Accuracy differences in aboveground woody biomass estimation with terrestrial laser scanning for trees in urban and rural forests and different leaf conditions

Abstract

We want to acknowledge the Michigan State University W.J. Beal Botanical Gardens and Campus Arboretum (Frank W. Telewski, and Jeffrey Wilson) and the Michigan State University Department of Infrastructure, Planning and Facilities (Jerry Wahl) who assisted with the urban data collection. Furthermore, we want to thank the research staff of Harvard Forest (David Orwig, Audrey Barker Plotkin), Alan Strahler and UNAVCO for coordinating the rural forest data collection. We would also like to thank Samuel Clark, Garret Dettmann and the field group of Michigan State University, Jereme Frank (University of Maine), David Walker and Phil Radtke (Virginia Tech University) for their contribution to the collection of destructive tree measurements in Harvard Forest. Finally, we want to acknowledge the contribution of Matheus 2 B. Vicari (University College London) for processing the point clouds of the evergreen species scanned by K. Calders in Harvard Forest.Key Message: TLS data can be converted to reliable woody AGB estimates, but estimation quality is influenced by growing environment, leaf condition, and variation in tree density affecting volume to mass conversion.

Modeling Management-Relevant Urban Forest Stand Characteristics to Optimize Carbon Storage and Sequestration

“…We used allometric equations created for rural forests in the urban street tree PPA, despite the apparent dissimilarity in the shape of urban and rural canopies due to their different growing conditions. Open-grown trees, such as urban street trees, typically have larger, more complex crowns than rural trees growing in a forest stand, which creates differences in the overall shape of the tree and how it distributes its biomass [63]. McHale et al [64] show a lack of consistency in the accuracy of predicting biomass of urban trees using rural allometric equations, with both underand over-estimations possible.…”

Comparison of Three Approaches for Estimating Understory Biomass in Yanshan Mountains

“…In comparison to ALS, terrestrial laser scanning (TLS) can collect a much higher point cloud density (1000 pts/m 2 ), greatly improving the measurement accuracy of vegetation structure. Although the scanning range is limited, it is suitable for collecting 3D structures at a fine scale and minimizing canopy occlusion in contrast with ALS [15], enabling the possibility of quantifying volume or biomass [21,29,30] and calculating leaf indices, including the leaf area index (LAI) and leaf area density (LAD) [31][32][33]. The voxel-based approach has proven to be effective in quantifying structural parameters and estimating the biomass of trees and surface fuelbeds using TLS data [34][35][36].…”

Enhancing Tree Species Identification in Forestry and Urban Forests through Light Detection and Ranging Point Cloud Structural Features and Machine Learning

“…In forestry, laser scanning techniques, such as Airborne Laser Scanning (ALS), Terrestrial Laser Scanning (TLS), including mobile LiDAR systems such as handheld systems, and Mobile Laser Scanning (MLS), have been widely investigated for applications in forest inventory [2,3]. These techniques provide efficient means for acquiring detailed three-dimensional (3D) data from vegetation, enabling the extraction of tree and forest parameters such as tree height, crown dimensions, and biomass [4][5][6][7][8][9][10][11][12][13][14][15][16].…”