Automatic intercalibration of night-time light imagery using robust regression

Li, Xi; Chen, Xiaoling; Zhao, Ying; Xu, Jun; Chen, Fengrui; Li, Hui

doi:10.1080/2150704x.2012.687471

Cited by 78 publications

(67 citation statements)

References 6 publications

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“…Temperate ecosystems have also experienced considerable increases in exposure to artificial light, ranging between 5% and 16% of the area for global ecosystem types. These regions largely coincide with rapid growth of artificial light in Europe, North America and China [1,10,15]. In the Tropical biome, the ecosystems that have experienced greatest increases in artificial light are the subtropical needleleaf and mixed broadleaf/needleleaf forests (16% and 19% respectively).…”

Section: Resultsmentioning

confidence: 99%

“…Quantifying changes is complicated by the lack of calibration between sensors and constant (but unknown) adjustment of the gain control of the optical instrument to provide consistent imagery of cloud. Nevertheless, careful intercalibration of the data can help to standardize the images and minimize both error and bias in order to map and detect changes over time [15,20,21]. Here we use a robust regression technique, quantile regression on the median [10] to intercalibrate DMSP/OLS images and detect changes in brightness over the period 1992 to 2012 (full details given in Methods section).…”

Section: Introductionmentioning

confidence: 99%

“…In addition to providing a measurement of emitted light itself [1,10,13], satellite images of artificial light at night have been shown to be a proxy measure of urbanization, human population density and economic activity at national and regional scales [14][15][16][17][18]. From the perspective of biodiversity conservation, satellite-sensed nighttime lights represent a measure not only of the influence of artificial light, but also of other threats associated with biodiversity loss, such as habitat fragmentation and loss, industrial pollution, resource extraction and human-wildlife conflict.…”

Section: Introductionmentioning

confidence: 99%

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Global Trends in Exposure to Light Pollution in Natural Terrestrial Ecosystems

et al. 2015

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Abstract:The rapid growth in electric light usage across the globe has led to increasing presence of artificial light in natural and semi-natural ecosystems at night. This occurs both due to direct illumination and skyglow -scattered light in the atmosphere. There is increasing concern about the effects of artificial light on biological processes, biodiversity and the functioning of ecosystems. We combine intercalibrated Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) images of stable night-time lights for the period 1992 to 2012 with a remotely sensed landcover product (GLC2000) to assess recent changes in exposure to artificial light at night in 43 global ecosystem types. We find that Mediterranean-climate ecosystems have experienced the greatest increases in exposure, followed by temperate ecosystems. Boreal, Arctic and montane systems experienced the lowest increases. In tropical and subtropical regions, the greatest increases are in mangroves and subtropical needleleaf and mixed forests, and in arid regions increases are mainly in forest and agricultural areas. The global ecosystems experiencing the greatest increase in exposure to artificial light are already localized and fragmented, and often of particular conservation importance due to high levels of diversity, endemism and rarity. Night time remote sensing can play a key role in identifying the extent to which natural ecosystems are exposed to light pollution.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Global Trends in Exposure to Light Pollution in Natural Terrestrial Ecosystems

et al. 2015

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“…Urban structures, including structure height, can be retrieved via SAR data to identify urban change. Likewise, nighttime lights provide input for measuring the rate of change from undeveloped to developed land when inter-calibration methods are used for consistency in multitemporal images [13]. While lights appear in rural human settlements, which are altered landscapes that are less urban; there is a higher brightness factor in urban areas (Figure 2).…”

Section: Urban Extentmentioning

confidence: 99%

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

et al. 2014

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This paper reviews how remotely sensed data have been used to understand the impact of urbanization on global environmental change. We describe how these studies can support the policy and science communities' increasing need for detailed and up-to-date information on the multiple dimensions of cities, including their social, biological, physical, and infrastructural characteristics. Because the interactions between urban and surrounding areas are complex, a synoptic and spatial view offered from remote sensing is integral to measuring, modeling, and understanding these relationships. Here we focus on OPEN ACCESSRemote Sens. 2014, 6 3880 three themes in urban remote sensing science: mapping, indices, and modeling. For mapping we describe the data sources, methods, and limitations of mapping urban boundaries, land use and land cover, population, temperature, and air quality. Second, we described how spectral information is manipulated to create comparative biophysical, social, and spatial indices of the urban environment. Finally, we focus how the mapped information and indices are used as inputs or parameters in models that measure changes in climate, hydrology, land use, and economics.

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“…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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Automatic intercalibration of night-time light imagery using robust regression

Cited by 78 publications

References 6 publications

Global Trends in Exposure to Light Pollution in Natural Terrestrial Ecosystems

Global Trends in Exposure to Light Pollution in Natural Terrestrial Ecosystems

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

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

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