Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth
The development of terrestrial laser scanning (TLS) has opened new avenues in the study of trees. Although TLS provides valuable information on structural elements, fine-scale analysis, e.g., at the annual shoots (AS) scale, is currently not possible. We present a new model to segment and classify A...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-03-01
|
| Series: | Forests |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4907/12/4/391 |
| _version_ | 1797539926436216832 |
|---|---|
| author | Bastien Lecigne Sylvain Delagrange Olivier Taugourdeau |
| author_facet | Bastien Lecigne Sylvain Delagrange Olivier Taugourdeau |
| author_sort | Bastien Lecigne |
| collection | DOAJ |
| description | The development of terrestrial laser scanning (TLS) has opened new avenues in the study of trees. Although TLS provides valuable information on structural elements, fine-scale analysis, e.g., at the annual shoots (AS) scale, is currently not possible. We present a new model to segment and classify AS from tree skeletons into a finite set of “physiological ages” (i.e., state of specialization and physiological age (PA)). When testing the model against perfect data, 90% of AS year and 99% of AS physiological ages were correctly extracted. AS length-estimated errors varied between 0.39 cm and 2.57 cm depending on the PA. When applying the model to tree reconstructions using real-life simulated TLS data, 50% of the AS and 77% of the total tree length are reconstructed. Using an architectural automaton to deal with non-reconstructed short axes, errors associated with AS number and length were reduced to 5% and 12%, respectively. Finally, the model was applied to real trees and was consistent with previous findings obtained from manual measurements in a similar context. This new method could be used for determining tree phenotype or for analyzing tree architecture. |
| first_indexed | 2024-03-10T12:53:44Z |
| format | Article |
| id | doaj.art-f1898644d0554eeca2d74e4696d245d8 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-10T12:53:44Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-f1898644d0554eeca2d74e4696d245d82023-11-21T12:09:35ZengMDPI AGForests1999-49072021-03-0112439110.3390/f12040391Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic GrowthBastien Lecigne0Sylvain Delagrange1Olivier Taugourdeau2Department of Biological Sciences, Centre for Forest Research (CEF) and NSERC/Hydro-Québec Chair on Tree Growth Control, Université du Québec à Montréal, Centre-Ville Station, P.O. Box 8888, Montreal, QC H3C 3P8, CanadaDepartment of Natural Resources, Institute of Temperate Forest Sciences and Centre for Forest Research (CEF), Université du Québec en Outaouais, 58 Rue Principale, Ripon, QC J0V 1V0, CanadaValorhiz, 1900 Boulevard de la Lironde, 34980 Montferrier sur Lez, FranceThe development of terrestrial laser scanning (TLS) has opened new avenues in the study of trees. Although TLS provides valuable information on structural elements, fine-scale analysis, e.g., at the annual shoots (AS) scale, is currently not possible. We present a new model to segment and classify AS from tree skeletons into a finite set of “physiological ages” (i.e., state of specialization and physiological age (PA)). When testing the model against perfect data, 90% of AS year and 99% of AS physiological ages were correctly extracted. AS length-estimated errors varied between 0.39 cm and 2.57 cm depending on the PA. When applying the model to tree reconstructions using real-life simulated TLS data, 50% of the AS and 77% of the total tree length are reconstructed. Using an architectural automaton to deal with non-reconstructed short axes, errors associated with AS number and length were reduced to 5% and 12%, respectively. Finally, the model was applied to real trees and was consistent with previous findings obtained from manual measurements in a similar context. This new method could be used for determining tree phenotype or for analyzing tree architecture.https://www.mdpi.com/1999-4907/12/4/391terrestrial laser scanerLiDARtree architectureannual shootaxes specializationphysiological age |
| spellingShingle | Bastien Lecigne Sylvain Delagrange Olivier Taugourdeau Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth Forests terrestrial laser scaner LiDAR tree architecture annual shoot axes specialization physiological age |
| title | Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth |
| title_full | Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth |
| title_fullStr | Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth |
| title_full_unstemmed | Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth |
| title_short | Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth |
| title_sort | annual shoot segmentation and physiological age classification from tls data in trees with acrotonic growth |
| topic | terrestrial laser scaner LiDAR tree architecture annual shoot axes specialization physiological age |
| url | https://www.mdpi.com/1999-4907/12/4/391 |
| work_keys_str_mv | AT bastienlecigne annualshootsegmentationandphysiologicalageclassificationfromtlsdataintreeswithacrotonicgrowth AT sylvaindelagrange annualshootsegmentationandphysiologicalageclassificationfromtlsdataintreeswithacrotonicgrowth AT oliviertaugourdeau annualshootsegmentationandphysiologicalageclassificationfromtlsdataintreeswithacrotonicgrowth |