Drought assessment using remote sensing and GIS.
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Uganda’s worst-affected communities along the livestock route.The spatial and temporal drought indices were used: the Standardized Precipitation Index (SPI) and the Normalized Difference northern and central parts of the Nakasongola district have little rainfall and sparse vegetation, station from the Uganda National Meteorological Authority to compute, determine and graphically drought were used to split the study region into three categories. The findings revealed that the Difference Vegetation Index for the years; 2010, 2015 and 2020. In addition, SPI was utilized in illustrate trend in drought severity from 1990 to 2020 at various time scales (3,6 and 12 month). this study. To measure drought using rainfall data and satellite imagery, twodifferent drought risk zones in Nakasongola district were assessed using integrated GIS and remote sensing data in are common in Uganda’s cattle corridor, but there islimitedinformation on their occurrence and periodic rainy periods and droughtsand few long term conditions, according to SPI. The findings severity. As a result, this issue prompted this study in Nakasongola district, which is one of Vegetation Index (NDVI). and communities around the world every year due to its slow onset and cascading effects.Droughts may be useful in developing drought management plans and in exposing the genuine drought Finally, usingweighted overlay method;spatial-temporal drought risk maps were created utilizing NDVI, seasonal rainfall, LULC, and SPI parameters. Mild drought, moderate drought, and severe making it the location with the highest drought prevalence. Rainfall variability was indicated, with Drought is a recurring and most complex weather-related natural phenomenon, affecting vast areas Drought was assessed using Landsat 8 OLI and 7 ETM temporal images based on Normalized conjunction with DrinC software and meteorological (monthly rainfall) data for nabiwera weather situation in the area.