Geomatics and Environmental Engineering, vol. 17, no. 3

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Vol. 17 No. 3 (2023): Geomatics and Environmental Engineering

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Submitted
Apr 29, 2022
Accepted
Jan 13, 2023
Published
Feb 11, 2023
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Monitoring of Land Surface Temperature from Landsat Imagery: A Case Study of Al-Anbar Governorate in Iraq

https://doi.org/10.7494/geom.2023.17.3.61
Salem Morsy
Cairo University, Faculty of Engineering, Public Works Department, Giza, Egypt
https://orcid.org/0000-0002-1683-2050
Shaker Ahmed
Cairo University, Faculty of Engineering, Public Works Department, Giza, Egypt; University of Anbar, College of Engineering, Department of Civil Engineering, Ramadi, Iraq

Corresponding Author(s) : Salem Morsy

smorsy@eng.cu.edu.eg

Geomatics and Environmental Engineering, Vol. 17 No. 3 (2023): Geomatics and Environmental Engineering

Abstract

Land surface temperature (LST) estimation is a crucial topic for many applications related to climate, land cover, and hydrology. In this research, LST estimation and monitoring of the main part of Al-Anbar Governorate in Iraq is presented using Landsat imagery from five years (2005, 2010, 2015, 2016 and 2020). Images of the years 2005 and 2010 were captured by Landsat 5 (TM) and the others were captured by Landsat 8 (OLI/TIRS). The Single Channel Algorithm was applied to retrieve the LST from Landsat 5 and Landsat 8 images. Moreover, the land use/land cover (LULC) maps were developed for the five years using the maximum likelihood classifier. The difference in the LST and normalized difference vegetation index (NDVI) values over this period was observed due to the changes in LULC. Finally, a regression analysis was conducted to model the relationship between the LST and NDVI. The results showed that the highest LST of the study area was recorded in 2016 (min = 21.1°C, max = 53.2°C and mean = 40.8°C). This was attributed to the fact that many people were displaced and had left their agricultural fields. Therefore, thousands of hectares of land which had previously been green land became desertified. This conclusion was supported by comparing the agricultural land areas registered throughout the presented years. The polynomial regression analysis of LST and NDVI revealed a better coefficient of determination (R2) than the linear regression analysis with an average R2 of 0.423.

Keywords

land surface temperature Landsat single channel algorithm NDVI land use/land cover classification regression
Morsy, S., & Ahmed, S. (2023). Monitoring of Land Surface Temperature from Landsat Imagery: A Case Study of Al-Anbar Governorate in Iraq. Geomatics and Environmental Engineering, 17(3), 61–81. https://doi.org/10.7494/geom.2023.17.3.61
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