Estimating Urban Tree Metrics Using Terrestrial LiDAR Scanning

Trees grown in an urban environment are typically from a selected list of suitable species due to their appearance and other factors. A popular oak species in recent decades has been the Nuttall oak (Quercus texana). A total of seven Nuttall oaks were scanned using a terrestrial LiDAR scanner and modeled for comparison to manual measurements. These trees were then destructively sampled in place to measure their above-ground biomass. The biomass data were compiled and statistically compared against digital models of each tree that were created from the LiDAR scans. This resulted in a Pearson coefficient of .977 and linear regression R2 value of .99 for the LiDAR derived measurements predictive ability in comparison to the manually derived measurements. This indicates an ability of this ground based LiDAR model to predict both the linear dimensions and volumetrics of the standing specimens without the need for such labor intensive and expensive sampling given the sensitivity and value of urban forests.

Data e Risorse

Campo Valore
encoding utf8
harvest_url https://ckan.americaview.org/dataset/233ab8fb-3e2d-4926-9331-b43a59a8aa7e
Gruppi
  • Academia
  • AmeriGEOSS
Tag
  • afrigeo
  • afrigeoss
  • americaview
  • amerigeo
  • amerigeoss
  • ckan
  • eurogeo
  • eurogeoss
  • geo
  • geoss
  • global
  • remote-sensing
isopen True
license_id cc-by
license_title Creative Commons Attribution
license_url http://www.opendefinition.org/licenses/cc-by
metadata_created 2026-02-11T15:26:07.675244
metadata_modified 2026-02-11T15:26:07.675248
notes Trees grown in an urban environment are typically from a selected list of suitable species due to their appearance and other factors. A popular oak species in recent decades has been the Nuttall oak (Quercus texana). A total of seven Nuttall oaks were scanned using a terrestrial LiDAR scanner and modeled for comparison to manual measurements. These trees were then destructively sampled in place to measure their above-ground biomass. The biomass data were compiled and statistically compared against digital models of each tree that were created from the LiDAR scans. This resulted in a Pearson coefficient of .977 and linear regression R2 value of .99 for the LiDAR derived measurements predictive ability in comparison to the manually derived measurements. This indicates an ability of this ground based LiDAR model to predict both the linear dimensions and volumetrics of the standing specimens without the need for such labor intensive and expensive sampling given the sensitivity and value of urban forests.
num_resources 1
num_tags 12
title Estimating Urban Tree Metrics Using Terrestrial LiDAR Scanning