Spatiotemporal Analysis of Vegetation Cover (NDVI) Using Remote Sensing and SVM in Al-Rutbah District, Iraq
DOI:
https://doi.org/10.31185/wjfh.Vol22.Iss3/Pt1.1822Keywords:
NDVI; Climate change; ; Temperature; Remote sensinAbstract
In recent decades western Iraq particularly Al-Rutbah District has experienced significant climatic variability including declining rainfall and rising temperatures, These changes have negatively affected vegetation cover leading to degradation and posing risks to environmental sustainability and agriculture. This study analyzes vegetation dynamics in relation to temperature and rainfall from 1990 to 2024 using artificial intelligence tools, The Normalized Difference Vegetation Index (NDVI) was derived from satellite imagery via Google Earth Engine while climatic data were modeled using the Support Vector Machine (SVM) algorithm.
Results show a strong negative correlation between NDVI and temperature (r = –0.84), indicating reduced vegetation density with increasing temperatures, In contrast, NDVI has a strong positive correlation with rainfall (r = 0.91) emphasizing its importance for vegetation growth Rainfall explains 38–41% of NDVI variability The study highlights the value of integrating remote sensing and machine learning to support environmental management in arid regions.
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Copyright (c) 2026 Ali Suleiman Erzik Al-Karbouli 1 , Bilal Moayad Abdulrahim Al-Alousi
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