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Author Avtar, R.; Tripathi, S.; Aggarwal, A.K. url  doi
openurl 
  Title Assessment of Energy–Population–Urbanization Nexus with Changing Energy Industry Scenario in India Type Journal Article
  Year 2019 Publication Land Abbreviated Journal Land  
  Volume 8 Issue 8 Pages 124  
  Keywords Remote Sensing  
  Abstract The demand for energy has been growing worldwide, especially in India partly due to the rapid population growth and urbanization of the country. To meet the ever-increasing energy requirement while maintaining an ecological balance is a challenging task. However, the energy industry-induced effect on population and urbanization has not been addressed before. Therefore, this study investigates the linkages between energy, population, and urbanization. The study also aims to find the quantifiable indicators for the population growth and rate of urbanization due to the expanding energy industry. The integrated framework uses a multi-temporal Landsat data to analyze the urbanization pattern, a census data for changes in population growth, night time light (NTL) data as an indicator for economic development and energy production and consumption data for energy index. Multi-attribute model is used to calculate a unified metric, termed as the energy–population–urbanization (EPU) nexus index. The proposed approach is demonstrated in the National Thermal Power Corporation (NTPC) Dadri power plant located in Uttar Pradesh, India. Landsat and NTL data clearly shows the urbanization pattern, economic development, and electrification in the study area. A comparative analysis based on various multi-attribute decision model assessment techniques suggests that the average value of EPU nexus index is 0.529, which significantly large compared to other studies and require special attention by policymakers because large EPU index indicates stronger correlation among energy, population, and urbanization. The authors believe that it would help the policymakers in planning and development of future energy projects, policies, and long-term strategies as India is expanding its energy industry.  
  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 2073-445X ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2620  
Permanent link to this record
 

 
Author Avtar, R.; Tripathi, S.; Aggarwal, A.K. url  doi
openurl 
  Title Assessment of Energy–Population–Urbanization Nexus with Changing Energy Industry Scenario in India Type Journal Article
  Year 2019 Publication Land Abbreviated Journal Land  
  Volume 8 Issue 8 Pages 124  
  Keywords Remote Sensing  
  Abstract The demand for energy has been growing worldwide, especially in India partly due to the rapid population growth and urbanization of the country. To meet the ever-increasing energy requirement while maintaining an ecological balance is a challenging task. However, the energy industry-induced effect on population and urbanization has not been addressed before. Therefore, this study investigates the linkages between energy, population, and urbanization. The study also aims to find the quantifiable indicators for the population growth and rate of urbanization due to the expanding energy industry. The integrated framework uses a multi-temporal Landsat data to analyze the urbanization pattern, a census data for changes in population growth, night time light (NTL) data as an indicator for economic development and energy production and consumption data for energy index. Multi-attribute model is used to calculate a unified metric, termed as the energy–population–urbanization (EPU) nexus index. The proposed approach is demonstrated in the National Thermal Power Corporation (NTPC) Dadri power plant located in Uttar Pradesh, India. Landsat and NTL data clearly shows the urbanization pattern, economic development, and electrification in the study area. A comparative analysis based on various multi-attribute decision model assessment techniques suggests that the average value of EPU nexus index is 0.529, which significantly large compared to other studies and require special attention by policymakers because large EPU index indicates stronger correlation among energy, population, and urbanization. The authors believe that it would help the policymakers in planning and development of future energy projects, policies, and long-term strategies as India is expanding its energy industry.  
  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 2073-445X ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2659  
Permanent link to this record
 

 
Author Xu, C.; Wang, H.-J.; Yu, Q.; Wang, H.-Z.; Liang, X.-M.; Liu, M.; Jeppesen, E. url  doi
openurl 
  Title Effects of Artificial LED Light on the Growth of Three Submerged Macrophyte Species during the Low-Growth Winter Season: Implications for Macrophyte Restoration in Small Eutrophic Lakes Type Journal Article
  Year 2019 Publication Water Abbreviated Journal Water  
  Volume 11 Issue 7 Pages 1512  
  Keywords Plants  
  Abstract Eutrophication of lakes is becoming a global environmental problem, leading to, among other things, rapid reproduction of phytoplankton, increased turbidity, loss of submerged macrophytes, and the recovery of these plants following nutrient loading reduction is often delayed. Artificial light supplement could potentially be a useful method to help speeding up recovery. In this study, three common species of submerged macrophytes, Vallisneria natans, Myriophyllum spicatum and Ceratophyllum demersum, were exposed to three LED light treatments (blue, red and white) and shaded (control) for 100 days (from 10 November 2016 to 18 January 2017) in 12 tanks holding 800 L of water. All the three LED light treatments promoted growth of the three macrophyte species in terms of shoot number, length and dry mass. The three light treatments differed in their effects on the growth of the plants; generally, the red light had the strongest promoting effects, followed by blue and white. The differences in light effects may be caused by the different photosynthetic photon flux density (PPFD) of the lights, as indicated by an observed relationship of PPFD with the growth variables. The three species also responded differently to the light treatments, V. natans and C. demersum showing higher growth than M. spicatum. Our findings demonstrate that artificial light supplement in the low-growth winter season can promote growth and recovery of submerged macrophytes and hence potentially enhance their competitiveness against phytoplankton in the following spring. More studies, however, are needed to elucidate if LED light treatment is a potential restoration method in small lakes, when the growth of submerged macrophytes are delayed following a sufficiently large external nutrient loading reduction for a shift to a clear macrophyte state to have a potential to occur. Our results may also be of relevance when elucidating the role of artificial light from cities on the ecosystem functioning of lakes in urban areas.  
  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 2073-4441 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2606  
Permanent link to this record
 

 
Author Paranunzio, R.; Ceola, S.; Laio, F.; Montanari, A. url  doi
openurl 
  Title Evaluating the Effects of Urbanization Evolution on Air Temperature Trends Using Nightlight Satellite Data Type Journal Article
  Year 2019 Publication Atmosphere Abbreviated Journal Atmosphere  
  Volume 10 Issue 3 Pages 117  
  Keywords Remote Sensing  
  Abstract Confounding factors like urbanization and land-use change could introduce uncertainty to the estimation of global temperature trends related to climate change. In this work, we introduce a new way to investigate the nexus between temporal trends of temperature and urbanization data at the global scale in the period from 1992 to 2013. We analyze air temperature data recorded from more than 5000 weather stations worldwide and nightlight satellite measurements as a proxy for urbanization. By means of a range of statistical methods, our results quantify and outline that the temporal evolution of urbanization affects temperature trends at multiple spatial scales with significant differences at regional and continental scales. A statistically significant agreement in temperature and nightlight trends is detected, especially in low and middle-income regions, where urbanization is rapidly growing. Conversely, in continents such as Europe and North America, increases in temperature trends are typically detected along with non-significant nightlight trends.  
  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 2073-4433 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2249  
Permanent link to this record
 

 
Author Sun, L.; Tang, L.; Shao, G.; Qiu, Q.; Lan, T.; Shao, J. url  doi
openurl 
  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.  
  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 2800  
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