MPSGIS Senior Lecturer Dr. Jonathan Resop, collaborating with researchers at Virginia Tech, has published some of their recent work in comparing drone-based and aerial-based lidar for modeling riverscape topography and vegetation in the MDPI journal Drones. This research is part of a larger effort to utilize the ultra-high resolution point cloud data produced from drone laser scanning for quantifying physical parameters of the riverscape (an ecosystem spanning from the stream channel to the floodplain), such as terrain roughness and vegetation cover. These parameters are critical for applications such as hydraulic modeling, flood mapping, and habitat modeling.
Title:
"Drone Laser Scanning for Modeling Riverscape Topography and Vegetation: Comparison with Traditional Aerial Lidar"
Full Text Manuscript Link:
https://www.mdpi.com/2504-446X/3/2/35
Abstract:
Lidar remote sensing has been used to survey stream channel and floodplain topography for decades. However, traditional platforms, such as aerial laser scanning (ALS) from an airplane, have limitations including flight altitude and scan angle that prevent the scanner from collecting a complete survey of the riverscape. Drone laser scanning (DLS) or unmanned aerial vehicle (UAV)-based lidar offer ways to scan riverscapes with many potential advantages over ALS. We compared point clouds and lidar data products generated with both DLS and ALS for a small gravel-bed stream, Stroubles Creek, located in Blacksburg, VA. Lidar data points were classified as ground and vegetation, and then rasterized to produce digital terrain models (DTMs) representing the topography and canopy height models (CHMs) representing the vegetation. The results highlighted that the lower-altitude, higher-resolution DLS data were more capable than ALS of providing details of the channel profile as well as detecting small vegetation on the floodplain. The greater detail gained with DLS will provide fluvial researchers with better estimates of the physical properties of riverscape topography and vegetation.
Virginia Tech StREAM Lab:
