Intercalibration of DMSP-OLS night-time light data by the invariant region method

Wu, Jiansheng; He, Shengbin; Peng, Jian; Liu, Weifeng; Zhong, X. Y.

doi:10.1080/01431161.2013.820365

Cited by 161 publications

(122 citation statements)

References 24 publications

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“…Because DMSP/OLS nighttime light data were produced by different satellites and OLS sensors which had no on-board calibration, we could not directly use the multi-year nighttime light to map the statistical energy consumption, due to the lack of continuity and comparability [35,36]. We first preprocessed the nighttime light data, according to the methodology developed by Liu et al (2012) [37].…”

Section: Mapping Statistical Energy Consumption Datamentioning

confidence: 99%

The Impact of Energy Consumption on the Surface Urban Heat Island in China’s 32 Major Cities

Liao

Wang

et al. 2017

Remote Sensing

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Supported by the rapid economic development in the last few decades, China has become the largest energy consumer in the world. Alongside this, the effect of the anthropogenic heat released from energy consumption is increasingly apparent. We quantified the daytime and nighttime surface urban heat island intensity (SUHII) for the 32 major cities in mainland China, using MODIS land surface temperature data from 2008 to 2012, and estimated the energy consumption intensity (ECI) based on the correlation between energy consumption and the sum of nighttime lights. On this basis, the impact of energy consumption on the surface urban heat island in China's 32 major cities was analyzed, by directly examining the relationship between SUHII and the urban-suburban difference in ECI. The results show that energy consumption has a significantly positive correlation with the nighttime SUHII, but no correlation with the daytime SUHII. It indicates that the cities with a larger urban-suburban difference in ECI have a far greater impact on SUHII during the nighttime. Therefore, the statistical analysis of the historical observation data in this study provides evidence for a long-held hypothesis that the anthropogenic heat released from energy consumption is an important contributor to the urban thermal environment.

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Section: Mapping Statistical Energy Consumption Datamentioning

confidence: 99%

The Impact of Energy Consumption on the Surface Urban Heat Island in China’s 32 Major Cities

Liao

Wang

et al. 2017

Remote Sensing

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“…These include the invariant region and the quadratic regression method proposed by Elvidge et al [23], the second-order regression and optimal threshold method proposed by Liu et al [24], and a power-law regression method proposed by Wu et al [25]. Although studies based on these calibration methods showed performance improvement after the rectification [24,25], the assumption that the nighttime light remains stable over time in a particular area requires a careful choice of the invariant region manually.…”

Section: −1°mentioning

confidence: 99%

“…The absence of inter-satellite calibration and onboard calibration strongly limits the inter-annual comparability of light intensity represented by the digital number (DN) values. To overcome this limitation, different inter-calibration methods, largely based on an assumption that light intensity in some areas did not change throughout the timeframe of interest, were proposed by researchers [23][24][25]36].…”

Section: Types Of Dmsp/ols Datasetsmentioning

confidence: 99%

Application of DMSP/OLS Nighttime Light Images: A Meta-Analysis and a Systematic Literature Review

et al. 2014

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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 OPEN ACCESS Remote Sens. 2014, 6 6845 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.

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“…To resolve the inherent problem of saturation in brightly lit areas, especially urban centers, saturation correction is needed for DMSP-OLS stable light products. A series of saturation correction methods have been proposed so far, and among these the invariant method [30][31][32][33] is widely used and well received. In this paper, the invariant method proposed by Shi et al [31] was used for data processing.…”

Section: Data Collectionmentioning

confidence: 99%

The Uncertainty of Nighttime Light Data in Estimating Carbon Dioxide Emissions in China: A Comparison between DMSP-OLS and NPP-VIIRS

Zhang

Peng

et al. 2017

Remote Sensing

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Nighttime light data can characterize urbanization, economic development, population density, energy consumption and other human activities. Additionally, carbon dioxide (CO 2 ) emissions are closely related to the scope and intensity of human activities. In this study, we assess the utility of nighttime light data as a powerful tool to reflect CO 2 emissions from energy consumption, analyze the uncertainty associated with different nighttime light data for modeling CO 2 emissions, and provide guidance and a reference for modeling CO 2 emissions based on nighttime light data. In this paper, Mainland China was taken as a case study, and nighttime light datasets (the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) nighttime light data and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) nighttime light data) as well as a global gridded CO 2 emissions dataset (PKU-CO 2 ) were used to perform simple regressions at provincial, prefectural and 0.1 • × 0.1 • grid levels, respectively. The analyses are aimed at exploring the accuracy and uncertainty of DMSP-OLS and NPP-VIIRS nighttime light data in modeling CO 2 emissions at different spatial scales. The improvement of nighttime light index and the potential factors influencing the effects of modeling CO 2 emissions based on nighttime light datasets were also explored. The results show that DMSP-OLS is superior to NPP-VIIRS in modeling CO 2 emissions at all spatial scales, and the bigger the scale, the more evident the advantages of DMSP-OLS. When modeling CO 2 emissions with nighttime light datasets, not only the total amount of lights within a given statistical unit but also the agglomeration degree of lights should be taken into account. Furthermore, the geographical location and socio-economic conditions at the study site, such as gross regional product per capita (GRP per capita), population, and urbanization were shown to have an impact on the regression effect of the nighttime lights-CO 2 emissions model. The regression effect was found to be better at higher latitude and longitude areas with higher GRP per capita and higher urbanization, while population showed little effect on the regression effect of the nighttime lights -CO 2 emissions model. The limitation of this study is that the thresholds of potential factors are unclear and the quantitative guidance is insufficient.

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Intercalibration of DMSP-OLS night-time light data by the invariant region method

Cited by 161 publications

References 24 publications

The Impact of Energy Consumption on the Surface Urban Heat Island in China’s 32 Major Cities

The Impact of Energy Consumption on the Surface Urban Heat Island in China’s 32 Major Cities

Application of DMSP/OLS Nighttime Light Images: A Meta-Analysis and a Systematic Literature Review

The Uncertainty of Nighttime Light Data in Estimating Carbon Dioxide Emissions in China: A Comparison between DMSP-OLS and NPP-VIIRS

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