Thursday, June 25, 2020
Global Water Futures’ upcoming 2-part mini virtual workshop will focus on Canadian agricultural water management issues, capabilities, advances and needs to help inform the federal government in its development of the Canada Water Agency.
Featuring The Honourable Ralph Goodale, this workshop will build on the discussion that began with the National Water Policy Panel on May 13th by diving deeper into priority water issues as they relate to agriculture. The workshop will include the latest scientific advances from GWF projects, science-to-policy discussions with ag-water leaders, and opportunities for participants from diverse agricultural sectors to offer perspectives and identify science synergies and policy implementation.
Part 1: GWF agriculture and water science: issues, capabilities, advances and needs
8:00-9:30am PDT | 9:00-10:30am MDT/CST | 11:00am-12:30pm EDT | 12:00-1:30pm ADT
Part 2: Water science to agriculture policy discussions
10:30am-12pm PDT | 11:30am-1:00pm MDT/CST | 1:30-3:00pm EDT | 2:30-4:00 pm ADT
*Please note that Part 2 has been changed to an earlier start since previous save-the-date communications.
View workshop format and agenda here.
Stephanie Merrill, University of Saskatchewan GWF Knowledge Mobilization Specialist: firstname.lastname@example.org
Nancy Goucher, University of Waterloo GWF Knowledge Mobilization Specialist: email@example.com
Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques
Phillip Harder, John Pomeroy, Warren Helgason
Published June 15th, 2020
The Cryosphere, volume 14, issue 6, pages 1919–1935
Vegetation has a tremendous influence on snow processes and snowpack dynamics, yet remote sensing techniques to resolve the spatial variability of sub-canopy snow depth are not always available and are difficult from space-based platforms. Unmanned aerial vehicles (UAVs) have had recent widespread application to capture high-resolution information on snow processes and are herein applied to the sub-canopy snow depth challenge. Previous demonstrations of snow depth mapping with UAV structure from motion (SfM) and airborne lidar have focussed on non-vegetated surfaces or reported large errors in the presence of vegetation. In contrast, UAV-lidar systems have high-density point clouds and measure returns from a wide range of scan angles, increasing the likelihood of successfully sensing the sub-canopy snow depth. The effectiveness of UAV lidar and UAV SfM in mapping snow depth in both open and forested terrain was tested in a 2019 field campaign at the Canadian Rockies Hydrological Observatory, Alberta, and at Canadian prairie sites near Saskatoon, Saskatchewan, Canada. Only UAV lidar could successfully measure the sub-canopy snow surface with reliable sub-canopy point coverage and consistent error metrics (root mean square error (RMSE) <0.17 m and bias −0.03 to −0.13 m). Relative to UAV lidar, UAV SfM did not consistently sense the sub-canopy snow surface, the interpolation needed to account for point cloud gaps introduced interpolation artefacts, and error metrics demonstrated relatively large variability (RMSE<0.33 m and bias 0.08 to −0.14 m). With the demonstration of sub-canopy snow depth mapping capabilities, a number of early applications are presented to showcase the ability of UAV lidar to effectively quantify the many multiscale snow processes defining snowpack dynamics in mountain and prairie environments.
For the full article, go here.