LiDAR forestry surveys to assist carbon reduction policies

Forest canopy structure characterization using Routescene LiDAR drone system

This LiDAR forestry case study with the University of Wisconsin-Madison (UW-Madison), employed an Unoccupied Aerial System (UAS/ drone) with a LiDAR payload to acquire high density point cloud data of the dominant forest tree species in Northern Wisconsin, USA. The 3D mapping LiDAR system mounted on a drone has the ability to detect and visualize the full tree structure from the top of the canopy right through to the understory vegetation. Structure characterization of the forest canopy represents a keystone dataset to better understand forest function.

CHEESEHEAD project overview

The UAS LIDAR survey was part of a wider project to understand how water and carbon interact across a variety of landscapes like forests. The aim of the CHEESEHEAD, “The Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High density Extensive Array of Detectors” project, was to study interactions and feedbacks between the land surface and atmosphere and to improve how these interactions are represented in weather and climate models. The project aims to help improve weather forecasting by helping to understand how vegetation and forest management influence the atmosphere, which may play a role in helping decision makers to craft policies to more effectively target reductions in carbon emissions and climate change.

During 2019 from June to October scientists and students from the US and Europe, led by UW-Madison Professor of Atmospheric and Oceanic Sciences Ankur Desai, used flux towers, aircraft, lasers, weather balloons, and drones in the Chequamegon-Nicolet National Forest to test science questions about how vegetation influences the weather and climate.

The project deployed a number of flux towers to allow researchers to make measurements across both time and space like never before. The telescoping towers reach as high as 100 feet gathering data on humidity, temperature, greenhouse gases like carbon dioxide and methane, and more. These observations are being used with large eddy simulation and scaling experiments to better understand processes and improve formulations in numerical weather and climate models.

Representation of the type, shape and composition of the forest canopy is a key component for estimation and modeling of forest land-atmosphere water and carbon budgets. Hence the need for a UAS LiDAR forest survey and work to identify and visualize the different forest tree species and structures in the areas around the flux towers.

Routescene LiDAR foresty Northern Wisconsin

Tree structure point cloud of the main tree species around the flux towers

Study site

The Routescene drone with LiDAR system was deployed in June 2019, within the footprint of CHEESEHEAD site flux towers in Price County, WI, USA.

The CHEESEHEAD study site

UAV LiDAR flight plans in yellow in the CHEESEHEAD study area. Flux tower sites (circles) and field canopy

The UAS LiDAR surveys

Forestry drone mapping surveys were conducted within footprints of 11 flux towers and surveyed areas ranged between 0.25 to 1 squared km per site (see table below). Six forest types were targeted including Aspen, Pine, Poplar, Larch, Cedar and Hardwood. This study represents the first effort to characterize the forest canopy structure in Northern Wisconsin employing LiDAR UAV mapping systems for forestry.

Routescene UAV LiDAR system at CHEESEHEAD project

The hexacopter drone was equipped with a Routescene LidarPod payload

LiDAR acquisition by drone was collected with a Routescene LidarPod LiDAR system that includes a 32-laser LiDAR sensor with a GNSS RTK system. Point cloud density averaged 600 points per meter squared with a vertical accuracy of 2-5cm.

UAS LiDAR forestry point clouds of different sites and canopy types colored by elevation

UAS LiDAR forestry point clouds of different sites and canopy types colored by elevation (blue = lower, red = higher. Star indicates flux tower.

Routescene system performed exceptionally well

Christian Andresen from the University of Wisconsin-Madison commented, “The biggest challenge was the flying conditions where the tall canopy (approximately 20-30m) obstructed visibility and we were only able to fly smaller footprints (500m x 500m) with line of sight. Also, we had to be pretty mobile and set up for each of the nine sites.

Routescene UAV LidarPod set up at CHEESEHEAD

The team collecting UAV LiDAR data with the Routescene drone LiDAR system

Christian added, “The system really made it easy for this situation. The Routescene UAS LiDAR system was flawless. With a crew of two people and we managed to fly 4.2 km² in 3 days. We achieved all we had planned, we were particularly impressed with the density of overlapping flight lines and the mapping of the forest structure.

Aerial view of flux tower

Aerial view of flux tower SE-3 dominated by Aspen. Arrow indicates the location of flux tower

UAV LiDAR point cloud of area around flux tower

LiDAR point cloud colored by height of area around flux tower SE-3. The arrow shows the location of the flux tower.

Summary

This study represents the first effort to characterize the forest canopy structure by tree type in Northern Wisconsin employing a drone with LiDAR. In addition, this study serves as a key baseline dataset for up-scaling ecosystem structure and modeling land-atmosphere gas interactions at a regional level. Additional information on leaf-on canopy structure is also being used to identify surface roughness in the flux footprints of the towers. The spatial data are being used as input drivers within machine learning.

The project contributed to the wider CHEESEHEAD experiment to help generate knowledge that advances the science of surface flux measurement and modeling, relevant to many scientific applications such as numerical weather prediction, climate change, energy resources, and computational fluid dynamics.

Future plans from Christian and the University of Wisconsin-Madison team include:

  • Extrapolating LiDAR data and associated ecosystem structure and function to the regional scale using airborne LiDAR data by the Wisconsin Department of Natural Resources.
  • Integrating airborne hyperspectral imagery for cover classification and derivation of main structural patterns for tree species.
  • Developing a fine-scale classification of LiDAR point clouds by tree species based on structural patterns.

For more information on the CHEESEHEAD project please visit:

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