Forest Structural Estimates Derived Using a Practical, Open-Source Lidar-Processing Workflow

Abstract

Lidar data is increasingly available over large spatial extents and can also be combined with satellite imagery to provide detailed vegetation structural metrics. To fully realize the benefits of lidar data, practical and scalable processing workflows are needed. In this study, we used the lidR R software package, a custom forest metrics function in R, and a distributed cloud computing environment to process 11 TB of airborne lidar data covering ~22,900 km2 into 28 height, cover, and density metrics. We combined these lidar outputs with field plot data to model basal area, trees per acre, and quadratic mean diameter. We compared lidar-only models with models informed by spectral imagery only, and lidar and spectral imagery together. We found that lidar models outperformed spectral imagery models for all three metrics, and combination models performed slightly better than lidar models in two of the three metrics. One lidar variable, the relative density of low midstory canopy, was selected in all lidar and combination models, demonstrating the importance of midstory forest structure in the study area. In general, this open-source lidar-processing workflow provides a practical, scalable option for estimating structure over large, forested landscapes. The methodology and systems used for this study offered us the capability to process large quantities of lidar data into useful forest structure metrics in compressed timeframes.

Influences of aquatic and terrestrial habitat characteristics on abundance patterns of adult wood turtles

“…We calculated several measures of 3D vegetation structure from the LiDAR point cloud (Appendix ). We adapted the relative density canopy cover function from St. Peter et al (2021) to calculate forest structure metrics at a 10‐m resolution to capture both vertical and horizontal variation of canopy cover and height. We calculated height as the mean of all returns >1 m (hereafter mean return height), upper canopy height as the mean of all returns >5 m, and vertical variation of height as the standard deviation for both.…”

“…We calculated several measures of 3D vegetation structure from the LiDAR point cloud (Appendix A). We adapted the relative density canopy cover function from St. Peter et al (2021) to calculate forest structure metrics at a 10-m resolution to capture both vertical and horizontal variation of canopy cover and height.…”

CACTUS: Content-Aware Compression and Transmission Using Semantics for Automotive LiDAR Data

“…The idea of sharing LiDAR PC data and processing them in a collaborative and distributed fashion has been recently investigated in many applications ranging from cultural and natural preservation [ 2 , 29 , 30 ] to automotive applications [ 31 , 32 ].…”