sustainable development of the basin in a profound manner. Full article

Rapid urbanization complicates the precise, timely quantification of urban spatial morphology. This study examined urban spatial morphology in Fujian Province, integrating DMSP-OLS and NPP-VIIRS nighttime light imagery from 1992 to 2022 to extract the built-up urban footprint via the constructed VMNUI. This method achieved an overall accuracy >0.95 and a Kappa coefficient of 0.80 when the results were compared against land use samples. Utilizing Centroid Migration Analysis, clustering, Geographical Detector, and GTWR, we quantitatively analyzed Fujian’s urban spatial form and its driving mechanisms. The results indicate that the calibration and integration of NTL data effectively resolved saturation and overflow issues in the DMSP data, revealing an urban expansion rate of 3.79%, which centered on coastal areas. Geographical Detector analysis identified fixed-asset investment (q = 0.83), population (0.80), precipitation (0.78), and highway density (0.76) as dominant factors; GDP ∩ fixed-asset investment yielded the strongest interaction (0.873). GTWR further identified that slope aspect, GDP, and secondary industry share accelerated expansion in eastern Fujian, whereas population, urbanization rate, and mean temperature were key drivers of expansion in the west. This study analyzed the spatiotemporal evolution patterns and driving mechanisms of urban spatial form development in Fujian Province over a long period, and based on the results, actionable, science-based optimization strategies with practical implications are proposed for sustainable development in the region. Full article

Understanding the directionality of the relationship between artificial light at night (ALAN) and economic activity is crucial for evidence-based policymaking aimed at accelerating and sustaining development. In particular, this knowledge may help to ensure that ALAN does not serve just a proxy for economic activity, when information is unavailable, but may also become a meaningful development indicator on its own. However, the question remains about the directionality of the GDP–ALAN relationship: Does an increase in GDP simply leads to more nighttime light emissions, while the reverse link is negligible, or is this relationship two-directional, with ALAN affecting economic development as well. The present study attempts to answer this question by applying the Granger directionality test to time series panel data available for 1300+ EU NUTS-3 regions over the period of 2001–2021. The study aims to determine the directionality of the relationship between GDP and ALAN in European NUTS-3 regions, distinguishing between Western and Eastern Europe and between different measurement eras (DMSP-OLS vs. VIIRS). The analysis reveals a complex and bidirectional relationship that varies in strength. In particular, for the years 2001–2013, the analysis showed that GDP led to more ALAN emissions, while the reverse link was much weaker and negative. However, after 2013, this relationship has become unidirectional, with GDP continuing to lead to more ALAN emissions, but not vice versa. These findings highlight the importance of considering long-term trends when interpreting ALAN emissions as an indicator of economic development, which is widely used in empirical studies at present. Full article

Urban boundary delineation is essential for understanding spatial structure, monitoring urbanization, and guiding sustainable land management. Nighttime light (NTL) data effectively capture urban dynamics across multiple spatial scales. This study integrates NTL data with spectral indices to delineate the urban boundaries of the Barcelona Metropolitan Region (BMR) from 2006 to 2018. Through multivariate regression analysis, the normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) are identified as key indicators of urban spatial heterogeneity. These indices are combined with brightness thresholds derived from three NTL datasets, DMSP-OLS, Black Marble, and VIIRS, to delineate urban areas more accurately. Results indicate that VIIRS achieved the highest precision in identifying construction land and urbanized areas, with an overall accuracy exceeding 90% and consistency with population density and GDP distribution. A strong spatial correlation between urban distribution and the NDVI–NDBI relationship is confirmed in the BMR. The coupling of multisource remote sensing data improves the accuracy, stability, and reliability of urban boundary delineation, overcoming single-source limitations. This integrated method supports urban planning and sustainable land management through consistent, objective urban mapping and offers a practical reference for applying remote sensing technologies to monitor urbanization dynamics across broader spatial and temporal contexts. Full article

Accurate delineation of urban built-up areas is critical for urban monitoring and planning. We evaluated the performance and consistency of three widely used methods—thresholding, multi-temporal image fusion, and support vector machine (SVM)—across three major nighttime light (NTL) datasets (DMSP/OLS, SNPP/VIIRS, and Luojia-1). We developed a unified methodological framework and applied it to Wuhan, China, encompassing data preprocessing, feature construction, classification, and cross-dataset validation. The results show that SNPP/VIIRS combined with thresholding or SVM achieved highest accuracy (kappa coefficient = 0.70 and 0.61, respectively) and spatial consistency (intersection over union, IoU = 0.76), attributable to its high radiometric sensitivity and temporal stability. DMSP/OLS exhibited robust performance with SVM (kappa = 0.73), likely benefiting from its long historical coverage, while Luojia-1 was constrained by limited temporal availability, hindering its suitability for temporal fusion methods. This study highlights the critical influence of sensor characteristics and method–dataset compatibility on extraction outcomes. While traditional methods provide interpretability and computational efficiency, the findings suggest a need for integrating deep learning models and hybrid strategies in future work. These advancements could further improve accuracy, robustness, and transferability across diverse urban contexts. Full article

With the rapid urbanization process, the construction industry has become a significant source of urban carbon emissions in China. The carbon emissions from buildings in the urban clusters of the Yangtze River Economic Belt, a crucial region for China’s economic development, have attracted considerable attention. This study focuses on urban buildings and aims to investigate the primary influencing factors of building carbon emissions in the urban clusters of the Yangtze River Economic Belt. The study highlights the innovative use of nighttime light remote sensing data to analyze urban carbon emissions and provides an in-depth exploration of the spatiotemporal characteristics of building carbon emissions in the urban clusters of the Yangtze River Economic Belt. Utilizing nighttime light remote sensing data similar to DMSP-OLS and provincial-level building carbon emissions, combined with spatial autocorrelation and spatiotemporal geographically weighted regression models, the study estimates and analyzes the building carbon emissions from 2012 to 2021 in 71 prefecture-level and above administrative regions within the three major urban clusters of the Yangtze River Economic Belt. The results indicate a continuous increase in total building carbon emissions in the three major urban clusters of the Yangtze River Economic Belt, with an accelerating growth rate. Spatially, urban building carbon emissions exhibit enhanced convergence but decreasing correlation over time, demonstrating evolving spatiotemporal patterns. Furthermore, the study identifies economic development level, population size, built-up area, and industrial structure as the main factors influencing building carbon emissions, with industrial structure showing significant impact. Full article

Nightly images offer a special data source for monitoring fishing activities. This study used images from the Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) to analyze the early development of lit fisheries in the open South China Sea (SCS), which mainly occurred around the Zhong Sha and Xi Sha Islands. Based on peak detection and a fixed threshold, lit fishing positions were extracted well from filtered, high-quality DMSP/OLS images. The results indicated that fisheries experienced an apparent rise and fall from 2005 to 2012, with the numbers of lit fishing boats rising to a maximum of ~60 from 2005 to 2008, almost disappearing in 2009, peaking at ~130 from 2010 to 2011, and starting to decline in 2012. The fish price of major fishing targets declined by ~60% in 2009, which obviously impacted the year’s fishing operations. The reason for declined fishing operations in 2012 was that most of the lit fishing operations shifted farther south to fishing grounds around the Nan Sha Islands. We also explored factors shaping the distribution patterns of lit fisheries by using MaxEnt models to relate fishing positions to environmental variables. Major environmental factors influencing the distribution of lit fishing boats varied with years, of which water depth was the most important factor across years, with an optimal depth range of 1000–2000 m. In addition to depth, the distribution of lit fisheries was also influenced by SST, especially for the years 2005–2008, and a suitable SST was found between 26 and 28 °C. This study fills the knowledge gaps of the inception of lit fisheries and their dynamic changes in the SCS. Full article

Urban areas in sub-Saharan Africa are facing significant developmental challenges due to rapid population growth and urban expansion, this study aims to predict urban growth and assess the SDG 11.3.1 indicator in the Chambishi multi-facility economic zone (CFEMZ) in Zambia through the integration of remote sensing data and spatial cooperative simulation so as to realize sustainable development goals (SDGs). The study utilized DMSP-OLS and VIIRS nighttime light data between 2000 and 2020 to extract the urban built-up area by applying the Pseudo-Invariant Features (PIFs) method to determine thresholds. The land-use and population changes under several development scenarios in 2030 were simulated in the study using the Spatial Cooperative Simulation (SCS) approach. The changes in SDG 11.3.1 indicators were also calculated in the form of a spatialized kilometer grid. The findings show a substantial rise in the built-up area and especially indicate a most notable increase in Chambishi. The primary cause of this growth is the development of industrial parks, which act as the region’s principal engine for urban expansion. Under the natural scenario, the land-use distribution in the study area presents an unplanned state that will make it difficult to realize SDGs. The results of the spatialization form of the SDG 11.3.1 indicator demonstrate the areas and problems of imbalance between urban construction and population growth in the CMFEZ. This study demonstrates the importance of remote sensing of nighttime lighting and spatial simulation in urban planning to achieve SDG 11.3.1 for sustainable urbanization in industrial cities. Full article

By studying the development law of urbanization, the problems of disorderly expansion and resource wastage in urban built-up areas can be effectively avoided, which is crucial for the long-term sustainable development of cities. This study proposes a high-precision urban built-up-area extraction method for county-level cities for small and medium-sized towns in county-level regions. Our process is based on the Defense Meteorological Satellite/Operational Linescan System (DMSP/OLS) and the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS), which develops long-term series of coordinated night-time light (NTL) datasets. We then combined this with the Normalized Vegetation Index (NDVI) to calculate the Vegetation-Adjusted NTL Urban Index (VANUI). We combine land use data and a support vector machine (SVM) for semi-supervised classification learning to propose a high-precision urban built-up-area extraction method for county-level cities. We achieved the following results: (1) we fit binary polynomials to the DMSP/OLS and VIIRS NTL datasets based on the correspondence of the mean values to construct a consistent time series of NTL data. (2) Our method effectively improves the accuracy of urban built-up-area extraction, especially for county-level cities, with an overall accuracy of 91.84% and a Kappa coefficient of 0.83. (3) Our method can perform a long-time series of urban built-up-area extraction, and, by studying the spatial and temporal changes in urban built-up areas, it can provide valuable information for sustainable urban development and urban planning. Full article

The accelerated urbanization taking place across Xinjiang in recent years has vastly improved the quality of life for people living in the region. However, to achieve rational urban growth and sustainable regional development, a deeper understanding of the spatial and temporal patterns, spatial morphology, and driving factors of urban sprawl is crucial. Nighttime light (NTL) data provide a novel approach for studying the spatial and temporal changes in urban expansion. In this study, based on DMSP-OLS and NPP-VIIRS data, we analyze the spatiotemporal characteristics of urban changes using the standard deviation ellipse and employ the geographical detector to analyze the impact of natural environmental and socioeconomic factors on the dynamic rate of urban expansion. The results reveal the following. (1) The overall accuracy of urban area extraction is above 80%, and the urban area of Xinjiang has expanded about 9.1 times over the past 30 years. Further, the growth rate from 2007 to 2017 exceeds the growth rate from 1992 to 1997, with the center of gravity of urban development shifting to the southwest. (2) The 5a sliding average temperature and average annual precipitation in the study area in 1992–2022 are 6.08 °C and 169.72 mm, respectively, showing a decrease in the urbanization rate followed by an increase, due to a rise in temperature and precipitation levels. (3) By combining the results of geographical detector factor detection and interaction detection, precipitation is determined to be the main controlling factor, while air temperature and GDP are secondary factors. This study presents new findings on the correlation between urban spatial and temporal changes and climate in Xinjiang, thus providing a scientific reference for future research on urban expansion and natural environment evolution. Full article

Land use and land cover (LULC) classification plays a significant role in the analysis of climate change, evidence-based policies, and urban and regional planning. For example, updated and detailed information on land use in urban areas is highly needed to monitor and evaluate urban development plans. Machine learning (ML) algorithms, and particularly ensemble ML models support transferability and efficiency in mapping land uses. Generalization, model consistency, and efficiency are essential requirements for implementing such algorithms. The transfer-ensemble learning approach is increasingly used due to its efficiency. However, it is rarely investigated for mapping complex urban LULC in Global South cities, such as India. The main objective of this study is to assess the performance of machine and ensemble-transfer learning algorithms to map the LULC of two metropolitan cities of India using Landsat 5 TM, 2011, and DMSP-OLS nightlight, 2013. This study used classical ML algorithms, such as Support Vector Machine-Radial Basis Function (SVM-RBF), SVM-Linear, and Random Forest (RF). A total of 480 samples were collected to classify six LULC types. The samples were split into training and validation sets with a 65:35 ratio for the training, parameter tuning, and validation of the ML algorithms. The result shows that RF has the highest accuracy (94.43%) of individual models, as compared to SVM-RBF (85.07%) and SVM-Linear (91.99%). Overall, the ensemble model-4 produces the highest accuracy (94.84%) compared to other ensemble models for the Kolkata metropolitan area. In transfer learning, the pre-trained ensemble model-4 achieved the highest accuracy (80.75%) compared to other pre-trained ensemble models for Delhi. This study provides innovative guidelines for selecting a robust ML algorithm to map urban LULC at the metropolitan scale to support urban sustainability. Full article

Scientific estimation and monitoring of regional long-term carbon emission change rules are the data support and scientific basis for developing differentiated emission reduction strategies. Based on the estimation data of energy carbon emissions from 2010 to 2021, DMSP/OLS and NPP/VIIRS lighting data, and the ESDA, Kaya identity, and LMDI models, the temporal and spatial changes and driving mechanism of carbon emissions in Shenyang were discussed. The results showed that: (1) During the study period, the carbon emission of energy consumption in Shenyang showed an upward trend, but the growth rate increased first and then decreased, and the carbon peak was not reached; (2) The spatial distribution of carbon emissions showed a radiative pattern decreasing from the center to the periphery; (3) The global Moran’s I of carbon emission is greater than zero, forming a high-high concentration distribution in the central region, low-low concentration distribution in the peripheral region, and low-high concentration distribution in the Yuhong region; (4) Economic development, population size, and energy efficiency are significant carbon-increasing factors, while industrial structure and energy structure factors are significant carbon-reducing factors. The order of driving factors is as follows: industrial structure > economic development > energy efficiency > population size > energy structure. Full article

The spatial extent and values of nighttime light (NTL) data are widely used to reflect the scope and intensity of human activities, such as extracting urban boundaries, spatializing population density, analyzing economic development levels, etc. DMSP-OLS and NPP-VIIRS are widely used global NTL datasets, but their severe inconsistencies hinder long-time series studies. At present, global coverage, long time series, and public NTL products are still rare and have room for improvement in terms of pixel-scale correction, temporal and spatial consistency, etc. We proposed a set of inter-correction methods for DMSP-OLS and NPP-VIIRS based on two corrected DMSP-OLS and NPP-VIIRS products, i.e., CCNL-DMSP and VNL-VIIRS, with the goal of temporal and spatial consistency at the pixel-scale. A pixel-scale corrected nighttime light dataset (PCNL, 1992–2021) that met the needs of pixel-scale studies was developed through outlier removal, resampling, masking, regression, and calibration processes, optimizing spatial and temporal consistency. To examine the quality of PCNL, we compared it with two existing global long time series NTL products, i.e., LiNTL and ChenNTL, in terms of overall accuracy, spatial consistency, temporal consistency, and applicability in the socio-economic field. PCNL demonstrates great overall accuracy at both the pixel-scale (R²: 0.93) and the city scale (R²: 0.98). In developing, developed, and war regions, PCNL shows excellent spatial consistency. At global, national, urban, and pixel-scales, PCNL has excellent temporal consistency and can portray stable trends in stable developing regions and abrupt changes in areas experiencing sudden development or disaster. Globally, PCNL has a high correlation coefficient with GDP (r: 0.945) and population (r: 0.971). For more than half of the countries, the correlation coefficients of PCNL with GDP and population are higher than the results of ChenNTL and LiNTL. PCNL can analyze the dynamic changes in socio-economic characteristics over the past 30 years at global, regional, and pixel-scales. Full article

Urban shrinkage has gradually become an issue of world-concerning social matter. As urbanization progresses, some Chinese cities are experiencing population loss and economic decline. Our study attempts to correct and integrate DMSP/OLS and NPP/VIIRS data to complete the identification and measurement of shrinking cities in China’s Yangtze River Economic Belt (YREB). We identified 36 shrinking cities and 644 shrinking counties on the municipal and county scales. Based on this approach, we established the average urban shrinkage intensity index and the urban shrinkage frequency index, attempting to find out the causes of shrinking cities for different shrinkage characteristics, city types and shrinkage frequencies. The results show that (1) the shrinking cities are mainly concentrated in the Yangtze River Delta city cluster, the midstream city cluster and the Chengdu–Chongqing economic circle. (2) Most shrinking cities have a moderate frequency of shrinking, dominated by low–low clusters. Resource-based, heavy industrial, small and medium-sized cities are more inclined to shrink. (3) The single economic structure, the difficulty of industrial transformation and the lack of linkage among county-level cities are possible reasons for the urban shrinkage in the YREB. Exploring the causes of urban shrinkage from a more micro perspective will be an inevitable task for sustainable development in YREB and even in China. Full article

Scientific estimations and the dynamic monitoring of the development trend of carbon emissions from energy consumption with a long time series can provide the scientific basis for formulating and implementing regional carbon-reduction strategies. Based on DMSP-OLS and NPP-VIIRS night-time light data, a pixel-scale estimation model of energy-consumption carbon emissions in Jiangsu Province from 2000 to 2019 was constructed. The spatiotemporal evolution characteristics and influencing factors were analyzed using the GIS method and a GTWR (geographically and temporally weighted regression) model. The results showed that: (1) The goodness of fit of the image-fusion correction of the two night-time light data sources from 2012 to 2013 was 0.894; the goodness of fit of the carbon-emission estimation model by stages was above 0.99; and the average relative error was 7.71%, which met the requirement for the estimation accuracy. (2) During the study period, the total carbon emissions from energy consumption in Jiangsu Province continued to increase, rising from 94.7618 million tons to 313.3576 million tons, with an annual growth rate of 6.50%; and the growth rate presented an upward trend of “slow-accelerate-decelerate”. Spatially, it showed an unbalanced distribution pattern of “low north and high south”. (3) Per-capita GDP and energy intensity were the core driving factors affecting carbon emissions in Jiangsu Province over the past 20 years. Energy intensity had the greatest driving effect on carbon emissions in southern Jiangsu, while per-capita GDP had the greatest influence in central and northern Jiangsu. Coordinating the relationship between central, north, and south Jiangsu is of great significance for the realization of the sustainable economic and social development of the double carbon goal. Full article