Spatial-Temporal Monitoring of Agricultural Dynamics Using Spectral Indicators and GIS: A Case Study of Zubaidiya District, Wasit (2017–2024)

Abstract

Agricultural dynamics are highly sensitive to climatic variability in arid and semi-arid regions, water availability, and human-induced land management practices. The focused aim of this study is to evaluate the applicability of satellite-based remote sensing data for monitoring vegetation growth and environmental change assessment, with particular prominence on the use of vegetation indices of enhanced vegetation index (EVI) in comparison with other frequently used indicators, involved normalized difference vegetation Index, for environmental and agricultural analysis. In this study, quantitative analysis of multispectral satellite imagery was derived from Landsat sensors. The surface reflectance data were processed to compute vegetation indices, which were then used to analyze spatial patterns of vegetation cover and biomass. The results from NDVI in comparison with EVI, is more sensitive in densely vegetated areas and more successful at reducing background noise and atmospheric effects. The findings show that EVI-Based metrics are more useful for tracking ecosystem dynamics by demonstrating the distinct relationships that exist between vegetation patterns, land cover change, and current environmental conditions. The significance of agricultural production in environmental system studies, Landsat-8 data were used to analyze the spatial-temporal pattern of vegetation covers from 2017 to 2024. The simulation results show a general decrease in agricultural Land area with significant changes in vegetationdensity brought on by land cover variation. This study further investigates the relationship between enhanced vegetation indices using remote sensing by focusing on their implications for integrated land management and significant economic sectors within agriculturally dominant regions.