ESTIMATING INDUSTRIAL STRUCTURE CHANGES IN CHINA USING DMSP – OLS NIGHT-TIME LIGHT DATA DURING 1999–2012

The Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) night-time light imagery has been proved to be a powerful tool to monitor economic development with its relatively high spatial resolution at large scales. Night-time lights caused by human activities derived from DMSP-OLS satellite imagery are widely used in socioeconomic parameter estimations and urbanization monitoring. In this paper, DMSP-OLS night-time stable light data from 1999 to 2012 are utilized to analyze inter-annual variation in GDP of per unit light intensity (RGDP) in China. Furthermore, RGDP was compared with statistical data of the tertiary industry structure for 28 provincial regions. The results show that the provincial RGDP decreased abruptly in 2001–2002, 2008–2009 and 2011–2012, which is consistent with the proportional growth of the tertiary industry in GDP. These results indicate that the changes in RGDP can reflect tertiary industry structural changes in China's province-level regions

Application of DMSP/OLS nighttime light images : a meta-analysis and a systematic literature review

© The Author(s), 2014. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Remote Sensing 6 (2014): 6844-6866, doi:10.3390/rs6086844.Since the release of the digital archives of Defense Meteorological Satellite Program Operational Line Scanner (DMSP/OLS) nighttime light data in 1992, a variety of datasets based on this database have been produced and applied to monitor and analyze human activities and natural phenomena. However, differences among these datasets and how they have been applied may potentially confuse researchers working with these data. In this paper, we review the ways in which data from DMSP/OLS nighttime light images have been applied over the past two decades, focusing on differences in data processing, research trends, and the methods used among the different application areas. Five main datasets extracted from this database have led to many studies in various research areas over the last 20 years, and each dataset has its own strengths and limitations. The number of publications based on this database and the diversity of authors and institutions involved have shown promising growth. In addition, researchers have accumulated vast experience retrieving data on the spatial and temporal dynamics of settlement, demographics, and socioeconomic parameters, which are “hotspot” applications in this field. Researchers continue to develop novel ways to extract more information from the DMSP/OLS database and apply the data to interdisciplinary research topics. We believe that DMSP/OLS nighttime light data will play an important role in monitoring and analyzing human activities and natural phenomena from space in the future, particularly over the long term. A transparent platform that encourages data sharing, communication, and discussion of extraction methods and synthesis activities will benefit researchers as well as public and political stakeholders.This work is supported by the 111 project “Hazard and Risk Science Base at Beijing Normal University” under Grant B08008 (Ministry of Education and State Administration of Foreign Experts Affairs, PRC), the State Key Laboratory of Earth Surface Processes and Resource Ecology of Beijing Normal University (No. 2013-RC-03), and the Fundamental Research Funds for the Central Universities (Grant No. 201413037)

Spatiotemporal Variations of City-Level Carbon Emissions in China during 2000–2017 Using Nighttime Light Data

China is one of the largest carbon emitting countries in the world. Numerous strategies have been considered by the Chinese government to mitigate carbon emissions in recent years. Accurate and timely estimation of spatiotemporal variations of city-level carbon emissions is of vital importance for planning of low-carbon strategies. For an assessment of the spatiotemporal variations of city-level carbon emissions in China during the periods 2000–2017, we used nighttime light data as a proxy from two sources: Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data and the Suomi National Polar-orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The results show that cities with low carbon emissions are located in the western and central parts of China. In contrast, cities with high carbon emissions are mainly located in the Beijing-Tianjin-Hebei region (BTH) and Yangtze River Delta (YRD). Half of the cities of China have been making eorts to reduce carbon emissions since 2012, and regional disparities among cities are steadily decreasing. Two clusters of high-emission cities located in the BTH and YRD followed two dierent paths of carbon emissions owing to the diverse political status and pillar industries. We conclude that carbon emissions in China have undergone a transformation to decline, but a very slow balancing between the spatial pattern of high-emission versus low-emission regions in China can be presumed

Nighttime Lights as a Proxy for Economic Performance of Regions

Studying and managing regional economic development in the current globalization era demands prompt, reliable, and comparable estimates for a region’s economic performance. Night-time lights (NTL) emitted from residential areas, entertainment places, industrial facilities, etc., and captured by satellites have become an increasingly recognized proxy for on-ground human activities. Compared to traditional indicators supplied by statistical offices, NTLs may have several advantages. First, NTL data are available all over the world, providing researchers and official bodies with the opportunity to obtain estimates even for regions with extremely poor reporting practices. Second, in contrast to non-standardized traditional reporting procedures, the unified NTL data remove the problem of inter-regional comparability. Finally, NTL data are currently globally available on a daily basis, which makes it possible to obtain these estimates promptly. In this book, we provide the reader with the contributions demonstrating the potential and efficiency of using NTL data as a proxy for the performance of regions

Ghost city extraction and rate estimation in China based on NPP-VIIRS night-time light data

The ghost city phenomenon is a serious problem resulting from the rapid urbanization process in China. Estimation of the ghost city rate (GCR) can provide information about vacant dwellings. This paper developed a methodology to quantitatively evaluate GCR values at the national scale using multi-resource remote sensing data. The Suomi National Polar-Orbiting Partnership–Visible Infrared Imaging Radiometer (NPP-VIIRS) night-time light data and moderate resolution imaging spectroradiometer (MODIS) land cover data were used in the evaluation of the GCR values in China. The average ghost city rate (AGCR) was 35.1% in China in 2013. Shanghai had the smallest AGCR of 21.7%, while Jilin has the largest AGCR of 47.27%. There is a significant negative correlation between both the provincial AGCR and the per capita disposable income of urban households (R

Utility of High Resolution Human Settlement Data for Assessment of Electricity Usage Patterns

Electricity is vital for modern human civilization, and its demands are expected to significantly rise due to urban growth, transportation modernization, and increasing industrialization and energy accessibility. Meeting the present and future demands while minimizing the environmental degradation from electricity generation pathways presents a significant sustainability challenge. Urban areas consume around 75% of global energy supply yet urban energy statistics are scarce all over the world, creating a severe hindrance for the much-needed energy sustainability studies. This work explores the scope of geospatial data-driven analysis and modeling to address this challenge. Identification and measurements of human habitats, a key measure, is severely misconceived. A multi-scale analysis of high, medium, and coarse resolution datasets in Egypt and Taiwan illustrates the increasing discrepancies from global to local scales. Analysis of urban morphology revealed that high-resolution datasets could perform much better at all scales in diverse geographies while the power of other datasets rapidly diminishes from the urban core to peripheries. A functional inventory of urban settlements was developed for three cities in the developing world using very high-resolution images and texture analysis. Analysis of correspondence between nighttime lights emission, a proxy of electricity consumption, and the settlement inventory was the conducted. The results highlight the statistically significant relationship between functional settlement types and corresponding light emission, and underline the potential of remote sensing data-driven methods in urban energy usage assessment. Lastly, the lack of urban electricity data was addressed by a geospatial modeling approach in the United States. The estimated urban electricity consumption was externally validated and subsequently used to quantify the effects of urbanization on electricity consumption. The results indicate a 23% lowering of electricity consumption corresponding to a 100% increase in urban population. The results highlight the potential of urbanization in lowering per-capita energy usage. The opportunity and limits to such energy efficiency were identified with regards to urban population density. The findings from this work validate the applicability of geospatial data in urban energy studies and provide unique insights into the relationship between urbanization and electricity demands. The insights from this work could be useful for other sustainability studies

Electrification, Power Outages and Employment

Although electrification rates have increased in developing countries, the poor quality of electricity still remains a challenge. This paper studies the effects of electrification at the intensive margin, using a fixed effects approach. I find that power outages significantly reduce employment, earnings, and hours of work. A key channel through which outages affect employment is decreased prevalence of small and medium enterprises (SMEs) among households. Evidence indicates that severe outages reduce opportunities for households to indulge in income generating activities. The decrease in employment opportunities is further exacerbated by reduced industrial growth and changes in the industrial composition. The results suggest that unreliable electricity may have a negative implication for job creation in developing countries

Spatial heterogeneity of human activities and its driving factors in karst areas of Southwest China over the past 20 years

Guizhou Province is located in the karst mountain regions of Southwest China, where the ecological environment is extremely fragile and particularly sensitive to human activities. Therefore, understanding the changing characteristics and driving factors of human activity in recent decades is urgent. In this study, least squares, correlation analysis, spatial autocorrelation analysis, and GeoDetector model were used. Based on a large collection of nightlight, topography, and meteorological data, combined with geographical information technology, we investigated the spatial distribution, intensity change, and degree of impact of human activity from 2000 to 2020. The results showed that during the study period, human activities intensity and range were expanding. The human activity areas expanded from 15,963 to 86,923 km2 at an increasing rate of 4,279.2 km2/a, which was 1,118.4 km2/a from 2000 to 2010 and 6,375.3 km2/a from 2010 to 2020. The human activity intensity increased from 0.009 to 0.018, with an increasing rate of 0.0006/a, which was 0.00028/a from 2000 to 2010 and 0.00073/a from 2010 to 2020. The rate of increase in the human activity intensity and the rate of expansion in the human activity range from 2010–2020 were significantly higher than that from 2000–2010, becoming more notable (p ≤ 0.01), especially after 2012. With the expansion of human activities, the center of gravity of human activity moved towards the northeast by 20.71 km. The human activities were primarily distributed in areas with the gentlest slopes (6–15°), middle and low altitudes (489–1,982 m), suitable temperatures (12.36–17.74°C), and abundant precipitation (1,001.99–1,276.99 mm). The research results using the GeoDetector model indicate that slope had the greatest impact on human activities with a q value of 0.1338; precipitation, elevation, and temperature had q values of 0.0626, 0.0253, and 0.0136, respectively. The combined impact between the precipitation and slope was the greatest with a q value of 0.1803. In Guizhou Province, under policy guidance, human activities that promoted vegetation change accounted for 79.60%. This study attempts to enhance sustainable development and provides valuable information on the environmental protection of karst mountain regions