Journal of Geo-information Science ›› 2020, Vol. 22 ›› Issue (5): 1153-1160.doi: 10.12082/dqxxkx.2020.190549

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Preliminary Application of Night Light Remote Sensing based on Passenger Aircraft in Hong Kong Economic Activity Zone Changes Identification

WANG Yongquan1,2, WANG Chisheng1,2,3,*(), WANG Lehan1, SHE Jiani1, LI Qingquan1   

  1. 1. Guangdong Key Laboratory of Urban Informatics, School of Architecture & Urban Planning, Shenzhen University, Shenzhen 518060, China
    2. Key Laboratory for Geo-Environmental Monitoring of Great Bay Area of Ministry of Natural Resources, Shenzhen University, Shenzhen 518060, China
    3. Key Laboratory of Urban Land Resources Monitoring and Simulation of Ministry of Natural Resources, Shenzhen 518060, China
  • Received:2019-09-25 Revised:2020-03-24 Online:2020-05-25 Published:2020-07-25
  • Contact: WANG Chisheng E-mail:wangchisheng@163.com
  • Supported by:
    Shenzhen Scientific Research and Development Funding Program(KQJSCX20180328093453763);Shenzhen Scientific Research and Development Funding Program(JCYJ20180305125101282);National Nature Science Foundation of China(41974006);Key Laboratory of Urban Land Resources Monitoring and Simulation of Ministry of Natural Resources(KF-2018-03-004);Shenzhen University NatureScienceFunding Program(2018073)

Abstract:

Large-scale sustained demonstrations will seriously affect the stability of social order. Since June 2019, Hong Kong's economy has been affected to some extent by the impact of continuous demonstrations on various industries. Rapid and accurate identification of areas affected by demonstrations plays a very important role in loss assessment, economic recovery, and government governance. In this paper, we proposed a novel method to identify the affected areas using night light remote sensing and VGI data. Firstly, we captured the highly overlapping photographs of the study area by a mobile phone on passenger aircraft on August 28, 2019. Following regular photogrammetry steps, night light remote sensing image was generated. Then we compared it with POI density maps and Luojia-01 night light image, and initially marked the affected areas, which were further validated by VGI photos. We finally confirmed two affected areas in Kwun Tong District. A simple quantitative analysis was performed to assess the influence on affected areas. We conclude that the proposed method can quickly identify the areas where economic activities are affected in Hong Kong. Our method can also be used to study economic changes in other cities, which is of great application value in precise urban governance.

Key words: passenger aircraft, night light remote sensing, VGI data, protest movements, socio-economic changes, impact area identification, brightness, Luojia 1-01