Vol. 2 No. 2 (2024): SJESR - June 2024
Articles

Land use/ Land cover assessment for Karbala City by using geographic information systems (GIS) technique

Suhail Tuama Khazaal Department of Civil Engineering, College of Engineering, University of Kerbala, Kerbala
Waqed H. Hussan Department of Civil Engineering, College of Engineering, University of Kerbala, Kerbala. College of Engineering, University of Warith Al-Anbiyaa, Kerbala
Musa Habib AL-Shammari Department of Civil Engineering, College of Engineering, University of Kerbala, Kerbala.
Vol. 2 No. 2 (2024)

Published 2024-07-03

Keywords

  • : Karbala city, LUALC geographical information systems (GIS)

How to Cite

Land use/ Land cover assessment for Karbala City by using geographic information systems (GIS) technique. (2024). Samarra Journal of Engineering Science and Research, 2(2), 108-120. https://doi.org/10.65115/7zq3sp06

Abstract

The Earth's surface is experiencing swift changes in (LUALC) due to diverse socioeconomic activity and natural events. The primary aim of this research is to acquire a quantitative comprehension of the alterations in (LUALC) in Karbala from 2000 to 2023. The study utilized the supervised classification-maximum likelihood approach in Geographic Information Systems (GIS) to identify (LUALC) changes in Karbala. Multispectral satellite data from Landsat 5 and 9 were employed for 2000 and 2023, respectively. The maps were categorized into four primary (LUALC) classifications: water, vegetation, building, and soil. The results explained an increase in the area of ​​building and vegetation by 7.67% equivalent to 388.7668km2 and 7.81% equivalent to 395.9646km2, respectively, and a decrease in the area of ​​water and soil, by -5.81% equivalent to -294.6234km2 and -9.67% equivalent to -490.188km2, respectively. The accuracy evaluation was conducted, revealing that the photos classified for the years 2000 and 2023 had overall accuracies of 90.90% and 93.33%, respectively. Finally, the Kappa coefficient values for the two photos were 0.8779 and 0.9095, respectively. Therefore, these statistics Demonstrate that the precision of (LUALC) classifications is convenient.

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