Noninteger Dimension of Seasonal Land Surface Temperature (LST)

• Published in: Axioms
• Main Authors: Sepideh Azizi, Tahmineh Azizi
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2023
• Subjects: Land Surface Temperature (LST); Earth’s surface temperature; climate changes; Moderate Resolution Imaging Spectroradiometer (MODIS); NASA’s Terra satellite; fractal geometry; Mathematics; QA1-939; Greenland; Riley; Antarc*; Antarctica
• Online Access: https://doi.org/10.3390/axioms12060607; https://doaj.org/article/ee7c249ff47f4cf48a27f5d2256f130d

During the few last years, climate change, including global warming, which is attributed to human activities, and its long-term adverse effects on the planet’s functions have been identified as the most challenging discussion topics and have provoked significant concern and effort to find possible solutions. Since the warmth arising from the Earth’s landscapes affects the world’s weather and climate patterns, we decided to study the changes in Land Surface Temperature (LST) patterns in different seasons through nonlinear methods. Here, we particularly wanted to estimate the noninteger dimension and fractal structure of the Land Surface Temperature. For this study, the LST data were obtained during the daytime by a Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite. Depending on the time of the year data were collected, temperatures changed in different ranges. Since equatorial regions remain warm, and Antarctica and Greenland remain cold, and also because altitude affects temperature, we selected Riley County in the US state of Kansas, which does not belong to any of these location types, and we observed the seasonal changes in temperature in this county. According to our fractal analysis, the fractal dimension may provide a complexity index to characterize different LST datasets. The multifractal analysis confirmed that the LST data may define a self-organizing system that produces fractal patterns in the structure of data. Thus, the LST data may not only have a wide range of fractal dimensions, but also they are fractal. The results of the present study show that the Land Surface Temperature (LST) belongs to the class of fractal processes with a noninteger dimension. Moreover, self-organized behavior governing the structure of LST data may provide an underlying principle that might be a general outcome of human activities and may shape the Earth’s surface temperature. We explicitly acknowledge the important role of fractal geometry when analyzing and tracing settlement patterns and ...