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Exploring Spatial Influence of Remotely Sensed PM 2.5 Concentration Using a Developed Deep Convolutional Neural Network Model

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  • Junming Li

    (School of Statistics, Shanxi University of Finance and Economics, Wucheng Road 696, Taiyuan 030006, China)

  • Meijun Jin

    (College of Architecture and Civil Engineering, Taiyuan University of Technology, Yingze Street 79, Taiyuan 030024, China)

  • Honglin Li

    (Shanxi Centre of Remote Sensing, 136 Street Yingze, Taiyuan 030001, China)

Abstract

Currently, more and more remotely sensed data are being accumulated, and the spatial analysis methods for remotely sensed data, especially big data, are desiderating innovation. A deep convolutional network (CNN) model is proposed in this paper for exploiting the spatial influence feature in remotely sensed data. The method was applied in investigating the magnitude of the spatial influence of four factors—population, gross domestic product (GDP), terrain, land-use and land-cover (LULC)—on remotely sensed PM 2.5 concentration over China. Satisfactory results were produced by the method. It demonstrates that the deep CNN model can be well applied in the field of spatial analysing remotely sensed big data. And the accuracy of the deep CNN is much higher than of geographically weighted regression (GWR) based on comparation. The results showed that population spatial density, GDP spatial density, terrain, and LULC could together determine the spatial distribution of PM 2.5 annual concentrations with an overall spatial influencing magnitude of 97.85%. Population, GDP, terrain, and LULC have individual spatial influencing magnitudes of 47.12% and 36.13%, 50.07% and 40.91% on PM 2.5 annual concentrations respectively. Terrain and LULC are the dominating spatial influencing factors, and only these two factors together may approximately determine the spatial pattern of PM 2.5 annual concentration over China with a high spatial influencing magnitude of 96.65%.

Suggested Citation

  • Junming Li & Meijun Jin & Honglin Li, 2019. "Exploring Spatial Influence of Remotely Sensed PM 2.5 Concentration Using a Developed Deep Convolutional Neural Network Model," IJERPH, MDPI, vol. 16(3), pages 1-11, February.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:3:p:454-:d:203501
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    References listed on IDEAS

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    Cited by:

    1. Shixiong Cheng & Jiahui Xie & De Xiao & Yun Zhang, 2019. "Measuring the Environmental Efficiency and Technology Gap of PM 2.5 in China’s Ten City Groups: An Empirical Analysis Using the EBM Meta-Frontier Model," IJERPH, MDPI, vol. 16(4), pages 1-22, February.
    2. Mei-Hsin Chen & Yao-Chung Chen & Tien-Yin Chou & Fang-Shii Ning, 2023. "PM2.5 Concentration Prediction Model: A CNN–RF Ensemble Framework," IJERPH, MDPI, vol. 20(5), pages 1-13, February.

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