Temporal Analysis of Remote Sensing Indices with MATLAB: Comparative Study of 2016 vs. 2019 Vegetation Cover Index in Abu Ghraib's Irrigation Projects
Keywords:
Remote Sensing Indices, MATLAB, Temporal Comparison, Landsat 8, GISAbstract
This study employs remote sensing techniques supported by MATLAB-based analysis to conduct a temporal comparison of key vegetation indices—including the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Soil Adjusted Vegetation Index (SAVI), Green Chlorophyll Index (GCI), Enhanced Vegetation Index (EVI), and Normalized Difference Water Index (NDWI)—for the years 2016 and 2019. Landsat 8 satellite imagery, obtained from the United States Geological Survey (USGS), was analyzed to detect spatial and seasonal changes in vegetation cover across irrigation zones in the Abu Ghraib district. Descriptive statistics (mean, standard deviation), Pearson correlation coefficients, and NDVI overlap computations were used to assess the distribution of vegetation indices and detect temporal variations in vegetation health. The findings indicate significant vegetation index change in 2019 over 2016 with significant vegetation index recovery in dry times indicating the success of the irrigation project in increasing the vegetation resilience. This research answers and presented quantitative information about vegetation patterns and can justify data-based agricultural surveillance and sustainable resource control management in semi-arid landscapes.
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