Comparison of Three Algorithms to Estimate Tree Stem Diameter from Terrestrial Laser Scanner Data
Terrestrial laser scanners provide accurate and detailed point clouds of forest plots, which can be used as an alternative to destructive measurements during forest inventories. Various specialized algorithms have been developed to provide automatic and objective estimates of forest attributes from...
Published in: | Forests |
---|---|
Main Authors: | , , , , , , |
Format: | Text |
Language: | English |
Published: |
Multidisciplinary Digital Publishing Institute
2019
|
Subjects: | |
Online Access: | https://doi.org/10.3390/f10070599 |
id |
ftmdpi:oai:mdpi.com:/1999-4907/10/7/599/ |
---|---|
record_format |
openpolar |
spelling |
ftmdpi:oai:mdpi.com:/1999-4907/10/7/599/ 2023-08-20T04:08:05+02:00 Comparison of Three Algorithms to Estimate Tree Stem Diameter from Terrestrial Laser Scanner Data Joris Ravaglia Richard A. Fournier Alexandra Bac Cédric Véga Jean-François Côté Alexandre Piboule Ulysse Rémillard agris 2019-07-18 application/pdf https://doi.org/10.3390/f10070599 EN eng Multidisciplinary Digital Publishing Institute Forest Inventory, Modeling and Remote Sensing https://dx.doi.org/10.3390/f10070599 https://creativecommons.org/licenses/by/4.0/ Forests; Volume 10; Issue 7; Pages: 599 forest inventory stem diameter diameter at breast height (DBH) terrestrial laser scanner STEP algorithm CompuTree SimpleTree LiDAR Text 2019 ftmdpi https://doi.org/10.3390/f10070599 2023-07-31T22:26:52Z Terrestrial laser scanners provide accurate and detailed point clouds of forest plots, which can be used as an alternative to destructive measurements during forest inventories. Various specialized algorithms have been developed to provide automatic and objective estimates of forest attributes from point clouds. The STEP (Snakes for Tuboid Extraction from Point cloud) algorithm was developed to estimate both stem diameter at breast height and stem diameters along the bole length. Here, we evaluate the accuracy of this algorithm and compare its performance with two other state-of-the-art algorithms that were designed for the same purpose (i.e., the CompuTree and SimpleTree algorithms). We tested each algorithm against point clouds that incorporated various degrees of noise and occlusion. We applied these algorithms to three contrasting test sites: (1) simulated scenes of coniferous stands in Newfoundland (Canada), (2) test sites of deciduous stands in Phalsbourg (France), and (3) coniferous plantations in Quebec, Canada. In most cases, the STEP algorithm predicted diameter at breast height with higher R2 and lower RMSE than the other two algorithms. The STEP algorithm also achieved greater accuracy when estimating stem diameter in occluded and noisy point clouds, with mean errors in the range of 1.1 cm to 2.28 cm. The CompuTree and SimpleTree algorithms respectively produced errors in the range of 2.62 cm to 6.1 cm and 1.03 cm to 3.34 cm, respectively. Unlike CompuTree or SimpleTree, the STEP algorithm was not able to estimate trunk diameter in the uppermost portions of the trees. Our results show that the STEP algorithm is more adapted to extract DBH and stem diameter automatically from occluded and noisy point clouds. Our study also highlights that SimpleTree and CompuTree require data filtering and results corrections. Conversely, none of these procedures were applied for the implementation of the STEP algorithm. Text Newfoundland MDPI Open Access Publishing Canada Forests 10 7 599 |
institution |
Open Polar |
collection |
MDPI Open Access Publishing |
op_collection_id |
ftmdpi |
language |
English |
topic |
forest inventory stem diameter diameter at breast height (DBH) terrestrial laser scanner STEP algorithm CompuTree SimpleTree LiDAR |
spellingShingle |
forest inventory stem diameter diameter at breast height (DBH) terrestrial laser scanner STEP algorithm CompuTree SimpleTree LiDAR Joris Ravaglia Richard A. Fournier Alexandra Bac Cédric Véga Jean-François Côté Alexandre Piboule Ulysse Rémillard Comparison of Three Algorithms to Estimate Tree Stem Diameter from Terrestrial Laser Scanner Data |
topic_facet |
forest inventory stem diameter diameter at breast height (DBH) terrestrial laser scanner STEP algorithm CompuTree SimpleTree LiDAR |
description |
Terrestrial laser scanners provide accurate and detailed point clouds of forest plots, which can be used as an alternative to destructive measurements during forest inventories. Various specialized algorithms have been developed to provide automatic and objective estimates of forest attributes from point clouds. The STEP (Snakes for Tuboid Extraction from Point cloud) algorithm was developed to estimate both stem diameter at breast height and stem diameters along the bole length. Here, we evaluate the accuracy of this algorithm and compare its performance with two other state-of-the-art algorithms that were designed for the same purpose (i.e., the CompuTree and SimpleTree algorithms). We tested each algorithm against point clouds that incorporated various degrees of noise and occlusion. We applied these algorithms to three contrasting test sites: (1) simulated scenes of coniferous stands in Newfoundland (Canada), (2) test sites of deciduous stands in Phalsbourg (France), and (3) coniferous plantations in Quebec, Canada. In most cases, the STEP algorithm predicted diameter at breast height with higher R2 and lower RMSE than the other two algorithms. The STEP algorithm also achieved greater accuracy when estimating stem diameter in occluded and noisy point clouds, with mean errors in the range of 1.1 cm to 2.28 cm. The CompuTree and SimpleTree algorithms respectively produced errors in the range of 2.62 cm to 6.1 cm and 1.03 cm to 3.34 cm, respectively. Unlike CompuTree or SimpleTree, the STEP algorithm was not able to estimate trunk diameter in the uppermost portions of the trees. Our results show that the STEP algorithm is more adapted to extract DBH and stem diameter automatically from occluded and noisy point clouds. Our study also highlights that SimpleTree and CompuTree require data filtering and results corrections. Conversely, none of these procedures were applied for the implementation of the STEP algorithm. |
format |
Text |
author |
Joris Ravaglia Richard A. Fournier Alexandra Bac Cédric Véga Jean-François Côté Alexandre Piboule Ulysse Rémillard |
author_facet |
Joris Ravaglia Richard A. Fournier Alexandra Bac Cédric Véga Jean-François Côté Alexandre Piboule Ulysse Rémillard |
author_sort |
Joris Ravaglia |
title |
Comparison of Three Algorithms to Estimate Tree Stem Diameter from Terrestrial Laser Scanner Data |
title_short |
Comparison of Three Algorithms to Estimate Tree Stem Diameter from Terrestrial Laser Scanner Data |
title_full |
Comparison of Three Algorithms to Estimate Tree Stem Diameter from Terrestrial Laser Scanner Data |
title_fullStr |
Comparison of Three Algorithms to Estimate Tree Stem Diameter from Terrestrial Laser Scanner Data |
title_full_unstemmed |
Comparison of Three Algorithms to Estimate Tree Stem Diameter from Terrestrial Laser Scanner Data |
title_sort |
comparison of three algorithms to estimate tree stem diameter from terrestrial laser scanner data |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2019 |
url |
https://doi.org/10.3390/f10070599 |
op_coverage |
agris |
geographic |
Canada |
geographic_facet |
Canada |
genre |
Newfoundland |
genre_facet |
Newfoundland |
op_source |
Forests; Volume 10; Issue 7; Pages: 599 |
op_relation |
Forest Inventory, Modeling and Remote Sensing https://dx.doi.org/10.3390/f10070599 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/f10070599 |
container_title |
Forests |
container_volume |
10 |
container_issue |
7 |
container_start_page |
599 |
_version_ |
1774720163796484096 |