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Advances in Remote Sensing with Nighttime Lights

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (31 August 2019) | Viewed by 75578

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Special Issue Editors

Dr. Tilottama Ghosh

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Guest Editor

Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines, Golden, CO 80401, USA
Interests: nighttime light remote sensing; socio-economic studies; demography; land use and land cover change; urbanization
Special Issues, Collections and Topics in MDPI journals

Dr. Feng Chi Hsu

E-Mail Website
Guest Editor

Earth Observation Group, the Payne Institute for Public Policy, Colorado School of Mines, 1600 Jackson St, Golden, GO 80401, USA
Interests: nighttime remote sensing; space observed socio-economic activity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The view of the world at night is not only stunning, but also a vivid testimony of human presence on earth. Consequently, nighttime light remote sensing has been popular among researchers for studying presence of human population; patterns of urbanization and land-use; economic activity; impact of natural disasters and wars through loss of lighting; air pollution; light pollution and its impacts on human health and other animals; boat detections; and studies related to fires and flares.

The United States Air Force Defence Meteorological Satellite Program (DMSP) Operation Linescan System (OLS) has been collecting global low light imaging data for more than forty years, and it has been digitally archived and processed by the Earth Observation Group at NOAA since 1992. Even with its known imperfections, the DMSP-OLS low light images established the possibilities of spatial approximations of various socio-economic, and demographic variables, which are otherwise difficult to measure and map at finer spatial resolutions.

In 2011, the first Visible Infrared Imaging Radiometer Suite (VIIRS) instrument aboard the joint NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite was launched. The VIIRS low light imaging data provides considerable improvements over the DMSP-OLS data in terms of spatial resolution, dynamic range, and on-board calibration. The second VIIRS instrument aboard Joint Polar Satellite System (JPSS-1) was launched on November, 2017, and continues to collect low light imaging data. In addition, there are other satellites such as the Israel’s EROS-B, which provides high spatial resolution images of nighttime lights, and China’s Jilin-1 which captures high resolution night-time videos; and the astronaut photos from the International Space Station.

This special issue aims to publish original manuscripts of recent advances in research focusing on nighttime lights and its scientific applications. Review contributions are also welcome. We invite papers covering the following topics:

Potential of new sensors and satellites in estimating nighttime brightness at higher spatial resolutions
Applications of nighttime lights to study various socio-economic, environmental and demographic phenomena
Use of nighttime lights in detecting combustion sources
Studies related to light pollution and its impacts
Amalgamation of nighttime lights and other remote sensing data
Spectral analysis of nighttime lights
New sensor design recommendations for nighttime lights

Dr. Tilottama Ghosh
Dr. Feng Chi Hsu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Nighttime light remote sensing
DMSP-OLS
Suomi NPP VIIRS
Socio-economic applications
Environmental applications
Light Pollution
Detection of combustion sources
Urbanization and urban area mapping
EROS-B
Jilin-1

Published Papers (13 papers)

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21 pages, 3837 KiB

Open AccessArticle

Human Lights

by Ilari Määttä and Christian Lessmann

Remote Sens. 2019, 11(19), 2194; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11192194 - 20 Sep 2019

Cited by 8 | Viewed by 3904

Abstract

Satellite nighttime light data open new opportunities for economic research. The data are objective and suitable for the study of regions at various territorial levels. Given the lack of reliable official data, nightlights are often a proxy for economic activity, particularly in developing countries. However, the commonly used product, Stable Lights, has difficulty separating background noise from economic activity at lower levels of light intensity. The problem is rooted in the aim of separating transient light from stable lights, even though light from economic activity can also be transient. We propose an alternative filtering process that aims to identify lights emitted by human beings. We train a machine learning algorithm to learn light patterns in and outside built-up areas using Global Human Settlements Layer (GHSL) data. Based on predicted probabilities, we include lights in those places with a high likelihood of being man-made. We show that using regional light characteristics in the process increases the accuracy of predictions at the cost of introducing a mechanical spatial correlation. We create two alternative products as proxies of economic activity. Global Human Lights minimizes the bias from using regional information, while Local Human Lights maximizes accuracy. The latter shows that we can improve the detection of human-generated light, especially in Africa. Full article

(This article belongs to the Special Issue Advances in Remote Sensing with Nighttime Lights)

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23 pages, 7737 KiB

Open AccessFeature PaperArticle

The Interannual Calibration and Global Nighttime Light Fluctuation Assessment Based on Pixel-Level Linear Regression Analysis

by Zihao Zheng, Zhiwei Yang, Yingbiao Chen, Zhifeng Wu and Francesco Marinello

Remote Sens. 2019, 11(18), 2185; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11182185 - 19 Sep 2019

Cited by 16 | Viewed by 3387

Abstract

The Operational Linescan System (OLS) carried by the National Defense Meteorological Satellite Program (DMSP) can capture the weak visible radiation emitted from earth at night and produce a series of annual cloudless nighttime light (NTL) images, effectively supporting multi-scale, long-term human activities and urbanization process research. However, the interannual instability and sensor bias of NTL time series products greatly limit further studies of lighting data in time series with OLS. Several calibration models for OLS have been proposed to implement interannual corrections to improve the continuity and consistency of time series NTL products; however, due to the subjective factors intervention and insufficient automation in the calibration process, the interannual correction study of NTL time series images is still worth being developed further. Therefore, to avoid the involvement of subjective factors and to optimize the Pseudo-Invariant Features (PIF) identification, an interannual calibration model Pixel-based PIF (PBPIF) is proposed, which identifies PIF by pixel fluctuation characteristics. Results show that a PBPIF-based model can reduce subjective interference and improve the degree of automation during the NTL interannual calibration process. The calibration performance evaluation based on Total Sum of Lights (TSOL) and Sum of the Normalized Difference Index (SNDI) shows that compared to the traditional PIF-based (tPIF-based) and Ridgeline Sampling Regression based (RSR-based) models, the PBPIF-based one achieves better performance in reducing NTL interannual turbulence and minimizing the deviation between sensors. In addition, based on the corrected NTL time series products, pixel-level linear regression analysis is implemented to maximize the potential of the NTL resolution to produce global Light Intensity Change Coefficient (LICC). The results of global LICC can be widely applied to the detailed study of the characteristics of economic development and urbanization. Full article

(This article belongs to the Special Issue Advances in Remote Sensing with Nighttime Lights)

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19 pages, 4251 KiB

Open AccessArticle

Assessing the Impact of Nightlight Gradients on Street Robbery and Burglary in Cincinnati of Ohio State, USA

by Hanlin Zhou, Lin Liu, Minxuan Lan, Bo Yang and Zengli Wang

Remote Sens. 2019, 11(17), 1958; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11171958 - 21 Aug 2019

Cited by 27 | Viewed by 5185

Abstract

Previous research has recognized the importance of edges to crime. Various scholars have explored how one specific type of edges such as physical edges or social edges affect crime, but rarely investigated the importance of the composite edge effect. To address this gap, this study introduces nightlight data from the Visible Infrared Imaging Radiometer Suite sensor on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS) to measure composite edges. This study defines edges as nightlight gradients—the maximum change of nightlight from a pixel to its neighbors. Using nightlight gradients and other control variables at the tract level, this study applies negative binomial regression models to investigate the effects of edges on the street robbery rate and the burglary rate in Cincinnati. The Akaike Information Criterion (AIC) of models show that nightlight gradients improve the fitness of models of street robbery and burglary. Also, nightlight gradients make a positive impact on the street robbery rate whilst a negative impact on the burglary rate, both of which are statistically significant under the alpha level of 0.05. The different impacts on these two types of crimes may be explained by the nature of crimes and the in-situ characteristics, including nightlight. Full article

(This article belongs to the Special Issue Advances in Remote Sensing with Nighttime Lights)

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20 pages, 8290 KiB

Open AccessEditor’s ChoiceArticle

Automated Extraction of Built-Up Areas by Fusing VIIRS Nighttime Lights and Landsat-8 Data

by Chang Liu, Kang Yang, Mia M. Bennett, Ziyan Guo, Liang Cheng and Manchun Li

Remote Sens. 2019, 11(13), 1571; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11131571 - 02 Jul 2019

Cited by 25 | Viewed by 4941

Abstract

As the world urbanizes and builds more infrastructure, the extraction of built-up areas using remote sensing is crucial for monitoring land cover changes and understanding urban environments. Previous studies have proposed a variety of methods for mapping regional and global built-up areas. However, most of these methods rely on manual selection of training samples and classification thresholds, leading to low extraction efficiency. Furthermore, thematic accuracy is limited by interference from other land cover types like bare land, which hinder accurate and timely extraction and monitoring of dynamic changes in built-up areas. This study proposes a new method to map built-up areas by combining VIIRS (Visible Infrared Imaging Radiometer Suite) nighttime lights (NTL) data and Landsat-8 multispectral imagery. First, an adaptive NTL threshold was established, vegetation and water masks were superimposed, and built-up training samples were automatically acquired. Second, the training samples were employed to perform supervised classification of Landsat-8 data before deriving the preliminary built-up areas. Third, VIIRS NTL data were used to obtain the built-up target areas, which were superimposed onto the built-up preliminary classification results to obtain the built-up area fine classification results. Four major metropolitan areas in Eurasia formed the study areas, and the high spatial resolution (20 m) built-up area product High Resolution Layer Imperviousness Degree (HRL IMD) 2015 served as the reference data. The results indicate that our method can accurately and automatically acquire built-up training samples and adaptive thresholds, allowing for accurate estimates of the spatial distribution of built-up areas. With an overall accuracy exceeding 94.7%, our method exceeded accuracy levels of the FROM-GLC and GUL built-up area products and the PII built-up index. The accuracy and efficiency of our proposed method have significant potential for global built-up area mapping and dynamic change monitoring. Full article

(This article belongs to the Special Issue Advances in Remote Sensing with Nighttime Lights)

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18 pages, 5383 KiB

Open AccessArticle

Aligning Pixel Values of DMSP and VIIRS Nighttime Light Images to Evaluate Urban Dynamics

by Kang Wu and Xiaonan Wang

Remote Sens. 2019, 11(12), 1463; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11121463 - 20 Jun 2019

Cited by 48 | Viewed by 5200

Abstract

The brightness of pixels in nighttime light images (NTL) has been regarded as the proxy of the urban dynamics. However, the great difference between the pixel values of NTL from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) and the Suomi National Polar-orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite (Suomi NPP/VIIRS) poses obstacles to analyze economic and social development with NTL in a continuous temporal sequence. This research proposes a methodology to align the pixel values of both NTL by calibrating annual DMSP images between the years 1992–2013 with a robust regression algorithm with a quadratic polynomial regression model and simulating annual DMSP images with VIIRS images between years 2012 and 2018 with a model consisting of a power function and a Gaussian low pass filter. As a result, DMSP annual images between years 1992–2018 can be produced. Case study of Beijing and Yiwu are conducted and evaluated with local gross domestic product (GDP). Compared with the values of DMSP and VIIRS annual composites, the Pearson correlation coefficients of DMSP and simulated DMSP annual composites in 2012 and in 2013 increase significantly, while the root mean square error (RMSE) decrease evidently. In addition, the correlation of the sum of light of NTL and local GDP is enhanced with a simulation process. These results demonstrate the feasibility of the proposed method in narrowing the gap between DMSP and VIIRS NTL in pixel values. Full article

(This article belongs to the Special Issue Advances in Remote Sensing with Nighttime Lights)

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18 pages, 5806 KiB

Open AccessArticle

Mapping Urban Extent at Large Spatial Scales Using Machine Learning Methods with VIIRS Nighttime Light and MODIS Daytime NDVI Data

by Xue Liu, Alex de Sherbinin and Yanni Zhan

Remote Sens. 2019, 11(10), 1247; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11101247 - 27 May 2019

Cited by 31 | Viewed by 7822

Abstract

Urbanization poses significant challenges on sustainable development, disaster resilience, climate change mitigation, and environmental and resource management. Accurate urban extent datasets at large spatial scales are essential for researchers and policymakers to better understand urbanization dynamics and its socioeconomic drivers and impacts. While high-resolution urban extent data products - including the Global Human Settlements Layer (GHSL), the Global Man-Made Impervious Surface (GMIS), the Global Human Built-Up and Settlement Extent (HBASE), and the Global Urban Footprint (GUF) - have recently become available, intermediate-resolution urban extent data products including the 1 km SEDAC’s Global Rural-Urban Mapping Project (GRUMP), MODIS 1km, and MODIS 500 m still have many users and have been demonstrated in a recent study to be more appropriate in urbanization process analysis (around 500 m resolution) than those at higher resolutions (30 m). The objective of this study is to improve large-scale urban extent mapping at an intermediate resolution (500 m) using machine learning methods through combining the complementary nighttime Visible Infrared Imaging Radiometer Suite (VIIRS) and daytime Moderate Resolution Imaging Spectroradiometer (MODIS) data, taking the conterminous United States (CONUS) as the study area. The effectiveness of commonly-used machine learning methods, including random forest (RF), gradient boosting machine (GBM), neural network (NN), and their ensemble (ESB), has been explored. Our results show that these machine learning methods can achieve similar high accuracies across all accuracy metrics (>95% overall accuracy, >98% producer’s accuracy, and >92% user’s accuracy) with Kappa coefficients greater than 0.90, which have not been achieved in the existing data products or by previous studies; the ESB is not able to produce significantly better accuracies than individual machine learning methods; the total misclassifications generated by GBM are more than those generated by RF, NN, and ESB by 14%, 16%, and 11%, respectively, with NN having the least total misclassifications. This indicates that using these machine learning methods, especially NN and RF, with the combination of VIIRS nighttime light and MODIS daytime normalized difference vegetation index (NDVI) data, high accuracy intermediate-resolution urban extent data products at large spatial scales can be achieved. The methodology has the potential to be applied to annual continental-to-global scale urban extent mapping at intermediate resolutions. Full article

(This article belongs to the Special Issue Advances in Remote Sensing with Nighttime Lights)

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15 pages, 2684 KiB

Open AccessArticle

A Rapid and Automated Urban Boundary Extraction Method Based on Nighttime Light Data in China

by Xiaojiang Liu, Xiaogang Ning, Hao Wang, Chenggang Wang, Hanchao Zhang and Jing Meng

Remote Sens. 2019, 11(9), 1126; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11091126 - 10 May 2019

Cited by 24 | Viewed by 3782

Abstract

As urbanization has progressed over the past 40 years, continuous population growth and the rapid expansion of urban land use have caused some regions to experience various problems, such as insufficient resources and issues related to the environmental carrying capacity. The urbanization process can be understood using nighttime light data to quickly and accurately extract urban boundaries at large scales. A new method is proposed here to quickly and accurately extract urban boundaries using nighttime light imagery. Three types of nighttime light data from the DMSP/OLS (US military’s defense meteorological satellite), NPP-VIIRS (National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite), and Luojia1-01 data sets are selected, and the high-precision urban boundaries obtained from a high-resolution image are selected as the true value. Next, 15 cities are selected as the training samples, and the Jaccard coefficient is introduced. The spatial data comparison method is then used to determine the optimal threshold function for the urban boundary extraction. Alternative high-precision urban boundary truth-values for the 13 cities are then selected, and the accuracy of the urban boundary extraction results obtained using the optimal threshold function and the mutation detection method are evaluated. The following observations are made from the results: (i) The average relative errors for the urban boundary extraction results based on the three nighttime light data sources (DMSP/OLS, NPP-VIIRS, and Luojia1-01) using the optimal threshold functions are 29%, 20%, and 39%, respectively. Compared with the mutation detection method, these relative errors are reduced by 83%, 18%, and 77%, respectively; (ii) The average overall classification accuracies of the extracted urban boundaries are 95%, 96%, and 93%, respectively, which are 5%, 1%, and 7% higher than those for the mutation detection method; (iii) The average Kappa coefficients of the extracted urban boundaries are 61%, 71%, and 61%, respectively, which are 5%, 4%, and 12% higher than for the mutation detection method. Full article

(This article belongs to the Special Issue Advances in Remote Sensing with Nighttime Lights)

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26 pages, 4105 KiB

Open AccessArticle

Cross-Matching VIIRS Boat Detections with Vessel Monitoring System Tracks in Indonesia

by Feng-Chi Hsu, Christopher D. Elvidge, Kimberly Baugh, Mikhail Zhizhin, Tilottama Ghosh, David Kroodsma, Adi Susanto, Wiryawan Budy, Mochammad Riyanto, Ridwan Nurzeha and Yeppi Sudarja

Remote Sens. 2019, 11(9), 995; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11090995 - 26 Apr 2019

Cited by 46 | Viewed by 8500

Abstract

A methodology had been proposed for cross-matching visible infrared imaging radiometer suite (VIIRS) boat detections (VBD) with vessel monitoring system (VMS) tracks. The process involves predicting the probable location of VMS vessels at the time of each VIIRS data collection with an orbital model. Thirty-two months of Indonesian VMS data was segmented into fishing and transit activity types and then cross-matched with the VBD record. If a VBD record is found within 700 m and 5 s of the predicted location, it is marked as a match. The cross-matching indicates that 96% of the matches occur while the vessel is fishing. Small pelagic purse seiners account for 27% of the matches. Other gear types with high match rates include hand line tuna, squid dip net, squid jigging, and large pelagic purse seiners. Low match rates were found for gillnet, trawlers, and long line tuna. There is an indication that VMS vessels using submersible lights can be identified based on consistently low average radiances and match rates under 45%. Overall, VBD numbers exceed VMS vessel numbers in Indonesia by a nine to one ratio, indicating that VIIRS detects large numbers of fishing boats under the 30 Gross Tonnage (GT) level set for the VMS requirement. The cross-matching could be used to identify “dark” vessels that lack automatic identification system (AIS) or VMS. Full article

(This article belongs to the Special Issue Advances in Remote Sensing with Nighttime Lights)

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18 pages, 5685 KiB

Open AccessArticle

Estimation of Poverty Using Random Forest Regression with Multi-Source Data: A Case Study in Bangladesh

by Xizhi Zhao, Bailang Yu, Yan Liu, Zuoqi Chen, Qiaoxuan Li, Congxiao Wang and Jianping Wu

Remote Sens. 2019, 11(4), 375; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11040375 - 13 Feb 2019

Cited by 103 | Viewed by 9472

Abstract

Spatially explicit and reliable data on poverty is critical for both policy makers and researchers. However, such data remain scarce particularly in developing countries. Current research is limited in using environmental data from different sources in isolation to estimate poverty despite the fact that poverty is a complex phenomenon which cannot be quantified either theoretically or practically by one single data type. This study proposes a random forest regression (RFR) model to estimate poverty at 10 km × 10 km spatial resolution by combining features extracted from multiple data sources, including the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) nighttime light (NTL) data, Google satellite imagery, land cover map, road map and division headquarter location data. The household wealth index (WI) drawn from the Demographic and Health Surveys (DHS) program was used to reflect poverty level. We trained the RFR model using data in Bangladesh and applied the model to both Bangladesh and Nepal to evaluate the model’s accuracy. The results show that the R² between the actual and estimated WI in Bangladesh is 0.70, indicating a good predictive power of our model in WI estimation. The R² between actual and estimated WI of 0.61 in Nepal also indicates a good generalization ability of the model. Furthermore, a negative correlation is observed between the district average WI and the poverty head count ratio (HCR) in Bangladesh with the Pearson Correlation Coefficient of -0.6. Using Gini importance, we identify that proximity to urban areas is the most important variable to explain poverty which contribute to 37.9% of the explanatory power. Compared to the study that used NTL and Google satellite imagery in isolation to estimate poverty, our method increases the accuracy of estimation. Given that the data we use are globally and publicly available, the methodology reported in this study would also be applicable in other countries or regions to estimate the extent of poverty. Full article

(This article belongs to the Special Issue Advances in Remote Sensing with Nighttime Lights)

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24 pages, 7420 KiB

Open AccessArticle

Modeling Polycentric Urbanization Using Multisource Big Geospatial Data

by Zhiwei Xie, Xinyue Ye, Zihao Zheng, Dong Li, Lishuang Sun, Ruren Li and Samuel Benya

Remote Sens. 2019, 11(3), 310; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11030310 - 04 Feb 2019

Cited by 23 | Viewed by 4050

Abstract

Understanding the dynamics of polycentric urbanization is important for urban studies and management. This paper proposes an analytical model that uses multisource big geospatial data to characterize such dynamics to facilitate policy making. There are four main steps: (1) main centers and subcenters are identified using spatial cluster analysis and geographically weighted regression (GWR) based on Visible Infrared Imaging Radiometer Suite (VIIRS)/NPP and social media check-in data; (2) the built-up areas are extracted by using Defense Meteorological Satellite Program—Operational Linescan System (DMSP/OLS) gradient images; (3) the economic corridors that connect the main center and subcenters are constructed using road network data from Open Street Map (OSM) with the least-cost distance method; and (4) the major urban development direction is identified by analyzing the changes in built-up areas within the economic corridors. The model is applied to three major cities in northeastern, central, and northwestern China (Shenyang, Wuhan, and Xi’an) from 1992 to 2012. Full article

(This article belongs to the Special Issue Advances in Remote Sensing with Nighttime Lights)

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14 pages, 2346 KiB

Open AccessArticle

Estimation and Mapping of Sub-National GDP in Uganda Using NPP-VIIRS Imagery

by Xuantong Wang, Mickey Rafa, Jonathan D. Moyer, Jing Li, Jennifer Scheer and Paul Sutton

Remote Sens. 2019, 11(2), 163; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11020163 - 16 Jan 2019

Cited by 39 | Viewed by 10303

Abstract

Uganda is one of the poorest nations in the world. It is important to obtain accurate, timely data on socio-economic characteristics sub-nationally, so as to target poverty reduction strategies to those most in need. Many studies have demonstrated that nighttime lights (NTL) can be used to measure human activities. Nevertheless, the methods developed from these studies (1) suffer from coarse resolutions, (2) fail to capture the nonlinearity and multi-scale variability of geospatial data, and (3) perform poorly for agriculture-dependent regions. This study proposes a new enhanced light intensity model (ELIM) to estimate the gross domestic product (GDP) for sub-national units within Uganda. This model is developed by combining the NTL data from the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS), the population data from the Global Human Settlement Layer (GHSL), and information on agricultural production and market prices across several commodity types. This resulted in a gridded dataset for Uganda’s GDP at sub-national levels, to capture the spatial heterogeneity in the economic activity. Full article

(This article belongs to the Special Issue Advances in Remote Sensing with Nighttime Lights)

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16 pages, 4880 KiB

Open AccessArticle

Urbanization and Spillover Effect for Three Megaregions in China: Evidence from DMSP/OLS Nighttime Lights

by Xiaoxin Zhang, Shan Guo, Yanning Guan, Danlu Cai, Chunyan Zhang, Klaus Fraedrich, Han Xiao and Zhuangzhuang Tian

Remote Sens. 2018, 10(12), 1888; https://0-doi-org.brum.beds.ac.uk/10.3390/rs10121888 - 27 Nov 2018

Cited by 14 | Viewed by 3977

Abstract

Urbanization drives human social development and natural environmental changes and shows complex implications for sustainability and challenges of future development, particularly in emerging countries. While extensive studies focus on extracting urban areas more precisely, less attention has been devoted to understand megaregion evolution and its related socioeconomic processes, not by socioeconomic statistics, but by comparing remote sensing based spatiotemporal evolution and the related spillover effect. Three main megaregions (with large area, high population and total gross domestic product) in China are selected for the analysis of development changes in an urbanization (magnitude, development)-diagram, of growth pattern changes based on Gravity Center and weighted Standard Deviation Ellipses and of the megaregions’ spillover effect. Employing the spatiotemporally continuous lighted areas (DN ≥ 12) from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime signal (1992–2013) to the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) leads to the following results: (i) Developments in the (magnitude, development)-diagram indicate 25.97%, 45.95%, and 39.10% of the first (high urbanization, fast development) class of the BTH, YRD, and PRD megaregions are rapidly developing into highly urbanized regions. The first class may slow down in the future like the second (high urbanization, slow development) class acting from 1992 to 2013, and the third (moderate urbanization, fast development) class shows potential to become the first class in the future. (ii) The original core function zones of YRD and PRD have highly developed till 1992 and expanding out with fast development from 1992 to 2013. Contrarily, BTH indicates more fast development toward the original core function zones while spatial expansion. (iii) The gravity distance evolution of the three megaregions shows a tendency towards the geometric distance 2013. However, YRD and PRD (BTH) indicate a light intensity expansion (concentration). This may relate to a positive spillover effect of YRD and PRD upon their neighbor cities, with the strongest signal in the early 21st Century and thereafter adjusting and followed by another positive spillover. Full article

(This article belongs to the Special Issue Advances in Remote Sensing with Nighttime Lights)

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13 pages, 673 KiB

Open AccessFeature PaperLetter

Nighttime Lights and Population Migration: Revisiting Classic Demographic Perspectives with an Analysis of Recent European Data

by Xi Chen

Remote Sens. 2020, 12(1), 169; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12010169 - 03 Jan 2020

Cited by 15 | Viewed by 4091

Abstract

This study examines whether the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime lights can be used to predict population migration in small areas in European Union (EU) countries. The analysis uses the most current data measured at the smallest administrative unit in 18 EU countries provided by the European Commission. The ordinary least squares regression model shows that, compared to population size and gross domestic product (GDP), lights data are another useful predictor. The predicting power of lights is similar to population but it is much stronger than GDP per capita. For most countries, regression models with lights can explain 50–90% of variances in small area migrations. The results also show that the annual VIIRS lights (2015–2016) are slightly better predictors for migration population than averaged monthly VIIRS lights (2014–2017), and their differences are more pronounced in high latitude countries. Further, analysis of quadratic models, models with interaction effects and spatial lag, shows the significant effect of lights on migration in the European region. The study concludes that VIIRS nighttime lights hold great potential for studying human migration flow, and further open the door for more widespread application of remote sensing information in studying dynamic demographic processes. Full article

(This article belongs to the Special Issue Advances in Remote Sensing with Nighttime Lights)

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