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Author Zhao, N., Zhang, W., Liu, Y., Samson, E. L., Chen, Y., & Cao, G.
Title Improving Nighttime Light Imagery With Location-Based Social Media Data Type Journal Article
Year 2018 Publication IEEE Transactions on Geoscience and Remote Sensing Abbreviated Journal
Volume 57 Issue 4 Pages
Keywords Remote Sensing
Abstract Location-based social media have been extensively utilized in the concept of “social sensing” to exploit dynamic information about human activities, yet joint uses of social sensing and remote sensing images are underdeveloped at present. In this paper, the close relationship between the number of Twitter users and brightness of nighttime lights (NTL) over the contiguous United States is calculated and geotagged tweets are then used to upsample a stable light image for 2013. An associated outcome of the upsampling process is the solution of two major problems existing in the NTL image, pixel saturation, and blooming effects. Compared with the original stable light image, digital number (DN) values of the upsampled stable light image have larger correlation coefficients with gridded population (0.47 versus 0.09) and DN values of the new generation NTL image product (0.56 versus 0.52), i.e., the Visible Infrared Imaging Radiometer Suite day/night band image composite. In addition, total personal incomes of states are disaggregated to each pixel in proportion to the DN value of the pixel in the NTL images and then aggregate by counties. Personal incomes distributed by the upsampled NTL image are closer to the official demographic data than those distributed by the original stable light image. All of these results explore the potential of geotagged tweets to improve the quality of NTL images for more accurately estimating or mapping socioeconomic factors.
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Call Number IDA @ intern @ Serial 2353
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Author Wang, L.; Wang, S.; Zhou, Y.; Liu, W.; Hou, Y.; Zhu, J.; Wang, F.
Title Mapping population density in China between 1990 and 2010 using remote sensing Type Journal Article
Year 2018 Publication Remote Sensing of Environment Abbreviated Journal Remote Sensing of Environment
Volume 210 Issue Pages 269-281
Keywords Remote Sensing
Abstract Knowledge of the spatial distribution of populations at finer spatial scales is of significant value and fundamental to many applications such as environmental change, urbanization, regional planning, public health, and disaster management. However, detailed assessment of the population distribution data of countries that have large populations (such as China) and significant variation in distribution requires improved data processing methods and spatialization models. This paper described the construction of a novel population spatialization method by combining land use/cover data and night-light data. Based on the analysis of data characteristics, the method used partial correlation analysis and geographically weighted regression to improve the distribution accuracy and reduce regional errors. China's census data for the years 1990, 2000, and 2010 were assessed. The results showed that the method was better at population spatialization than methods that use only night-light data or land use/cover data and global linear regression. Evaluation of overall accuracies revealed that the coefficient of correlation R-square was >0.90 and increased by >0.13 in the years 1990, 2000, and 2010. Moreover, the local R-square of over 90% of the samples (counties) was higher than the adjusted R-square of the general linear regression model. Furthermore, the gridded population density datasets obtained by this method can be used to analyse spatial-temporal patterns of population density and provide population distribution information with increased accuracy and precision compared to conventional models.
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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 2480
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Author Tan, M.; Li, X.; Li, S.; Xin, L.; Wang, X.; Li, Q.; Li, W.; Li, Y.; Xiang, W.
Title Modeling population density based on nighttime light images and land use data in China Type Journal Article
Year 2018 Publication Applied Geography Abbreviated Journal Applied Geography
Volume 90 Issue Pages 239-247
Keywords Remote Sensing
Abstract Population change is a key variable that influences climate change, ecological construction, soil and water use, and economic growth. Census data are always point data, whereas planar data are often required in scientific research. By using nighttime light (NTL) images and land use data, combined with the fifth and sixth census data of China at the county level, we carried out spatial matching on the population of each county, respectively, and established population density diagrams of China for 2000 and 2010, which had a spatial resolution of 1 × 1 km. The method proposed in this paper is relatively simple and has a high simulation precision. The results showed that during the first ten years of the 21st century, there are some remarkable characteristics in Chinese population spatial pattern change: 1) the “disappearance” of intermediate-density regions; namely, areas with a population density between 500 and 1500 persons/km2 have decreased by 41% during the ten years; 2) continuous growth of high-density regions; namely, areas with a population density of more than 1500 persons/km2 have increased by 76%; 3) an expansion tendency of low-density regions similar to high-density regions.
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0143-6228 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2481
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Author Shi, K.; Yu, B.; Huang, C.; Wu, J.; Sun, X.
Title Exploring spatiotemporal patterns of electric power consumption in countries along the Belt and Road Type Journal Article
Year 2018 Publication Energy Abbreviated Journal Energy
Volume 150 Issue Pages 847-859
Keywords Remote Sensing
Abstract Fully understanding spatiotemporal patterns of electric power consumption (EPC) is one of the key questions related to sustainable socioeconomic and environmental development in countries along the Silk Road Economic Belt and the 21st-Century Maritime Silk Road (hereinafter referred to as the Belt and Road countries). However, studies about spatiotemporal patterns of EPC in the Belt and Road countries are still scarce due to the lack of reliable data. This study attempted to investigate spatiotemporal patterns of EPC in the Belt and Road countries from multiple perspectives. Firstly, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime stable light data were used to estimate EPC from 1992 to 2013. Subsequently, the mathematical statistic method, standard deviational ellipse, rank size rule, and correlation analysis were employed to evaluate the EPC change in detail. The results reveal that the EPC growth mainly occurs in the developing countries, especially in China. The geographical distribution of EPC in the Belt and Road countries is oriented in the Northwest-Southeast direction between 1992 and 2013. Based on the rank size rule analysis, the slope values of q are −2.392 and −2.175 between 1992 and 2013, with an average R2 value of 0.664, indicating a clear clustering pattern of EPC. It is also proved that GDP is a more important impact factor to EPC than the population. Our findings can offer an effective way to understand spatiotemporal evolution characteristics of EPC in the Belt and Road countries, and provide references for regional socioeconomic development and cooperation.
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Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0360-5442 ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number GFZ @ kyba @ Serial 2487
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Author Vaaja, M. T., Kurkela, M., Maksimainen, M., Virtanen, J., Kukko, A., Lehtola, V. V., Hyyppä, J., & Hyyppä, H.
Title MOBILE MAPPING OF NIGHT-TIME ROAD ENVIRONMENT LIGHTING CONDITIONS Type Journal Article
Year 2018 Publication The Photogrammetric Journal of Finland Abbreviated Journal
Volume 26 Issue 1 Pages
Keywords Lighting; Remote Sensing
Abstract The measurement of 3D geometry for road environments is one of the main applications of mobile mapping systems (MMS). We present mobile mapping applied to a night-time road environment. We integrate the measurement of luminances into a georeferenced 3D point cloud. The luminance measurement and the 3D point cloud acquired with an MMS are used in assessing road environment lighting conditions. Luminance (cd/m2) was measured with a luminance-calibrated panoramic camera system, and point cloud was produced by laser scanners. The relative orientation between the GNSS, IMU, camera, and laser scanner sensors was solved in order to

integrate the data sets into the same coordinate system. Hence, the georeferenced luminance values are transferable into geographic information systems (GIS). The method provides promising results for future road lighting assessment. In addition, this article demonstrates the night-time mobile mapping principle applied to a road section in Helsinki, Finland. Finally, we discuss the

future applications of mobile-mapped luminance point clouds.
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Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes Approved no
Call Number IDA @ intern @ Serial 2650
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