Assessing Satellite-Observed Nighttime Lights for Monitoring Socioeconomic Parameters in the Republic of Kazakhstan

This paper describes an initial assessment of human-induced nighttime lights acquired by the Defence Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) with respect to its applicability in monitoring settlement patterns, population, electricity consumption, gross domestic product (GDP), and carbon dioxide emissions at different spatial levels in the Republic of Kazakhstan. The results revealed the suitability of DMSP-OLS data to detect both urban expansion and contraction over last two decades caused by the new economic situation following the independence of Kazakhstan in 1991. Relationships between DMSP-OLS urban lit area and the socioeconomic parameters were quantified. The DMSP-OLS data proved to be an effective tool in the monitoring of both the spatial and temporal variability of the examined socioeconomic parameters.     

Use of an inside buffer method to extract the extent of urban areas from DMSP/OLS nighttime light data in North China

Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) nighttime imagery provides a valuable data source for mapping urban areas. However, the spatial extents of large cities are often overestimated because of the effect of over-glow from nighttime light if a fixed thresholding technique is used. In the work reported here, an inside buffer method was developed to solve this issue. The method is based on the fact that the area overestimated is proportional to the extent of the lit area if a fixed threshold is used to extract urban areas in a region/county. Using this method, the extents of urban areas in North China were extracted and validated by interpretations from Landsat Thematic Mapper images. The results showed that the lit areas had a significant linear relationship with the urban areas for 120 representative cities in North China in 2000, with an R² value of over 0.95. This demonstrates that the inside buffer method can be used to extract urban areas. The validation results showed that the inside buffer model developed in 2000 can be directly used to extract the extent of urban areas using more recent nighttime light imagery. This is of great value for the timely updating of urban area databases in large regions or countries.     

New urban map of Eurasia using MODIS and multi-source geospatial data

Urban areas are of paramount significance to both the individuals and communities at local and regional scales. However, the rapid growth of urban areas exerts effects on climate, biodiversity, hydrology, and natural ecosystems worldwide. Therefore, regular and up-to-date information related to urban extent is necessary to monitor the impacts of urban areas at local, regional, and potentially global scales. This study presents a new urban map of Eurasia at 500 m resolution using multi-source geospatial data, including Moderate Resolution Imaging Spectroradiometer (MODIS) data of 2013, population density of 2012, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime lights of 2012, and constructed Impervious Surface Area (ISA) data of 2010. The Eurasian urban map was created using the threshold method for these data, combined with references of fine resolution Landsat and Google Earth imagery. The resultant map was compared with nine global urban maps and was validated using random sampling method. Results of the accuracy assessment showed high overall accuracy of the new urban map of 94%. This urban map is one product of the 20 land cover classes of the next version of Global Land Cover by National Mapping Organizations.     

DMSP-OLS与NPP-VIIRS夜间灯光遥感影像数据整合

DMSP-OLS夜间灯光遥感数据截至2013年,现已被NPP-VIIRS夜间灯光数据取代。因此,要获得长时间序列且稳定的夜间灯光数据集,需要整合两类夜间灯光数据。基于此,本文提出了基于重采样的两类数据整合方法,对2013—2020年NPP-VIIRS数据进行模拟,最终建立了1992—2020年长时间序列校正—模拟DMSP-OLS夜光遥感数据集。结果表明,基于重采样的整合方法效果良好(城市区域Pearson相关系数ρ=0.9852,RMSE=3.4607),整合数据集与相关社会经济参考量高度契合(影像DN值总和与GDP的相关系数ρ=0.946,与人口的相关系数ρ=0.971,二次多项式模型拟合R²≈0.98,RMSE<5.55),优于已有研究。因此,利用该方法整合后的数据集能更好地支撑基于夜间灯光影像的长时间序列研究。     

基于珞珈一号的岳麓区第二、三产业GDP公里网格空间化研究

现代化城市中,夜间活动一般需要黑暗环境中灯光照明的支持,从而使夜间灯光可以间接反映城市中的各类生产生活的活跃情况,而利用遥感手段从高空获得的夜间灯光遥感数据可以大面积地折射城市中的社会经济情况.对于研究城市中主要以非第一产业的GDP指标的二、三产业分布研究具有一定的参考价值.同时考虑到第二、三产业活动的特殊性,通过FR...     

Spatialization of electricity consumption of China using saturation-corrected DMSP-OLS data

Electricity is one of the most important components in energy consumption, which is directly related to economic growth, CO₂ emission and global warming. This research intends to estimate spatial distribution of electricity consumption in China, the largest developing country, and analyze the temporal and spatial change of electricity consumption during 1994–2009. The spatial modeling is based on the total electricity consumption of each province and DMSP (Defense Meteorological Satellite Program) – Operational Line-scan System (OLS) data, the latter provides the nighttime light information corresponding to electricity consumption, GDP and population. A simple method was developed to correct the saturated pixels with digital number of 63 in non-radiance-corrected DMSP-OLS data, using cities’ GDP data. The spatial electricity consumption maps were produced during 1994–2009, and they were validated by the electricity consumption records of 101 cities. Finally, the spatial–temporal changes of electricity consumption were analyzed. The results of this research can help to understand the regional discrepancy, especially rural and urban areas of China, of electricity consumption and economic development.     

Monitoring urbanization dynamics in India using DMSP/OLS night time lights and SPOT-VGT data

India is a rapidly urbanizing country and has experienced profound changes in the spatial structure of urban areas. This study endeavours to illuminate the process of urbanization in India using Defence Meteorological Satellites Program – Operational Linescan System (DMSP-OLS) night time lights (NTLs) and SPOT vegetation (VGT) dataset for the period 1998–2008. Satellite imagery of NTLs provides an efficient way to map urban areas at global and national scales. DMSP/OLS dataset however lacks continuity and comparability; hence the dataset was first intercalibrated using second order polynomial regression equation. The intercalibrated dataset along with SPOT-VGT dataset for the year 1998 and 2008 were subjected to a support vector machine (SVM) method to extract urban areas. SVM is semi-automated technique that overcomes the problems associated with the thresholding methods for NTLs data and hence enables for regional and national scale assessment of urbanization. The extracted urban areas were validated with Google Earth images and global urban extent maps. Spatial metrics were calculated and analyzed state-wise to understand the dynamism of urban areas in India. Significant changes in urban proportion were observed in Tamil Nadu, Punjab and Kerala while other states also showed a high degree of changes in area wise urban proportion.     

Observations of urban and suburban environments with global satellite scatterometer data

A global and consistent characterization of land use and land change in urban and suburban environments is crucial for many fundamental social and natural science studies and applications. Presented here is a dense sampling method (DSM) that uses satellite scatterometer data to delineate urban and intraurban areas at a posting scale of about 1 km. DSM results are analyzed together with information on population and housing censuses, with Landsat Enhanced Thematic Mapper Plus (ETM+) imagery, and with Defense Meteorological Satellite Program (DMSP) night-light data. The analyses include Dallas-Fort Worth and Phoenix in the United States, Bogotá in Colombia, Dhaka in Bangladesh, Guangzhou in China, and Quito in Ecuador. Results show that scatterometer signatures correspond to buildings and infrastructures in urban and suburban environments. City extents detected by scatterometer data are significantly smaller than city light extents, but not all urban areas are detectable by the current SeaWinds scatterometer on the QuikSCAT satellite. Core commercial and industrial areas with high buildings and large factories are identified as high-backscatter centers. Data from DSM backscatter and DMSP nighttime lights have a good correlation with population density. However, the correlation relations from the two satellite datasets are different for different cities indicating that they contain complementary information. Together with night-light and census data, DSM and satellite scatterometer data provide new observations to study global urban and suburban environments and their changes. Furthermore, the capability of DSM to identify hydrological channels on the Greenland ice sheet and ecological biomes in central Africa demonstrates that DSM can be used to observe persistent structures in natural environments at a km scale, providing contemporaneous data to study human impacts beyond urban and suburban areas.     

基于DMSP-OLS和MODIS数据的宁夏城市化与生态环境耦合协调分析

科学认识城市化与生态环境耦合协调机制,对推动黄河流域及黄河所经地区生态保护和高质量发展具有重要意义。本文以宁夏回族自治区为例,采用长时间序列DMSP-OLS夜间灯光数据建立了适用于宁夏的城市夜间灯光指数（UNLI）,并利用MOD09A1、MOD11A2提出了一种面向大区域尺度的遥感生态指数（RSEI）构建方法。在此基础上,采用耦合协调度模型（CCDM）,探讨分析了2000—2012年宁夏5个地级市城市化与生态环境耦合协调发展过程。结果表明：①基于DMSP-OLS数据建立的宁夏城市夜间灯光指数与人均GDP,以及第一、二、三产业GDP相关性显著（p<0.001）,能够较好地反映宁夏城市化水平。②基于MOD09A1、MOD11A2建立的遥感生态指数能够较好地反映宁夏生态环境质量时空变化特征。③宁夏城市化与生态环境耦合协调度呈稳定上升趋势,整体处于基本协调-生态环境滞后的转型阶段。研究结果对沿黄城市生态保护和高质量发展具有重要参考价值。     

基于NPP-VIIRS夜光遥感数据的京津冀城市群空间结构变化分析

本文基于NPP-VIIRS夜光遥感数据对京津冀城市群2015—2019年的空间结构进行分析,通过灯光值统计、城市位序-规模法则、空间关联测度等方法探究京津冀城市群空间结构及时空变化特征,结合城市总体规划浅析时空变化的原因。结果表明：①京津冀城市群灯光总量整体呈上升趋势,北京市和天津市的灯光增长率最高,其次为河北省廊坊市和石家庄市。②灯光集中分布在高位序城市北京和天津,中小城市不够发达。从时间序列上看,中小型城市快速发展,京津冀各城市有趋于ZIPF理想状态分布的态势。③随着区域交通一体化的推进,城市间的关联度整体增强,北京市与天津市的总体关联度占主导地位,但北部城市与中南地区的交通联系不足,关联度较弱。     

基于夜间灯光数据的南京城镇用地提取

针对多时相夜间灯光影像无法直接对比的问题,该文提出了一种基于不变目标进行不同年份不同卫星夜间灯光数据的校正方法。在系统校正多时相夜间灯光数据的基础上,应用支持向量机分类算法提取城镇用地信息,并分析1992—2013年南京城镇扩张动态变化规律。结果表明:该数据校正方法可以有效减少年际夜间灯光影像之间的异常差异,提高不同年份数据间的连续性和可比较性;支持向量机分类算法提取的4个样区城镇用地信息总体精度和Kappa系数平均值分别为88.35%和0.56,能够准确反映区域城镇发展的实际情况;1992—2013年南京城镇经历先缓慢后快速的扩张过程,主城区在1992年城镇基础上往四周扩张,并沿长江及南北交通走廊发展。     

DMSP-OLS与NPP-VIIRS夜间灯光数据测算统计指标能力评估——以京津冀地区县域GDP、人口及能源消耗为例

夜间灯光数据记录了地球表面的人造灯光强度,是估计社会统计指标的有效手段之一。为了评估DMSP-OLS和NPP-VIIRS 2种夜间灯光数据对社会统计指标的模拟潜力,采用4种常用的灯光校正方法分别对2种夜间灯光数据进行灯光饱和性校正,根据校正后的夜间灯光数据分别建立与京津冀地区县域GDP、人口和能源消耗3种社会统计指标间的线性回归模型,从模型拟合的相关系数、F统计量值与概率p值中分析并评价了2种夜间灯光数据对GDP、人口和能源消耗3种社会统计指标的测算能力。本文研究结果表明：EANTLI法是2种夜间灯光数据的最佳校正方式,而HSI法不适用于夜间灯光数据校正后与县域社会统计指标的线性关系拟合2种夜间灯光数据对GDP的拟合效果都较好,NPP-VIIRS夜间灯光数据估算社会统计指标的拟合能力要优于DMSP-OLS数据。     

Remote sensing and social sensing for socioeconomic systems: A comparison study between nighttime lights and location-based social media at the 500 m spatial resolution

With the advent of “social sensing” in the Big Data era, location-based social media (LBSM) data are increasingly used to explore anthropogenic activities and their impacts on the environment. This study converts a typical kind of LBSM data, geo-tagged tweets, into raster images at the 500 m spatial resolution and compares them with the new generation nighttime lights (NTL) image products, the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) monthly image composites. The results show that the monthly tweet images are significantly correlated with the VIIRS-DNB images at the pixel level. The tweet images have nearly the same ability on estimating electric power consumption and better performance on assessing personal incomes and population than the NTL images. Tweeted areas (i.e. the pixels with at least one posted tweet) are closer to satellite-derived built-up/urban areas than lit areas in NTL imagery, making tweet images an alternative to delimit extents of human activities. Moreover, the monthly tweet images do not show apparent seasonal changes, and the values of tweet images are more stable across different months than VIIRS-DNB monthly image composites. This study explores the potential of LBSM data at relatively fine spatiotemporal resolutions to estimate or map socioeconomic factors as an alternative to NTL images in the United States.     

A global inventory of coral reef stressors based on satellite observed nighttime lights

In this article, we present a satellite-based approach to gather information about the threat to coral reefs worldwide. Three chosen reef stressors – development, gas flaring and heavily lit fishing boat activity – are analysed using nighttime lights data derived from the Defense Meteorological Satellite Program (DMSP) produced at the National Oceanic & Atmospheric Administration, National Geophysical Data Center (NOAA/NGDC). Nighttime lights represent a direct threat to coral reef ecosystems and are an excellent proxy measure for associated human-caused stressors. A lights proximity index (LPI) is calculated, measuring the distance of coral reef sites to each of the stressors and incorporating the stressor's intensity. Colourized maps visualize the results on a global scale. Area rankings clarify the effects of artificial night lighting on coral reefs on a regional scale. The results should be very useful for reef managers and for state administrations to implement coral reef conservation projects and for the scientific world to conduct further research.     

Monitoring growth of built-up areas in indo-gangetic plain using multi-sensor remote sensing data

This paper provides an approach for rapid and accurate estimation of built-up areas on a per pixel-basis using a integration of two coarse spatial resolution remote sensing data namely DMSP-OLS and MODIS NDVI. The DMSP-OLS data due to its free availability, high temporal resolution and wide swath was used for regional level mapping of built-up areas. However, due to its low radiometric resolution, the built-up areas cannot be estimated accurately from the DMSP-OLS data. In present study, the DMSP-OLS data was combined with MODIS NDVI data to develop an Human Settlement Index (HSI) image, which estimated the fraction of built-up area on a per pixel basis. The resultant HSI image conveys more information than both the individual datasets. These temporal HSI images were then used for monitoring urban growth in Indo-Gangetic plains during the 2001–2007 time period. Thus, the present research can be very useful for regional level monitoring of built-up areas from coarse resolution data within limited time and minimal cost.     

These lit areas are undeveloped: Delimiting China’s urban extents from thresholded nighttime light imagery

Nighttime light imagery is a powerful tool to study urbanization because it can provide a uniform metric, lit area, to delimit urban extents. However, lit area is much larger than actual urban area, so thresholds of digital number (DN) values are usually needed to reduce the lit area. The threshold varies greatly among different regions, but at present it is still not very clear what factors impact the changes of the threshold. In this study, urban extent by province for China is mapped using official statistical data and four intercalibrated and geometrically corrected nighttime light images between 2004 and 2010. Lit area in the imagery for most provinces is at least 94% greater than the official amount of urban area. Regression analyses show a significant correlation between optimal thresholds and GDP per capita, and larger thresholds more commonly indicate higher economic level. Size and environmental condition may explain a province’s threshold that is disproportionate to GDP. Findings indicate one threshold DN is not appropriate for multiple (adjacent) province urban extent mapping, and optimal thresholds for one year may be notably different than the next. Province-level derived thresholds are not appropriate for other geographic levels. Brightness of nighttime lights is an advantage over imagery that relies on daylight reflection, and decreases in brightness indicate faster growth in the horizontal direction than the vertical. A province’s optimal threshold does not always maintain an increase with population and economic growth. In the economically developed eastern provinces, urban population densities decreased (and this is seen in the brightness data), while urban population increased.     

SNPP-VIIRS夜间灯光影像去噪方法研究

高精度的夜间灯光数据能够有效反映人类空间活动特征,传统的SNPP-VⅡRS夜间灯光影像去噪方法容易忽略新增加或消失的有效灯光,且未处理灯光异常值,随着时间推移,去噪误差会逐渐增大。因此,本文提出了利用中值滤波与低阈值去噪相结合的方法过滤SNPP-VⅡRS夜间灯光影像中的异常值及背景噪声,并将去噪后的夜间灯光总量与GDP进行相关性分析及精度验证。试验结果表明,通过中值滤波与低阈值去噪结合的方法提取的夜间灯光总量与GDP的相关性优于传统方法,利用该方法建立的模型估算出的亮度值精度更高,证明该方法具有更高的去噪精度。     

Exploring improvement of impervious surface estimation at national scale through integration of nighttime light and Proba-V data

Directly mapping impervious surface area (ISA) at national and global scales using nighttime light data is a challenge due to the complexity of land surface components and the impacts of unbalanced economic conditions. Previous research mainly used the coarse spatial resolution Defense Meteorological Satellite Program’s Operational Linescan System (DMSP OLS) and Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI) data for ISA mapping; the improved spatial resolution and data quality in the Suomi National Polar-orbiting Partnership, Visible Infrared Imaging Radiometer Suite’s Day/Night Band (VIIRS DNB) and in Proba-V data provide a new opportunity to accurately map ISA distribution at the national scale, which has not been explored yet. This research aimed to develop a new index – modified impervious surface index (MISI) – based on VIIRS DNB and Proba-V data to improve ISA estimation and to compare the results with those from the combination of VIIRS DNB and MODIS NDVI data. Landsat data were used to develop ISA data for the typical sites for use as reference data. Regression analysis was used to establish the ISA estimation model in which the dependent variable was from the Landsat data and the independent variable was from the MISI, as well as the previously used Large-scale Impervious Surface Index (LISI). The results indicate that the major error is from the very small or very large proportion of ISA in a unit; improvement of spatial resolution through use of higher spatial resolution nighttime light data (e.g., VIIRS DNB) or NDVI (e.g., Proba-V NDVI) data is an effective approach to improve ISA estimation. Although different indices for the combination of nighttime light and NDVI data have been used, the MISI is especially valuable for reducing the estimation errors for the regions with a small or large ISA proportion.     

Remote sensing of human beings – a perspective from nighttime light

City lights, fishing boats, and oil fields are the major sources of nighttime lights, therefore the nighttime light images provide a unique source to map human beings and their activities from outer space. While most of the scholars focused on application of nighttime light remote sensing in urbanization and regional development, the actual fields are much wider. This paper summarized the applications of nighttime light remote sensing into fields such as the estimation of socioeconomic parameters, monitoring urbanization, evaluation of important events, analyzing light pollution, fishery, etc. For estimation of socioeconomic parameters, the most promising progress is that Gross Domestic Product and its growth rate have been estimated with statistical data and nighttime light data using econometric models. For monitoring urbanization, urban area and its dynamics can be extracted using different classification methods, and spatial analysis has been employed to map urban agglomeration. As sharp changes of nighttime light are associated with important socioeconomic events, the images have been used to evaluate humanitarian disasters, especially in the current Syrian and Iraqi wars. Light pollution is another hotspot of nighttime light application, as the night light is related to some diseases and abnormal behavior of animals, and the nighttime light images can provide light pollution information on large scales so that it is much easier to analyze the effects of light pollutions. In each field, we listed typical cases of the applications. At last, future studies of nighttime light remote sensing have been predicted.     

World energy consumption pattern as revealed by DMSP-OLS nighttime light imagery

Remotely sensed nighttime light (NTL) from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) provides a spatially consistent and cost-effective mean to estimate energy consumption pattern. While previous researches have documented the application of NTL to predict electric power consumption (EPC) with varying degrees of success, few have systematically studied the possible factors affecting the EPC-NTL relationship. Moreover, no substantial research effort has been made to relate overall energy consumption (OEC) to NTL. This study investigated key factors governing the EPC/OEC-NTL relationship by examining the influences of affluence, urbanization, technology, temperature, and NTL pattern. Results show that EPC increased with higher per capital GDP, urbanization rate, and high-technology exports, and lower agricultural development, both globally and regionally. Meanwhile, EPC generally reduced with higher temperature and more agglomerate human activities. A strong OEC-NTL relationship was found; but the influencing factors to the OEC-NTL relationship varied across regions due to the natures of energy use. These factors must be considered especially for the studies of less-affluent regions where NTL was undetectable by the DMSP-OLS sensor.