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Author Lieske, D.J.; Tranquilla, L.M.F.; Ronconi, R.A.; Abbott, S. url  doi
openurl 
  Title “Seas of risk”: Assessing the threats to colonial-nesting seabirds in Eastern Canada Type Journal Article
  Year 2020 Publication Marine Policy Abbreviated Journal Marine Policy  
  Volume 115 Issue Pages 103863  
  Keywords Animals  
  Abstract This study presents the results of the first broad-scale, spatial cumulative impact analysis (SCIA) conducted for colonial-nesting seabirds at-sea in eastern Canada. Species distribution models, based on at-sea tracking data for thirteen species/groups of seabirds (n = 520 individuals), were applied to over 5000 species-specific colonies to map relative abundance patterns across the entire region. This information was combined with distributional data for a number of key anthropogenic threats to quantify exposure to fisheries, light and ship-source oil pollution, and marine traffic. As a final step, information about species-specific sensitivity to each threat was integrated to compute region-wide cumulative risk.

The data products permit the visualization of the interaction between species and threats, and confirm that large portions of the coastal zones of Nova Scotia and Newfoundland, as well as the Grand Banks shelf break, constitute areas where breeding seabirds experience the highest potential impact. The cumulative risk maps revealed that species which were either widespread throughout coastal areas (e.g., gulls), or capable of foraging long distance (Leach's Storm-Petrel), were most at risk. Cumulative risk maps help identify appropriate and potentially effective management and conservation actions, and are of value to federal regulators responsible for managing cumulative effects as part of the new Canadian Impact Assessment Act. They also can assist marine planners achieve the Aichi marine conservation targets as specified by the Convention on Biodiversity. By filling a knowledge gap for a large potion of the northwest Atlantic, these results help to counter the “shifting baselines syndrome”.
 
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0308597X ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial (down) 2941  
Permanent link to this record
 

 
Author Zheng, Q.; Weng, Q.; Wang, K. url  doi
openurl 
  Title Correcting the Pixel Blooming Effect (PiBE) of DMSP-OLS nighttime light imagery Type Journal Article
  Year 2020 Publication Remote Sensing of Environment Abbreviated Journal Remote Sensing of Environment  
  Volume 240 Issue Pages 111707  
  Keywords *instrumentation; Remote Sensing  
  Abstract In the last two decades, the advance in nighttime light (NTL) remote sensing has fueled a surge in extensive research towards mapping human footprints. Nevertheless, the full potential of NTL data is largely constrained by the blooming effect. In this study, we propose a new concept, the Pixel Blooming Effect (PiBE), to delineate the mutual influence of lights from a pixel and its neighbors, and an integrated framework to eliminate the PiBE in radiance calibrated DMSP-OLS datasets (DMSPgrc). First, lights from isolated gas flaring sources and a Gaussian model were used to model how the PiBE functions on each pixel through point spread function (PSF). Second, a two-stage deblurring approach (TSDA) was developed to deconvolve DMSPgrc images with Tikhonov regularization to correct the PiBE and reconstruct PiBE-free images. Third, the proposed framework was assessed by synthetic data and VIIRS imagery and by testing the resulting image with two applications. We found that high impervious surface fraction pixels (ISF > 0.6) were impacted by the highest absolute magnitude of PiBE, whereas NTL pattern of low ISF pixels (ISF < 0.2) was more sensitive to the PiBE. By using TSDA the PiBE in DMSPgrc images was effectively corrected which enhanced data variation and suppressed pseudo lights from non-built-up pixels in urban areas. The reconstructed image had the highest similarity to reference data from synthetic image (SSIM = 0.759) and VIIRS image (r = 0.79). TSDA showed an acceptable performance for linear objects (width > 1.5 km) and circular objects (radius > 0.5 km), and for NTL data with different noise levels (<0.6σ). In summary, the proposed framework offers a new opportunity to improve the quality of DMSP-OLS images and subsequently will be conducive to NTL-based applications, such as mapping urban extent, estimating socioeconomic variables, and exploring eco-impact of artificial lights.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0034-4257 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial (down) 2940  
Permanent link to this record
 

 
Author Yeh, C.; Perez, A.; Driscoll, A.; Azzari, G.; Tang, Z.; Lobell, D.; Ermon, S.; Burke, M. url  doi
openurl 
  Title Using publicly available satellite imagery and deep learning to understand economic well-being in Africa Type Journal Article
  Year 2020 Publication Nature Communications Abbreviated Journal Nat Commun  
  Volume 11 Issue 1 Pages 2583  
  Keywords Remote Sensing  
  Abstract Accurate and comprehensive measurements of economic well-being are fundamental inputs into both research and policy, but such measures are unavailable at a local level in many parts of the world. Here we train deep learning models to predict survey-based estimates of asset wealth across ~ 20,000 African villages from publicly-available multispectral satellite imagery. Models can explain 70% of the variation in ground-measured village wealth in countries where the model was not trained, outperforming previous benchmarks from high-resolution imagery, and comparison with independent wealth measurements from censuses suggests that errors in satellite estimates are comparable to errors in existing ground data. Satellite-based estimates can also explain up to 50% of the variation in district-aggregated changes in wealth over time, with daytime imagery particularly useful in this task. We demonstrate the utility of satellite-based estimates for research and policy, and demonstrate their scalability by creating a wealth map for Africa's most populous country.  
  Address National Bureau of Economic Research, 1050 Massachusetts Avenue, Cambridge, MA, 02138-5398, USA. mburke@stanford.edu  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2041-1723 ISBN Medium  
  Area Expedition Conference  
  Notes PMID:32444658 Approved no  
  Call Number GFZ @ kyba @ Serial (down) 2939  
Permanent link to this record
 

 
Author Wang, H.; Li, J.; Gao, M.; Chan, T.-C.; Gao, Z.; Zhang, M.; Li, Y.; Gu, Y.; Chen, A.; Yang, Y.; Ho, H.C. url  doi
openurl 
  Title Spatiotemporal variability in long-term population exposure to PM2.5 and lung cancer mortality attributable to PM2.5 across the Yangtze River Delta (YRD) region over 2010–2016: A multistage approach Type Journal Article
  Year 2020 Publication Chemosphere Abbreviated Journal Chemosphere  
  Volume in press Issue Pages 127153  
  Keywords Remote Sensing  
  Abstract The Yangtze River Delta region (YRD) is one of the most densely populated regions in the world, and is frequently influenced by fine particulate matter (PM2.5). Specifically, lung cancer mortality has been recognized as a major health burden associated with PM2.5. Therefore, this study developed a multistage approach 1) to first create dasymetric population data with moderate resolution (1 km) by using a random forest algorithm, brightness reflectance of nighttime light (NTL) images, a digital elevation model (DEM), and a MODIS-derived normalized difference vegetation index (NDVI), and 2) to apply the improved population dataset with a MODIS-derived PM2.5 dataset to estimate the association between spatiotemporal variability of long-term population exposure to PM2.5 and lung cancer mortality attributable to PM2.5 across YRD during 2010–2016 for microscale planning. The created dasymetric population data derived from a coarse census unit (administrative unit) were fairly matched with census data at a fine spatial scale (street block), with R2 and RMSE of 0.64 and 27,874.5 persons, respectively. Furthermore, a significant urban-rural difference of population exposure was found. Additionally, population exposure in Shanghai was 2.9–8 times higher than the other major cities (7-year average: 192,000 μg·people/m3·km2). More importantly, the relative risks of lung cancer mortality in high-risk areas were 28%–33% higher than in low-risk areas. There were 12,574–14,504 total lung cancer deaths attributable to PM2.5, and lung cancer deaths in each square kilometer of urban areas were 7–13 times higher than for rural areas. These results indicate that moderate-resolution information can help us understand the spatiotemporal variability of population exposure and related health risk in a high-density environment.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0045-6535 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial (down) 2938  
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Author Zangeneh, P.; Hamledari, H.; McCabe, B. url  doi
openurl 
  Title Quantifying Remoteness for Risk and Resilience Assessment Using Nighttime Satellite Imagery Type Journal Article
  Year 2020 Publication Journal of Computing in Civil Engineering Abbreviated Journal J. Comput. Civ. Eng.  
  Volume 34 Issue 5 Pages 04020026  
  Keywords Remote Sensing  
  Abstract Remoteness has a crucial role in risk assessments of megaprojects, resilience assessments of communities and infrastructure, and a wide range of public policymaking. The existing measures of remoteness require an extensive amount of population census and of road and infrastructure network data, and often are limited to narrow scopes. This paper presents a methodology to quantify remoteness using nighttime satellite imagery. The light clusters of nighttime satellite imagery are direct yet unintended consequences of human settled populations and urbanization; therefore, the absence of illuminated clusters is considered as evidence of remoteness. The proposed nighttime remoteness index (NIRI) conceptualizes the remoteness based on the distribution of nighttime lights within radii of up to 1,000 km. A predictive model was created using machine learning techniques such as multivariate adaptive regression splines and support vector machines regressions to establish a reliable and accurate link between nighttime lights and the Accessibility/Remoteness Index of Australia (ARIA). The model was used to establish NIRI for the United States and Canada, and in different years. The index was compared with the Canadian remoteness indexes published by Statistics Canada.  
  Address  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0887-3801 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial (down) 2937  
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