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赵鹏程
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Personal Information
  • Supervisor of Master's Candidates
  • Name (Pinyin):Zhao Pengcheng
  • Date of Birth:1993-09-05
  • E-Mail:
  • Date of Employment:2019-12-07
  • Administrative Position:高级实验师
  • Education Level:With Certificate of Graduation for Doctorate Study
  • Business Address:武汉大学信息学部遥感信息工程学院（5号楼）315办公室
  • Gender:Male
  • Contact Information:+86 15972003670
  • Status:Employed
  • Alma Mater:武汉大学
  • Teacher College:School of Remote Sensing and Information Engineering
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Paper Publications
Current position: Home   >   Scientific Research   >   Paper Publications

An Approach of Identifying and Extracting Urban Commercial Areas Using the Nighttime Lights Satellite Imagery

  • Date of Publication:2025-01-07
  • Hits:

  • DOI number: 

    10.3390/rs12061029

  • Affiliation of Author(s): 

    School of Remote Sensing and Information Engineering, Wuhan University, China

  • Journal: 

    REMOTE SENSING

  • Key Words: 

    urban commercial area,nighttime lights satellite imagery,exploratory spatial data analysis,urban hot spots detection

  • Abstract: 

    Urban commercial areas can reflect the spatial distribution of business activities. However, the scope of urban commercial areas cannot be easily detected by traditional methods because of difficulties in data collection. Considering the positive correlation between business scale and nighttime lighting, this paper proposes a method of urban commercial areas detection based on nighttime lights satellite imagery. First, an imagery preprocess model is proposed to correct imageries and improve efficiency of cluster analysis. Then, an exploratory spatial data analysis and hotspots clustering method is employed to detect commercial areas by geographic distribution metric with urban commercial hotspots. Furthermore, four imageries of Wuhan City and Shenyang City are selected as an example for urban commercial areas detection experiments. Finally, a comparison is made to find out the time and space factors that affect the detection results of the commercial areas. By comparing the results with the existing map data, we are convinced that the nighttime lights satellite imagery can effectively detect the urban commercial areas. The time of image acquisition and the vegetation coverage in the area are two important factors affecting the detection effect. Harsh weather conditions and high vegetation coverage are conducive to the effective implementation of this method. This approach can be integrated with traditional methods to form a fast commercial areas detection model, which can then play a role in large-scale socio-economic surveys and dynamic detection of commercial areas evolution. Hence, a conclusion can be reached that this study provides a new method for the perception of urban socio-economic activities.

  • Co-author: 

    Mingyao, Pengcheng,Zhao, Qingwu, Xuzhe,Duan,Ai, Shaohua,Wang,Hu

  • Indexed by: 

    Journal paper

  • Discipline: 

    Engineering

  • Document Type: 

    J

  • Volume: 

    12

  • Issue: 

    6

  • Translation or Not: 

    no

  • CN No.: 

    WOS:000526820600131

  • Date of Publication: 

    2020-03-01

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