Improvement of Monitoring Production Status of Iron and Steel Factories Based on Thermal Infrared Remote Sensing

被引:2
|
作者
Han, Fang [1 ,2 ]
Zhao, Fei [3 ]
Li, Fuxing [1 ]
Shi, Xiaoli [1 ]
Wei, Qiang [1 ]
Li, Weimiao [1 ]
Wang, Wei [1 ]
机构
[1] Hebei Normal Univ, Hebei Remote Sensing Technol Identificat Innovat C, Sch Geog Sci, Hebei Lab Environm Evolut & Ecol Construction, Shijiazhuang 050024, Peoples R China
[2] Hebei Univ Econ & Business, Hebei Collaborat Innovat Ctr Urban Rural Integrate, Sch Publ Management, Shijiazhuang 050061, Peoples R China
[3] China Satellite Commun Co Ltd Beijing, Beijing 100190, Peoples R China
关键词
production status; ISHII; seasonal decomposition model; TIRS; LAND-SURFACE TEMPERATURE; SPLIT-WINDOW ALGORITHM; RETRIEVAL;
D O I
10.3390/su15118575
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Thermal infrared remote satellite (TIRS) images combined with high-resolution optical images in a time series can be used to analyze the production status of iron and steel factories (ISF) in China, which is more objective compared with statistical data. In previous studies, based on the land surface temperature (LST) data retrieved from Landsat-8 TIRS data, the heat island intensity index of an ISF (hereinafter referred to as ISHII) evaluates the LST difference between the main production area and other areas, and it can show the production status partly in one ISF. However, deviations in the LST due to seasonal changes can cause inaccuracies in the monitoring production status. In this study, we propose a modified method that introduces a seasonal-trend decomposition procedure based on regression (hereinafter referred to as STR) into the ISHII data to build a seasonal decomposition model. First, on the basis of a previously proposed time series of ISHII data from January 2013 to October 2017 for three ISF samples, the seasonal decomposition of the ISHII model was used to decompose the ISHII data into three components: trend, seasonality, and random. Then, we analyzed the relationships between these three components and the production status in the three ISFs. Additionally, to verify the precision of this method, we used high-resolution optical images to visually detect surface changes in the facilities at specific times. Finally, results showed that the trend curve can represent the entire factory development status, the seasonality curve shows the regular seasonal fluctuation, and the random component sensitively reflects the production status changes of one ISF. After decomposition, the capacity of a random component to reflect production changes has doubled or tripled compared to previous methods. In conclusion, this study provides a modified method with a seasonal decomposition model to improve prediction accuracy on the long-term production status of ISFs. Then, based on the change obtained from high-resolution optical images and Internet data on the ISF production status, we verified the accuracy of this modified method. This research will provide powerful supports for sustainable industrial development and policy decision-making in China.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] Thermal infrared remote sensing of vegetation: Current status and perspectives
    Neinavaz, Elnaz
    Schlerf, Martin
    Darvishzadeh, Roshanak
    Gerhards, Max
    Skidmore, Andrew K.
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2021, 102
  • [2] Hyperspectral thermal infrared remote sensing: Current status and perspectives
    Wu H.
    Li X.
    Li Z.
    Duan S.
    Qian Y.
    National Remote Sensing Bulletin, 2021, 25 (08) : 1567 - 1590
  • [3] Monitoring Method of Underground Coal Fire Based on Night Thermal Infrared Remote Sensing Technology
    Jiang Wei-guo
    Wu Jian-jun
    Gu Lei
    Yang Bo
    Chen Qiang
    Liu Xiao-chen
    SPECTROSCOPY AND SPECTRAL ANALYSIS, 2011, 31 (02) : 357 - 361
  • [4] Review and improvement of an algorithm for determining emissivity of a heterogeneous cavity in thermal infrared remote sensing
    Badenas, C
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 1998, 19 (04) : 731 - 741
  • [5] INFRARED REMOTE-SENSING FOR MONITORING RAINFALL
    MOORE, DG
    HARLAN, JC
    HEILMAN, JL
    OHLEN, DO
    ROSENTHAL, WD
    AGRICULTURAL WATER MANAGEMENT, 1983, 7 (1-3) : 363 - 378
  • [6] Thermal infrared remote sensing data downscaling investigations: An overview on current status and perspectives
    Pu, Ruiliang
    Bonafoni, Stefania
    REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT, 2023, 29
  • [7] Simulation of thermal infrared remote sensing imaging based on QuickBird data
    Gu, You-Lin
    Guangxue Jishu/Optical Technique, 2008, 34 (06): : 877 - 880
  • [8] Hydrothermal monitoring in Yellowstone National Park using airborne thermal infrared remote sensing
    Neale, C. M. U.
    Jaworowski, C.
    Heasler, H.
    Sivarajan, S.
    Masih, A.
    REMOTE SENSING OF ENVIRONMENT, 2016, 184 : 628 - 644
  • [9] Remote sensing measurements in the thermal infrared band
    Prevot, L
    Laville, P
    Gu, XF
    INRA BIOCLIMATOLOGY DEPARTMENT RESEARCH COURSE, VOL 2: FROM PLANT CANOPY TO THE REGION, 1996, : 69 - 80
  • [10] Remote Sensing for Monitoring Potato Nitrogen Status
    Alfadhl Alkhaled
    Philip A. Townsend
    Yi Wang
    American Journal of Potato Research, 2023, 100 : 1 - 14