Nighttime Lights Time Series Research Articles

Spatio-temporal variations and influencing factors of energy-related carbon emissions for Xinjiang cities in China based on time-series nighttime light data

Spatio-temporal variations and influencing factors of energy-related carbon emissions for Xinjiang cities in China based on time-series nighttime light data

A New Framework for Reconstructing Time Series DMSP-OLS Nighttime Light Data Using the Improved Stepwise Calibration (ISC) Method

Calibration and reconstruction of time series DMSP-OLS nighttime light images are critical for understanding urbanization processes and the evolution of urban spatial patterns from a unique perspective. In this study, we developed an improved stepwise calibration (ISC) method based on numerical constancy to correct and reconstruct the time series of China’s regional nighttime light data, thus eliminating the drawbacks of the invariant target region method. We evaluated the different calibration methods and quantitatively validated the calibrated nighttime light data using gross domestic product (GDP) and electricity consumption (EC) at municipal, provincial, and national scales. The results indicated that the ISC method demonstrated its advantage in screening stable lit pixels and maintaining the temporal variability of multi-year nighttime light variation. The variation curve of reconstructed multi-year nighttime light obtained by the ISC method based on numerical constancy was more consistent with the actual urban development. The ISC method retained the original data’s most abundant and complete information than other calibration methods. Moreover, the significant advantages of this method in the low-light high-variation regions and high-light low-variation regions offered new possibilities for understanding the development of small- and medium-sized nighttime light centers such as towns and villages from a nighttime light perspective. This is an advantage that other calibration methods do not offer. The correlation between the multi-year nighttime light dataset obtained by the ISC method and the socio-economic data was significantly improved. The correlation coefficients with GDP and EC are 0.9695 and 0.9923, respectively. Last but not least, the ISC method is more straightforward to implement. The new framework developed in this study produces a more accurate and reliable long time series nighttime light dataset and provides quality assurance for subsequent research in socio-economic development, urban development, natural disasters, and other fields.

Exploring the industrial growth and poverty alleviation through space-time data mining from night-time light images: a case study in Eastern Economic Corridor (EEC), Thailand

ABSTRACT In low-middle income countries, industrial development is essential in driving economic growth, improving social progress, and creating employment to alleviate poverty. This study used the time series of night-time light (NTL) from 2000 to 2020 to assess the industrial park growth and its impact on poverty reduction at the village level. The emerging hotspot analysis has been used to identify the space-time pattern of night-time light data over the study period. Our study reveals that industrial parks keep growing and generate new neighbourhood growth. NTL growth patterns and Gross Provincial Product (GPP) are significantly correlated. Also, the multidimensional poverty index (MPI) increases with the distance from industrial park areas. Conclusively, using space-time data mining with NTL time series data can help the local government understand and monitor industrial growth at a low cost. The NTL data is a suitable proxy indicator for studying human activities and solving the incomplete and discontinued dataset. A further study utilizing the time series NTL data is still beneficial for developing countries.

Trend analysis and Applicability of Fuzzy Time Series Models to Night-time Luminosity Data of Uttarakhand state, India

Objectives: Application of trend detection and fuzzy time series models to predict the growth of Uttarakhand state using night-Time Lights (NTL) dataset (Visible Infrared Imaging Radiometer Suite (VIIRS) night-time lights). Methods: In this work, we propose a methodology to analyze trends and estimate different fuzzy time series models for prediction in the context of forecasting the economic growth of a low-earning income Uttarakhand state. Mann- Kendall tests are used for performing trend analysis of Uttarakhand (India) using night-time luminosity data sets. Forecasting methods based on fuzzy time series models have been presented to deal with uncertainties induced by ambiguities, vagueness, and some other non-probabilistic factors, and have widely been employed in many problems domains. The simulation is done on the Jupyter Notebook platform using night-time luminosity datasets. Findings: We conclude that Uttarakhand and Himachal Pradesh show positive growth in trends for their night-time lights monthly average intensity with magnitude of 0.002182 and 0.002558 respectively. Night time luminosity database can be used as a proxy indicator for the economic growth of the Uttarakhand state as the correlation between NTL time series data of Uttarakhand and its Gross Development Product (GDP) is 0.795641021. Fuzzy time series models can also be used to predict the growth of the Uttarakhand state and the High Order Fuzzy Time Series model gives the accuracy metrics of root mean square (RMSE) as 0.11. Novelty: No previous research work is performed on trend analysis and prediction of growth of a Uttarakhand state using night-time luminosity data sets. Keywords: Fuzzy Systems; Fuzzy Time Series; Prediction; Night time luminosity; Satellite image

A consistent and corrected nighttime light dataset (CCNL 1992–2013) from DMSP-OLS data

Remote sensing of nighttime light can observe the artificial lights at night on the planet’s surface. The Defense Meteorological Satellite Program’s Operational Line Scan (DMSP-OLS) data (1992–2013) provide planet-scale nighttime light data over a long-time span and have been widely used in areas such as urbanization monitoring, socio-economic parameters estimation, and disaster assessment. However, due to the lack of an on-board calibration system, sensor design defects, limited light detection range, and inadequate quantization levels, the applications of DMSP-OLS data are greatly limited by interannual inconsistency, saturation, and blooming problems. To address these issues, we used the power function model based on pseudo-invariant feature, the saturation correction method based on regression model and radiance-calibrated data (SARMRC), and the self-adjusting model (SEAM) to improve the quality of DMSP data, and generated a Consistent and Corrected Nighttime Light dataset (CCNL 1992–2013). CCNL dataset shows good performance in interannual consistency, spatial details of urban centers, and light blooming, which is helpful to fully explore the application potentials of long time series nighttime light data.

Consistent nighttime light time series in 1992–2020 in Northern Africa by combining DMSP-OLS and NPP-VIIRS data

ABSTRACT Human activities modulate the impact of environmental forcing in general and of climate in particular. Information on the spatial and temporal patterns of human activities is in high demand, but scarce in sparsely populated and data-poor regions such as Northern Africa. The intensity and spatial distribution of nighttime lights provide useful information on human activities and can be observed by space-borne imaging radiometers. Our study helps to bridge the gap between the DMSP-OLS data available until 2013 and the NPP-VIIRS data available since 2013. The approach to calibrate the OLS data includes three steps: a) inter-calibrate the OLS DN data acquired by different sensors in 1992–2013; b) calibrate the OLS DN data using VIIRS data in 2013; c) generate synthetic OLS radiance data by degrading the VIIRS data in 2013–2020. We generated a) a time series of calibrated OLS nighttime light radiance data (1992–2013); b) mean annual VIIRS radiance on stable lights at the OLS spatial resolution for 2013–2020; c) synthetic OLS radiance data generated using VIIRS radiance data degraded to match the radiometric specifications of OLS for 2013–2020. The evaluation of these data products in 2013 documented their accuracy and consistency.

A global dataset of annual urban extents (1992–2020) from harmonized nighttime lights

Abstract. Understanding the spatiotemporal dynamics of global urbanization over a long time series is increasingly important for sustainable development goals. The harmonized nighttime light (NTL) time-series composites created by fusing multi-source NTL observations provide a long and consistent record of the nightscape for characterizing and understanding global urban dynamics. In this study, we generated a global dataset of annual urban extents (1992–2020) using consistent NTL observations and analyzed the spatiotemporal patterns of global urban dynamics over nearly 30 years. The urbanized areas associated with locally high intensity human activities were mapped from the global NTL time-series imagery using a new stepwise-partitioning framework. This framework includes three components: (1) clustering of NTL signals to generate potential urban clusters, (2) identification of optimal thresholds to delineate annual urban extents, and (3) check of temporal consistency to correct pixel-level urban dynamics. We found that the global urban land area percentage of the Earth's land surface rose from 0.22 % to 0.69 % between 1992 and 2020. Urban dynamics over the past 3 decades at the continent, country, and city levels exhibit various spatiotemporal patterns. Our resulting global urban extents (1992–2020) were evaluated using other urban remote sensing products and socioeconomic data. The evaluations indicate that this dataset is reliable for characterizing spatial extents associated with intensive human settlement and high-intensity socioeconomic activities. The dataset of global urban extents from this study can provide unique information to capture the historical and future trajectories of urbanization and to understand and tackle urbanization impacts on food security, biodiversity, climate change, and public well-being and health. This dataset can be downloaded from https://doi.org/10.6084/m9.figshare.16602224.v1 (Zhao et al., 2021).

Intercalibration of Luojia1-01 and Suomi-NPP-VIIRS Monthly Nighttime Light Composite Using a Spatial-Temporal Residuals Correction Random Forest Model

Satellite-derived Nighttime light (NTL) data from the Suomi National Polar-orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) and Luojia1-01 (LJ1-01) have been widely used in multiple urban aspects. However, the inconsistency in NTL intensity (the same spatial coverage and month) between the two sensors impedes their integration on finer scales. To alleviate this issue, a novel model was developed to simulate LJ1-01-like NTL in March 2019, considering Guangzhou main city (GZMC) as the study area, Shenzhen, Dongguan, and Huizhou as validation cities to confirm the spatial simulation ability of the model. First, the spatial features originated from VIIRS NTL and geographical data to enhance the prior spatial information for NTL. Second, the temporal features were derived from the VIIRS NTL time series between 2018 and 2020, such as inter-annual and intra-annual trends, to describe the temporal fluctuation of NTL. Third, a spatial-temporal residuals correction random forest (STRCRF) regression model was proposed to generate LJ1-01-like NTL validation with 10-fold cross-validation (CV). It established the nonlinear relationship between LJ1-01 NTL intensity and spatial-temporal features at the pixel level. It generated LJ1-01-like NTL data with a CV-R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 0.982 and a CV-RMSE of 3469. The validation accuracy with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ranged from 0.918 to 0.945, and RMSE varied from 4139 to 7298 in Shenzhen, Dongguan, and Huizhou. The evaluations of LJ1-01-like NTL based on the statistic analyses, spatial patterns, and profile analyses indicated the effectiveness of the STRCRF model in improving NTL intensity consistency at the pixel and landscape level. Moreover, the STRCRF model outperformed the previous related studies and improved the CV-R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> by 0.4. Our approach offers the potential for simulating LJ1-01-like NTL by taking advantage of the prior spatial and temporal information and the ensemble regression with residuals correction. This method could contribute to a higher quality NTL dataset for mapping human activities and monitoring the urban environment.

Modeling the direction and magnitude of angular effects in nighttime light remote sensing

Remote sensing of nighttime light (NTL) offers a unique opportunity to monitor urban dynamics and human socioeconomic activities directly from space. However, angular observations lead to inconsistencies among observations over the same area on different days, introducing uncertainty into daily NTL time series. This study aims to investigate this angular effect and its drivers using the Visible Infrared Imaging Radiometer Suite/Suomi (VIIRS) Black Marble NTL dataset. First, we proposed a conceptual model of the angular effect and hypothesized the mechanism of how urban three-dimensional (3D) landscapes form the anisotropic characteristics of artificial light observations. Second, we quantified the spatial patterns of the angular effect within five representative cities, and identified three distinctive types of angular effects: negative, U-shaped, and positive. Subsequently, the contribution of landscape factors to the direction (i.e., the type) and magnitude (i.e., NTL change rate with angle) of the angular effect is quantified using multinomial logistic regression and mediation analysis, respectively. The results show that the direction of the angular effect is mainly controlled by building height which determines the blocked and visible parts of artificial light at different satellite viewing angles. The magnitude of the angular effect is determined by both NTL brightness and landscape factors. The mediation analysis shows that landscape factors can have a direct effect on the magnitude of the angular effect as well as an indirect effect on the magnitude by affecting NTL brightness. Among the landscape factors, both vegetation and buildings are indicated to be significantly influential factors with direct and indirect effects. The findings of this research deepen our understanding of the NTL angular effect, guide the development of technologies for reconstructing high-quality daily NTL time series by correcting the angular effect, and help us better monitor high-frequency socioeconomic activities.

The Impact of modern Tobacco Agriculture planning on the Spatio-temporal Evolution of Urban construction Land

In recent years, the world tobacco industry has shown a trend of continuous growth in market demand and increasingly concentrated tobacco market, which brings new opportunities to the tobacco agriculture development. However, the modern tobacco agriculture is bound to occupy a large amount of agricultural land, which will have a certain impact on the distribution of urban construction land (UCL). Therefore, reasonable and effective modern tobacco agricultural planning plays a vital role in promoting modern agricultural and urban development.In this paper, we took Baiyangdian basin as the study area, applying the long time series nighttime light (NTL) data to extract UCL and analysing the impact of modern tobacco agriculture planning on the spatio-temporal evolution of UCL. Firstly, we used a power function model to fit the two kinds of international mainstream NTL data to form a long time series NTL dataset from 1992 to 2018 to make the NTL data comparable over a long time. Then, the threshold segmentation method based on land use data calibration was applied to extract the UCL in Baiyangdian basin in 2000, 2010 and 2018, and to analyze its spatio-temporal evolution characteristics and patterns by combining the landscape metrics and gravity model. The results show that the UCL in Baiyangdian basin shows the expansion trend centered on the main urban area of Baoding City, land intensification degree has increased, and the modern tobacco agriculture planning has a profound impact on the spatio-temporal distribution of UCL. Our study will provide technical support and experience for the scientific modern tobacco agriculture planning.

Identifying and Classifying Shrinking Cities Using Long-Term Continuous Night-Time Light Time Series

Shrinking cities—cities suffering from population and economic decline—has become a pressing societal issue of worldwide concern. While night-time light (NTL) data have been applied as an important tool for the identification of shrinking cities, the current methods are constrained and biased by the lack of using long-term continuous NTL time series and the use of unidimensional indices. In this study, we proposed a novel method to identify and classify shrinking cities by long-term continuous NTL time series and population data, and applied the method in northeastern China (NEC) from 1996 to 2020. First, we established a long-term consistent NTL time series by applying a geographically weighted regression model to two distinct NTL datasets. Then, we generated NTL index (NI) and population index (PI) by random forest model and the slope of population data, respectively. Finally, we developed a shrinking city index (SCI), based on NI and PI to identify and classify city shrinkage. The results showed that the shrinkage pattern of NEC in 1996–2009 (stage 1) and 2010–2020 (stage 2) was quite different. From stage 1 to stage 2, the shrinkage situation worsened as the number of shrinking cities increased from 102 to 162, and the proportion of severe shrinkage increased from 9.2% to 30.3%. In stage 2, 85.4% of the cities exhibited population decline, and 15.7% of the cities displayed an NTL decrease, suggesting that the changes in NTL and population were not synchronized. Our proposed method provides a robust and long-term characterization of city shrinkage and is beneficial to provide valuable information for sustainable urban planning and decision-making.

Quantifying spatiotemporal patterns of shrinking cities in urbanizing China: A novel approach based on time-series nighttime light data

The shrinking of cities has become an increasingly global phenomenon, posing challenges for sustainable urban development. However, most focus remains on Europe and North America, and relatively little attention has been paid to the East Asia, especially the urbanizing China. Nighttime light (NL) dataset and its features (long-term time-series free access and large coverage) provide an alternative means to quantify shrinking cities. Here, we developed a new approach to identify shrinking cities and measure urban shrinkage, using corrected-integrated DMSP/OLS and NPP/VIIRS NL data. Based on this approach, we quantified the spatiotemporal patterns of shrinking cities in China from 1992 to 2019. Our study identified 153 shrinking cities in China during the study period, accounting for 23.39% of all 654 cities. These shrinking cities were widely distributed across eight economic regions and most provinces. The number of shrinking cities changed periodically and peaked following the Asian Financial Crisis in 1997 and again after the Global Economic Crisis in 2008. The cities that experienced the greatest shrinkage intensity were mainly distributed in northeast China, with severe urban shrinkage occurring between 2008 and 2013. The new approach proposed in this study can effectively identify shrinking city hotspots and key periods of urban shrinkage. Our findings suggest that sustainable urban development in China must consider shrinking cities, which are faced with challenging and urgent sustainability issues different from those by rapidly growing cities.

Quantifying uncertainties in nighttime light retrievals from Suomi-NPP and NOAA-20 VIIRS Day/Night Band data

Satellite observations of nighttime lights (NTL) from Suomi-NPP and NOAA-20 VIIRS Day/Night Band data have been widely used to estimate human activities. Long-term changes such as urban development and abrupt short-term changes such as power outages have been monitored from temporal NTL acquired by satellites. While high temporal NTL variation has been found across NTL data of varying temporal scale (e.g., daily, monthly, and annual composites), the sources of measurement error and uncertainty are poorly understood. This paper quantifies the sources of VIIRS-derived NTL uncertainty due to view-illumination geometry, surface Bidirectional Reflectance Distribution Function (BRDF)/albedo, and the effects of snow cover, lunar irradiance, aerosol loading, cloud mask, vegetation, geometry, and ephemeral artifacts (e.g., the Aurora Borealis). Based on this current assessment of NASA Black Marble retrievals (VNP46, Collection V001), we found that angular and atmospheric effects dominate retrieval uncertainty. Errors introduced by upstream data inputs (e.g., a coarser nighttime snow cover flag and misclassification errors in the existing VIIRS nighttime cloud mask) were also found to impact retrieval quality. Despite these challenges, a consistent daily NTL time series record can be routinely generated from top-of-atmosphere VNP46 radiances. Key recommendations include: (1) the use of lunar-BRDF adjusted and atmospherically corrected NTL (i.e., as identified as high-quality retrievals in the VNP46 QA fields), (2) development and improvement to the VIIRS snow cover and cloud masks algorithms to accurately reflect NTL retrieval conditions, (3) characterizing seasonal variations in NTL due to vegetation and snow, (4) reducing geometric effects due to the spatial mismatch of gridded pixel and observation footprint, (5) employing angularly-consistent NTL observations from multiple VIIRS instruments (i.e., Suomi-NPP and NOAA-20) to reduce pixel-based uncertainties and address persistent data gaps, and (6) being mindful of surface-reflected radiance from aurora events at mid-to-high latitudes.

Mapping Impervious Surface Areas Using Time-Series Nighttime Light and MODIS Imagery

Mapping impervious surface area (ISA) dynamics at the regional and global scales is an important task that supports the management of the urban environment and urban ecological systems. In this study, we aimed to develop a new method for ISA percentage (ISA%) mapping using Nighttime Light (NTL) and MODIS products. The proposed method consists of three major steps. First, we calculated the Enhanced Vegetation Index (EVI)-adjusted NTL index (EANTLI) and performed intra-annual and inter-annual corrections on the DMSP-OLS data. Second, based on the geographically weighted regression (GWR) model, we built a consistent NTL product from 2000 to 2019 by performing an intercalibration between DMSP-OLS and VIIRS images. Third, we adopted a GA-BP neural network model to monitor ISA% dynamics using NTL imagery, MODIS imagery, and population data. Taking the Guangdong–Hong Kong–Macao Greater Bay as the study area, our results indicate that the ISA% in our study area increased from 7.97% in 2000 to 17.11% in 2019, with a mean absolute error (MAE) of 0.0647, root mean square error (RMSE) of 0.1003, Pearson’s coefficient of 0.9613, and R2 (R-squared) of 0.9239. Specifically, these results demonstrate the effectiveness of the proposed method in mapping ISA and investigating ISA dynamics using temporal features extracted from consistent NTL and MODIS products. The proposed method is feasible when generating ISA% at a large scale at high frequency, given the ease of implementation and the availability of input data sources.

Evaluation of Light Pollution in Global Protected Areas from 1992 to 2018

Light pollution, a phenomenon in which artificial nighttime light (NTL) changes the form of brightness and darkness in natural areas such as protected areas (PAs), has become a global concern due to its threat to global biodiversity. With ongoing global urbanization and climate change, the light pollution status in global PAs deserves attention for mitigation and adaptation. In this study, we developed a framework to evaluate the light pollution status in global PAs, using the global NTL time series data. First, we classified global PAs (30,624) into three pollution categories: non-polluted (5974), continuously polluted (8141), and discontinuously polluted (16,509), according to the time of occurrence of lit pixels in/around PAs from 1992 to 2018. Then, we explored the NTL intensity (e.g., digital numbers) and its trend in those polluted PAs and identified those hotspots of PAs at the global scale with consideration of global urbanization. Our study shows that global light pollution is mainly distributed within the range of 30°N and 60°N, including Europe, north America, and East Asia. Although the temporal trend of NTL intensity in global PAs is increasing, Japan and the United States of America (USA) have opposite trends due to the implementation of well-planned ecological conservation policies and declining population growth. For most polluted PAs, the lit pixels are close to their boundaries (i.e., less than 10 km), and the NTL in/around these lit areas has become stronger over the past decades. The identified hotspots of PAs (e.g., Europe, the USA, and East Asia) help support decisions on global biodiversity conservation, particularly with global urbanization and climate change.

Annual Time Series of Global VIIRS Nighttime Lights Derived from Monthly Averages: 2012 to 2019

A consistently processed annual global nighttime lights time series (2012–2019) was produced using monthly cloud-free radiance averages made from low light imaging day/night band (DNB) data collected by the NASA/NOAA Visible Infrared Imaging Radiometer Suite (VIIRS). The processing steps are modified from the original methods developed to produce annual nighttime lights products from nightly data. Only two years of VIIRS nighttime lights (VNL) were produced with the V.1 methods: 2015 and 2016. Here we report on methods used to produce a V.2 VNL time series from the monthly averages with filtering to remove extraneous features such as biomass burning, aurora, and background. In this case, outlier removal is achieved with a twelve-month median, which discards high and low radiance outliers, thus isolating the background to a narrow range of radiances under 1 nW/cm2/sr. Background areas with no detectable lighting are further isolated using a statistical measure of texture, 3 × 3 data range (DR). The DR threshold for zeroing out background rises as the number of cloud-free observations falls. The V.2 method extends the temporal leverage in the noise filtering by developing the DR threshold from a multiyear maximum DR and a multiyear percent cloud-free grid. Additional noise filtering is achieved by zeroing out grid cells that have low average radiances (&lt;0.6 nW/cm2/sr) and detection in only one or two years out of eight. The spatial extent and average radiance levels are compared for the V.1 and V.2 2015 VNL. For the vast majority of grid cells, the average radiances are nearly the same in the two products. However, the V.2 product has more areas of dim lighting detected. The key advantages of the V.2 time series include consistent processing and threshold levels across all years, thus optimizing the set for change detection analyses.

Characterizing urban land changes of 30 global megacities using nighttime light time series stacks

Worldwide urbanization has brought about diverse types of urban land use and land cover (LULC) changes. The diversity of urban land changes, however, have been greatly under studied, since the major focus of past research has been on urban growth. In this study, we proposed a framework to characterize diverse urban land changes of 30 global megacities using monthly nighttime light time series from VIIRS data. First, we developed a Logistic-Harmonic model to fit VIIRS time series. Second, by leveraging the uniqueness of urban land change and nighttime light data, we incorporated temporal information of VIIRS time series and proposed a new classification scheme to produce monthly maps of built-up areas and to disentangle urban land changes into five categories. Third, we provided an in-depth analysis and comparison of urban land change patterns of the selected megacities. Results demonstrated that the Logistic-Harmonic model yielded a robust performance in fitting VIIRS time series. Temporal features based classification can not only significantly improve the mapping accuracy of built-up areas, especially for regions with heterogeneous built-up and non-built-up landscapes, but also promoted temporal consistency and classification efficiency. Urban land changes occurred in 51% of the built-up pixels of the megacities. Compared with urban growth, other types of urban land change, particularly land use intensification, contributed to an unexpectedly large proportion of the changes (83%). The findings of this study offer an insightful understanding on global urbanization processes in megacities, and evoke further investigation on the environmental and ecological implications of urban land changes.

Intercalibration Between Night-Time DMSP/OLS Radiance Calibrated Images and NPP/VIIRS Images Using Stable Pixels

In this study, we proposed a method for intercalibration between DMSP/OLS radiance calibrated images and NPP/VIIRS images based on stable pixels, while the challenge is that these two datasets do not have overlapped periods which makes it difficult to find the stable pixels. In this study, the outliers, which are the changed pixels, were iteratively removed considering the radiometric and spatial differences between the two kinds of images, and then the stable pixels were obtained. Using the stable pixels, the DMSP/OLS radiance calibrated images and NPP/VIIRS images were intercalibrated based on a regression model. Taking mainland China as an experimental region, we obtained DMSP-like VIIRS images from 2013 to 2019. The growth of night-time light (NTL) from 2010 to later years (e.g., 2013–2019) in provincial regions of mainland China is well correlated to the growth of gross domestic product (GDP), and the averaged ${R^2}$ between the two variables is 0.7126. The growth of NTL from 2010 to later years (e.g., 2013–2019) in 236 municipal regions is also well correlated to the growth of GDP, and the averaged ${R^2} $ is 0.5974. Compared with a multitemporal logistic model which also aims to intercalibrate the two kinds of images, our method showed better a better performance for the intercalibration as our method can generate time series NTL images with stronger correlation to economic growth. Finally, we also found that the NTL, derived from the intercalibrated images, is growing faster in the area where urbanization is faster. This study shows that the proposed method is effective for the intercalibration between the two sets of images which do not have an overlapped time series.

A Relative Radiation Normalization Method of ISS Nighttime Light Images Based on Pseudo Invariant Features

The International Space Station (ISS) offers a unique view from space that provides nighttime light (NTL) images of many parts of the globe. Compared with other NTL remote sensing data, ISS NTL multispectral images taken by astronauts with commercial digital single-lens reflex (DSLR) cameras have the characteristics of free access, high spatial resolution, abundant data and no light saturation, so it plays a unique advantage in the research of small-scale urban planning, optimization of lighting resource allocation and blue light pollution. In order to improve the radiation consistency of ISS NTL images, a relative radiation normalization method of ISS NTL images is proposed in this paper. Pseudo invariant features (PIF) were identified in the cloud-free Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) time series NTL remote sensing annual composite product, and then they were used to derive the relative radiation normalization model of ISS NTL images. The results show that the radiation brightness of ISS NTL images in different regions is normalized to the same gray level with that of DMSP/OLS NTL remote sensing images in the same year, which improves the radiation brightness comparability between different regions of ISS NTL images. This method is universally applicable to all ISS NTL images, which is beneficial to the NTL comparability of ISS NTL image in the regional horizontal and temporal vertical.

Remote sensing modeling of urban density dynamics across 36 major cities in China: Fresh insights from hierarchical urbanized space

Rapid urbanization is considered to be of high relevance for global/local environment change, but its multi-dimensional change features over time have rarely been reported, especially for the process, states and consequences of urban density growth at hierarchical scales of urbanized area. To fill this gap, here, we firstly integrated 27-years’ worth of consistent nighttime light (NTL) time series with land cover dataset, to reconstruct 4 basic, 13 archetypes of temporal trajectories of urban density dynamics with an objected-oriented analysis, and measured their spatio-temporal patterns over hierarchical urbanized space that spanned from landscape patches to 36 major cities in China. Our results showed that, from the micro patch dynamics, the logistic pattern is striking among 4 models over most of time periods, accounting for ~45.63% of the total patch amounts and ~43.73% of the total patch area, respectively, while the exponential pattern contributes the least. Moreover, the structural transition of thematic features revealed their spatial gradient delay from established patches to newly developed patches, from infilling patches to outlying patches. In terms of the space of cities, seven typical clusters of urbanization evolution were identified, however, under different policies, reform practice and developmental history. Additional three alternative states of urban density were also organized for estimated future pathways. Therefore, according to our new insights into inter- and intra-city analysis, both the differentiated strategies for horizontal cities and trade-offs of spatial configuration for urban interior areas were informed to formulate effective science-policy interface.