A Multitemporal and Multilevel Land Surface Temperature Regional Attribute Change Analysis in Henan, China, Using MODIS Imagery

Temperature is an important aspect of land–atmosphere studies and plays a key role in urban environmental change. With the continuous development satellite remote sensing sensors, technology has become means obtaining large-scale land surface temperature (LST) data. LST can be calculated from thermal infrared band data images to analyze changes determine its relationship with type. In this study, multitemporal multilevel (MTML) method for analyzing remotely sensed presented that analyzes attribute correlations different periods at levels. First, were obtained under same climatic conditions times, influence on was excluded. Threshold superposition analysis then performed generate temperature-connected regions levels, tree structure constructed. Each node represented connected region. Finally, information levels calculated, between times analyzed. five MODIS datasets 15 May 2006, 1 2010, 7 2014, 29 April 2017, 8 2021 Henan Province China obtained, MTML carried out. The experimental results showed negative correlation exists vegetation index, while positive built-up index. However, increase level, feature type index decreased. addition, there more concentrated high-temperature areas northern, central, western lower temperatures eastern southern regions.