Nighttime Light Data Research Articles

Developing a Pixel-Scale Corrected Nighttime Light Dataset (PCNL, 1992–2021) Combining DMSP-OLS and NPP-VIIRS

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.

Factors impacting ecological network in Changsha-Zhuzhou-Xiangtan urban agglomeration, China—Based on the perspective of functional performance

Research on ecological network of highly urbanised areas still suffers from a lack of species specificity in the identification of sources and corridors, solidified identification range with errors, and neglect of stepping stones. This study examined the evolution of ecological network components (habitats and corridors) of the active urban birds, with factors that impact them, from the perspective of functional performance, with the aim of evaluating ecological networks using a flexible identification approach. Habitat function performance (level of ecological source function performance, SFP; level of ecological stepping-stone function performance, SsFP) and corridor function performance (CFP) per grid were constructed using a flexible identification strategy to ensure that the ecological network function of each grid was not overlooked. Additionally, nonlinear relationships between bird habitats in ecological network components and three driving factor types such as topography, resistance, and landscape pattern were explored at the global scale using the Moran’s I, geographical detector, and regression equations. Local regressions were used to determine the impact of regional landscape patterns on CFP for management recommendations. The following results were observed: (1) Of the 238 grids with significant continuous changes in SFP and SsFP, scenario 16 (S16: SFP continuously decreasing with SsFP continuously increasing) accounted for 33.40%, indicating more grids had a transfer in primary function performance from source to stepping stones, mainly in the central part of Changsha-Zhuzhou-Xiangtan urban agglomeration (CZXUA). The amount of change in the continuous change scenario was concentrated within the range 0.2–0.6. (2) Spatial correlation analysis showed that the positive indicators, percentage of construction land (CLP) and nighttime lighting data (NLD), and the negative indicator, normalised difference vegetation index (NDVI), in resistance had consistently increasing negative and positive correlations with SFP, whereas the correlations with the landscape pattern indices remained stable. Topography and resistance factors contributed more to the spatial heterogeneity of SFP, whereas their contribution to SsFP demonstrated the opposite trend. The regression relationship between habitat function performance and explanatory variables showed a high R2 value for nonlinear fitting. (3) Multiscale CFP was identified based on habitat function and the contribution of resistance factors. Geographically weighted regression analysis showed significant positive and negative impacts on CFP at the local scale, with landscape diversity and patchy irregularities providing more spaces for potential ecological corridor functions, and that regional landscape regulation strategies need to be weighed from multiple perspectives. Overall, this study proposes a flexible method for identifying ecological networks and multi-scale urban ecological management strategies.

The Impact of Urban Sprawl on Carbon Emissions from the Perspective of Nighttime Light Remote Sensing: A Case Study in Eastern China

This paper collects the nighttime light data and six control variables of 77 cities in the eastern China region from 2000 to 2020 and analyzes the spatio-temporal changes of urban sprawl and carbon emissions in the eastern China region. Using the baseline regression model, the correlation and influencing factors between urban sprawl and carbon emissions are explored. The results show that although the impact of urban sprawl on carbon emissions in the eastern China region is positive, it is the result of a trade-off of various factors. Different factors have different impacts on carbon emissions, and urban expansion of different city sizes also has different impacts on carbon emissions in the eastern China region. This paper uses nighttime light data to measure the urban sprawl index more finely, directly explores its impact on carbon emissions from the perspective of urban sprawl and conducts an in-depth analysis of multiple external factors and different city types, providing references for decision-makers to construct sustainable low-carbon city development models and low-carbon city planning systems from different dimensions.

Identification and Measurement of Shrinking Cities Based on Integrated Time-Series Nighttime Light Data: An Example of the Yangtze River Economic Belt

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.

Explaining and modeling the impact of industrial co-agglomeration on regional economic growth in China: integrated a quality concern of night-time light perspective.

The double-wheel drive of manufacturing industries and producer services industries is one of the key pathways to promote high-quality development relying on a modern industrial system. This paper explores the impacts of industrial co-agglomeration on regional economic growth in China with systematic consideration of static, dynamic, and spatio-temporal perspectives based on panel data for 285 prefecture-level cities from 2003 to 2020, employing the consolidated night-time light data. The empirical results show that industrial co-agglomeration significantly accelerates regional economic growth, especially high-tech intensity producer services industries, information industries, and finance industries. In addition, its spatial spillover effects are evidently established, which are characterized by cyclic accumulative, feedback features, and distance attenuation. Carrying out robustness tests, the preliminary regression results are verified. The heterogeneous influences are established across cities with different geographical locations, innovation capacities, and resource endowments. The further mechanism analysis indicates that industrial upgrading, technological progress, and efficiency enhancements account for the main channels for a sharp promotion in regional economic growth over the sample period. Furthermore, the government moderates this process more significantly than market forces do, especially when it comes to macroexogenous shocks that are regulated by the government. The findings of this study recommend policymakers to give full play to the positive externalities of industrial co-agglomeration and accelerate the industrial co-agglomeration process in a reasonable manner, so as to promote high-quality economic growth in China.

Spatial compactness and carbon emission: Nighttime light satellite‐based exposure assessment

AbstractScholars of urban sustainability are increasingly interested in the influence of spatial configuration on local environmental performance, with a focus on urban compactness. Spatial compactness with urban density boosts energy‐efficient construction and infrastructure, subsequently encouraging lower carbon emissions. In this study, we first documented the distributional pattern of spatial compactness in the prefectures of Japan using refined measurement and nighttime light satellite data, which is a more consistent indicator of economic activities. Further, we investigate the underlying impact of this compactness on the local carbon emission intensity with quantitative evidence. The regression results indicate that higher compactness in a prefecture corresponds to lower carbon intensity even in different settings. Moreover, robustness checks verify the consistency of the abatement effects. Additionally, the mitigating role of compactness presents a heterogeneous pattern across sectors. The effect is statistically significant only in the manufacturing and household sectors. These findings add to the value of urban compactness and offer important policy implications for addressing sustainable societies in the long term.

Identifying the operational status of container terminals from high-resolution nighttime-light satellite image for global supply chain network optimization

Container terminals are cargo gateways in the global maritime supply chain network. Major container terminals generally operate throughout the year, but do not operate at night, when container vessels are not calling at ports, or when there is no need to handle containers. Terminal congestion can delay containers’ shipping schedules, which impacts the supply chain network. To optimize global logistics, it is therefore important to understand fully the daily operational status of container terminals. A vessels’ automatic identification system data are not sufficient to determine whether containers are being handled in container terminals at night. Remote sensing, especially nighttime-light (NTL) imagery, might solve this problem. Recently, high-resolution images for the CE-SAT-IIB satellite with a pixel resolution of 5.1 m became available to observe NTL. This study assessed the operational status of container terminals based on satellite image taken at night. Eight terminals in the Port of Tokyo, Japan, were selected for the study. A Sentinel-2A image recorded during the day on 7 April 2021, and a CE-SAT-IIB image recorded during the night on 6 April 2021, were obtained. The digital numbers (DNs) of each red-, green-, and blue-(RGB) band image were analyzed, revealing that the red, green, and blue bands, in that order, had higher DNs in the Sentinel-2A daytime image and the CE-SAT-IIB NTL image at all terminals. One of the eight terminals had a low DN in the CE-SAT-IIB RGB image because its lights were off at the time the image was taken. The operational status of the terminals could be verified from the CE-SAT-IIB image by setting the DN threshold to the green or red bands. We also found that the CE-SAT-IIB image could distinguish white-light-emitting diode (LED) lamps from high-pressure sodium lamps based on color differences in the DNs of the RGB bands. If high-resolution NTL sensors were placed onboard microsatellites, a high-frequency observation constellation network could be constructed using a combination of NTL data and daytime images. This study showed the benefits and usefulness of NTL images of ports; the results will contribute to the overall optimization of the global maritime supply chain network.

Spatiotemporal Evolution and Correlation Analysis of Carbon Emissions in the Nine Provinces along the Yellow River since the 21st Century Using Nighttime Light Data

Monitoring carbon emissions is crucial for assessing and addressing economic development and climate change, particularly in regions like the nine provinces along the Yellow River in China, which experiences significant urbanization and development. However, to the best of our knowledge, existing studies mainly focus on national and provincial scales, with fewer studies on municipal and county scales. To address this issue, we established a carbon emission assessment model based on the “NPP-VIIRS-like” nighttime light data, aiming to analyze the spatiotemporal variation of carbon emissions in three different levels of nine provinces along the Yellow River since the 21st century. Further, the spatial correlation of carbon emissions at the county level was explored using the Moran’s I spatial analysis method. Results show that, from 2000 to 2021, carbon emissions in this region continued to rise, but the growth rate declined, showing an overall convergence trend. Per capita carbon emission intensity showed an overall upward trend, while carbon emission intensity per unit of GDP showed an overall downward trend. Its spatial distribution generally showed high carbon emissions in the eastern region and low carbon emissions in the western region. The carbon emissions of each city mainly showed a trend of “several”; that is, the urban area around the Yellow River has higher carbon emissions. Meanwhile, there is a trend of higher carbon emissions in provincial capitals. Moran’s I showed a trend of decreasing first and then increasing and gradually tended to a stable state in the later stage, and the pattern of spatial agglomeration was relatively fixed. “High–High” and “Low–Low” were the main types of local spatial autocorrelation, and the number of counties with “High–High” agglomeration increased significantly, while the number of counties with “Low–Low” agglomeration gradually decreased. The findings of this study provide valuable insights into the carbon emission trends of the study area, as well as the references that help to achieve carbon peaking and carbon neutrality goals proposed by China.

Relationship between the degree and spatial pattern of urban shrinkage based on nighttime light data: a case study of the old industrial area in Northeast China

ABSTRACT Urban shrinkage is a common phenomenon worldwide, but the relationship between the degree and spatial pattern of urban shrinkage is unclear. This paper takes the old northeastern industrial base, a region with a concentration of shrinking cities in China, as the study area. With the help of multi-source remote sensing images, this study first identified the degree and spatial pattern of urban shrinkage, and then investigated their relationship using the chi-square test. A total of 63 physical urban areas were identified as shrinking cities during the period 2013–2018. Among them, the proportion of cities with moderate and severe shrinking was relatively high. The spatial patterns of urban shrinkage are classified as central shrinkage, peripheral shrinkage, perforation shrinkage and global shrinkage. The results of the chi-square test proved that the spatial pattern and degree of urban shrinkage are correlated. Mild shrinkage was usually peripheral shrinkage; moderate shrinkage was usually central and perforation shrinkage; and severe shrinkage was usually global shrinkage. Identifying the degree and spatial pattern of urban shrinkage and investigating their relationship help deepen the understanding of the urban shrinkage phenomenon at a theoretical level and help urban planners and policy makers propose multidimensional strategies to alleviate the development dilemma of shrinking cities on a practical level.

Analysis of Construction Networks and Structural Characteristics of Pearl River Delta and Surrounding Cities Based on Multiple Connections

The Pearl River Delta (PRD) is one of three world-class city clusters in China, which is important for the strategical deployment of the national “Belt and Road”. Based on nighttime lighting data, Baidu index, and train stopping times, this study analyzed the network of spatial patterns and structural evolution of the PRD and surrounding cities via social network analysis and dynamic network visualization, providing new perspectives and ideas for the study of intercity linkages and urban networks. The results provide decision support to the government for urban cluster planning. From 2014 to 2020, the economic network evolved from a uniaxial structure to an “inverted V” structure. The transportation network evolved from a uniaxial structure to a “△” structure. The information network did not show any obvious structural changes during its development, except for a star-shaped radial structure. The PRD city cluster and its surrounding cities exhibited a spatially non-uniform distribution in terms of spatial connections. The total connections between Guangzhou and Foshan and the surrounding cities in terms of economic, transportation, and information functions account for 30%, 28%, and 10% of the total urban connections, respectively. The graph entropy growth rates of the PRD city cluster and surrounding cities in economic, transportation, and information networks from 2014 to 2020 were 39.9%, 115.4%, and 5.1%, respectively. The network structures of economic and transportation networks stabilized eventually. The information network structures are stable.

Spatiotemporal variation characteristics analysis of infrastructure iron stock in China based on nighttime light data

Abstract Iron is one of the most important basic materials in infrastructure development, spatial and temporal variation characteristics analysis of infrastructure iron stocks is conducive to revealing its distribution and change patterns from different scales, which can provide a scientific basis for sustainable urban development and iron resource management in China. In this article, we first calculated provincial infrastructure iron stock data from 2000 to 2020. Then, fitting equations between nighttime lighting data and infrastructure iron stock are constructed to simulate the spatial distribution of China’s infrastructure iron stock at 500 m resolution from 2000 to 2020. Finally, the spatial and temporal dynamics of China’s infrastructure iron stock is analyzed from four scales: national, regional, provincial, and urban agglomeration. The results show as follows: (1) China’s infrastructure iron stock grew at an average annual rate of 26.42% from 2000 to 2020, with China’s infrastructure iron stock increasing 6.28 times over the 21 years. Construction facilities are the most important part of the infrastructure iron stock, and its share is still increasing. (2) On a regional scale, the high-growth type of infrastructure iron stock is mainly distributed in the eastern region, while the no-obvious-growth type is mainly distributed in the western region. The high grade of infrastructure iron stock is mainly distributed in the eastern region, while the low grade is mainly distributed in the western region. (3) On a provincial scale, the highest share of no-obvious-growth type of infrastructure iron stock is in Xinjiang. The highest proportion of infrastructure iron stock of high-growth type is in Jiangsu. The highest proportion of low-grade infrastructure iron stock is in Xinjiang. The highest proportion of infrastructure iron stock of high grade is in Beijing. (4) In terms of urban agglomerations, the high-growth type of infrastructure iron stock is mainly located in Shanghai–Nanjing–Hangzhou, while the no-obvious-growth type is mainly located in the Middle south of Liaoning. The high-grade infrastructure iron stock is mainly distributed in Shanghai–Nanjing–Hangzhou, while the low grade is mainly distributed in Sichuan–Chongqing.

Spatio-temporal Variation and Multi-dimensional Detection of Driving Mechanism of PM2.5 Concentration in the Chengdu-Chongqing Urban Agglomeration from 2000 to 2021

Studies on the spatio-temporal variation and driving mechanism of PM2.5 concentration in the Chengdu-Chongqing urban agglomeration are of great significance for regional atmospheric environment protection and national economic sustainable development. Based on PM2.5 remote sensing data, DEM data, in situ meteorological data, MODIS NDVI data, population density data, nighttime lighting data, road network data, and land use type data, a series of mathematical methods such as Theil-Sen Medium analysis and Mann-Kendall significance test, combined with the Geo-detector model were used to analyze the spatio-temporal variation and multi-dimensional detection of the driving mechanism of PM2.5 concentration in the Chengdu-Chongqing urban agglomeration. The results showed that the overall PM2.5 concentration showed a fluctuating downward trend in the Chengdu-Chongqing urban agglomeration from 2000 to 2021, and the PM2.5 pollution was the most prominent in winter. PM2.5 concentration exhibited obvious spatial heterogeneity with "high in the middle and low in the surrounding areas." The high-PM2.5 concentration areas were mainly concentrated in Zigong, Neijiang, Ziyang, and Guang'an, and the areas with a PM2.5 concentration decrease were mainly concentrated in the west of Chongqing. Influencing detection results showed that the spatial heterogeneity of PM2.5 concentration in the Chengdu-Chongqing urban agglomeration was influenced by the combined effects of climate factors, topographic factors, vegetation cover, and anthropogenic factors. Furthermore, elevation, slope, and road network density were regarded as the dominant factors influencing the spatial heterogeneity of PM2.5 concentration in the study area. Topographic factors and climate factors showed the highest and lowest contribution rate to the spatial heterogeneity of PM2.5 concentration, respectively. The contribution rate of topographic factors and anthropogenic factors had gradually increased, and the contribution rate of climate factors and vegetation cover had gradually decreased in the study area from 2000 to 2021. Interaction detection results showed that the spatial heterogeneity of PM2.5 concentration in the Chengdu-Chongqing urban agglomeration was mostly affected by the interaction effects of elevation and road network density, slope, precipitation, sunshine duration, and land use type. The interaction detection results exhibited obvious regional differences on the city level. For instance, the spatial heterogeneity of PM2.5 concentration in Chengdu, Deyang, and Leshan was mostly affected by the interaction between different influencing types, and the spatial heterogeneity of PM2.5 concentration in Dazhou, Meishan, Ya'an, Ziyang, Neijiang, and Zigong was mostly affected by the interaction within a single influencing type.

Spatiotemporal Heterogeneity of the Characteristics and Influencing Factors of Energy-Consumption-Related Carbon Emissions in Jiangsu Province Based on DMSP-OLS and NPP-VIIRS

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.

The Changes in Nighttime Lights Caused by the Turkey–Syria Earthquake Using NOAA-20 VIIRS Day/Night Band Data

The Turkey–Syria earthquake on 6 February 2023 resulted in losses such as casualties, road damage, and building collapses. We mapped and quantified the areas impacted by the earthquake at different distances and directions using NOAA-20 VIIRS nighttime light (NTL) data. We then explored the relationship between the average changes in the NTL intensity, population density, and building density using the bivariate local indicators of the spatial association (LISA) method. In Turkey, Hatay, Gaziantep, and Sanliurfa experienced the largest NTL losses. Ar Raqqah was the most affected city in Syria, with the highest NTL loss rate. A correlation analysis showed that the number of injured populations in the provinces in Turkey and the number of pixels with a decreased NTL intensity exhibited a linear correlation, with an R-squared value of 0.7395. Based on the changing value of the NTL, the areas with large NTL losses were located 50 km from the earthquake epicentre in the east-by-south and north-by-west directions and 130 km from the earthquake epicentre in the southwest direction. The large NTL increase areas were distributed 130 km from the earthquake epicentre in the north-by-west and north-by-east directions and 180 km from the earthquake epicentre in the northeast direction, indicating a high resilience and effective earthquake rescue. The areas with large NTL losses had large populations and building densities, particularly in the areas approximately 130 km from the earthquake epicentre in the south-by-west direction and within 40 km of the earthquake epicentre in the north-by-west direction, which can be seen from the low–high (L-H) pattern of the LISA results. Our findings provide insights for evaluating natural disasters and can help decision makers to plan post-disaster reconstruction and determine risk levels on a national or regional scale.

Downscaling satellite night-time lights imagery to support within-city applications using a spatially non-stationary model

For mapping and monitoring socioeconomic activities in cities, night-time lights (NTL) satellite sensor images are used widely, measuring the light intensity during the night. However, the main challenge to mapping human activities in cities using such NTL satellite sensor images is their coarse spatial resolution. To address this drawback, spatial downscaling of satellite nocturnal images is a plausible solution. However, common approaches for spatial downscaling employ spatially stationary models that may not be optimal where the data are spatially heterogeneous. In this research, a geostatistical model termed Random Forest area-to-point regression Kriging (RFATPK) was employed to disaggregate coarse spatial scale VIIRS NTL images (450 m) to a fine spatial scale (100 m). The RF predicts at a coarse resolution from fine spatial resolution variables, such as a Population raster. ATPK then downscales the coarse residuals from the RF prediction. In numerical experiments, RFATPK was compared with three benchmark techniques, including the simple Allocation of pixel values from the coarse resolution NTL data, Machine Learning with Splines and Geographically Weighted Regression. The downscaled results were validated using fine resolution LuoJia 1-01 satellite sensor imagery. RFATPK produced more accurate disaggregated images than the three benchmark approaches, with mean root mean square errors (RMSE) for the year 2018 of 13.89 and 6.74 nWcm−2 sr−1, for Mumbai and New Delhi, respectively. Also, the property of perfect coherence, measured by the Correlation Coefficient, was preserved consistently when applying RFATPK and was almost 1 for all years. The applicability of the disaggregated NTL data to monitor socioeconomic activities at the within-city scale against the reference NTL was illustrated by utilizing them as a proxy for the Gross National Income (GNI) per capita and the Night Light Development Index. The GNI estimation from the downscaled NTL outperformed the coarse resolution NTL when examining their coefficients of determination, with R2 of 0.67 and 0.47 for the GNI estimation using the fine and coarse resolution NTL data, respectively. For the Night Light Development Index (NLDI), the results of the index were compared by measuring their correlation with the Human Development Index (HDI). The NLDI from the downscaled NTL outperformed the coarse resolution NTL when measuring the correlation with the HDI, with Pearson’s correlation coefficients of −0.48 and −0.35 for the NLDI using the fine and coarse resolution NTL data, respectively, for New Delhi. The outcomes indicate that RFATPK provides more accurate predictions than the three benchmark techniques and the downscaled NTL data are more suitable for fine scale socioeconomic applications, as demonstrated by the NLDI and GNI. This research, thus, shows that the RFATPK solution for NTL disaggregation can facilitate data enhancement for fine-scale sub-national applications in social sciences and can be generalized worldwide by including other cities as well as other applications.

Drought monitoring based on temperature vegetation dryness index and its relationship with anthropogenic pressure in a subtropical humid watershed in China

Drought has serious impacts on agricultural production and ecological environment in China. Study on the causes of drought and drought monitoring technology is crucial for drought mitigation. This study conducts drought monitoring based on temperature vegetation dryness index (TVDI) and investigates with the impact of anthropogenic pressure in the Poyang Lake basin (PYLB). In the study, the MODIS normalized vegetation index (NDVI) and land surface temperature (LST) products from 2000 to 2021 were selected to construct NDVI-LST feature space processed with elevation correction, from which the monthly TVDI data was calculated. The land use data and nighttime light data were used to construct the anthropogenic pressure index (API). The results indicate that the frequency of drought in the PYLB is higher in the south and lower in the north. According to drought influence scope (DIS), the occurrence frequency of basin-wide drought (DIS ≥ 50%) in the PYLB is the highest, followed by regional drought (33%≤DIS < 50%), partial local drought (25%≤DIS < 33%), and local drought (10%≤DIS < 25%). The basin as a whole showed an obvious wetting trend in spring and a drying trend in autumn. During the study period, API in the north of PYLB was obviously higher than that in the south, and showed a general increasing trend. The contribution of human activities to drought in the PYLB increased exponentially with the improvement of social development level. Meanwhile, the impact of human activities on drought shows spatial differentiation. Ecological protection and green development have been implemented in the northerly region of the PYLB, and anthropogenic pressure inhibited the degree of drought. Oppositely, extensive socioeconomic development in the central and southern parts of the PYLB has caused damage to the environment and indirectly aggravated regional drought. This study makes important contributions to exploring the response between remote sensing drought monitoring and anthropogenic pressure and promoting regional drought mitigation in humid areas.

Polycentric Spatial Structure Evolution and Influencing Factors of the Kunming–Yuxi Urban Agglomeration: Based on Multisource Big Data Fusion

The polycentric spatial structure is the most common spatial form of urban agglomerations, so exploring the evolution of this structure and analyzing its influencing factors is of great significance for the optimization of the spatial structure of urban agglomerations. However, there are relatively few studies on the topic that fuse multisource big data analysis, especially in the urban agglomeration of Western China. Therefore, this study uses a fusion of nighttime light (NTL) data, point of interest (POI) data and LandScan data to identify the polycentric spatial structure and its evolution in the Kunming–Yuxi (Kunyu) urban agglomeration and analyzes the factors that have dominated its evolution at different periods using geographic detectors. Results show that the fusion of multisource big data are more in line with the actual development process of the Kunyu urban agglomeration and the factors that have dominated the spatial evolution at different periods vary but the government and sectors have gradually become increasingly important. This study provides a feasible path for exploring urban spatial evolution through the fusion analysis of multisource big data in the Kunyu urban agglomeration and provides a reference for the key directions of urban agglomeration planning and development at different periods.

Correcting the nighttime lighting data underestimation effect based on light source detection and luminance reconstruction

Nighttime light remote sensing provides a unique perspective on monitoring human activities and socioeconomic conditions. However, early studies on nighttime lighting usually neglected the evaluation of light source detection and luminance conditions in underdeveloped areas. Recent studies have identified a significant nighttime lighting underestimation effect in underdeveloped areas, which makes it difficult to use nighttime lighting as a valid proxy for other variables. To address this problem, this paper proposes a method based on light source detection and luminance reconstruction. Missing luminous pixels will be identified using the rich information provided by daily-scale lighting data and light blooming patterns, and diverse ancillary data will be utilized for the regression reconstruction of luminance values to supplement annual data and to correct the underestimation effect. The effectiveness of this method was verified using data from 7,776 townships in underdeveloped areas in China. The results demonstrated that the proposed method using the corrected NPP/VIIRS data improved light source detection accuracy by 23.23% and 17.71% at the village and township scales, respectively. The correlations between night lighting and proxy variables were also improved. Spearman's rank correlation coefficients between lighting and population, GDP, and electricity consumption at the county scale increased by 0.045, 0.020, and 0.018, respectively. The correlations between lighting and agriculture-related variables also increased to different degrees at both the township and county scales. The proposed method can contribute to improving modeling accuracy and correcting the nighttime lighting underestimation effect in underdeveloped areas. This will provide better support for sustainable development studies.

Local Climate Zone Classification Using Daytime Zhuhai-1 Hyperspectral Imagery and Nighttime Light Data

The tremendous advancement of cities has caused changes to the urban subsurface. Urban climate problems have become increasingly prominent, especially with regard to the intensification of the urban heat island (UHI) effect. The local climate zone (LCZ) is a new quantitative method for analyzing urban climate that is based on the kind of urban surface and can effectively deal with the problem of the hazy distinction between urban and rural areas in UHI effect research. LCZs are widely used in regional climate modeling, urban planning, and thermal comfort surveys. Existing large-scale LCZ classification methods usually use visual features of optical images, such as spectral and textural features. There are many problems with hyperspectral LCZ extraction over large areas. LCZ is an integrated concept that includes features of the geography, society, and economy. Consequently, it makes sense to consider the characteristics of human activity and the visual features of the images to interpret them accurately. ALOS_DEM data can depict the city’s physical characteristics; however, images of nighttime lights are crucial indicators of human activity. These three datasets can be used in combination to portray the urban environment. Therefore, this study proposes a method for fusing daytime and nighttime data for LCZ mapping, i.e., fusing daytime Zhuhai-1 hyperspectral images and their derived feature indices, ALOS_DEM data, and nighttime light data from Luojia-1. By combining daytime and nighttime information, the proposed approach captures the temporal dynamics of urban areas, providing a more complete representation of their characteristics. The integration of the data allows for a more refined identification and characterization of urban land cover. It comprehensively integrates daytime and nighttime data, exploits synergistic information from multiple sources, and provides higher accuracy and resolution for LCZ mapping. First, we extracted various features, namely spectral, red-edge, and textural features, from the Zhuhai-1 images, ALOS_DEM data, and nighttime light data from Luojia-1. Random forest (RF) and XGBoost classifiers were used, and the average impurity reduction method was employed to assess the significance of the variables. All the input variables were optimized to select the best combination of variables. The results from a study of the 5th ring road area of Beijing, China, revealed that the technique achieved LCZ mapping with good precision, with a total accuracy of 87.34%. In addition, to examine and contrast the effects of various feature indices on the LCZ classification accuracy, feature combination methods were used. The results of the study showed that the accuracies of LCZ classification in terms of spectral and textural were improved by 2.33% and 2.19% using the RF classifier, respectively. The radiation brightness value (RBV) (GI value = 0.0212) attained the classification’s highest variable importance value; the DEM also produced a high GI value (0.0159), indicating that night lighting and landform features strongly influence LCZ classification.

Identification and Optimization Strategy for the Ecological Security Pattern in Henan Province Based on Matching the Supply and Demand of Ecosystem Services

Ecological security pattern construction is a fundamental approach to ensuring regional ecological security and enhancing human welfare. Taking Henan Province, a typical region of China’s main agricultural production area, as the study area, we use multi-source spatial data to calculate the high-value areas of ecosystem services and identify ecological source sites. On the basis of calculating the degree of land use development and GDP per land and population density to determine high-demand areas of the ecosystem, the ecological resistance surface coefficient is modified with nighttime lighting data, and the ecological corridor between the source site and the high-demand area is extracted using the minimum cumulative resistance model, so as to construct and optimize the regional ecological security pattern. The following results are presented. (1) The total area of ecological source sites in Henan Province is 3.02 × 104 km2, accounting for 18.12% of the total study area, which is concentrated in the mountainous areas of East Henan and South Henan. (2) The high-demand area of ecosystem services has a total area of 4.1 × 104 km2, accounting for 24.73% of the total study area, mainly concentrated in the central and eastern regions of Henan, with poor spatial matching of ecosystem service supply and demand. (3) The total length of ecological corridors is 1062.3 km, and the overall pattern forms the main corridor axes. The identification of ecological corridors focuses on the ecological demand space and puts forward suggestions for the optimization of the regional ecological security pattern based on it.