EFFECTIVENESS OF LIDAR FOR RAPID CHANNEL GEOMORPHIC ASSESSMENT

Channel geomorphology is dynamically linked to the nature of landscape and the processes acting on them. Changes in land cover and climate in an area can cause significant impact on urban streams including, channel erosion, bank undercutting, loss of vegetation cover and habitat, floodplain loss, and water quality impairment. Minimizing these negative impacts requires collection of qualitative and quantitative geomorphic data from the field for analysis. River managers need an efficient and cost effective method to assess channel conditions. Traditional field surveys are accurate, but often time consuming, expensive, and occasionally limited by inaccessible sites. However, in recent times, various remote sensing based data, such as high resolution DEM and LiDAR based data have proven useful in geomorphic studies. Remote sensing based data gives us the ability to analyze larger area quickly and cost effectively. The main goal of this research was to examine the effectiveness of using available LiDAR data and 10 m DEM data to extract channel geomorphic variables and compare them to field based measurements of the same variables.

Channel geometry (channel width and depth at flood-prone height and bankful width and depth) measurements were made for 10 channel sections located within the Enoree and the Saluda river basins in Upstate of South Carolina. Similar measurements were also made using a TIN surface derived from bare Earth LiDAR data set. The LiDAR based surface model did not provide enough details to identify the bankful conditions and hence those measurements were not included in the analysis. The LiDAR method also produced dubious stream cross sections at three of the 10 sites, therefore, only 7 of the total 10 sites were used for final analysis. Results indicate that overall LiDAR based measurements were consistently higher (by 23 % average) than field measurements for width measurement but consistently lower(by -21 % average) than for depth measurements. Linear regressions of field based versus LiDAR based measurements resulted in R² values of 0.49 (Flood-Prone Width), and 0.02 (depth at Flood-Prone Height). In conclusion, our research indicates that LiDAR data can be useful as a preliminary information but not ready for replacing basic field based measurements as yet.