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Non-Integer Dimension of Seasonal Land Surface Temperature (LST)
Version 1
: Received: 17 April 2023 / Approved: 17 April 2023 / Online: 17 April 2023 (07:01:24 CEST)
A peer-reviewed article of this Preprint also exists.
Azizi, S.; Azizi, T. Noninteger Dimension of Seasonal Land Surface Temperature (LST). Axioms 2023, 12, 607. Azizi, S.; Azizi, T. Noninteger Dimension of Seasonal Land Surface Temperature (LST). Axioms 2023, 12, 607.
Abstract
During few last years, climate change including global warming which is attributed to
human activities and also its long-term adverse effects on the planet’s functions have
been identified as the most challenging discussion topics which have arisen many
concerns and efforts to find the possible solutions. Since the warmth arising from
Earth’s landscapes affects the world’s weather and climate patterns, we decided to study
the changes in the Land Surface Temperature (LST) patterns in different seasons
through non-linear methods. Here, we particularly want to estimate the non-integer
dimension and fractal structure of the land surface temperature. For this study, the
(LST) data has been obtained during the daytime by the Moderate Resolution Imaging
Spectroradiometer (MODIS) on NASA’s Terra satellite. Depending on what time of the
year data has been collected, temperatures change 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 U.S. state of Kansas,
which does not belong to any of this type locations and we are interested to observe the
seasonal changes in temperature in this county. The results of the present study show
that the Land Surface Temperature (LST) belongs to the class of fractal process with
non-integer dimension.
Keywords
Land Surface Temperature; Climate Change; Fractal Geometry; Discrete Wavelet Transform
Subject
Computer Science and Mathematics, Geometry and Topology
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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