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Author Vandersteen, J.; Kark, S.; Sorrell, K.; Levin, N.
Title Quantifying the Impact of Light Pollution on Sea Turtle Nesting Using Ground-Based Imagery Type Journal Article
Year 2020 Publication Remote Sensing Abbreviated Journal Remote Sensing
Volume 12 Issue 11 Pages 1785
Keywords Animals; Skyglow
Abstract Remote sensing of anthropogenic light has substantial potential to quantify light pollution levels and understand its impact on a wide range of taxa. Currently, the use of space-borne night-time sensors for measuring the actual light pollution that animals experience is limited. This is because most night-time satellite imagery and space-borne sensors measure the light that is emitted or reflected upwards, rather than horizontally, which is often the light that is primarily perceived by animals. Therefore, there is an important need for developing and testing ground-based remote sensing techniques and methods. In this study, we aimed to address this gap by examining the potential of ground photography to quantify the actual light pollution perceived by animals, using sea turtles as a case study. We conducted detailed ground measurements of night-time brightness around the coast of Heron Island, a coral cay in the southern Great Barrier Reef of Australia, and an important sea turtle rookery, using a calibrated DSLR Canon camera with an 8 mm fish-eye lens. The resulting hemispheric photographs were processed using the newly developed Sky Quality Camera (SQC) software to extract brightness metrics. Furthermore, we quantified the factors determining the spatial and temporal variation in night-time brightness as a function of environmental factors (e.g., moon light, cloud cover, and land cover) and anthropogenic features (e.g., artificial light sources and built-up areas). We found that over 80% of the variation in night-time brightness was explained by the percentage of the moon illuminated, moon altitude, as well as cloud cover. Anthropogenic and geographic factors (e.g., artificial lighting and the percentage of visible sky) were especially important in explaining the remaining variation in measured brightness under moonless conditions. Night-time brightness variables, land cover, and rock presence together explained over 60% of the variation in sea turtle nest locations along the coastline of Heron Island, with more nests found in areas of lower light pollution. The methods we developed enabled us to overcome the limitations of commonly used ground/space borne remote sensing techniques, which are not well suited for measuring the light pollution to which animals are exposed. The findings of this study demonstrate the applicability of ground-based remote sensing techniques in accurately and efficiently measuring night-time brightness to enhance our understanding of ecological light pollution.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2072-4292 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2975
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Author Cox, D.T.C.; Sánchez de Miguel, A.; Dzurjak, S.A.; Bennie, J.; Gaston, K.J.
Title National Scale Spatial Variation in Artificial Light at Night Type Journal Article
Year 2020 Publication Remote Sensing Abbreviated Journal Remote Sensing
Volume 12 Issue 10 Pages 1591
Keywords Remote Sensing; United Kingdom; National parks; skyglow; VIIRS-DNB; albedo; landcover; light emissions; light pollution; protected areas; skyglow; sky brightness; urbanization
Abstract The disruption to natural light regimes caused by outdoor artificial nighttime lighting has significant impacts on human health and the natural world. Artificial light at night takes two forms, light emissions and skyglow (caused by the scattering of light by water, dust and gas molecules in the atmosphere). Key to determining where the biological impacts from each form are likely to be experienced is understanding their spatial occurrence, and how this varies with other landscape factors. To examine this, we used data from the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band and the World Atlas of Artificial Night Sky Brightness, to determine covariation in (a) light emissions, and (b) skyglow, with human population density, landcover, protected areas and roads in Britain. We demonstrate that, although artificial light at night increases with human density, the amount of light per person decreases with increasing urbanization (with per capita median direct emissions three times greater in rural than urban populations, and per capita median skyglow eleven times greater). There was significant variation in artificial light at night within different landcover types, emphasizing that light pollution is not a solely urban issue. Further, half of English National Parks have higher levels of skyglow than light emissions, indicating their failure to buffer biodiversity from pressures that artificial lighting poses. The higher per capita emissions in rural than urban areas provide different challenges and opportunities for mitigating the negative human health and environmental impacts of light pollution.
Address Environment and Sustainability Institute, University of Exeter, Penryn, Cornwall TR10 9FE, UK; d.t.c.cox(at )exeter.ac.uk
Corporate Author Thesis
Publisher MDPI Place of Publication Editor
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2072-4292 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number IDA @ john @ Serial 2920
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Author Cox, D.T.C.; Sánchez de Miguel, A.; Dzurjak, S.A.; Bennie, J.; Gaston, K.J.
Title National Scale Spatial Variation in Artificial Light at Night Type Journal Article
Year 2020 Publication Remote Sensing Abbreviated Journal Remote Sensing
Volume 12 Issue 10 Pages 1591
Keywords Skyglow; Remote Sensing
Abstract The disruption to natural light regimes caused by outdoor artificial nighttime lighting has significant impacts on human health and the natural world. Artificial light at night takes two forms, light emissions and skyglow (caused by the scattering of light by water, dust and gas molecules in the atmosphere). Key to determining where the biological impacts from each form are likely to be experienced is understanding their spatial occurrence, and how this varies with other landscape factors. To examine this, we used data from the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band and the World Atlas of Artificial Night Sky Brightness, to determine covariation in (a) light emissions, and (b) skyglow, with human population density, landcover, protected areas and roads in Britain. We demonstrate that, although artificial light at night increases with human density, the amount of light per person decreases with increasing urbanization (with per capita median direct emissions three times greater in rural than urban populations, and per capita median skyglow eleven times greater). There was significant variation in artificial light at night within different landcover types, emphasizing that light pollution is not a solely urban issue. Further, half of English National Parks have higher levels of skyglow than light emissions, indicating their failure to buffer biodiversity from pressures that artificial lighting poses. The higher per capita emissions in rural than urban areas provide different challenges and opportunities for mitigating the negative human health and environmental impacts of light pollution.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2072-4292 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2926
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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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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2072-4292 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2800
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Author Chen, X.
Title Nighttime Lights and Population Migration: Revisiting Classic Demographic Perspectives with an Analysis of Recent European Data Type Journal Article
Year 2020 Publication Remote Sensing Abbreviated Journal Remote Sensing
Volume 12 Issue 1 Pages 169
Keywords Remote Sensing
Abstract This study examines whether the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime lights can be used to predict population migration in small areas in European Union (EU) countries. The analysis uses the most current data measured at the smallest administrative unit in 18 EU countries provided by the European Commission. The ordinary least squares regression model shows that, compared to population size and gross domestic product (GDP), lights data are another useful predictor. The predicting power of lights is similar to population but it is much stronger than GDP per capita. For most countries, regression models with lights can explain 50–90% of variances in small area migrations. The results also show that the annual VIIRS lights (2015–2016) are slightly better predictors for migration population than averaged monthly VIIRS lights (2014–2017), and their differences are more pronounced in high latitude countries. Further, analysis of quadratic models, models with interaction effects and spatial lag, shows the significant effect of lights on migration in the European region. The study concludes that VIIRS nighttime lights hold great potential for studying human migration flow, and further open the door for more widespread application of remote sensing information in studying dynamic demographic processes.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2072-4292 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2794
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