Spatiotemporal variation characteristics analysis of infrastructure iron stock in China based on nighttime light data

被引:0
|
作者
Huang, Junchang [1 ,2 ]
Yue, Shuaijun [1 ,3 ]
Ji, Guangxing [1 ,3 ]
Cheng, Mingyue [1 ,3 ]
Ma, Hengyun [2 ]
Hua, Xuanke [1 ,3 ]
机构
[1] Henan Agr Univ, Coll Resources & Environm, Zhengzhou 450002, Peoples R China
[2] Henan Agr Univ, Coll Econ & Management, Zhengzhou 450002, Peoples R China
[3] Henan Engn Res Ctr Land Consolidat & Ecol Restorat, Zhengzhou 450002, Peoples R China
关键词
night lighting; iron stock; China; infrastructure; IN-USE STOCKS; STEEL; BUILDINGS;
D O I
10.1515/geo-2022-0510
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Iron is one of the most important basic materials in infrastructure development, spatial and temporal variation characteristics analysis of infrastructure iron stocks is conducive to revealing its distribution and change patterns from different scales, which can provide a scientific basis for sustainable urban development and iron resource management in China. In this article, we first calculated provincial infrastructure iron stock data from 2000 to 2020. Then, fitting equations between nighttime lighting data and infrastructure iron stock are constructed to simulate the spatial distribution of China's infrastructure iron stock at 500 m resolution from 2000 to 2020. Finally, the spatial and temporal dynamics of China's infrastructure iron stock is analyzed from four scales: national, regional, provincial, and urban agglomeration. The results show as follows: (1) China's infrastructure iron stock grew at an average annual rate of 26.42% from 2000 to 2020, with China's infrastructure iron stock increasing 6.28 times over the 21 years. Construction facilities are the most important part of the infrastructure iron stock, and its share is still increasing. (2) On a regional scale, the high-growth type of infrastructure iron stock is mainly distributed in the eastern region, while the no-obvious-growth type is mainly distributed in the western region. The high grade of infrastructure iron stock is mainly distributed in the eastern region, while the low grade is mainly distributed in the western region. (3) On a provincial scale, the highest share of no-obvious-growth type of infrastructure iron stock is in Xinjiang. The highest proportion of infrastructure iron stock of high-growth type is in Jiangsu. The highest proportion of low-grade infrastructure iron stock is in Xinjiang. The highest proportion of infrastructure iron stock of high grade is in Beijing. (4) In terms of urban agglomerations, the high-growth type of infrastructure iron stock is mainly located in Shanghai-Nanjing-Hangzhou, while the no-obvious-growth type is mainly located in the Middle south of Liaoning. The high-grade infrastructure iron stock is mainly distributed in Shanghai-Nanjing-Hangzhou, while the low grade is mainly distributed in Sichuan-Chongqing.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Spatiotemporal Variation Characteristics Analysis of Anthropogenic Heat Fluxes Based on Nighttime Lighting Data
    Huang, Junchang
    Yue, Shuaijun
    Wang, Songling
    Ji, Guangxing
    Cheng, Mingyue
    Li, Ling
    POLISH JOURNAL OF ENVIRONMENTAL STUDIES, 2024, 33 (03): : 3183 - 3192
  • [2] Analysis of the spatiotemporal expansion and pattern evolution of urban areas in Anhui Province, China, based on nighttime light data
    Xu, Yazhou
    Hao, Shuang
    Cui, Yuhuan
    Li, Pengfei
    Sheng, Liangliang
    Liao, Congcong
    ECOLOGICAL INDICATORS, 2023, 157
  • [3] Spatiotemporal Characteristics of Urbanization in the Taiwan Strait Based on Nighttime Light Data from 1992 to 2020
    Ye, Yuqing
    Yun, Guoliang
    He, Yuanrong
    Lin, Ruijin
    He, Tingting
    Qian, Zhiheng
    REMOTE SENSING, 2023, 15 (13)
  • [4] Analysis of Spatiotemporal Characteristics and Influencing Factors of Land Urbanization Level in China at Different Scales Based on Nighttime Light Remote Sensing
    Zhang, Zhaoxu
    Liu, Xingchi
    Li, Jiayi
    Fu, Shihong
    Sun, Yuanheng
    Qiao, Rongfeng
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2024, 17 : 9811 - 9827
  • [5] In-use stock analysis using satellite nighttime light observation data
    Takahashi, Kazue Ichino
    Terakado, Ryutaro
    Nakamura, Jiro
    Adachi, Yoshihiro
    Elvidge, Christopher D.
    Matsuno, Yasunari
    RESOURCES CONSERVATION AND RECYCLING, 2010, 55 (02) : 196 - 200
  • [6] Study on Spatiotemporal Evolution and Driving Factors of Urban Expansion Based on Nighttime Light Data: Case of Anhui Province, China
    Xie, Xinwei
    Qin, Xuemin
    Hu, Xiaoxuan
    JOURNAL OF URBAN PLANNING AND DEVELOPMENT, 2024, 150 (03)
  • [7] Analysis and Prediction of Expansion of Central Cities Based on Nighttime Light Data in Hunan Province, China
    Liu, Yuxin
    He, Tian
    Wang, Yi
    Peng, Changhui
    Du, Hui
    Yuan, Shuai
    Li, Peng
    SUSTAINABILITY, 2021, 13 (21)
  • [8] Material stock analysis of urban road from nighttime light data based on a bottom-up approach
    Zhao, Fei
    Wu, Huixia
    Zhu, Sijin
    Zeng, Hongyun
    Zhao, Zhifang
    Yang, Xutao
    Zhang, Sujin
    ENVIRONMENTAL RESEARCH, 2023, 228
  • [9] Analysis of In-Use Stock of Copper by Using Satellite Nighttime Light Observation Data
    Takahashi, Kazue Ichino
    Terakado, Ryutaro
    Nakamura, Jiro
    Daigo, Ichiro
    Matsuno, Yasunari
    Adachi, Yoshihiro
    JOURNAL OF THE JAPAN INSTITUTE OF METALS, 2008, 72 (11) : 852 - 855
  • [10] Analyzing Spatiotemporal Variation Modes and Industry-Driving Force Research Using VIIRS Nighttime Light in China
    Song, Xiaoke
    Chen, Yunhao
    Li, Kangning
    REMOTE SENSING, 2020, 12 (17)