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Ye, Y.; Xue, X.; Huang, L.; Gan, M.; Tong, C.; Wang, K.; Deng, J. |
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A new perspective to map the supply and demand of artificial night light based on Loujia1-01 and urban big data |
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Journal Article |
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2020 |
Publication |
Journal of Cleaner Production |
Abbreviated Journal |
Journal of Cleaner Production |
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276 |
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123244 |
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Remote Sensing |
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The notable increase in artificial night light (ANL) induced by the rapid urbanization process has been widely studied, but a deep understanding of the supply and demand status of ANL is still lacking. This paper attempts to map the supply and demand of ANL from the human perspective by using advanced Loujia1-01 nighttime imagery and social media derived population density (PD) data, which provides a new tool for light regulation in urban management. The bivariate clustering based k-means algorithm and template matching technique are integrated to delineate mismatch regions at the block scale to further analyze the underlying reason for unbalanced status. The results showed that the high supply but low demand (HSLD) ANL status was the leading component in the mismatch regions, occupying more than 650,000 ha and mainly occurring in the city center. The HSLD proportion was considerable in terms of public services (44%), commercial (40%), industrial (39%), transportation (56%), and green space areas (53%). Moreover, the HSLD area notably increased 946 ha over time from 18:00 to 22:00. The measurements for validation obtained by field investigation showed highly linear relationship with ANL (R2 = 0.75) and PD (R2 = 0.62), and the mapping results were consistent with the actual conditions. This study reveals the highly unbalanced ANL status, and appeals to planners for the establishment of optimal lighting regulations to alleviate disruptive effects. |
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0959-6526 |
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GFZ @ kyba @ |
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3070 |
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Levin, N.; Johansen, K.; Hacker, J.M.; Phinn, S. |
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A new source for high spatial resolution night time images -- The EROS-B commercial satellite |
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Journal Article |
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Year |
2014 |
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Remote Sensing of Environment |
Abbreviated Journal |
Remote Sensing of Environment |
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149 |
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1-12 |
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Night lights; EROS-B; Land cover; Land use; Fine spatial resolution; remote sensing; satellite; light at night |
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City lights present one of humankind's most unique footprints on Earth as seen from space. Resulting light pollution from artificial lights obscures the night sky for astronomy and has negative impacts on biodiversity as well as on human health. However, remote sensing studies of night lights to date have been mostly limited to coarse spatial resolution sensors such as the DMSP-OLS. Here we present a new source for high spatial resolution mapping of night lights from space, derived from a commercial satellite. We tasked the Israeli EROS-B satellite to acquire two night-time light images (at a spatial resolution of 1 m) of Brisbane, Australia, and analyzed their radiometric quality and content with respect to land cover and land use. The spatial distribution of night lights as imaged by EROS-B corresponded with night-time images acquired by an airborne camera, although EROS-B was not as sensitive to low light levels. Using land cover and land use data at the statistical local area level, we could statistically explain 89% of the variability in night-time lights. Arterial roads and commercial and service areas were found to be some of the brightest land use types. Overall, we found that EROS-B imagery provides fine spatial resolution images of night lights, opening new avenues for studying light pollution in cities worldwide. |
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Department of Geography, The Hebrew University of Jerusalem, Mt. Scopus, Jerusalem 91905, Israel. |
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Elsevier |
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0034-4257 |
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IDA @ john @ |
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307 |
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Zheng, Q.; Weng, Q.; Huang, L.; Wang, K.; Deng, J.; Jiang, R.; Ye, Z.; Gan, M. |
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A new source of multi-spectral high spatial resolution night-time light imagery—JL1-3B |
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2018 |
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Remote Sensing of Environment |
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Remote Sensing of Environment |
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215 |
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300-312 |
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Remote Sensing |
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Artificial light at night (ALAN) provides a unique footprint of human activities and settlements. However, the adverse effects of ALAN on human health and ecosystems have not been well understood. Because of a lack of high resolution data, studies of ALAN in China have been confined to coarse resolution, and fine-scale details are missing. The fine details of ALAN are pertinent, because the highly dense population in Chinese cities has created a distinctive urban lighting pattern. In this paper, we introduced a new generation of high spatial resolution and multi-spectral night-time light imagery from the satellite JL1-3B. We examined its effectiveness for monitoring the spatial pattern and discriminating the types of artificial light based on a case study of Hangzhou, China. Specifically, local Moran's I analysis was applied to identify artificial light hotspots. Then, we analyzed the relationship between artificial light brightness and land uses at the parcel-level, which were generated from GF-2 imagery and open social datasets. Third, a machine learning based method was proposed to discriminate the type of lighting sources – between high pressure sodium lamps (HPS) and light-emitting diode lamps (LED) – by incorporating their spectral information and morphology feature. The result shows a complicated heterogeneity of illumination characteristics across different land uses, where main roads, commercial and institutional areas were brightly lit while residential area, industrial area and agricultural land were dark at night. It further shows that the proposed method was effective at separating light emitted by HPS and LED, with an overall accuracy and kappa coefficient of 83.86% and 0.67, respectively. This study demonstrates the effectiveness of JL1-3B and its superiority over previous night-time light data in detecting details of lighting objects and the nightscape pattern, and suggests that JL1-3B and alike could open up new opportunities for the advancement of night-time remote sensing. |
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0034-4257 |
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GFZ @ kyba @ |
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1945 |
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Fasciani, I.; Petragnano, F.; Aloisi, G.; Marampon, F.; Rossi, M.; Francesca Coppolino, M.; Rossi, R.; Longoni, B.; Scarselli, M.; Maggio, R. |
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A new threat to dopamine neurons: the downside of artificial light |
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Journal Article |
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2020 |
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Neuroscience |
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Neuroscience |
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in press |
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in press |
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Review; Human Health; Parkinson's disease; artificial light; dopamine neurons; melatonin; opsins; photoactivation |
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Growing awareness of adverse impacts of artificial light on human health has led to recognize light pollution as a significant global environmental issue. Despite, a large number of studies in rodent and monkey models of Parkinson's disease have reported that near infrared light has neuroprotective effects on dopaminergic neurons, recent findings have shown that prolonged exposure of rodents and birds to fluorescent artificial light results in an increase of neuromelanin granules in substantia nigra and loss of dopaminergic neurons. The observed detrimental effect seems to be dependent on a direct effect of light on the substantia nigra rather than a secondary effect of the alterations of circadian rhythms. Moreover, inferences from animal models to human studies have shown a positive correlation between the prevalence of Parkinson's disease and light pollution. The present article discusses experimental evidence supporting a potentially deleterious impact of light on dopaminergic neurons and highlights the mechanisms whereby light might damage neuronal tissue. Moreover, it analyses epidemiological evidence that suggests light pollution to be an environmental risk factor for Parkinson's disease. |
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Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy. Electronic address: roberto.maggio@univaq.it |
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English |
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0306-4522 |
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PMID:32142863 |
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GFZ @ kyba @ |
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2839 |
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Liu, Y.; Yang, Y.; Jing, W.; Yao, L.; Yue, X.; Zhao, X. |
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A New Urban Index for Expressing Inner-City Patterns Based on MODIS LST and EVI Regulated DMSP/OLS NTL |
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Journal Article |
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2017 |
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Remote Sensing |
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Remote Sensing |
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9 |
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8 |
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777 |
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Remote Sensing |
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With the rapid pace of urban expansion, comprehensively understanding urban spatial patterns, built environments, green-spaces distributions, demographic distributions, and economic activities becomes more meaningful. Night Time Lights (NTL) images acquired through the Operational Linescan System of the US Defense Meteorological Satellite Program (DMSP/OLS NTL) have long been utilized to monitor urban areas and their expansion characteristics since this system detects variation in NTL emissions. However, the pixel saturation phenomenon leads to a serious limitation in mapping luminance variations in urban zones with nighttime illumination levels that approach or exceed the pixel saturation limits of OLS sensors. Consequently, we propose an NTL-based city index that utilizes the Moderate-resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI) images to regulate and compensate for desaturation on NTL images acquired from corresponding urban areas. The regulated results achieve good performance in differentiating central business districts (CBDs), airports, and urban green spaces. Consequently, these derived imageries could effectively convey the structural details of urban cores. In addition, compared with the Vegetation Adjusted NTL Urban Index (VANUI), LST-and-EVI-regulated-NTL-city index (LERNCI) reveals superior capability in delineating the spatial structures of selected metropolis areas across the world, especially in the large cities of developing countries. The currently available results indicate that LERNCI corresponds better to city spatial patterns. Moreover, LERNCI displays a remarkably better “goodness-of-fit†correspondence with both the Version 1 Nighttime Visible Infrared Imaging Radiometer Suite Day/Night Band Composite (NPP/VIIRS DNB) data and the WorldPop population-density data compared with the VANUI imageries. Thus, LERNCI can act as a helpful indicator for differentiating and classifying regional economic activities, population aggregations, and energy-consumption and city-expansion patterns. LERNCI can also serve as a valuable auxiliary reference for decision-making processes that concern subjects such as urban planning and easing the central functions of metropolis. |
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2072-4292 |
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LoNNe @ kyba @ |
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1713 |
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