Analysis of change trends in spectral drought indicators in Babylon Governorate using remote sensing techniques: Al-Hashimiya District as an applied model
DOI:
https://doi.org/10.31185/eduj.Vol64.Iss2.5315Keywords:
Change trend, drought, spectral indices, remote sensing, study areaAbstract
This research aims to reveal the trend of change in spectral aridity indices in the study area during the period (2015–2025) using remote sensing techniques. The research relied on a set of key spectral indices (NDVI, LSWI, SI, NDSI, and CI) to monitor and assess aridity. Regarding vegetation cover, bare land decreased by (-626 km²), while moisture indices showed a water degradation with an increase in arid land (+107 km²) and a decrease in highly moist land (-55.8 km²). Salinity indices recorded an expansion of highly saline land (+222.66 km²) and a decrease in non-saline land (-157.83 km²), with a sharp seasonal fluctuation in the NDSI index (+524.50 km²) for moderately saline land during the wet season. The results confirm the increasing risks of aridity and salinization, necessitating the adoption of continuous spectral monitoring systems to support natural resource planning and management.
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1_ World Meteorological Organization (WMO) and Global Water Partnership (GWP), (2016): Handbook of Drought Indicators and Indices, Integrated Drought Management Tools and Guidelines Series 2. Geneva. Available online:https://www.droughtmanagement.info/literature/GWP_Handbook_of_Drought_Indicators_and_Indices_2016.pdf
2_ A.H.I, Al-Musawi (2025), Drought Indicators changing in Al-Najaf Al-Ashraf Governorate Using Remote Sensing Techniques, College Arts, University of Kufa.
3_ H.H. Ahmed and et al (2025), Geospatial Assessment of the Effect of Groundwater Salinity on NDVI in Western Karbala, Mesopotamian Journal of Marine Sciences, 40 (2). Available online: https://doi.org/10.58629/mjms.v40i2.
4_ A.H.A, Harbi and et al (2025), Monitoring the Normalized Difference Vegetation Index (NDVI) in the Kurdistan Region of Iraq using Remote Sensing Techniques, Iraqi Journal of Environmental Sciences, Vol. 01, No. 01. Available online: https://doi.org/10.71109/ijes.i1.9
5_ I.H. Mohammed and et al (2024), Drought in Iraq: remote sensing assessment using LSWI-Index and Landsat imagery, Engineering and Technology Journal, 42 (11). Available online: http://doi.org/10.30684/etj.2024.150522.1764
6_ A. Allbed and et al(2014), Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: Applications in a date palm dominated region, Geoderma Jour., vol230-231. Available online: https://doi.org/10.1016/j.geoderma.2014.03.025
7_ A. Voitik and et al(2023), COMPARISON OF NDVI, NDRE, MSAVI AND NDSI INDICES FOR EARLY DIAGNOSIS OF CROP PROBLEMS, Agricultural Engineering, Vol. 2 7 ,No.1. Available online: https://doi.org/10.2478/agriceng-2023-0004
8_ Xu Ma and et al(2024), A new hyperspectral index of biological crust in remote sensing, BIO Web of Conferences 100, Available online: https://doi.org/10.1051/bioconf/202410002035.
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