An Evaluation of The Impact of Hali Dam on The Vegetation Cover During Dry and Wet months between (2006-2020)

Abstract

Damming can cause the vegetation to decrease as hydrological alterations can create water stress problems in the downstream areas. This study focuses on evaluating the impact on the vegetation cover which occurred as a result of the construction of Hali dam in August 2010 in Saudi Arabia. The satellite images of Landsat-7 and Landsat-8 were chosen based on the determination of the Dry and Wet months using satellite-based rain information and surface air temperature. The selected 28 images between (2006-2020) were processed using the unsupervised classification technique to calculate the change in the vegetation area. NDVI and NDWI indices were utilized with thresholds of (0.2) and (0.15) respectively, to calculate the areas of the moderately healthy to healthy vegetation and the areas of the medium to high leaf water content, respectively. The results revealed that the average vegetation area decreased from 15.7 km² before the dam was constructed to 13.2 km² after construction. Additionally, the vegetation classified as moderately healthy to healthy had an average area of 1.9 km² before the construction of the dam and decreased to 1.5 km² after the dam. Moreover, the vegetation classified as medium to high leaf water content area had an average of 0.6 km² before the dam and increased to 0.8 km² after the dam. The average of the highest values of NDVI before the construction of the dam was (0.36). After the construction of the dam the average was (0.38). NDWI highest values, the average before the dam was constructed was (0.30), after the construction of the dam the average NDVI highest values was (0.31). It was found that the decreases in the vegetation area, healthiness and leaf water content can be reversed with periodic releases of water from the dam, it was concluded that the release of water from the dam that occurred in 2019 which lasted for two months caused the vegetation to increase to a level that is the highest among the 28 analyzed satellite images.