| Records |
| Author |
Min, M.; Zheng, J.; Zhang, P.; Hu, X.; Chen, L.; Li, X.; Huang, Y.; Zhu, L. |
Title  |
A low-light radiative transfer model for satellite observations of moonlight and earth surface light at night |
Type |
Journal Article |
| Year |
2020 |
Publication |
Journal of Quantitative Spectroscopy and Radiative Transfer |
Abbreviated Journal |
Journal of Quantitative Spectroscopy and Radiative Transfer |
| Volume |
in press |
Issue |
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Pages |
106954 |
| Keywords |
Remote Sensing; Instrumentation |
| Abstract |
Lunar sun-reflected light can be effectively measured through a low-light band or a day/night band (DNB) implemented on space-based optical sensors. Based on moonlight, nocturnal observations for artificial light sources at night can be achieved. However, to date, an open-sourced and mature Low-Light Radiative Transfer Model (LLRTM) for the further understanding of the radiative transfer problem at night is still unavailable. Therefore, this study develops a new LLRTM at night with the correction of the lunar and active surface light sources. First, the radiative transfer equations with an active surface light source are derived for the calculation based on the lunar spectral irradiance (LSI) model. The simulation from this new LLRTM shows a minimal bias when compared with the discrete ordinates radiative transfer (DISORT) model. The simulated results of radiance and reflectance at the top of the atmosphere (TOA) also show that the surface light source has a remarkable impact on the radiative transfer process. In contrast, the change in the lunar phase angle has minimal influence. Also, comparing with space-based DNB radiance observations, LLRTM shows the potential to simulate space-based low-light imager observations under an effective surface light source condition during the night. |
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| ISSN |
0022-4073 |
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no |
| Call Number |
GFZ @ kyba @ |
Serial |
2850 |
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| Author |
Sun, L.; Tang, L.; Shao, G.; Qiu, Q.; Lan, T.; Shao, J. |
| Title |
A Machine Learning-Based Classification System for Urban Built-Up Areas Using Multiple Classifiers and Data Sources |
Type |
Journal Article |
| Year |
2020 |
Publication |
Remote Sensing |
Abbreviated Journal |
Remote Sensing |
| Volume |
12 |
Issue |
1 |
Pages |
91 |
| Keywords |
Remote Sensing |
| Abstract |
Information about urban built-up areas is important for urban planning and management. However, obtaining accurate information about urban built-up areas is a challenge. This study developed a general-purpose built-up area intelligent classification (BAIC) system that supports various types of data and classifiers. All of the steps in the BAIC were implemented using Python modules including Numpy, Pandas, matplotlib, and scikit-learn. We used the BAIC to conduct a classification experiment that involved seven types of input data; namely, Point of Interest (POI), Road Network (RN), nighttime light (NTL), a combination of POI and RN data (POIRN), a combination of POI and NTL data (POINTL), a combination of RN and NTL data (RNNTL), and a combination of POI, RN, and NTL data (POIRNNTL), and five classifiers, namely, Logistic Regression (LR), Decision Tree (DT), Random Forests (RF), Gradient Boosted Decision Trees (GBDT), and AdaBoost. The results show the following: (1) among the 35 combinations of the five classifiers and seven types of input data, the overall accuracy (OA) ranged from 76 to 89%, F1 values ranged from 0.73 to 0.86, and the area under the receiver operating characteristic (ROC) curve (AUC) ranged from 0.83 to 0.95. The largest F1 value and OA were obtained using the POIRNNTL data and AdaBoost, while the largest AUC was obtained using POIRNNTL and POINTL data against AdaBoost, LR, and RF; and (2) the advantages of the BAIC include its support for multi-source input data, its objective accuracy assessment, and its robust classifiers. The BAIC can quickly and efficiently realize the automatic classification of urban built-up areas at a reasonably low cost and can be readily applied to other urban areas in the world where any kind of POI, RN, or NTL data coverage is available. The results of this study are expected to provide timely and effective reference information for urban planning and urban management departments, and could also potentially be used to develop large-scale maps of urban built-up areas in the future. |
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2072-4292 |
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GFZ @ kyba @ |
Serial |
2800 |
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| Author |
Sanders, D.; Frago, E.; Kehoe, R.; Patterson, C.; Gaston, K.J. |
| Title |
A meta-analysis of biological impacts of artificial light at night |
Type |
Journal Article |
| Year |
2020 |
Publication |
Nature Ecology & Evolution |
Abbreviated Journal |
Nat Ecol Evol |
| Volume |
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Issue |
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Pages |
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| Keywords |
Ecology; meta-analysis; biology |
| Abstract |
Natural light cycles are being eroded over large areas of the globe by the direct emissions and sky brightening that result from sources of artificial night-time light. This is predicted to affect wild organisms, particularly because of the central role that light regimes play in determining the timing of biological activity. Although many empirical studies have reported such effects, these have focused on particular species or local communities and have thus been unable to provide a general evaluation of the overall frequency and strength of these impacts. Using a new database of published studies, we show that exposure to artificial light at night induces strong responses for physiological measures, daily activity patterns and life history traits. We found particularly strong responses with regards to hormone levels, the onset of daily activity in diurnal species and life history traits, such as the number of offspring, predation, cognition and seafinding (in turtles). So far, few studies have focused on the impact of artificial light at night on ecosystem functions. The breadth and often strength of biological impacts we reveal highlight the need for outdoor artificial night-time lighting to be limited to the places and forms-such as timing, intensity and spectrum-where it is genuinely required by the people using it to minimize ecological impacts. |
| Address |
Environment and Sustainability Institute, University of Exeter, Penryn, UK.; k.j.gaston ( at ) exeter.ac.uk |
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Nature |
Place of Publication |
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| Language |
English |
Summary Language |
English |
Original Title |
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Series Issue |
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Edition |
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| ISSN |
2397-334X |
ISBN |
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Medium |
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| Notes |
PMID:33139919 |
Approved |
no |
| Call Number |
IDA @ john @ |
Serial |
3197 |
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| Author |
Fiorentin, P.; Boscaro, F. |
| Title |
A method for measuring the light output of video advertising reproduced by LED billboards |
Type |
Journal Article |
| Year |
2019 |
Publication |
Measurement |
Abbreviated Journal |
Measurement |
| Volume |
138 |
Issue |
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Pages |
25-33 |
| Keywords |
Lighting; Energy; Instrumentation; Planning; Light-emitting diode displays; Photometry; Video recording; Image analysis; CCD image sensors; Luminance; Glare |
| Abstract |
Improving knowledge of the light output of digital billboards is important to better assess their effect on driver distraction when they are installed along roads. In this work the emission of an LED based billboard is measured when playing advertising video-clips. In particular the average and the maximum values of the luminance are evaluated. The same video-clips are also analyzed when shown on an LCD monitor, aiming at separating the variability of the videos and of the playing device. The results allow to evaluate an utilization factor of the billboard: the videos have an average luminance around 11% and a peak luminance of 35% of the maximum luminance obtainable from the billboard. The power consumption of the billboard is measured, aside the photometric analysis. The luminance of the device are found linearly dependent on both the power and the effective current absorbed by the device from the grid, with a discrepancy within 6%. It could be a useful information for billboard manufacturers to qualify their product when they do not own photometric instruments. |
| Address |
Department of Industrial Engineering, University of Padova, Padova, Italy; pietro.fiorentin(at)unipd.it |
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Elsevier |
Place of Publication |
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English |
Summary Language |
English |
Original Title |
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0263-2241 |
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Approved |
no |
| Call Number |
GFZ @ kyba @ |
Serial |
2214 |
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| Author |
Salat, H.; Smoreda, Z.; Schlapfer, M. |
| Title |
A method to estimate population densities and electricity consumption from mobile phone data in developing countries |
Type |
Journal Article |
| Year |
2020 |
Publication |
PloS one |
Abbreviated Journal |
PLoS One |
| Volume |
15 |
Issue |
6 |
Pages |
e0235224 |
| Keywords |
Remote Sensing |
| Abstract |
High quality census data are not always available in developing countries. Instead, mobile phone data are becoming a popular proxy to evaluate the density, activity and social characteristics of a population. They offer additional advantages: they are updated in real-time, include mobility information and record visitors' activity. However, we show with the example of Senegal that the direct correlation between the average phone activity and both the population density and the nighttime lights intensity may be insufficiently high to provide an accurate representation of the situation. There are reasons to expect this, such as the heterogeneity of the market share or the particular granularity of the distribution of cell towers. In contrast, we present a method based on the daily, weekly and yearly phone activity curves and on the network characteristics of the mobile phone data, that allows to estimate more accurately such information without compromising people's privacy. This information can be vital for development and infrastructure planning. In particular, this method could help to reduce significantly the logistic costs of data collection in the particularly budget-constrained context of developing countries. |
| Address |
Future Cities Laboratory, Singapore-ETH Centre, ETH Zurich, Singapore, Singapore |
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English |
Summary Language |
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| ISSN |
1932-6203 |
ISBN |
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| Notes |
PMID:32603345 |
Approved |
no |
| Call Number |
GFZ @ kyba @ |
Serial |
3030 |
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