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Modeling and mapping total freight traffic in China using NPP-VIIRS nighttime light composite data

机译:使用NPP-VIIRS夜间灯光组合数据对中国的总货运量进行建模和映射

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摘要

In early 2013, the first global Suomi National Polar-orbiting Partnership (NPP) visible infrared imaging radiometer suite (VIIRS) nighttime light composite data were released. Up to present, few studies have been conducted to evaluate the ability of NPP-VIIRS data to estimate the amount of freight traffic. This paper provides an exploratory evaluation on the NPP-VIIRS data for estimating the total freight traffic (TFT) in China, in comparison with the results derived from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime stable light composite data. We first corrected the original NPP-VIIRS data by employing a simple method to remove the outliers. The total nighttime light (TNL) which is measured by the sum value of all pixels from the nighttime light composite data was then regressed on TFT at the provincial level of China. Finally, the spatial distribution patterns of TFT were produced from the corrected NPP-VIIRS and DMSP-OLS data, respectively, and validated by the TFT statistics of 244 prefectures. The results have demonstrated that the corrected NPP-VIIRS data are more suitable for modeling TFT in China than the DMSP-OLS data.
机译:2013年初,发布了全球首个Suomi国家极地轨道伙伴关系(NPP)可见红外成像辐射计套件(VIIRS)夜间光合成数据。迄今为止,很少进行研究来评估NPP-VIIRS数据估计货运量的能力。与美国国防气象卫星计划-操作线扫描系统(DMSP-OLS)夜间稳定光复合数据得出的结果相比,本文对NPP-VIIRS数据进行了探索性评估,以估算中国的总货运量(TFT)。 。我们首先通过一种简单的方法来消除异常值,从而更正了原始的NPP-VIIRS数据。然后,根据夜间光合成数据中所有像素之和测得的夜间总光(TNL)在中国省级TFT上进行回归。最后,分别从校正后的NPP-VIIRS和DMSP-OLS数据得出TFT的空间分布图,并通过244个州的TFT统计数据进行验证。结果表明,校正后的NPP-VIIRS数据比DMSP-OLS数据更适合于中国的TFT建模。

著录项

  • 来源
    《GIScience & remote sensing》 |2015年第3期|274-289|共16页
  • 作者单位

    E China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China;

    E China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China;

    Univ Calif Santa Barbara, Dept Geog, Santa Barbara, CA 93106 USA;

    Northwest Univ, Coll Urban & Environm Sci, Xian 710127, Peoples R China;

    CSIRO Land & Water, Canberra, ACT 2601, Australia;

    E China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China;

    E China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China;

    E China Normal Univ, Minist Educ, Key Lab Geog Informat Sci, Shanghai 200241, Peoples R China;

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  • 正文语种 eng
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  • 入库时间 2022-08-18 03:40:56

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