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

Thermal infrared remote satellite (TIRS) images combined with high-resolution optical in a time series can be used to analyze the production status of iron and steel factories (ISF) China, which is more objective compared statistical data. In previous studies, based on land surface temperature (LST) data retrieved from Landsat-8 TIRS data, heat island intensity index an ISF (hereinafter referred as ISHII) evaluates LST difference between main area other areas, it show partly one ISF. However, deviations due seasonal changes cause inaccuracies monitoring status. this study, we propose modified method that introduces seasonal-trend decomposition procedure regression STR) into ISHII build model. First, basis previously proposed January 2013 October 2017 for three samples, model was decompose components: trend, seasonality, random. Then, analyzed relationships these components ISFs. Additionally, verify precision method, visually detect facilities at specific times. Finally, results showed trend curve represent entire factory development status, seasonality shows regular fluctuation, random component sensitively reflects After decomposition, capacity reflect has doubled or tripled methods. conclusion, study provides improve prediction accuracy long-term change obtained Internet verified method. This research will provide powerful supports sustainable industrial policy decision-making China.