[fr] The emergence of mobile laser scanning (MLS) systems that use simultaneous localization and mapping (SLAM) technology to map their environment opens up new opportunities for characterizing forest structure. The speed and accuracy of data acquisition makes them particularly adapted to operational inventories. MLS also shows great potential for estimating inventory attributes that are difficult to measure in the field, such as wood volume or crown dimensions. Hardwood species represent a significant challenge for wood volume estimation compared to softwoods because a substantial portion of the volume is included in the crown, making them more prone to allometric bias and more complex to model. This study assessed the potential of MLS data to estimate tree structural attributes in a temperate hardwood stand: height, crown dimensions, diameter at breast height (DBH), and merchantable wood volume. Merchantable wood volume estimates were evaluated to the third branching order using the quantitative structural modeling (QSM) approach. Destructive field measurements and terrestrial laser scanning (TLS) data of 26 hardwood trees were used as reference to quantify errors on wood volume and inventory attribute estimations from MLS data. Results reveal that SLAM-based MLS systems provided accurate estimates of tree height (RMSE = 0.42 m (1.78%), R2 = 0.93), crown projected area (RMSE = 3.23 m2 (5.75%), R2 = 0.99), crown volume (RMSE = 71.4 m3 (23.38%), R2 = 0.99), DBH (RMSE = 1.21 cm (3.07%), R2 = 0.99), and merchantable wood volume (RMSE = 0.39 m3 (18.57%), R2 = 0.95), when compared to TLS. They also estimated operational merchantable volume with good accuracy (RMSE = 0.42 m3 (21.82%), R2 = 0.94) compared to destructive measurements. Finally, the merchantable stem volume derived from MLS data was estimated with high accuracy compared to TLS (RMSE = 0.11 m3 (8.32%), R2 = 0.96) and regional stem taper models (RMSE = 0.16 m3 (14.7%), R2 = 0.93). We expect our results would provide a better understanding of the potential of SLAM-based MLS systems to support in-situ forest inventory.
Luoma V. Saarinen N. Wulder M.A. White J.C. Vastaranta M. Holopainen M. Hyyppä J. Assessing Precision in Conventional Field Measurements of Individual Tree Attributes Forests 2017 8 38 10.3390/f8020038
Achim A. Moreau G. Coops N.C. Axelson J.N. Barrette J. Bédard S. Byrne K.E. Caspersen J. Dick A.R. D’Orangeville L. et al. The Changing Culture of Silviculture For. Int. J. For. Res. 2021 95 143 152 10.1093/forestry/cpab047
McRoberts R.E. Westfall J.A. Effects of Uncertainty in Model Predictions of Individual Tree Volume on Large Area Volume Estimates For. Sci. 2014 60 34 42 10.5849/forsci.12-141
Muukkonen P. Generalized Allometric Volume and Biomass Equations for Some Tree Species in Europe Eur. J. For. Res. 2007 126 157 166 10.1007/s10342-007-0168-4
Forrester D.I. Pretzsch H. Tamm Review: On the Strength of Evidence When Comparing Ecosystem Functions of Mixtures with Monocultures For. Ecol. Manag. 2015 356 41 53 10.1016/j.foreco.2015.08.016
Vorster A.G. Evangelista P.H. Stovall A.E.L. Ex S. Variability and Uncertainty in Forest Biomass Estimates from the Tree to Landscape Scale: The Role of Allometric Equations Carbon Balance Manag. 2020 15 8 10.1186/s13021-020-00143-6
Calders K. Adams J. Armston J. Bartholomeus H. Bauwens S. Bentley L.P. Chave J. Danson F.M. Demol M. Disney M. et al. Terrestrial Laser Scanning in Forest Ecology: Expanding the Horizon Remote Sens Environ. 2020 251 112102 10.1016/j.rse.2020.112102
Wilkes P. Lau A. Disney M. Calders K. Burt A. Gonzalez de Tanago J. Bartholomeus H. Brede B. Herold M. Data Acquisition Considerations for Terrestrial Laser Scanning of Forest Plots Remote Sens Environ. 2017 196 140 153 10.1016/j.rse.2017.04.030
Raumonen P. Kaasalainen M. Markku Å. Kaasalainen S. Kaartinen H. Vastaranta M. Holopainen M. Disney M. Lewis P. Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data Remote Sens. 2013 5 491 520 10.3390/rs5020491
Hackenberg J. Spiecker H. Calders K. Disney M. Raumonen P. SimpleTree—An Efficient Open Source Tool to Build Tree Models from TLS Clouds Forests 2015 6 4245 4294 10.3390/f6114245
Demol M. Wilkes P. Raumonen P. Krishna Moorthy S. Calders K. Gielen B. Verbeeck H. Volumetric Overestimation of Small Branches in 3D Reconstructions of Fraxinus Excelsior Silva. Fennica. 2022 56 1 26 10.14214/sf.10550
Burt A. Boni Vicari M. da Costa A.C.L. Coughlin I. Meir P. Rowland L. Disney M. New Insights into Large Tropical Tree Mass and Structure from Direct Harvest and Terrestrial Lidar R. Soc. Open Sci. 2021 8 201458 10.1098/rsos.201458
Liang X. Hyyppä J. Kaartinen H. Lehtomäki M. Pyörälä J. Pfeifer N. Holopainen M. Brolly G. Francesco P. Hackenberg J. et al. International Benchmarking of Terrestrial Laser Scanning Approaches for Forest Inventories ISPRS J. Photogramm. Remote Sens. 2018 144 137 179 10.1016/j.isprsjprs.2018.06.021
Liang X. Kukko A. Hyyppä J. Lehtomäki M. Pyörälä J. Yu X. Kaartinen H. Jaakkola A. Wang Y. In-Situ Measurements from Mobile Platforms: An Emerging Approach to Address the Old Challenges Associated with Forest Inventories ISPRS J. Photogramm. Remote Sens. 2018 143 97 107 10.1016/j.isprsjprs.2018.04.019
di Stefano F. Chiappini S. Gorreja A. Balestra M. Pierdicca R. Mobile 3D Scan LiDAR: A Literature Review Geomat. Nat. Hazards Risk 2021 12 2387 2429 10.1080/19475705.2021.1964617
Liang X. Hyyppa J. Kukko A. Kaartinen H. Jaakkola A. Yu X. The Use of a Mobile Laser Scanning System for Mapping Large Forest Plots IEEE Geosci. Remote Sens. Lett. 2014 11 1504 1508 10.1109/LGRS.2013.2297418
Balenović I. Liang X. Jurjević L. Hyyppä J. Seletković A. Kukko A. Hand-Held Personal Laser Scanning—Current Status and Perspectives for Forest Inventory Application Croat. J. For. Eng. 2020 42 165 183 10.5552/crojfe.2021.858
Cabo C. del Pozo S. Rodríguez-Gonzálvez P. Ordóñez C. González-Aguilera D. Comparing Terrestrial Laser Scanning (TLS) and Wearable Laser Scanning (WLS) for Individual Tree Modeling at Plot Level Remote Sens. 2018 10 540 10.3390/rs10040540
Hartley R.J.L. Jayathunga S. Massam P.D. de Silva D. Estarija H.J. Davidson S.J. Wuraola A. Pearse G.D. Assessing the Potential of Backpack-Mounted Mobile Laser Scanning Systems for Tree Phenotyping Remote Sens. 2022 14 3344 10.3390/rs14143344
Donager J.J. Sánchez Meador A.J. Blackburn R.C. Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare? Remote Sens. 2021 13 2297 10.3390/rs13122297
Bauwens S. Bartholomeus H. Calders K. Lejeune P. Forest Inventory with Terrestrial LiDAR: A Comparison of Static and Hand-Held Mobile Laser Scanning Forests 2016 7 127 10.3390/f7060127
Chen S. Liu H. Feng Z. Shen C. Chen P. Applicability of Personal Laser Scanning in Forestry Inventory PLoS ONE 2019 14 e0211392 10.1371/journal.pone.0211392
Potter T.L. Mobile Laser Scanning in Forests: Mapping Beneath the Canopy Ph.D. Thesis University of Leicester Leicester, UK 2019 Available online: https://leicester.figshare.com/articles/thesis/Mobile_laser_scanning_in_forests_Mapping_beneath_the_canopy/11322848 (accessed on 2 August 2022)
Hyyppä E. Yu X. Kaartinen H. Hakala T. Kukko A. Vastaranta M. Hyyppä J. Comparison of Backpack, Handheld, under-Canopy UAV, and above-Canopy UAV Laser Scanning for Field Reference Data Collection in Boreal Forests Remote Sens. 2020 12 3327 10.3390/rs12203327
Arkin J. Coops N.C. Daniels L.D. Plowright A. Estimation of Vertical Fuel Layers in Tree Crowns Using High Density Lidar Data Remote Sens. 2021 13 4598 10.3390/rs13224598
Niță M.D. Testing Forestry Digital Twinning Workflow Based on Mobile Lidar Scanner and Ai Platform Forests 2021 12 1576 10.3390/f12111576
Bienert A. Georgi L. Kunz M. von Oheimb G. Maas H.G. Automatic Extraction and Measurement of Individual Trees from Mobile Laser Scanning Point Clouds of Forests Ann. Bot. 2021 128 787 804 10.1093/aob/mcab087
Jin S. Zhang W. Shao J. Wan P. Cheng S. Cai S. Yan G. Estimation of Larch Growth at the Stem, Crown and Branch Levels Using Ground-Based LiDAR Point Cloud 2021 Available online: https://assets.researchsquare.com/files/rs-910503/v1_covered.pdf?c=1632840255 (accessed on 2 August 2022)
Zelazny V.F. New Brunswick Department of Natural Resources New Brunswick Ecosystem Classsification Working Group Our Landscape Heritage: The Story of Ecological Land Classification in New Brunswick = Notre Patrimoine Du Paysage, l’histoire de La Classification Écologique Des Terres Au Nouveau-Brunswick New Brunswick Dept. of Natural Resources Fredericton, NB, Canada 2007 9781553962038
Colpitts M.C. Fahmy S.H. MacDougall J.E. Ng T.T.M. McInnis B.G. Zelazny V.F. Forest Soils of New Brunswick. CLBRR contribution No. 95-38 U.S. Department of Energy, Office of Scientific and Technical Information Oak Ridge, TN, USA 1995
Vandendaele B. Fournier R.A. Vepakomma U. Pelletier G. Lejeune P. Martin-ducup O. Estimation of Northern Hardwood Forest Inventory Attributes Using Uav Laser Scanning (Uls): Transferability of Laser Scanning Methods and Comparison of Automated Approaches at the Tree- and Stand-level Remote Sens. 2021 13 2796 10.3390/rs13142796
CloudCompare Version 2.11.3 GPL Software 2020 Available online: http://www.Cloudcompare.Org/ (accessed on 20 December 2021)
R Core Team R: A Language and Environment for Statistical Computing R Foundation for Statistical Computing Vienna, Austria 2017 Available online: https://www.R-Project.Org/ (accessed on 20 December 2021)
Schneider R. Calama R. Martin-Ducup O. Understanding Tree-to-Tree Variations in Stone Pine (Pinus Pinea l.) Cone Production Using Terrestrial Laser Scanner Remote Sens. 2020 12 173 10.3390/rs12010173
Gama J. Chernov N. Conicfit: Algorithms for Fitting Circles, Ellipses and Conics Based on the Work by Prof. Nikolai Chernov. R Package Version 1.0.4 2015 Available online: https://CRAN.R-Project.Org/Package=conicfit (accessed on 15 April 2022)
Lecigne B. Delagrange S. Messier C. Exploring Trees in Three Dimensions: VoxR, a Novel Voxel-Based R Package Dedicated to Analysing the Complex Arrangement of Tree Crowns Ann. Bot. 2018 121 589 601 10.1093/aob/mcx095
Martin-Ducup O. Mofack G. Wang D. Raumonen P. Ploton P. Sonké B. Barbier N. Couteron P. Pélissier R. Evaluation of Automated Pipelines for Tree and Plot Metric Estimation from TLS Data in Tropical Forest Areas Ann. Bot. 2021 128 753 766 10.1093/aob/mcab051 33876194
Martin-Ducup O. Lecigne B. ARchi: Quantitative Structural Model (‘QSM’) Treatment for Tree Architecture. R Package Version 2.1.0 Available online: https://CRAN.R-Project.Org/Package=aRchi (accessed on 20 April 2022)
Li R. Weiskittel A. Dick A.R. Kershaw J.A. Seymour R.S. Regional Stem Taper Equations for Eleven Conifer Species in the Acadian Region of North America: Development and Assessment North. J. Appl. For. 2012 29 5 14 10.5849/njaf.10-037
Weiskittel A. Li R. Development of Regional Taper and Volume Equations: Hardwood Species DendroMetrics, LLC Welches, OR, USA 2012
Bruce D. Schumacher F.X. Forest Mensuration McGraw-Hill New York, NY, USA 1950
Gollob C. Ritter T. Nothdurft A. Forest Inventory with Long Range and High-Speed Personal Laser Scanning (PLS) and Simultaneous Localization and Mapping (SLAM) Technology Remote Sens. 2020 12 1509 10.3390/rs12091509
del Perugia B. Giannetti F. Chirici G. Travaglini D. Influence of Scan Density on the Estimation of Single-Tree Attributes by Hand-Held Mobile Laser Scanning Forests 2019 10 277 10.3390/f10030277
Oveland I. Hauglin M. Gobakken T. Næsset E. Maalen-Johansen I. Automatic Estimation of Tree Position and Stem Diameter Using a Moving Terrestrial Laser Scanner Remote Sens. 2017 9 350 10.3390/rs9040350
Witzmann S. Matitz L. Gollob C. Ritter T. Kraßnitzer R. Tockner A. Stampfer K. Nothdurft A. Accuracy and Precision of Stem Cross-Section Modeling in 3D Point Clouds from TLS and Caliper Measurements for Basal Area Estimation Remote Sens. 2022 14 1923 10.3390/rs14081923
Giannetti F. Puletti N. Quatrini V. Travaglini D. Bottalico F. Corona P. Chirici G. Integrating Terrestrial and Airborne Laser Scanning for the Assessment of Single-Tree Attributes in Mediterranean Forest Stands Eur. J. Remote Sens. 2018 51 795 807 10.1080/22797254.2018.1482733
Jurjević L. Liang X. Gašparović M. Balenović I. Is Field-Measured Tree Height as Reliable as Believed—Part II, A Comparison Study of Tree Height Estimates from Conventional Field Measurement and Low-Cost Close-Range Remote Sensing in a Deciduous Forest ISPRS J. Photogramm. Remote Sens. 2020 169 227 241 10.1016/j.isprsjprs.2020.09.014
Hyyppä E. Kukko A. Kaijaluoto R. White J.C. Wulder M.A. Pyörälä J. Liang X. Yu X. Wang Y. Kaartinen H. et al. Accurate Derivation of Stem Curve and Volume Using Backpack Mobile Laser Scanning ISPRS J. Photogramm. Remote Sens. 2020 161 246 262 10.1016/j.isprsjprs.2020.01.018
Bienert A. Georgi L. Kunz M. Maas H.G. von Oheimb G. Comparison and Combination of Mobile and Terrestrial Laser Scanning for Natural Forest Inventories Forests 2018 8 395 10.3390/f9070395
Demol M. Calders K. Verbeeck H. Gielen B. Forest Above-Ground Volume Assessments with Terrestrial Laser Scanning: A Ground-Truth Validation Experiment in Temperate, Managed Forests Ann. Bot. 2021 128 805 819 10.1093/aob/mcab110
Abegg M. Boesch R. Schaepman M.E. Morsdorf F. Impact of Beam Diameter and Scanning Approach on Point Cloud Quality of Terrestrial Laser Scanning in Forests IEEE Trans. Geosci. Remote Sens. 2021 59 8153 8167 10.1109/TGRS.2020.3037763
Åkerblom M. Kaitaniemi P. Terrestrial Laser Scanning: A New Standard of Forest Measuring and Modelling? Ann. Bot. 2021 128 653 662 10.1093/aob/mcab111
Vaaja M.T. Virtanen J.-P. Kurkela M. Lehtola V. Hyyppä J. Hyyppä H. The Effect of Wind on Tree Stem Parameter Estimation Using Terrestrial Laser Scanning ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 2016 III-8 117 122 10.5194/isprs-annals-III-8-117-2016
Du S. Lindenbergh R. Ledoux H. Stoter J. Nan L. AdTree: Accurate, Detailed, and Automatic Modelling of Laser-Scanned Trees Remote Sens. 2019 11 2074 10.3390/rs11182074