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