The study was carried out in Omdurman and Sharg El-Neel to map and detect how vegetation cover 
impacts runoff and torrents. Satellite imageries of Landsat Thematic Mapper (TM) of 1994-2009 and Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) of 2018 processed to derive proper land use. Furthermore, land cover (LULC), using hybrid classification and normalized differences vegetation index (NDVI) of the study area. The results revealed changes in LULC classes during the study period (1994, 2009, and 2018). Omdurman area showed that the vegetation cover for the years 1994, 2009, and 2018 was 3.2%, 3.1%, and 4.2%, respectively. The respective sandy soil ratio was 18.2%, 34.9%, and 27.6%. The water bodies found to cover only 0.5%, 0.5%, and 0.7%, respectively. These results indicated that the area of vegetation cover was less than other LULC. Contrary results from Sharg El-Neel revealed that the percentage of vegetation cover for the years 1994, 2009, and 2018 accounted for 31.4%, 41.3%, and 24.7%, respectively. For these years, the sandy soil ratios were 26.3%, 14.8%, and 38.3%, respectively. The water bodies covered 0.5%, 2 0.6% and 1.3% respectively. The research concluded that changes in vegetation cover status play a role in water runoff and torrent events in the study areas. The study recommends further studies to help produce robust plans that consist of LULC status to minimize the water-related risks and hazards.
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                    Detecting and Mapping the Influences of Vegetation Cover on Runoff andTorrents in Khartoum State, Sudan
                
                                    
                
                
                    Published:
31 August 2021
by MDPI
in The 2nd International Electronic Conference on Forests — Sustainable Forests: Ecology, Management, Products and Trade
session Forest Inventory, Modeling and Remote Sensing
                
                                    
                        https://doi.org/10.3390/IECF2021-10800
                                                    (registering DOI)
                                            
                
                
                    Abstract: 
                                    
                        Keywords: Remote sensing, GIS technology, Satellite imageries, Operational Land Imager,  Thermal Infrared Sensor
                    
                
                
                
                
        
            