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Author NETL. url  openurl
  Title Lighting market characterization vol. 1 National lighting inventory and energy consumption estimate 9/02. Type Journal Article
  Year (up) 2002 Publication Abbreviated Journal  
  Volume Issue Pages  
  Keywords Remote Sensing  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number LoNNe @ kagoburian @ Serial 962  
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Author Pindar, A.; Papetti, M. url  openurl
  Title Public Procurement of Energy Saving Technologies in Europe (PROST) Report on the Country Study for Italy: Task 2a-Current Public Sector Purchasing. Type Journal Article
  Year (up) 2002 Publication Building, and Replacement Practices Task 4b-PICO Feasibility Study Abbreviated Journal  
  Volume Issue Pages  
  Keywords Remote Sensing  
  Abstract  
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  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number LoNNe @ kagoburian @ Serial 966  
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Author Szpir, M. ( url  openurl
  Title Clickworkers on Mars Type Journal Article
  Year (up) 2002 Publication American Scientist Abbreviated Journal  
  Volume 90 Issue 3 Pages 226  
  Keywords Remote Sensing  
  Abstract  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number LoNNe @ kagoburian @ Serial 975  
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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 (up) 2002 Publication 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  
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Author Sutton, P.C. url  doi
openurl 
  Title A scale-adjusted measure of “Urban sprawl” using nighttime satellite imagery Type Journal Article
  Year (up) 2003 Publication Remote Sensing of Environment Abbreviated Journal Remote Sensing of Environment  
  Volume 86 Issue 3 Pages 353-369  
  Keywords Urban sprawl; Sprawl Line; Nighttime satellite imagery; DMSP-OLS; remote sensing; satellite; llight at night  
  Abstract “Urban Sprawl” is a growing concern of citizens, environmental organizations, and governments. Negative impacts often attributed to urban sprawl are traffic congestion, loss of open space, and increased pollutant runoff into natural waterways. Definitions of “Urban Sprawl” range from local patterns of land use and development to aggregate measures of per capita land consumption for given contiguous urban areas (UA). This research creates a measure of per capita land use consumption as an aggregate index for the spatially contiguous urban areas of the conterminous United States with population of 50,000 or greater. Nighttime satellite imagery obtained by the Defense Meteorological Satellite Program's Operational Linescan System (DMSP OLS) is used as a proxy measure of urban extent. The corresponding population of these urban areas is derived from a grid of the block group level data from the 1990 U.S. Census. These numbers are used to develop a regression equation between Ln(Urban Area) and Ln(Urban Population). The ‘scale-adjustment’ mentioned in the title characterizes the “Urban Sprawl” of each of the urban areas by how far above or below they are on the “Sprawl Line” determined by this regression. This “Sprawl Line” allows for a more fair comparison of “Urban Sprawl” between larger and smaller metropolitan areas because a simple measure of per capita land consumption or population density does not account for the natural increase in aggregate population density that occurs as cities grow in population. Cities that have more “Urban Sprawl” by this measure tended to be inland and Midwestern cities such as Minneapolis–St. Paul, Atlanta, Dallas–Ft. Worth, St. Louis, and Kansas City. Surprisingly, west coast cities including Los Angeles had some of the lowest levels of “Urban Sprawl” by this measure. There were many low light levels seen in the nighttime imagery around these major urban areas that were not included in either of the two definitions of urban extent used in this study. These areas may represent a growing commuter-shed of urban workers who do not live in the urban core but nonetheless contribute to many of the impacts typically attributed to “Urban Sprawl”. “Urban Sprawl” is difficult to define precisely partly because public perception of sprawl is likely derived from local land use planning decisions, spatio-demographic change in growing urban areas, and changing values and social mores resulting from differential rates of international migration to the urban areas of the United States. Nonetheless, the aggregate measures derived here are somewhat different than similar previously used measures in that they are ‘scale-adjusted’; also, the spatial patterns of “Urban Sprawl” shown here shed some insight and raise interesting questions about how the dynamics of “Urban Sprawl” are changing.  
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  ISSN 0034-4257 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number IDA @ john @ Serial 233  
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