Land surface thermal alteration and pattern simulation based on influencing factors of rural landscape

Abstract

Is it true that rural areas, like urban areas, experience temperature changes over time? The aim of this mission was to find the season-wise trend associated with land surface thermal alteration based on satellite imagery from the last 30 years using the least square regression method, as well as to determine the stimulus of LST for the years 2027 and 2037 using Artificial Neural Network and Support Vector Machine techniques. The average temperature in the winter, summer, and Monsoon months has risen by 0.11 °C/year, 0.19 °C/year, and 0.07 °C/year, respectively, according to the analysis. Additionally, the simulated models reveal which extreme finish temperature group (>37.13 °C) may include more areas than the current one. For example, in 2017, a total area of 28.95 km2 was above the 37.13 °C temperature class, but this could increase to 37.91 km2 in 2027 and 42.67 km2 in 2037. Fragmentation analysis of the extreme temperature patches shows that the location of the high-temperature core steadily increases over time. The simulated water body, vegetation, built-up land, and bare land cover area show a decreasing trend in the first two parameters and an increasing trend in the last two, all of which influence temperature increase incident.