Evaluation of Surface Temperature Changes in Ilam City with a Combined Approach to Geostatistical Models and Spatial Information Systems

Vafaeinejad, A.; Hemati, F.; Haery, S.

doi:10.22061/jrsgr.2024.10977.1069

Document Type : Original Research Paper

Authors

¹ Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran

² Faculty of Environment and Energy, Islamic Azad University Science and Research Branch, Tehran, Iran

³ Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran,

https://doi.org/10.22061/jrsgr.2024.10977.1069

Abstract

Background and Objectives: Monitoring and analyzing variations in land surface temperature is essential for agriculture, biodiversity, human health, and water resources as it is a major indication of climate change. By analyzing and assessing these alterations, one can get a thorough understanding of the causes and effects of global warming and be better equipped to take preventative and remedial action against this rapidly spreading phenomena. Because remote sensing data may show the features of land phenomena and their spatial distribution at various scales, it is crucial to gather LST. Several factors need to be taken into account, including data distribution and spatial correlation analysis, associated geostatistical model investigation, model selection, and result validation. In light of the foregoing, the purpose of this research is to ascertain the trend of variations in LST in Ilam County and to propose a suitable mathematical model for the interpolation of meteorological station data in the area.
Methods: The thermal band of the Landsat 7 satellite has proven to be an effective tool in this research to study changes in surface temperature in Ilam city. The data in this band, which is recorded as thermal radiation emitted from the register, allows the surface to be calculated with a reasonable degree of accuracy. One benefit of using satellite images is their large coverage and periodicity, which allows the study of changes in surface temperature in a region and over time. This comprehensive view allows the temperature changes in Ilam city to be well-analyzed and the factors that affect these changes are identified in various locations. To do this, a regular grid with 291 points was sampled from satellite photos. Next, experimental variogram points were created using the geostatistical method, and various spatial models, including Gaussian, exponential, circular, and spherical, were fitted to the sampled data. Ultimately, distinct surface temperature maps have been produced using the normal kriging interpolation method and each of these models. The correctness of each map has been determined using statistical markers like the coefficient of determination and root mean square error.
Findings: The findings of the study demonstrate that the thermal band data from the Landsat 7 satellite exhibits a Gaussian geographical pattern, and this model provides a strong justification for the observed spatial variations in surface temperature. The findings demonstrate that the Gaussian spatial model fits the experimental variogram of surface temperature in the investigated region the best. This demonstrates that variations in surface temperature in this area are spatially autocorrelated, with the correlation between locations decreasing with increasing distance. High accuracy interpolated maps are produced by the traditional kriging approach using the Gaussian model. These maps' coefficient of determination was 0.94, indicating a good degree of agreement between the estimated and real surface temperature data.
Conclusion: The integration of remote sensing data and geostatistical methods offers a powerful tool for examining spatial variations and interpolating environmental data, including land surface temperature changes. In this study, the pattern of LST changes in Ilam County was determined using satellite remote sensing data, and the Gaussian model was introduced as the optimal spatial model for interpolating LST at weather stations. Data analysis revealed that LST in the region has increased significantly over the past few decades.

Keywords

Main Subjects

Geo-spatial Information System

COPYRIGHTS
© 2024 The Author(s). This is an open-access article distributed under the terms and conditions of the Creative Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/)

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