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Author Wang, J.; Qu, S.; Peng, K.; Feng, Y. url  doi
openurl 
  Title Quantifying Urban Sprawl and Its Driving Forces in China Type Journal Article
  Year 2019 Publication Discrete Dynamics in Nature and Society Abbreviated Journal Discrete Dynamics in Nature and Society  
  Volume 2019 Issue Pages 1-14  
  Keywords Remote Sensing  
  Abstract Against the background that urbanization has proceeded quickly in China over the last two decades, a limited number of empirical researches have been performed for analyzing the measurement and driving forces of urban sprawl at the national and regional level. The article aims at using remote sensing derived data and administrative data (for statistical purposes) to investigate the development status of urban sprawl together with its driving forces. Compared with existing studies, NPP/VIIRS data and LandScan data were used here to examine urban sprawl from two different perspectives: urban population sprawl and urban land sprawl. Furthermore, we used population density as a counter-indicator of urban sprawl, and the regression results also prove the superiority of the urban sprawl designed by us. The main results show that the intensity of urban population sprawl and urban land sprawl has been enhanced. However, the upside-down between the inflow of migrants and the supply of urban construction land among different regions aggravates the intensity of urban sprawl. According to the regression analyses, the driving mechanism of urban sprawl in the eastern region relying on land finance and financial development has lost momentum for the limitation of urban construction land supply. The continuous outflow of population and loosely land supply have accelerated the intensity of urban land sprawl in the central and western regions. The findings of the article may help people to realize that urban sprawl has become a staggering reality among Chinese cities; thereby urban planners as well as policymakers should make some actions to hinder the urban sprawl.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1026-0226 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2379  
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Author You, H.; Jin, C.; Sun, W. url  doi
openurl 
  Title Spatiotemporal Evolution of Population in Northeast China during 2012–2017: A Nighttime Light Approach Type Journal Article
  Year 2020 Publication Complexity Abbreviated Journal Complexity  
  Volume 2020 Issue Pages 1-12  
  Keywords Remote Sensing  
  Abstract Population is one of the key problematic factors that are restricting China’s economic and social development. Previous studies have used nighttime light (NTL) imagery to calculate population density. This study analyzes the spatiotemporal evolution of the population in Northeast China based on linear regression analyses of NPP-VIIRS NTL imagery and statistical population data from 36 cities in Northeast China from 2012 to 2017. Based on a comparison of the estimation results in different years, we observed the following. (1) The population of Northeast China showed an overall decreasing trend from 2012–2017, with population changes of +31,600, −960,800, −359,800, −188,000, and −1,127,600 in the respective years. (2) With the overall population loss trend in Northeast China, the population increased in only three cities, namely, Shenyang, Dalian, and Panjin, with an average increase during the six-year period of 24,200, 6,500, and 2,000 people, respectively. (3) The four major urban agglomerations in Northeast China (the Harbin-Daqing-Qiqihar Industrial Corridor, Changjitu Pilot Zone, Liaoning Coastal Economic Belt, and Shenyang Economic Zone) have annual populations far exceeding 4 million people. A correct appreciation of the population dynamics is vital to resource management and comprehensive management efforts. Making full use of natural resources and regional advantages could effectively improve and potentially solve the urban population loss problem and would be of great innovative significance for supporting the realization of the Millennium Development Goals.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1076-2787 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2981  
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Author Priyatikanto, R.; Mayangsari, L.; Prihandoko, R.A.; Admiranto, A.G. url  doi
openurl 
  Title Classification of Continuous Sky Brightness Data Using Random Forest Type Journal Article
  Year 2020 Publication Advances in Astronomy Abbreviated Journal Advances in Astronomy  
  Volume 2020 Issue Pages 1-11  
  Keywords Skyglow  
  Abstract Sky brightness measuring and monitoring are required to mitigate the negative effect of light pollution as a byproduct of modern civilization. Good handling of a pile of sky brightness data includes evaluation and classification of the data according to its quality and characteristics such that further analysis and inference can be conducted properly. This study aims to develop a classification model based on Random Forest algorithm and to evaluate its performance. Using sky brightness data from 1250 nights with minute temporal resolution acquired at eight different stations in Indonesia, datasets consisting of 15 features were created to train and test the model. Those features were extracted from the observation time, the global statistics of nightly sky brightness, or the light curve characteristics. Among those features, 10 are considered to be the most important for the classification task. The model was trained to classify the data into six classes (1: peculiar data, 2: overcast, 3: cloudy, 4: clear, 5: moonlit-cloudy, and 6: moonlit-clear) and then tested to achieve high accuracy (92%) and scores (F-score = 84% and G-mean = 84%). Some misclassifications exist, but the classification results are considerably good as indicated by posterior distributions of the sky brightness as a function of classes. Data classified as class-4 have sharp distribution with typical full width at half maximum of 1.5 mag/arcsec2, while distributions of class-2 and -3 are left skewed with the latter having lighter tail. Due to the moonlight, distributions of class-5 and -6 data are more smeared or have larger spread. These results demonstrate that the established classification model is reasonably good and consistent.  
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  ISSN 1687-7969 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2878  
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Author Kii, M., Kronprasert, N., & Satayopas, B. url  doi
openurl 
  Title ESTIMATION OF TRANSPORT DEMAND USING SATELLITE IMAGE: CASE STUDY OF CHIANG MAI, THAILAND Type Journal Article
  Year 2020 Publication International Journal of GEOMATE Abbreviated Journal  
  Volume 18 Issue 69 Pages 111-117  
  Keywords Remote Sensing  
  Abstract Transport demand is one of the essential datasets for urban / transport planning and policy development. However, the full size of travel demand survey requires large amount of cost, therefore the survey is merely conducted in developing countries. Their policy decision might be based on the old and limited datasets. In this study we propose a new approach to estimate transport demand using the night-time light satellite image based on the correlation of these two factors. Taking the case of Chiang Mai Metropolitan area, we found a soft relationship between the night-time light intensity and trip generation/trip attraction. Transport survey data is provided by Chiang Mai University for the year 2016. NOAA provides cloud free monthly composite of night-time light satellite image (VIIRS-DNB) by Suomi-NPP satellite of which resolution is 15 arc-second (about 500m by 500m at equator). It is spatially more precise than zones of travel demand survey and monthly frequency. Applying the relationship between transport demand and night-time light intensity, we propose a method to update the transport demand with higher spatial resolution.  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number IDA @ intern @ Serial 2963  
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Author Windle, A. E., Hooley, D. S., & Johnston, D. W. url  doi
openurl 
  Title Robotic Vehicles Enable High-Resolution Light Pollution Sampling of Sea Turtle Nesting Beaches Type Journal Article
  Year 2018 Publication Frontiers in Marine Science Abbreviated Journal  
  Volume 5 Issue 493 Pages  
  Keywords Instrumentation; Animals; Skyglow  
  Abstract Nesting sea turtles appear to avoid brightly lit beaches and often turn back to sea prematurely when exposed to artificial light. Observations and experiments have noted that nesting turtles prefer darker areas where buildings and high dunes act as light barriers. As a result, sea turtles often nest on darker beaches, creating spatial concentrations of nests. Artificial nighttime light, or light pollution, has been quantified using a variety of methods. However, it has proven challenging to make accurate measurements of ambient light at fine scales and on smaller nesting beaches. Additionally, light has traditionally been measured from stationary tripods perpendicular to beach vegetation, disregarding the point of view of a nesting sea turtle. In the present study, nighttime ambient light conditions were assessed on three beaches in central North Carolina: a developed coastline of a barrier island, a nearby State Park on the same barrier island comprised of protected and undeveloped land, and a completely uninhabited wilderness on an adjacent barrier island in the Cape Lookout National Seashore. Using an autonomous terrestrial rover, high resolution light measurements (mag/arcsec2) were collected every minute with two ambient light sensors along transects on each beach. Spatial comparisons between ambient light and nesting density at and between these locations reveal that highest densities of nests occur in regions with lowest light levels, supporting the hypothesis that light pollution from coastal development may influence turtle nesting distribution. These results can be used to support ongoing management strategies to mitigate this pressing conservation issue.  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number IDA @ intern @ Serial 2315  
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