A new perspective to map the supply and demand of artificial night light based on Loujia1-01 and urban big data

Abstract The notable increase in artificial night light (ANL) induced by the rapid urbanization process has been widely studied, but a deep understanding of the supply and demand status of ANL is still lacking. This paper attempts to map the supply and demand of ANL from the human perspective by using advanced Loujia1-01 nighttime imagery and social media derived population density (PD) data, which provides a new tool for light regulation in urban management. The bivariate clustering based k-means algorithm and template matching technique are integrated to delineate mismatch regions at the block scale to further analyze the underlying reason for unbalanced status. The results showed that the high supply but low demand (HSLD) ANL status was the leading component in the mismatch regions, occupying more than 650,000 ha and mainly occurring in the city center. The HSLD proportion was considerable in terms of public services (44%), commercial (40%), industrial (39%), transportation (56%), and green space areas (53%). Moreover, the HSLD area notably increased 946 ha over time from 18:00 to 22:00. The measurements for validation obtained by field investigation showed highly linear relationship with ANL (R2 = 0.75) and PD (R2 = 0.62), and the mapping results were consistent with the actual conditions. This study reveals the highly unbalanced ANL status, and appeals to planners for the establishment of optimal lighting regulations to alleviate disruptive effects.

[1]  P. Boyce.  The benefits of light at night , 2019, Building and Environment.

[2]  A. Tatem,et al.  Dynamic population mapping using mobile phone data , 2014, Proceedings of the National Academy of Sciences.

[3]  Guo Zhang,et al.  Evaluating the Potential of LJ1-01 Nighttime Light Data for Modeling Socio-Economic Parameters , 2019, Sensors.

[4]  Chen Peng,et al.  Urban Built-Up Area Extraction From Log- Transformed NPP-VIIRS Nighttime Light Composite Data , 2018, IEEE Geoscience and Remote Sensing Letters.

[5]  Kevin J. Gaston,et al.  Human alteration of natural light cycles: causes and ecological consequences , 2014, Oecologia.

[6]  Kendall R. Jones,et al.  Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation , 2016, Nature Communications.

[7]  Jinliang Huang,et al.  Monitoring Trends in Light Pollution in China Based on Nighttime Satellite Imagery , 2014, Remote. Sens..

[8]  Xuejun Du,et al.  Ecological and environmental effects of land use change in rapid urbanization: The case of hangzhou, China , 2017 .

[9]  T. Smyth,et al.  Why artificial light at night should be a focus for global change research in the 21st century , 2018, Global change biology.

[10]  Cecilia Nilsson,et al.  Bright lights in the big cities: migratory birds’ exposure to artificial light , 2019, Frontiers in Ecology and the Environment.

[11]  Guojin He,et al.  Potentiality of Using Luojia 1-01 Nighttime Light Imagery to Investigate Artificial Light Pollution , 2018, Sensors.

[12]  Frank Nielsen,et al.  Total Jensen divergences: Definition, properties and clustering , 2013, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[13]  Kevin J. Gaston,et al.  REVIEW: Reducing the ecological consequences of night‐time light pollution: options and developments , 2012, The Journal of applied ecology.

[14]  Peng Gong,et al.  Mapping Urban Land Use by Using Landsat Images and Open Social Data , 2016, Remote. Sens..

[15]  Pr Boyce,et al.  Road lighting and energy saving , 2009 .

[16]  Xi Li,et al.  Mapping Urban Extent Using Luojia 1-01 Nighttime Light Imagery , 2018, Sensors.

[17]  L. Guanter,et al.  Artificially lit surface of Earth at night increasing in radiance and extent , 2017, Science Advances.

[18]  C. Elvidge,et al.  Remote sensing of night lights: A review and an outlook for the future , 2020, Remote Sensing of Environment.

[19]  Shane D. Johnson,et al.  What is the effect of reduced street lighting on crime and road traffic injuries at night? A mixed-methods study , 2015 .

[20]  M. Krarti,et al.  Spatial distribution of building energy use in the United States through satellite imagery of the earth at night , 2018, Building and Environment.

[21]  K. Gaston,et al.  Benefits and costs of artificial nighttime lighting of the environment , 2015 .

[22]  M. Ciach,et al.  Ungulates in the city: light pollution and open habitats predict the probability of roe deer occurring in an urban environment , 2019, Urban Ecosystems.

[23]  K. Gaston,et al.  Quantifying the erosion of natural darkness in the global protected area system , 2015, Conservation biology : the journal of the Society for Conservation Biology.

[24]  Xin Huang,et al.  A novel locally adaptive method for modeling the spatiotemporal dynamics of global electric power consumption based on DMSP-OLS nighttime stable light data , 2019, Applied Energy.

[25]  Guo Zhang,et al.  On-Orbit Relative Radiometric Calibration of the Night-Time Sensor of the LuoJia1-01 Satellite , 2018, Sensors.

[26]  Stuart R. Phinn,et al.  A new source for high spatial resolution night time images — The EROS-B commercial satellite , 2014 .

[27]  Ting Ma,et al.  Multi-Level Relationships between Satellite-Derived Nighttime Lighting Signals and Social Media-Derived Human Population Dynamics , 2018, Remote. Sens..

[28]  Noam Levin,et al.  Quantifying urban light pollution — A comparison between field measurements and EROS-B imagery , 2016 .

[29]  C. Elvidge,et al.  Limiting the impact of light pollution on human health, environment and stellar visibility. , 2011, Journal of environmental management.

[30]  K. Gaston,et al.  Multiple night‐time light‐emitting diode lighting strategies impact grassland invertebrate assemblages , 2017, Global change biology.

[31]  Alexander Zipf,et al.  Twitter as an indicator for whereabouts of people? Correlating Twitter with UK census data , 2015, Comput. Environ. Urban Syst..

[32]  K. Casey,et al.  Spatial and temporal changes in cumulative human impacts on the world's ocean , 2015, Nature Communications.

[33]  Yutang Wang,et al.  A study of the impacts of urban expansion on vegetation primary productivity levels in the Jing-Jin-Ji region, based on nighttime light data , 2020 .

[34]  K. K. Ramakrishnan,et al.  Mining checkins from location-sharing services for client-independent IP geolocation , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[35]  G. A. Abubakar,et al.  Mapping the fine-scale spatial pattern of artificial light pollution at night in urban environments from the perspective of bird habitats. , 2019, The Science of the total environment.

[36]  Noam Levin,et al.  The impact of seasonal changes on observed nighttime brightness from 2014 to 2015 monthly VIIRS DNB composites , 2017 .

[37]  Gregory Dobler,et al.  Dynamics of the urban lightscape , 2015, Inf. Syst..

[38]  H. Kajitani,et al.  Development of a magnetic head suspension system for high-speed seeking performance , 1996 .

[39]  Hao Zhang,et al.  Land use dynamics, built-up land expansion patterns, and driving forces analysis of the fast-growing Hangzhou metropolitan area, eastern China (1978-2008) , 2012 .

[40]  C. Kerbiriou,et al.  Disentangling the relative effect of light pollution, impervious surfaces and intensive agriculture on bat activity with a national-scale monitoring program , 2016, Landscape Ecology.

[41]  Jihua Wang,et al.  Characterizing spatial patterns and driving forces of expansion and regeneration of industrial regions in the Hangzhou megacity, China , 2020 .

[42]  K. Gaston,et al.  Mapping artificial lightscapes for ecological studies , 2014 .

[43]  Kevin J. Gaston,et al.  Sustainability: A green light for efficiency , 2013, Nature.

[44]  Qi Dai,et al.  Quantification assessment of light pollution of façade lighting display in Shenzhen, China. , 2020, Optics express.

[45]  Bo Huang,et al.  Using multi-source geospatial big data to identify the structure of polycentric cities , 2017 .

[46]  Wenze Yue,et al.  Suburban industrial land development in transitional China: Spatial restructuring and determinants , 2018, Cities.

[47]  Qihao Weng,et al.  A new source of multi-spectral high spatial resolution night-time light imagery—JL1-3B , 2018, Remote Sensing of Environment.

[48]  Yuxia Huang,et al.  Association between nighttime artificial light pollution and sea turtle nest density along Florida coast: A geospatial study using VIIRS remote sensing data. , 2018, Environmental pollution.

[49]  Yuyu Zhou,et al.  Spatiotemporal variations of CO2 emissions and their impact factors in China: A comparative analysis between the provincial and prefectural levels , 2019, Applied Energy.

[50]  Dacheng Tao,et al.  Robust Visual Tracking Revisited: From Correlation Filter to Template Matching , 2018, IEEE Transactions on Image Processing.

[51]  W. Nordhaus,et al.  Using luminosity data as a proxy for economic statistics , 2011, Proceedings of the National Academy of Sciences.

[52]  Karen C. Seto,et al.  A Robust Method to Generate a Consistent Time Series From DMSP/OLS Nighttime Light Data , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[53]  B. Jiang,et al.  Spatial Distribution of City Tweets and Their Densities , 2016, 1603.02231.