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Author Hu, T.; Huang, X. url  doi
openurl 
  Title A novel locally adaptive method for modeling the spatiotemporal dynamics of global electric power consumption based on DMSP-OLS nighttime stable light data Type Journal Article
  Year 2019 Publication (up) Applied Energy Abbreviated Journal Applied Energy  
  Volume 240 Issue Pages 778-792  
  Keywords Remote Sensing  
  Abstract Timely and reliable estimation of electricity power consumption (EPC) is essential to the rational deployment of electricity power resources. Nighttime stable light (NSL) data from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) have the potential to model global 1-km gridded EPC. A processing chain to estimate EPC includes: (1) NSL data correction; and (2) regression model between EPC statistics and NSL data. For the global gridded EPC estimation, the current approach is to correct the global NSL image in a uniform manner and establish the linear relationships between NSL and EPC. However, the impacts of local socioeconomic inconsistencies on the NSL correction and model establishment are not fully considered. Therefore, in this paper, we propose a novel locally adaptive method for global EPC estimation. Firstly, we set up two options (with or without the correction) for each local area considering the global NSL image is not saturated everywhere. Secondly, three directions (forward, backward, or average) are alternatives for the inter-annual correction to remove the discontinuity effect of NSL data. Thirdly, four optional models (linear, logarithmic, exponential, or second-order polynomial) are adopted for the EPC estimation of each local area with different socioeconomic dynamic. Finally, the options for each step constitute all candidate processing chains, from which the optimal one is adaptively chosen for each local area based on the coefficient of determination. The results demonstrate that our product outperforms the existing one, at global, continental, and national scales. Particularly, the proportion of countries/districts with a high accuracy (MARE (mean of the absolute relative error)  ≤ 10%) increases from 17.8% to 57.8% and the percentage of countries/districts with inaccurate results (MARE > 50%) decreases sharply from 23.0% to 3.7%. This product can enhance the detailed understanding of the spatiotemporal dynamics of global EPC.  
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  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0306-2619 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2242  
Permanent link to this record
 

 
Author Fan, J., He, H., Hu, T., Zhang, P., Yu, X., & Zhou, Y. doi  openurl
  Title Estimation of Landscape Pattern Changes in BRICS from 1992 to 2013 Using DMSP-OLS NTL Images Type Journal Article
  Year 2019 Publication (up) Journal of the Indian Society of Remote Sensing Abbreviated Journal J Ind Soc Rem Sens  
  Volume 47 Issue 5 Pages 725–735  
  Keywords Remote Sensing; BRICS; Brazil; India; China; South Africa; nighttime light; night lights; DMSP-OLS  
  Abstract Nighttime light data from the Defense Meteorological Satellite Program’s Operational Linescan System are widely used for monitoring urbanization development. Brazil, Russia, India, China and South Africa (BRICS) countries have global economic and cultural influence in the new era. It was the first time for the researches about BRICS countries adopting nighttime light data to analyze the urbanization process. In this paper, we calibrated and extracted annual urbanized area patches from cities in BRICS based on a quadratic polynomial model. Nine landscape indexes were calculated to analyze urbanization process characteristics in BRICS. The results suggested that China and India both expanded more rapidly than other countries, with urban areas that increased by more than 100%. The expansion of large core cities was dominant in the urbanization of China, while emerging and expanding small urban patches were major forces in the urbanization of India. Since 1992, urbanization declined and urban areas shrunk in Russia, but core cities still maintained strength of urbanization. Due to economic recovery, urban areas near large cities in Russia began to expand. From 1992 to 2013, the urbanization process in South Africa developed slowly, as evidenced by time series fluctuations, but overall the development remained stable. The degree of urbanization in Brazil was greater than that in South Africa but less than that in Russia. Large-sized cities expanded slowly and small-sized cities clearly expanded in BRICS from 1992 to 2013.  
  Address School of Civil and Architectural Engineering,Shandong University of Technology, Zibo, China; anjf(at)sdut.edu.cn  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language English Original Title  
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  Notes Approved no  
  Call Number IDA @ intern @ Serial 2307  
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Author Gong, P.; Li, X.; Wang, J.; Bai, Y.; Chen, B.; Hu, T.; Liu, X.; Xu, B.; Yang, J.; Zhang, W.; Zhou, Y. url  doi
openurl 
  Title Annual maps of global artificial impervious area (GAIA) between 1985 and 2018 Type Journal Article
  Year 2020 Publication (up) Remote Sensing of Environment Abbreviated Journal Remote Sensing of Environment  
  Volume 236 Issue Pages in press  
  Keywords Remote Sensing  
  Abstract Artificial impervious areas are predominant indicators of human settlements. Timely, accurate, and frequent information on artificial impervious areas is critical to understanding the process of urbanization and land use/cover change, as well as of their impacts on the environment and biodiversity. Despite their importance, there still lack annual maps of high-resolution Global Artificial Impervious Areas (GAIA) with longer than 30-year records, due to the high demand of high performance computation and the lack of effective mapping algorithms. In this paper, we mapped annual GAIA from 1985 to 2018 using the full archive of 30-m resolution Landsat images on the Google Earth Engine platform. With ancillary datasets, including the nighttime light data and the Sentinel-1 Synthetic Aperture Radar data, we improved the performance of our previously developed algorithm in arid areas. We evaluated the GAIA data for 1985, 1990, 1995, 2000, 2005, 2010, and 2015, and the mean overall accuracy is higher than 90%. A cross-product comparison indicates the GAIA data are the only dataset spanning over 30 years. The temporal trend in GAIA agrees well with other datasets at the local, regional, and global scales. Our results indicate that the GAIA reached 797,076 km2 in 2018, which is 1.5 times more than that in 1990. China and the United States (US) rank among the top two in artificial impervious area, accounting for approximately 50% of the world's total in 2018. The artificial impervious area of China surpassed that of the US in 2015. By 2018, the remaining eight among the top ten countries are India, Russia, Brazil, France, Italy, Germany, Japan, and Canada. The GAIA dataset can be freely downloaded from http://data.ess.tsinghua.edu.cn.  
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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 2756  
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