| dc.description.abstract | 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. | en_US |
