Physics · Vol. 4 No. 3 (2025) · Pages 98-107

Study of Change Detection in the Shikeh Saad Region Using Meltitemporal Method from 2014 to 2024

Published

Authors

  • Manhal Hamead Jasim Department of physics, College of Education, Al-Qadisiya University, IRAQ
  • Israa Hussein Mohammed

DOI https://doi.org/10.31185/wjps.1278

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Received Oct. 24, 2026 Online Sep. 30, 2025

Abstract

The study was titled "Detecting Changes in Land Cover in the Sheikh Saad Area Between 2014 and 2024." The importance of the study stems from the importance of land cover as a resource for humans, including phenomena such as soil, which represents the foundation for natural plant growth and a place for crop cultivation. Water is important for humans and animals, as are trees. The study information was collected from books and references, map sites, and satellite images. The study used the European Landsat 8 satellite image to detect changes in land cover and land uses in their various forms. The study followed the descriptive and analytical approaches by comparing land cover between 2014 and 2024. The analysis used the ArcMap ArcGIS 10.8 program. The study concluded that land cover and land uses are classified into five main types, they are green lands, barren lands, wetlands (water), rugged lands, and pasture lands. It also showed that there was a change in land cover and land uses, with the percentage of change in land reaching Barren lands recorded a change rate of (-84.496 km2) due to environmental changes. Wetlands recorded a change rate of (-259.111) where we notice a decrease in their areas between the two periods due to climate change. Green lands recorded a change rate of (291.121 km2) where we notice a change in their areas between the two periods. Rough lands recorded a change rate of (21.82 km2) while pasture lands recorded a change of (30.666 km2). These changes were reflected in the change in the land cover in Sheikh Saad district.                                                                                                                                  

 

Keywords

  • NDVI; NDWI; Landsat 8; Samarra; classification; accuracy assessment
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