Characterizing Forest Canopy Structure Webinar

Characterizing Forest Canopy Structure Using High-Density UAS LiDAR

Thursday June 25th 2020 at 11:45am Eastern Daylight Time

Presented by Christian Andresen, University of Wisconsin

Author(s): Christian Andresen, Phil Townsend, Ankur Desai

Representation of the type, shape and composition of the canopy is a key component for estimation and modelling of forest land-atmosphere water and carbon budgets. The ability to gather multi-return LiDAR data allows the full detection of the forest structure from the top of the canopy to the understory vegetation.

This study employed a Routescene UAS LiDAR System to acquire high density point cloud data of the dominant forest tree species in Northern Wisconsin. UAV-based LiDAR data was collected with a hexacopter equipped with a Routescene LidarPod, an integrated system containing a number of sensors including a 32 laser Velodyne LiDAR sensor and a GNSS RTK system. The resulting point cloud density averaged 600 points per meter squared with a vertical accuracy of 2-5cm.

In coordination with the NSF-funded CHEESEHEAD project, surveys were conducted within footprints of 11 flux towers and areas surveyed ranged between 0.25-1 squared km per site. Six forest types were targeted including Aspen, Pine, Poplar, Larch, Cedar, Aspen and Hardwood and described their main canopy structure attributes.

This study represents the first effort to characterize the forest canopy structure by tree type in Northern Wisconsin employing UAV LiDAR data. In addition, this study serves as a key baseline dataset for up-scaling ecosystem structure and modelling land-atmosphere gas interactions at a regional level.