A Case for a New Satellite Mission for Remote Sensing of Night Lights

The physiology and behavior of most life at or near the Earth’s surface has evolved over billions of years to be attuned with our planet’s natural light–dark cycle of day and night. However, over a relatively short time span, humans have disrupted this natural cycle of illumination with the introduction and now widespread proliferation of artificial light at night (ALAN). Growing research in a broad range of fields, such as ecology, the environment, human health, public safety, economy, and society, increasingly shows that ALAN is taking a profound toll on our world. Much of our current understanding of light pollution comes from datasets generated by remote sensing, primarily from two missions, the Operational Linescan System (OLS) instrument of the now-declassified Defense Meteorological Satellite Program (DMSP) of the U.S. Department of Defense and its follow-on platform, the Day-Night Band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on board the Suomi National Polar-Orbiting Partnership satellite. Although they have both proved invaluable for ALAN research, sensing of nighttime lights was not the primary design objective for either the DMSP-OLS or VIIRS-DNB instruments; thus, they have some critical limitations. Being broadband sensors, both the DMSP-OLS and VIIRS-DNB instruments suffer from a lack of spectral information. Additionally, their spatial resolutions are too low for many ALAN research applications, though the VIIRS-DNB instrument is much improved over the DMSP-OLS in this regard, as well as in terms of dynamic range and quantization. Further, the very late local time of VIIRS-DNB observations potentially misses the true picture of ALAN. We reviewed both current literature and guiding advice from ALAN experts, aggregated from a diverse range of disciplines and Science Goals, to derive recommendations for a mission to expand knowledge of ALAN in areas that are not adequately addressed with currently existing orbital missions. We propose a stand-alone mission focused on understanding light pollution and its effects on our planet. Here we review the science cases and the subsequent mission recommendations for NITESat (Nighttime Imaging of Terrestrial Environments Satellite), a dedicated ALAN observing mission.

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Are Karst Rocky Desertification Areas Affected by Increasing Human Activity in Southern China? An Empirical Analysis from Nighttime Light Data

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16214175 ◽

2019 ◽

Vol 16 (21) ◽

pp. 4175

Author(s):

Shi ◽

Yang ◽

Keyword(s):

Human Activities ◽

Socioeconomic Development ◽

Infrared Imaging ◽

Southern China ◽

Annual Growth Rate ◽

Karst Rocky Desertification ◽

Rocky Desertification ◽

Nighttime Lights ◽

Nighttime Light ◽

Night Lights

Due to remarkable socioeconomic development, an increasing number of karst rocky desertification areas have been severely affected by human activities in southern China. Effectively analyzing human activities in karst rocky desertification areas is a critical prerequisite for managing and restoring areas with tremendous negative impacts from desertification. At present, a timely and accurate way of quantifying the spatiotemporal variations of human activities in karst rocky desertification areas is still lacking. In this communication, we attempted to quantify human activities from the corrected Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) nighttime light composite data from 2012 to 2018 based on statistical analysis. The results show that a significant increase of night lights could be clearly identified during the study period. The total nighttime lights (TL) related to severe karst rocky desertification (S) were particularly concentrated in Guizhou and Yunnan. The nighttime light intensity (LI) related to the S areas in Chongqing was the strongest due to its rapid socioeconomic development. The annual growth rate of nighttime lights (GL) has been slow or even negative in Guangdong because of its various karst rocky desertification restoration programs. This communication could provide an effective approach for quantifying human activities and provide useful information about where prompt attention is required for policy-making on the restoration of the karst rocky desertification areas.

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Tsunami Assessment and Evacuation Analysis Using Remote Sensing for Tianya District of Sanya City, China

Journal of Earthquake and Tsunami ◽

10.1142/s1793431119410033 ◽

2019 ◽

Vol 13 (05n06) ◽

pp. 1941003

Author(s):

Jingming Hou ◽

Zhiyuan Ren ◽

Peitao Wang ◽

Juncheng Wang ◽

Yi Gao

Keyword(s):

Remote Sensing ◽

Numerical Model ◽

Infrared Imaging ◽

Remote Sensing Data ◽

Tsunami Inundation ◽

Sensing Data ◽

Inundation Area ◽

Nighttime Lights ◽

Elevation Data ◽

Landsat Satellite

Tsunami is one of the world’s most dangerous marine disaster. In this paper, freely available remote sensing data are applied to study the hazard, vulnerability, and evacuation in the event that a tsunami strikes the district of Tianya in the city of Sanya. Tsunami inundation is calculated using a tsunami numerical model, and the tsunami vulnerability and evacuation in the inundation area are analyzed. Aster Global Digital Elevation Model elevation data are applied to provide input data for the tsunami numerical model and thus obtain tsunami inundation areas, while they are also used to study the surface slope for evacuation. Landsat satellite imagery is used to analyze land–water borders and land cover in both hazard assessment and evacuation analysis. Visible Infrared Imaging Radiometer Suite nighttime lights data provide information of the socioeconomic activity for vulnerability analysis. The analysis results show that the remote sensing data is suitable for tsunami assessment and evacuation analysis of China’s county-level region. We can get a general understanding about tsunami vulnerability and evacuation situation. One kind of remote sensing data can accomplish several tasks, avoiding the error caused by different source data. Remote sensing can play an important role in tsunami assessment.

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Cross-Sensor Nighttime Lights Image Calibration for DMSP/OLS and SNPP/VIIRS with Residual U-Net

Remote Sensing ◽

10.3390/rs13245026 ◽

2021 ◽

Vol 13 (24) ◽

pp. 5026

Author(s):

Dmitry Nechaev ◽

Mikhail Zhizhin ◽

Alexey Poyda ◽

Tilottama Ghosh ◽

Feng-Chi Hsu ◽

...

Keyword(s):

Infrared Imaging ◽

Dynamic Range ◽

Satellite System ◽

Sensor Calibration ◽

Environmental Light ◽

Nighttime Lights ◽

Military Conflicts ◽

Highly Correlated ◽

Polar Satellite

Remote sensing of nighttime lights (NTL) is widely used in socio-economic studies of economic growth, urbanization, stability of power grid, environmental light pollution, pandemics and military conflicts. Currently, NTL data are collected with two sensors: (1) Operational Line-scan System (OLS) onboard the satellites from the Defense Meteorology Satellite Program (DMSP) and (2) Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi NPP (SNPP) and NOAA-20 satellites from the Joint Polar Satellite System (JPSS). However, the nighttime images acquired by these two sensors are incompatible in spatial resolution and dynamic range. To address this problem, we propose a method for the cross-sensor calibration with residual U-net convolutional neural network (CNN). The CNN produces DMSP-like NTL composites from the VIIRS annual NTL composites. The pixel radiances predicted from VIIRS are highly correlated with NTL observed with OLS (0.96 < R2 < 0.99). The method can be used to extend long-term series of annual NTL after the end of DMSP mission or to cross-calibrate same year NTL from different satellites to study diurnal variations.

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Testing for changes in light emissions from certified International Dark Sky Places

International Journal of Sustainable Lighting ◽

10.26607/ijsl.v21i1.92 ◽

2019 ◽

Vol 21 (1) ◽

pp. 11-19 ◽

Cited By ~ 4

Author(s):

Emilee Hyde ◽

Sabine Frank ◽

John C Barentine ◽

Helga Kuechly ◽

Christopher C. M. Kyba

Keyword(s):

Infrared Imaging ◽

Light Emission ◽

Rate Of Change ◽

Light Pollution ◽

Voluntary Certification ◽

Sky Brightness ◽

Legislative Changes ◽

Night Lights ◽

Reduced Light ◽

Night Sky Brightness

The International Dark-Sky Places (IDSP) program of the International Dark-Sky Association is a voluntary certification in which communities commit via legislative changes to move towards more sustainable lighting that reduces light pollution. As over 115 IDSP have now been certified, it is interesting to ask the extent to which this certification results in reduced light emissions. In this paper, we compared trends in upward light emission of 98 communities located in or near IDSP to those of 98 similarly sized communities further away from the IDSP, using a night lights observing satellite (the Visible Infrared Imaging Radiometer Suite Day-Night Band). The current dataset is not sufficient to distinguish the hypothesis that IDSP certification results in a lower rate of change in upward light emissions from the null hypothesis that IDSP certification has no impact. This result is with regard to upward light emissions only: it is possible that certification has resulted in decreases in night sky brightness that the satellite is not able to observe.

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Remote Sensing of Night Lights—Beyond DMSP

Remote Sensing ◽

10.3390/rs11121472 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1472 ◽

Cited By ~ 3

Author(s):

Noam Levin ◽

Christopher C.M. Kyba ◽

Qingling Zhang

Keyword(s):

Remote Sensing ◽

Infrared Imaging ◽

Future Research ◽

Special Issue ◽

Defense Meteorological Satellite Program ◽

Sensing Platforms ◽

New Type ◽

Future Research Directions ◽

New Applications ◽

Night Lights

Remote sensing of night lights differs from other sources of remote sensing in its ability to directly observe human activity from space as well as in informing us on a new type of anthropogenic threat, that of light pollution. This special issue focuses on studies which used newer sensors than the Defense Meteorological Satellite Program - Operational Line-Scan System (DMSP/OLS). Most of the analyses focused on data from the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime sensor (also called the Day/Night Band, or VIIRS/DNB in short), for which the first instrument in the series was launched in 2011. In this editorial, we provide an overview of the 12 papers published in this special issue, and offer suggestions for future research directions in this field, both with respect to the remote sensing platforms and algorithms, and with respect to the development of new applications.

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Potentiality of Using Luojia 1-01 Nighttime Light Imagery to Investigate Artificial Light Pollution

Sensors ◽

10.3390/s18092900 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2900 ◽

Cited By ~ 50

Author(s):

Wei Jiang ◽

Guojin He ◽

Tengfei Long ◽

Hongxiang Guo ◽

Ranyu Yin ◽

...

Keyword(s):

Infrared Imaging ◽

Dynamic Range ◽

Light Pollution ◽

Artificial Light ◽

Radiometric Calibration ◽

Urban Core ◽

Total Light ◽

Successful Launch ◽

Nighttime Light ◽

Nighttime Light Imagery

The successful launch of Luojia 1-01 complements the existing nighttime light data with a high spatial resolution of 130 m. This paper is the first study to assess the potential of using Luojia 1-01 nighttime light imagery for investigating artificial light pollution. Eight Luojia 1-01 images were selected to conduct geometric correction. Then, the ability of Luojia 1-01 to detect artificial light pollution was assessed from three aspects, including the comparison between Luojia 1-01 and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS), the source of artificial light pollution and the patterns of urban light pollution. Moreover, the advantages and limitations of Luojia 1-01 were discussed. The results showed the following: (1) Luojia 1-01 can detect a higher dynamic range and capture the finer spatial details of artificial nighttime light. (2) The averages of the artificial light brightness were different between various land use types. The brightness of the artificial light pollution of airports, streets, and commercial services is high, while dark areas include farmland and rivers. (3) The light pollution patterns of four cities decreased away from the urban core and the total light pollution is highly related to the economic development. Our findings confirm that Luojia 1-01 can be effectively used to investigate artificial light pollution. Some limitations of Luojia 1-01, including its spectral range, radiometric calibration and the effects of clouds and moonlight, should be researched in future studies.

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Impact of Monsoon-Transported Anthropogenic Aerosols and Sun-Glint on the Satellite-Derived Spectral Remote Sensing Reflectance in the Indian Ocean

Remote Sensing ◽

10.3390/rs13020184 ◽

2021 ◽

Vol 13 (2) ◽

pp. 184

Author(s):

Rongjie Liu ◽

Jie Zhang ◽

Tingwei Cui ◽

Haocheng Yu

Keyword(s):

Remote Sensing ◽

Indian Ocean ◽

Infrared Imaging ◽

Atmospheric Correction ◽

Spatiotemporal Pattern ◽

Anthropogenic Aerosols ◽

Remote Sensing Reflectance ◽

The Indian Ocean ◽

Moderate Resolution Imaging Spectroradiometer ◽

Terra Modis

Spectral remote sensing reflectance (Rrs(λ), sr−1) is one of the most important products of ocean color satellite missions, where accuracy is essential for retrieval of in-water, bio-optical, and biogeochemical properties. For the Indian Ocean (IO), where Rrs(λ) accuracy has not been well documented, the quality of Rrs(λ) products from Moderate Resolution Imaging Spectroradiometer onboard both Terra (MODIS-Terra) and Aqua (MODIS-Aqua), and Visible Infrared Imaging Radiometer Suite onboard the Suomi National Polar-Orbiting Partnership spacecraft (VIIRS-NPP), is evaluated and inter-compared based on a quality assurance (QA) system, which can objectively grade each individual Rrs(λ) spectrum, with 1 for a perfect spectrum and 0 for an unusable spectrum. Taking the whole year of 2016 as an example, spatiotemporal pattern of Rrs(λ) quality in the Indian Ocean is characterized for the first time, and the underlying factors are elucidated. Specifically, QA analysis of the monthly Rrs(λ) over the IO indicates good quality with the average scores of 0.93 ± 0.02, 0.92 ± 0.02 and 0.92 ± 0.02 for VIIRS-NPP, MODIS-Aqua, and MODIS-Terra, respectively. Low-quality (~0.7) data are mainly found in the Bengal Bay (BB) from January to March, which can be attributed to the imperfect atmospheric correction due to anthropogenic absorptive aerosols transported by the northeasterly winter monsoon. Moreover, low-quality (~0.74) data are also found in the clear oligotrophic gyre zone (OZ) of the south IO in the second half of the year, possibly due to residual sun-glint contributions. These findings highlight the effects of monsoon-transported anthropogenic aerosols, and imperfect sun-glint removal on the Rrs(λ) quality. Further studies are advocated to improve the sun-glint correction in the oligotrophic gyre zone and aerosol correction in the complex ocean–atmosphere environment.

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Remote sensing to map influence of light pollution on Cory’s shearwater in São Miguel Island, Azores Archipelago

European Journal of Wildlife Research ◽

10.1007/s10344-011-0555-5 ◽

2011 ◽

Vol 58 (1) ◽

pp. 147-155 ◽

Cited By ~ 29

Author(s):

Pedro Rodrigues ◽

Christoph Aubrecht ◽

Artur Gil ◽

Travis Longcore ◽

Chris Elvidge

Keyword(s):

Remote Sensing ◽

Light Pollution ◽

Azores Archipelago ◽

Cory’S Shearwater

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Reviews and syntheses: Australian vegetation phenology: new insights from satellite remote sensing and digital repeat photography

Biogeosciences ◽

10.5194/bg-13-5085-2016 ◽

2016 ◽

Vol 13 (17) ◽

pp. 5085-5102 ◽

Cited By ~ 39

Author(s):

Caitlin E. Moore ◽

Tim Brown ◽

Trevor F. Keenan ◽

Remko A. Duursma ◽

Albert I. J. M. van Dijk ◽

...

Keyword(s):

Remote Sensing ◽

Satellite Remote Sensing ◽

Tropical Rainforests ◽

Vegetation Phenology ◽

Repeat Photography ◽

Tropical Savannas ◽

Diverse Range ◽

Canopy Development ◽

Continental Scale ◽

Digital Repeat Photography

Abstract. Phenology is the study of periodic biological occurrences and can provide important insights into the influence of climatic variability and change on ecosystems. Understanding Australia's vegetation phenology is a challenge due to its diverse range of ecosystems, from savannas and tropical rainforests to temperate eucalypt woodlands, semi-arid scrublands, and alpine grasslands. These ecosystems exhibit marked differences in seasonal patterns of canopy development and plant life-cycle events, much of which deviates from the predictable seasonal phenological pulse of temperate deciduous and boreal biomes. Many Australian ecosystems are subject to irregular events (i.e. drought, flooding, cyclones, and fire) that can alter ecosystem composition, structure, and functioning just as much as seasonal change. We show how satellite remote sensing and ground-based digital repeat photography (i.e. phenocams) can be used to improve understanding of phenology in Australian ecosystems. First, we examine temporal variation in phenology on the continental scale using the enhanced vegetation index (EVI), calculated from MODerate resolution Imaging Spectroradiometer (MODIS) data. Spatial gradients are revealed, ranging from regions with pronounced seasonality in canopy development (i.e. tropical savannas) to regions where seasonal variation is minimal (i.e. tropical rainforests) or high but irregular (i.e. arid ecosystems). Next, we use time series colour information extracted from phenocam imagery to illustrate a range of phenological signals in four contrasting Australian ecosystems. These include greening and senescing events in tropical savannas and temperate eucalypt understorey, as well as strong seasonal dynamics of individual trees in a seemingly static evergreen rainforest. We also demonstrate how phenology links with ecosystem gross primary productivity (from eddy covariance) and discuss why these processes are linked in some ecosystems but not others. We conclude that phenocams have the potential to greatly improve the current understanding of Australian ecosystems. To facilitate the sharing of this information, we have formed the Australian Phenocam Network (http://phenocam.org.au/).

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