Key points are not available for this paper at this time.
Natural disaster prevention requires the maintenance of the proper function of river flow during droughts and heavy rains caused by abnormal weather as well as the minimization of damage via rapid and accurate predictions. This study analyzed hydraulic characteristics by utilizing D-LiDAR (Drone LiDAR) topographic data and D-HSI (Drone Hyperspectral Image) data to quickly and accurately collect and analyze related data. The HEC-RAS model was used to compare and analyze the results obtained via the D-LiDAR topographic data of this study, the roughness coefficient of the river master plan, and the results of applying parameters by adding NDVI. As a result, in the case of a 100-year frequency flood discharge, the average velocity decreased by an average of 2.23 m/s when applying the resistance coefficient by area, based on NDVI. Moreover, the flood level increased by an average of 0.30 m, and the cross-sectional area of the flow capacity increased by an average of 20.17 m2. These results confirms that vegetation has a significant influence on the flow characteristics of natural streams. Hence, the data construction and analysis method used in this study can provide useful information regarding the rapid measurement and analysis of river flow characteristics during floods as well as and the development of prediction techniques and warning-related systems for drought and flood prevention.
Kim et al. (Fri,) studied this question.