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

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A Correction to this article was published on 24 February 2024

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Abstract

Key Message

Terrestrial laser scanning data can be converted to reliable woody aboveground biomass estimates, but estimation quality is influenced by growing environment, leaf condition, and variation in tree density affecting volume to mass conversion.

Abstract

Both rural and urban forests play an important role in terrestrial carbon cycling. Forest carbon stocks are typically estimated from models predicting the aboveground biomass (AGB) of trees. However, such models are often limited by insufficient data on tree mass, which generally requires felling and weighing parts of trees. In this study, thirty-one trees of both deciduous and evergreen species were destructively sampled in rural and urban forest conditions. Prior to felling, terrestrial laser scanning (TLS) data were used to estimate tree biomass based on volume estimates from quantitative structure models, combined with tree basic density estimates from disks sampled from stems and branches after scanning and felling trees, but also in combination with published basic density values. Reference woody AGB, main stem, and branch biomass were computed from destructive sampling. Trees were scanned in leaf-off conditions, except evergreen and some deciduous trees, to assess effects of a leaf-separation algorithm on TLS-based woody biomass estimates. We found strong agreement between TLS-based and reference woody AGB, main stem, and branch biomass values, using both measured and published basic densities to convert TLS-based volume to biomass, but use of published densities reduced accuracy. Correlations between TLS-based and reference branch biomass were stronger for urban trees, while correlations with stem mass were stronger for rural trees. TLS-based biomass estimates from leaf-off and leaf-removed point clouds strongly agreed with reference biomass data, showing the utility of the leaf-removal algorithm for enhancing AGB estimation.

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Data availability

The data generated and analyzed during the study are available upon reasonable request from the corresponding author DM.

Change history

Notes

  1. More information on this campaign can be found here: http://tlsrcn.bu.edu/index.php/harvard-forest-calibration-activity/.

References

  • Arseniou G, MacFarlane DW (2021) Fractal dimension of tree crowns explains species functional-trait responses to urban environments at different scales. Ecol Appl. https://doi.org/10.1002/EAP.2297

    Article  PubMed  Google Scholar 

  • Arseniou G, MacFarlane DW, Seidel D (2021a) Measuring the contribution of leaves to the structural complexity of urban tree crowns with terrestrial laser scanning. Remote Sens 13:2773. https://doi.org/10.3390/rs13142773

    Article  ADS  Google Scholar 

  • Arseniou G, MacFarlane DW, Seidel D (2021b) Woody surface area measurements with terrestrial laser scanning relate to the anatomical and structural complexity of urban trees. Remote Sens 13:3153. https://doi.org/10.3390/rs13163153

    Article  ADS  Google Scholar 

  • Baker ME, Schley ML, Sexton JO (2019) Impacts of expanding impervious surface on specific conductance in urbanizing streams. Water Resour Res. https://doi.org/10.1029/2019WR025014

    Article  Google Scholar 

  • Bang C, Sabo JL, Stanley HF (2010) Reduced wind speed improves plant growth in a desert city. PLoS ONE 5(6):1–8

    Article  Google Scholar 

  • Bournez E, Landes T, Saudreau M, Kastendeuch P, Najjar G (2017) From TLS point clouds to 3D models of trees: a comparison of existing algorithms for 3D tree reconstruction. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XLII, Part 2/W3 - Nafplio, Greece, March 1–3, 2017

  • Burt A, Disney M, Calders K (2018) Extracting individual trees from lidar point clouds using treeseg. Methods Ecol Evol 10:438–445. https://doi.org/10.1111/2041-210X.13121

    Article  Google Scholar 

  • Burt A, Boni Vicari M, da Costa ACL, Coughlin I, Meir P, Rowland L, Disney M (2021) New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar. R Soc Open Sci 8:201458. https://doi.org/10.1098/rsos.201458

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  • Calders K, Newnham G, Burt A, Murphy S, Raumonen P, Herold M, Culvenor D, Avitabile V, Disney MI, Armston J, Kaasalainen M (2015) Nondestructive estimates of aboveground biomass using terrestrial laser scanning. Methods Ecol Evol 6(2015):198–208

    Article  Google Scholar 

  • Calders K, Adams J, Armston J, Bartholomeus H, Bauwens S, Bentley LP, Chave J, Danson FM, Demol M, Disney M, Gaulton R, Moorthy SMK, Levick SR, Saarinen N, Schaaf C, Stovall A, Terryn L, Wilkes P, Verbeeck H (2020) Terrestrial laser scanning in forest ecology: expanding the horizon. Remote Sens Environ 251:112102

    Article  Google Scholar 

  • Canham CD, LePage PT, Coates KD (2004) A neighborhood analysis of canopy tree competition: effects of shading versus crowding. Can J for Res 34:778–787. https://doi.org/10.1139/X03-232

    Article  Google Scholar 

  • Casalegno S, Anderson K, Hancock S, Gaston KJ (2017) Improving models of urban green space: from vegetation surface cover to volumetric survey, using waveform laser scanning. Methods Ecol Evol 8(2017):1443–1452. https://doi.org/10.1111/2041-210X.12794

    Article  Google Scholar 

  • Champagne C, Sinha N (2004) Compound leaves: equal to the sum of their parts? Development 131:4401–4412

    Article  CAS  PubMed  Google Scholar 

  • Chave J, Coomes D, Jansen S, Lewis SL, Swenson NG, Zanne AE (2009) Towards a worldwide wood economics spectrum. Ecol Lett 12:351–366. https://doi.org/10.1111/j.1461-0248.2009.01285.x

    Article  PubMed  Google Scholar 

  • Demol M, Calders K, Moorthy SMK, Van den Bulcke J, Verbeeck H, Gielen B (2021) Consequences of vertical basic wood density variation on the estimation of aboveground biomass with terrestrial laser scanning. Trees. https://doi.org/10.1007/s00468-020-02067-7

    Article  Google Scholar 

  • Demol M, Wilkes P, Raumonen P, Krishna Moorthy SM, Calders K, Gielen B, Verbeeck H (2022) Volumetric overestimation of small branches in 3D reconstructions of Fraxinus excelsior. Silva Fennica 56(1 10550):26. https://doi.org/10.14214/sf.10550

    Article  Google Scholar 

  • Dettman GT, MacFarlane DW (2018) Trans-species predictors of tree leaf mass. Ecol Appl 29(1):e01817. https://doi.org/10.1002/eap.1817

    Article  Google Scholar 

  • Disney M (2019) Terrestrial LiDAR: a three-dimensional revolution in how we look at trees. New Phytol 222:1736–1741. https://doi.org/10.1111/nph.15517

    Article  PubMed  Google Scholar 

  • Disney MI, Vicari MB, Burt A, Calders K, Lewis SL, Raumonen P, Wilkes P (2018) Weighing trees with lasers: advances, challenges and opportunities. Interface Focus 8:20170048. https://doi.org/10.1098/rsfs.2017.0048

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Disney M, Burt A, Calders K, Schaaf C, Stovall A (2019) Innovations in ground and airborne technologies as reference and for training and validation: terrestrial laser scanning (TLS). Surv Geophys 40:937–958. https://doi.org/10.1007/s10712-019-09527-x

    Article  ADS  Google Scholar 

  • Disney M, Burt A, Wilkes P, Armston J, Duncanson L (2020) New 3D measurements of large redwood trees for biomass and structure. Sci Rep 10:16721. https://doi.org/10.1038/s41598-020-73733-6

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  • Fan G, Nan L, Dong Y, Su X, Chen F (2020) AdQSM: a new method for estimating aboveground biomass from TLS point clouds. Remote Sens 12:3089. https://doi.org/10.3390/rs12183089

    Article  ADS  Google Scholar 

  • Gardiner B, Berry P, Moulia B (2016) Review: wind impacts on plant growth, mechanics and damage. Plant Sci 245(2016):94–118

    Article  CAS  PubMed  Google Scholar 

  • Gonzalez de Tanago J, Lau A, Bartholomeus H, Herold M, Avitabile V, Raumonen P, Martius C, Goodman RC, Disney M, Manuri S, Burt A, Calders K (2018) Estimation of aboveground biomass of large tropical trees with terrestrial LiDAR. Methods Ecol Evol 2018(9):223–234. https://doi.org/10.1111/2041-210X.12904

    Article  Google Scholar 

  • Hackenberg J, Spiecker H, Calders K, Disney MI, Raumonen P (2015a) SimpleTree—An efficient open source tool to build tree models from TLS clouds. Forests 6(2015):4245–4294

    Article  Google Scholar 

  • Hackenberg J, Wassenberg M, Spiecker H, Sun D (2015b) Non destructive method for biomass prediction combining tls derived tree volume and wood density. Forests 6:1274–1300. https://doi.org/10.3390/f6041274

    Article  Google Scholar 

  • Holopainen M, Vastaranta M, Kankare M, Räty M, Vaaja M, Liang X, Yu X, Hyyppä J, Viitala R, Kaasalainen S (2011) Biomass estimation of individual trees using stem and crown diameter TLS measurements. IIn: nternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXVIII-5/W12, 2011

  • Hopkinson C, Chasmer L, Young-Pow C, Treitz P (2004) Assessing forest metrics with a ground-based scanning lidar. Can J for Res 34(2004):573–583

    Article  Google Scholar 

  • Jung SE, Kwak DA, Park T, Lee WK, Yoo S (2011) Estimating crown variables of individual trees using airborne and terrestrial laser scanners. Remote Sens 3:2346–2363

    Article  ADS  Google Scholar 

  • Kaasalainen S, Krooks A, Liski J, Raumonen P, Kaartinen H, Kaasalainen M, Puttonen E, Anttila K, Mäkipää R (2014) Change detection of tree biomass with terrestrial laser scanning and quantitative structure modeling. Remote Sensing 6(2014):3906–3922

    Article  ADS  Google Scholar 

  • Kankare V, Holocaine M, Vastaranta M, Puttonen E, Yu X, Hyyppä J, Vaaja M, Hyyppä H, Alho P (2013) Individual tree biomass estimation using terrestrial laser scanning. ISPRS J Photogramm Remote Sens 75(2013):64–75

    Article  ADS  Google Scholar 

  • Klingenberg CP, Duttke S, Whelan S, Kim M (2012) Developmental plasticity, morphological variation and evolvability: a multilevel analysis of morphometric integration in the shape of compound leaves. J Evol Biol 25(2012):115–129

    Article  CAS  PubMed  Google Scholar 

  • Kükenbrink D, Gardi O, Morsdorf M, Thürig E, Schellenberger A, Mathys L (2021) Above-ground biomass references for urban trees from terrestrial laser scanning data. Ann Bot XX:1–16. https://doi.org/10.1093/aob/mcab002

    Article  CAS  Google Scholar 

  • Lau A, Bentley LP, Martius C, Shenkin A, Bartholomeus H, Raumonen P, Malhi Y, Jackson T, Herold M (2018) Quantifying branch architecture of tropical trees using terrestrial LiDAR and 3D modelling. Trees 32:1219–1231. https://doi.org/10.1007/s00468-018-1704-1

    Article  CAS  Google Scholar 

  • Lau A, Martius C, Bartholumeus H, Shenkin A, Jackson T, Malhi Y, Herold M, Bentley LP (2019a) Estimating architecture-based metabolic scaling exponents of tropical trees using terrestrial LiDAR and 3D modelling. For Ecol Manag 439(2019):132–145

    Article  Google Scholar 

  • Lau A, Calders K, Bartholomeus H, Martius C, Raumonen P, Herold M, Vicari M, Sukhdeo H, Singh J (2019b) Tree biomass equations from terrestrial LiDAR: a case study in Guyana. Forests 10:527. https://doi.org/10.3390/f10060527

    Article  Google Scholar 

  • Lefsky M, McHale M (2008) Volume estimates of trees with complex architecture from terrestrial laser scanning. J Appl Remote Sens 2:023521

    Article  Google Scholar 

  • Liang X, Kankare V, Hyyppä J, Wang Y, Kukko A, Haggrén H, Yu X, Kaartinen H, Jaakkola A, Guan F, Holopainen M, Vastaranta M (2016) Terrestrial laser scanning in forest inventories. ISPRS J Photogramm Remote Sens 115(2016):63–77

    Article  ADS  Google Scholar 

  • Liang X, Hyyppä J, Kaartinen H, Lehtomäki M, Pyörälä J, Pfeifer N, Holopainen M, Brolly G, Francesco P, Hackenberg J, Huang H, Jo HW, Katoh M, Liu L, Mokros M, Morel J, Olofsson K, Poveda-Lopez J, Trochta J, Wang D, Wang J, Xi Z, Yang B, Zheng G, Kankare V, Luoma V, Yu X, Chen L, Vastaranta M, Saarinen N, Wang Y (2018) International benchmarking of terrestrial laser scanning approaches for forest inventories. ISPRS J Photogramm Remote Sens 144(2018):137–179

    Article  ADS  Google Scholar 

  • Lin L (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 45:255–268

    Article  CAS  PubMed  Google Scholar 

  • Lines ER, Zavala MA, Purves DW, Coomes DA (2012) Predictable changes in aboveground allometry of trees along gradients of temperature, aridity and competition. Glob Ecol Biogeogr 21:1017–1028

    Article  Google Scholar 

  • Maas HG, Bienert A, Scheller S, Keane E (2008) Automatic forest inventory parameter determination from terrestrial laser scanner data. Int J Remote Sens 29(2008):1579–1593

    Article  Google Scholar 

  • MacFarlane DW (2009) Potential availability of urban wood biomass in Michigan: Implications for energy production, carbon sequestration and sustainable forest management in the U.S.A. Biomass Bioenergy 33(2009):628–634

    Article  Google Scholar 

  • MacFarlane DW (2010) Predicting branch to bole volume scaling relationships from varying centroids of tree bole volume. Can J for Res 40:2278–2289

    Article  Google Scholar 

  • MacFarlane DW (2015) A generalized tree component biomass model derived from principles of variable allometry. For Ecol Manag 354(2015):43–55

    Article  Google Scholar 

  • MacFarlane DW (2020) Functional relationships between branch and stem wood density for temperate tree species in North America. Front for Glob Change 3:63. https://doi.org/10.3389/ffgc.2020.00063

    Article  Google Scholar 

  • MacFarlane DW, Kane B (2017) Neighbour effects on tree architecture: functional trade-offs balancing crown competitiveness with wind resistance. Funct Ecol 31:1624–1636

    Article  Google Scholar 

  • Malhi Y, Jackson T, Patrick Bentley L, Lau A, Shenkin A, Herold M, Calders K, Bartholomeus H, Disney MI (2018) New perspectives on the ecology of tree structure and tree communities through terrestrial laser scanning. Interface Focus 8:20170052

    Article  PubMed  PubMed Central  Google Scholar 

  • Markesteijn L, Poorter L, Bongers F, Paz H, Sack L (2011) Hydraulics and life history of tropical dry forest tree species: coordination of species’ drought and shade tolerance. New Phytol 191:480–495. https://doi.org/10.1111/j.1469-8137.2011.03708.x

    Article  PubMed  Google Scholar 

  • McHale MR, Burke IC, Lefsky MA, Peper PJ, McPherson EG (2009) Urban forest biomass estimates: is it important to use allometric relationships developed specifically for urban trees? Urban Ecosystems 12:95–113

    Article  Google Scholar 

  • McPherson EG (1998) Atmospheric carbon dioxide reduction by Sacramento’s urban forest. J Arboric 24(4):215–223

    Google Scholar 

  • Metz J, Seidel D, Schall P, Scheffer D, Schulze ED, Ammer C (2013) Crown modeling by terrestrial laser scanning as an approach to assess the effect of aboveground intra- and interspecific competition on tree growth. For Ecol Manag 310(2013):275–288

    Article  Google Scholar 

  • Miles PD, Smith WB (2009) Specific gravity and other properties of wood and bark for 156 tree species found in North America. Res. Note NRS-38. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station, p 35

  • Mohamed MA, Wood DH (2015) Computational study of the effect of trees on wind flow over a building. Wind, Water, and Solar 2:2. https://doi.org/10.1186/s40807-014-0002-9

    Article  Google Scholar 

  • Momo Takoudjou S, Ploton P, Sonke B, Hackenberg J, Griffon S, de Coligny F, Kamdem NG, Libalah M II, Mofack G, Le Moguedec G, Pelissier R (2018) Barbier N (2018) Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: a comparison with traditional destructive approach. Methods Ecol Evol 9:905–916. https://doi.org/10.1111/2041-210X.12933

    Article  Google Scholar 

  • Momo Takoudjou S, Ploton P, Martin-Ducup O, Lehnebach R, Fortunel C, Sagang LBT, Boyemba F, Couteron P, Fayolle A, Libalah M, Loumeto J, Medjibe V, Ngomanda A, Obiang D, Pelissier R, Rossi V, Yongo O, Sonke B, Barbier N, PREREDD Collaborators (2020) Leveraging signatures of plant functional strategies in wood density profiles of African trees to correct mass estimations from terrestrial laser data. Sci Rep 10:2001. https://doi.org/10.1038/s41598-020-58733-w

    Article  ADS  CAS  Google Scholar 

  • Moorthy I, Millera JR, Antonio Jimenez Bernic J, Zarco-Tejadac P, Hub B, Chend J (2010) Field characterization of olive (Olea europaea L.) tree crown architecture using terrestrial laser scanning data. Agric for Meteorol 151(2011):204–214

    ADS  Google Scholar 

  • Moorthy SMK, Calders K, Vicari MB, Verbeeck H (2020) Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests. IEEE Trans Geosci Remote Sens 58(2020):3057–3070

    Article  ADS  Google Scholar 

  • Moskal LM, Zheng G (2011) Retrieving forest inventory variables with terrestrial laser scanning (TLS) in urban heterogeneous forest. Remote Sens 4(2012):1–20

    Article  ADS  Google Scholar 

  • Nowak DJ, Greenfield EJ (2020) The increase of impervious cover and decrease of tree cover within urban areas globally (2012–2017). Urban for Urban Green 49:126638

    Article  Google Scholar 

  • Olagoke A, Proisy C, Féret JB, Blanchard E, Fromard F, Mehling U, Menezes MMD, Santos VFD, Berger U (2016) Extended biomass allometric equations for large mangrove trees from terrestrial LiDAR data. Trees 30:935–947. https://doi.org/10.1007/s00468-015-1334-9

    Article  Google Scholar 

  • Olschofsky K, Mues V, Köhl M (2016) Operational assessment of aboveground tree volume and biomass by terrestrial laser scanning. Comput Electron Agric 127(2016):699–707

    Article  Google Scholar 

  • Phillips TH, Baker ME, Lautar K, Yesilonis I, Pavao-Zuckerman MA (2019) The capacity of urban forest patches to infiltrate stormwater is influenced by soil physical properties and soil moisture. J Environ Manag 246(2019):11–18

    Article  Google Scholar 

  • Polo JRR, Sanz R, Llorens J, Arno J, Escola A, Ribes-Dasi M, Masip J, Camp F, Gracia F, Solanelles F, Palleja T, Val L, Planas S, Gil E, Palacin J (2009) A tractor-mounted scanning LIDAR for the non-destructive measurement of vegetative volume and surface area of tree-row plantations: A comparison with conventional destructive measurements. Biosys Eng 102(2009):128–134

    Article  Google Scholar 

  • Pretzsch H, Biber P, Uhl E, Dahlhausen J, Rötzer T, Caldentey J, Koike T, Van Con T, Chavanne A, Seifert T, Du Toit B, Farnden C, Pauleit S (2015) Crown size and growing space requirement of common tree species in urban centers, parks, and forests. Urban for Urban Green 14(2015):466–479

    Article  Google Scholar 

  • R Core Team (2015) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Available at: https://www.R-project.org/. Accessed 26 Mar 2021

  • Radtke P, Walker D, Frank J, Weiskittel A, DeYoung C, MacFarlane D, Domke G, Woodall C, Coulston J, Westfall J (2017) Improved accuracy of aboveground biomass and carbon estimates for live trees in forests of the eastern United States. Forestry 2017 90:32–46. https://doi.org/10.1093/forestry/cpw047

    Article  Google Scholar 

  • Rahman MZA, Bakar MAA, Razak KA, Rasib AW, Kanniah KD, Kadir WHW, Omar H, Faidi A, Kassim AR, Latif ZR (2017) Non-destructive, laser-based individual tree aboveground biomass estimation in a tropical rainforest. Forests 8:86. https://doi.org/10.3390/f8030086

    Article  Google Scholar 

  • Raumonen P, Kaasalainen M, Åkerblom M, Kaasalainen S, Kaartinen H, Vastaranta M, Holopainen M, Disney MI, Lewis PE (2013) Fast automatic precision tree models from terrestrial laser scanner data. Remote Sens 5(2013):491–520

    Article  ADS  Google Scholar 

  • Raumonen P, Casella E, Calders K, Murphy S, Åkerblom M, Kaasalainen M (2015) Massive-scale tree modelling from TLS data. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-3/W4, 2015.

  • Roxburgh SH, Paul KI, Clifford D, England JR, Raison RJ (2015) Guidelines for constructing allometric models for the prediction of woody biomass: how many individuals to harvest? Ecosphere 6:1–27

    Article  Google Scholar 

  • Sagang LBT, Momo Takoudjou S, Libalah Bakonck M, Rossi V, Fonton N, Mofack G II, Kamdem Guy N, Nguetsop François V, Sonké B, Pierre P (2018) Barbier N (2018) Using volume-weighted average wood specific gravity of trees reduces bias in aboveground biomass predictions from forest volume data. For Ecol Manage 424:519–528

    Article  Google Scholar 

  • Salim MH, Schlünzen KH, Grawe D (2015) Including trees in the numerical simulations of the wind flow in urban areas: Should we care? J Wind Eng Ind Aerodyn 144(2015):84–95

    Article  Google Scholar 

  • Seidel D, Ehbrecht M, Dorji Y, Jambay J, Ammer C, Annighöfer P (2019) Identifying architectural characteristics that determine tree structural complexity. Trees 33:911–919. https://doi.org/10.1007/s00468-019-01827-4

    Article  Google Scholar 

  • Sileshi GW (2014) A critical review of forest biomass estimation models, common mistakes and corrective measures. For Ecol Manag 329(2014):237–254

    Article  Google Scholar 

  • Stovall AEL, Vorster AG, Anderson RS, Evangelista PH, Shugart HH (2017) Non-destructive aboveground biomass estimation of coniferous trees using terrestrial LiDAR. Remote Sens Environ 200(2017):31–42

    Article  ADS  Google Scholar 

  • Stovall AEL, Anderson-Teixeira KJ, Shugart HH (2018) Assessing terrestrial laser scanning for developing non-destructive biomass allometry. For Ecol Manag 427(2018):217–229

    Article  Google Scholar 

  • Tanhuanpää T, Kankare V, Setälä H, Yli-Pelkonen V, Vastarantaa M, Niemi MT, Raisio J, Holopainen M (2017) Assessing aboveground biomass of open-grown urban trees: a comparison between existing models and a volume-based approach. Urban for Urban Green 21:239–246

    Article  Google Scholar 

  • Telewski FW (2012) Is windswept tree growth negative thigmotropism? Plant Sci 184(2012):20–28. https://doi.org/10.1016/j.plantsci.2011.12.001

    Article  CAS  PubMed  Google Scholar 

  • Telewski FW, Gardiner BA, White G, Plovanich-Jones A (1997) Wind flow around multi-storey buildings and its influence on tree growth. In: Conference Proceedings I of Plant Biomechanics (1997), pp 179–183

  • Tigges J, Tobia Lakes T (2017) High resolution remote sensing for reducing uncertainties in urban forest carbon offset life cycle assessments. Carbon Balance Manag 12:17. https://doi.org/10.1186/s13021-017-0085-

    Article  PubMed  PubMed Central  Google Scholar 

  • TreeQSM Version 2.3.0. Quantitative Structure Models of Single Trees from Laser Scanner Data. Copyright (C) 2013-2017 Pasi Raumonen. Available at: https://zenodo.org/record/844626#.Xvz_nW1KjIU. Accessed 26 Mar 2021

  • Van den Bulcke J, Boone MA, Dhaene J, Van LD, Van HL, Boone MN, Wyffels F, Beeckman H, Van AJ, De Mil T (2019) Advanced X-ray CT scanning can boost tree-ring research for earth-system sciences. Ann Bot. https://doi.org/10.1093/aob/mcz126

    Article  PubMed  PubMed Central  Google Scholar 

  • Ver Planck NR, MacFarlane DW (2014) Modelling vertical allocation of tree stem and branch volume for hardwoods. Forestry 87:459–469. https://doi.org/10.1093/forestry/cpu007

    Article  Google Scholar 

  • Ver Planck NR, MacFarlane DW (2015) A vertically integrated whole-tree biomass model. Trees 29:449–460. https://doi.org/10.1007/s00468-014-1123-x

    Article  Google Scholar 

  • Vicari MB (2017) TLSeparation—a Python library for material separation from tree/forest 3D point clouds. 10.5281/zenodo.1147705.

  • Vicari MB, Disney M, Wilkes P, Burt A, Calders K, Woodgate W (2019) Leaf and wood classification framework for terrestrial LiDAR point clouds. Methods Ecol Evol 10(2019):680–694. https://doi.org/10.1111/2041-210X.13144

    Article  Google Scholar 

  • Vonderach C, Voegtle T, Adler P, Norra S (2012) Terrestrial laser scanning for estimating urban tree volume and carbon content. Int J Remote Sens 33(21):6652–6667

    Article  Google Scholar 

  • Wang D, Takoudjou SM, Casella E (2019) LeWoS: a universal leaf-wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR. Methods Ecol Evol 11(2019):376–389

    Google Scholar 

  • Weiner J (2004) Allocation, plasticity and allometry in plants. Perspect Plant Ecol Evol Syst 6(4):207–215

    Article  ADS  Google Scholar 

  • Weiskittel AR, MacFarlane DW, Radtke PJ, Affleck DLR, Temesgen H, Woodall CW, Westfall JA, Coulston JW (2015) A call to improve methods for estimating tree biomass for regional and national assessments. J for 113(4):414–424

    Google Scholar 

  • Wilkes P, Lau A, Disney M, Calders K, Burt A, Tanago JG, Bartholomeus H, Brede B, Herold M (2017) Data acquisition considerations for Terrestrial Laser Scanning of forest plots. Remote Sens Environ 196(2017):140–153

    Article  ADS  Google Scholar 

  • Wilkes P, Disney M, Boni Vicari M, Calders K, Burt A (2018) Estimating urban above ground biomass with multi-scale LiDAR. Carbon Balance Manag 13:10. https://doi.org/10.1186/s13021-018-0098-0

    Article  PubMed  PubMed Central  Google Scholar 

  • Zheng Y, Jia W, Wang Q, Huang X (2019) Deriving individual-tree biomass from effective crown data generated by terrestrial laser scanning. Remote Sens 11:2793. https://doi.org/10.3390/rs11232793

    Article  ADS  Google Scholar 

  • Zhou X, Brandle JR, Awada TN, Schoeneberger MM, Martin DL, Xin Y, Tang Z (2011) The use of forest-derived specific gravity for the conversion of volume to biomass for open-grown trees on agricultural land. Biomass Bioenerg 35(2011):1721–1731

    Article  Google Scholar 

  • Zhou X, Schoeneberger MM, Brandle JR, Awada TN, Chu J, Martin DL, Li J, Li Y, Mize CW (2015) Analyzing the uncertainties in use of forest- derived biomass equations for open-grown trees in agricultural land. For Sci 61(1):144–161

    Google Scholar 

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Acknowledgements

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 B. Vicari (University College London) for processing the point clouds of evergreen species scanned by K. Calders in Harvard Forest.

Funding

This work was partially supported with funds from a joint venture agreement between Michigan State University and the United States Department of Agriculture Forest Service, Forest Inventory and Analysis Program, Northern Research Station. Part of D.W. MacFarlane’s time was paid for with funds from Michigan AgBioResearch, the USDA National Institute of Food and Agriculture. Part of G. Arseniou’s time was supported by a Bouyoukos Fellowship. Part of M. Baker's time was supported by NSF grant DEB no. 1637661 and DEB no. 1855277.

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Authors and Affiliations

  1. Department of Forestry, Michigan State University, East Lansing, MI, USA

    Georgios Arseniou & David W. MacFarlane

  2. CAVElab-Computational & Applied Vegetation Ecology Laboratory, Department of Environment, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium

    Kim Calders

  3. Department of Geography and Environmental Systems, University of Maryland-Baltimore County, Baltimore, MD, USA

    Matthew Baker

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  1. Georgios Arseniou
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  2. David W. MacFarlane
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  3. Kim Calders
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  4. Matthew Baker
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Correspondence to David W. MacFarlane.

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Arseniou, G., MacFarlane, D.W., Calders, K. et al. Accuracy differences in aboveground woody biomass estimation with terrestrial laser scanning for trees in urban and rural forests and different leaf conditions. Trees 37, 761–779 (2023). https://doi.org/10.1007/s00468-022-02382-1

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