Near-field remote sensing of Alaskan Rivers
The U.S. Geological Survey (USGS) Geomorphology and Sediment Transport Laboratory (GSTL), in collaboration with the U.S. Army Corps of Engineers Cold Regions Research and Engineering Laboratory (CRREL), acquired remotely sensed data from several Alaskan rivers in 2017 and 2018 with the goal of developing a methodology for measuring streamflow from a helicopter. CRREL operates a custom airborne lidar system that can be deployed in a helicopter-based pod (HeliPod). Data were collected with the HeliPod near existing USGS streamflow information stations on the Knik, Matanuska, Chena, and Salcha Rivers in both 2017 and 2018. Sites on the Tanana and Snow Rivers were added in 2018. In 2018, the HeliPod was modified to accommodate both a thermal infrared and a visible camera. The cameras were integrated with the flight management software to simultaneously acquire imagery with lidar. The Global Navigation Satellite System (GNSS) and inertial measurement unit (IMU) in the HeliPod were used to compute trajectories with precise position and orientation information needed for image orthorectification. The HeliPod sensors provide data for measuring river channel characteristics. Lidar can map the elevation of the water surface and thus be used to measure water-surface slopes and return intensity can be used to delineate the extent of the wetted river channel. Various approaches are currently being evaluated to estimate surface flow velocity from visible and thermal image time series. In this paper, we examine and compare water-surface elevation returns and slopes derived from the HeliPod lidar and found good agreement with measurements made using conventional field-based techniques.
|Near-field remote sensing of Alaskan Rivers
|Paul J. Kinzel, Carl J. Legleiter, Jonathan M. Nelson, Jeff Conaway, Adam LeWinter, Peter Gadomski, Dominic Filiano
|USGS Publications Warehouse
|Alaska Science Center; Colorado Water Science Center; National Research Program - Central Branch; WMA - Integrated Modeling and Prediction Division