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Author Levin, N.; Kark, S.; Crandall, D. url  doi
openurl 
  Title Where have all the people gone? Enhancing global conservation using night lights and social media Type Journal Article
  Year 2015 Publication (up) Ecological Applications Abbreviated Journal Ecological Applications  
  Volume 25 Issue 8 Pages 2153–2167  
  Keywords Remote Sensing  
  Abstract  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1051-0761 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number LoNNe @ christopher.kyba @ Serial 1150  
Permanent link to this record
 

 
Author Kosicki, J.Z. url  doi
openurl 
  Title Anthropogenic activity expressed as ‘artificial light at night’ improves predictive density distribution in bird populations Type Journal Article
  Year 2020 Publication (up) Ecological Complexity Abbreviated Journal Ecological Complexity  
  Volume 41 Issue Pages 100809  
  Keywords Remote Sensing; Animals; Ecology  
  Abstract Artificial Light At Night (ALAN) is one of the most important anthropogenic environmental components that affects biodiversity worldwide. Despite extensive knowledge on ALAN, being a measure of human activity that directly impacts numerous aspects of animal behaviour, such as orientation and distribution, little is known about its effects on density distribution on a large spatial scale. That is why we decided to explore by means of the Species Distribution Modelling approach (SDM) how ALAN as one of 33 predictors determines farmland and forest bird species densities. In order to safeguard study results from any inconsistency caused by the chosen method, we used two approaches, i.e. the Generalised Additive Model (GAM) and the Random Forest (RF). Within each approach, we developed two models for two bird species, the Black woodpecker and the European stonechat: the first with ALAN, and the second without ALAN as an additional predictor. Having used out-of-bag procedures in the RF approach, information-theoretic criteria for the GAM, and evaluation models based on an independent dataset, we demonstrated that models with ALAN had higher predictive density power than models without it. The Black woodpecker definitely and linearly avoids anthropogenic activity, defined by the level of artificial light, while the European stonechat tolerates human activity to some degree, especially in farmland habitats. What is more, a heuristic analysis of predictive maps based on models without ALAN shows that both species reach high densities in regions where they are deemed rare. Hence, the study proves that urbanisation processes, which can be reflected by ALAN, are among key predictors necessary for developing Species Density Distribution Models for both farmland and forest bird species.  
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  Corporate Author Thesis  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1476945X ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2776  
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Author Sutton, P.C.; Costanza, R. url  doi
openurl 
  Title Global estimates of market and non-market values derived from nighttime satellite imagery, land cover, and ecosystem service valuation Type Journal Article
  Year 2002 Publication (up) Ecological Economics Abbreviated Journal Ecological Economics  
  Volume 41 Issue 3 Pages 509-527  
  Keywords Remote Sensing  
  Abstract We estimated global marketed and non-marketed economic value from two classified satellite images with global coverage at 1 km2 resolution. GDP (a measure of marketed economic output) is correlated with the amount of light energy (LE) emitted by that nation as measured by nighttime satellite images. LE emitted is more spatially explicit than whole country GDP, may (for some nations or regions) be a more accurate indicator of economic activity than GDP itself, can be directly observed, and can be easily updated on an annual basis. As far as we know, this is the first global map of estimated economic activity produced at this high spatial resolution (1 km2). Ecosystem services product (ESP) is an important type of non-marketed value. ESP at 1 km2 resolution was estimated using the IGBP land-cover dataset and unit ecosystem service values estimated by Costanza et al. [Valuing Ecosystem Services with Efficiency, Fairness and Sustainability as Goals. Nature's Services, Island Press, Washington DC, pp. 49–70]. The sum of these two (GDP+ESP)=SEP is a measure of the subtotal ecological–economic product (marketed plus a significant portion of the non-marketed). The ratio: (ESP/SEP)×100=%ESP is a measure of proportion of the SEP from ecosystem services. Both SEP and %ESP were calculated and mapped for each 1 km2 pixel on the earth's surface, and aggregated by country. Results show the detailed spatial patterns of GDP, ESP, and SEP (also available at: http://www.du.edu/∼psutton/esiindexisee/EcolEconESI.htm). Globally, while GDP is concentrated in the northern industrialized countries, ESP is concentrated in tropical regions and in wetlands and other coastal systems. %ESP ranges from 1% for Belgium and Luxembourg to 3% for the Netherlands, 18% for India, 22% for the United States, 49% for Costa Rica, 57% for Chile, 73% for Brazil, and 92% for Russia. While GDP per capita has the usual northern industrialized countries at the top of the list, SEP per capita shows a quite different picture, with a mixture of countries with either high GDP/capita, high ESP/capita, or a combination near the top of the list. Finally, we compare our results with two other indices: (1) The 2001 Environmental Sustainability Index (ESI) derived as an initiative of the Global Leaders of Tomorrow Environment Task Force, World Economic Forum, and (2) Ecological Footprints of Nations: How much Nature do they use? How much Nature do they have? developed by Mathis Wackernagel and others. While both of these indices purport to measure sustainability, the ESI is actually mainly a measure of economic activity (and is correlated with GDP), while the Eco-Footprint index is a measure of environmental impact. The related eco-deficit (national ecological capacity minus national footprint) correlates well with %ESP.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0921-8009 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2477  
Permanent link to this record
 

 
Author Neri, L.; Coscieme, L.; Giannetti, B.F.; Pulselli, F.M. url  doi
openurl 
  Title Imputing missing data in non-renewable empower time series from night-time lights observations Type Journal Article
  Year 2018 Publication (up) Ecological Indicators Abbreviated Journal Ecological Indicators  
  Volume 84 Issue Pages 106-118  
  Keywords Remote Sensing  
  Abstract Emergy is an environmental accounting tool, with a specific set of indicators, that proved to be highly informative for sustainability assessment of national economies. The empower, defined as emergy per unit time, is a measure of the overall flow of resources used by a system in order to support its functioning. Continuous time-series of empower are not available for most of the world countries, due to the large amount of data needed for its calculation year by year. In this paper, we aim at filling this gap by means of a model that facilitates reconstruction of continuous time series of the non-renewable component of empower for a set of 57 countries of the world from 1995 to 2012. The reconstruction is based on a 3 year global emergy dataset and on the acknowledged relationships between the use of non-renewables, satellite observed artificial lights emitted at night, and Gross Domestic Product. Results show that this method provides accurate estimations of non-renewable empower at the country scale. The estimation model can be extended onward and backward in time and replicated for more countries, also using higher-resolution satellite imageries newly available. Besides representing an important advancement in emergy theory, this information is helpful for monitoring progresses toward Sustainable Development and energy use international goals.  
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  Series Volume Series Issue Edition  
  ISSN 1470160X ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number LoNNe @ kyba @ Serial 1706  
Permanent link to this record
 

 
Author Chen, J.; Fan, W.; Li, K.; Liu, X.; Song, M. url  doi
openurl 
  Title Fitting Chinese cities’ population distributions using remote sensing satellite data Type Journal Article
  Year 2019 Publication (up) Ecological Indicators Abbreviated Journal Ecological Indicators  
  Volume 98 Issue Pages 327-333  
  Keywords Remote Sensing  
  Abstract Remote sensing satellite data from 2012 to 2013 are used to fit the Chinese cities’ population distributions over the same period in order to verify the population distribution in China from a relatively objective perspective. Most scholars have used nighttime light data and vegetation indexes to fit the population distribution, but the fitting effect has not been satisfactory. In this paper, processed Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data, net primary productivity of vegetation (NPP), and average slope data were used to fit the population distribution from the three dimensions of economic growth, ecological environment, and topographic factors, respectively. The fitting effect was significantly improved compared with other studies (R2 values of 0.9244 and 0.9253 in 2012 and 2013, respectively). Therefore, this method provides a practical and effective way to fit the population distribution for remote cities or areas lacking census data. Furthermore, there is important practical significance for the government to formulate its population policies rationally, optimize the spatial distribution of population, and improve the ecological quality of the city.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1470160X ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2071  
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