COMPUTING AND PREDICTING THE VEGETATION COVER USING NDVI UNDER THE CONDITIONS OF CLIMATE CHANGES, FOR THE PERIOD 2000 - 2024: A CASE STUDY, KARBALA CITY, IRAQ

Vegetation cover plays a vital role in aiding to get rid of the atmospheric dust and harmful suspensions, so it can be considered as one of the most important factors affecting the ecosystem. Also, it reduces the differences in temperatures between days and nights, making the temperature more appropriate for living. Cycles of minerals, organic elements, water (including rain), and moisture are all affected by the vegetation cover. For this work, the Normalized Difference Vegetation Index (NDVI) is computed relying on images from the website of the United States Geological Survey (USGS) for Landsat7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat 8OLI (Operational Land Imager) satellites. The data of February months for the years 2000, 2014, and 2024 are chosen to calculate the green areas for the holy Karbala city utilizing the Geographic Information System software (GIS 10.3). Weather data from Nasa power data are used as well for the period (1995- 2024). Results from satellite images show that the vegetation cover has been increased by 245% from 2000 to 2014, and for the same period, the average percentage of precipitation amount is estimated to increase by 153%. The rainfalls were more concentrated on the eastern, northeastern, and southeastern sides of the city. Likewise, the period extending from the year 2015 to 2024 has witnessed a very large increase in vegetation areas by (37,750 %) in the western and eastern parts of the city because of 274% increase in the precipitation, and the projects that are adopted to convert desert areas into green agricultural areas by Holy Abbasid Shrine. For the future, the HadGEM2-rcp26 climate model was used, and the areas of vegetation cover were calculated for the periods (2024-2050, 2050-2070, and 2070-2080) in general, the area of vegetation to enhanced mean annual precipitation and decreased annual temperature for the period 2050-2080. Linear regression in was used to forecast vegetation cover. Data were analyzed through scatter plots and a fitted linear trend line, resulting in a predictive equation. The findings reveal a significant correlation, demonstrating the effectiveness of linear regression as a tool for forecasting vegetation cover based on rainfall.