Applying quantitative structure models to plot-based terrestrial laser data to assess dendrometric parameters in dense mixed forests

Chiara Torresan, Ugo Chiavetta, Jan Hackenberg


Aim of study: To assess terrestrial laser scanning (TLS) accuracy in estimating biometrical forest parameters at plot-based level in order to replace manual survey for forest inventory purposes.

Area of study: Monte Morello, Tuscany region, Italy

Material and methods: In 14 plots (10 m radius) in dense Mediterranean mixed conifer forests, diameter at breast height (DBH) and height were measured in Summer 2016. Tree volume was computed using the second Italian National Forest Inventory (INFC II) equations. TLS data were acquired in the same plots and quantitative structure models (QSMs) were applied to TLS data to compute dendrometric parameters. Tree parameters measured in field survey, i.e. DBH, height, and computed volume, were compared to those resulting from TLS data processing. The effect of distance from the plot boundary in the accuracy of DBH, height and volume estimation from TLS data was tested.

Main results: TLS-derived DBH showed a good correlation with the traditional forest inventory data (R2=0.98, RRMSE=7.81%), while tree height was less correlated with the traditional forest inventory data (R2=0.60, RRMSE=16.99%). Poor agreement was observed when comparing the volume from TLS data with volume estimated from the INFC II prediction equations.

Research highlights: The study demonstrated that the application of QSM to plot-based terrestrial laser data generates errors in plots with high density of coniferous trees. A buffer zone of 5 m would help reduce the error of 35% and 42% respectively in height estimation for all trees and in volume estimation for broadleaved trees.


LiDAR; geometrical modeling metrics; wood volume; forest inventory, tree segmentation; CompuTree; SimpleTree

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Åkerblom M, Raumonen P, Kaasalainen M, Casella E, 2015. Analysis of geometric primitives in quantitative structure models of tree stems. Remote Sens 7: 4581-4603.

Åkerblom M, Raumonen P, Mäkipää R, Kaasalainen M, 2017. Automatic tree species recognition with quantitative structure models. Remote Sens Environ 191: 1-12.

Arrigoni PV, Foggi B, Bechi N, Ricceri C, 1997. Documenti per la carta della vegetazione del Monte Morello (Provincia di Firenze). Parlatorea II: 73-100.

Aschoff T, Spiecker H, 2004. Algorithms for the automatic detection of trees in laser scanner data. ISPRS 36: 66-70.

Bauwens S, Bartholomeus H, Calders, Lejeune P, 2016. Forest inventory with terrestrial LiDAR: A comparison of static and hand-held mobile laser scanning. Forests 7 (6): 127-143.

Bentley LP, Stegen JC, Savage VM, Smith DD, von Allmen EI, Sperry S, Reich PB, Enquist BJ, 2013. An empirical assessment of tree branching networks and implications for plant allometric scaling models. Ecol Lett 16 (8): 1069-1078.

Bienert A, Hess C, Maas HG, Von Oheimb G, 2014. A Voxel-based technique to estimate the volume of trees from terrestrial laser scanner data. International archives of the photogrammetry, remote sensing and spatial information sciences - ISPRS Archives 40 (5): 101-106.

Brazeal R, 2013. Low cost spherical registration targets for terrestrial laser scanning. SUR 6905 - Point Cloud Analysis.

Brolly G, Kiraly G, 2009. Algorithms for stem mapping by means of terrestrial laser scanning. Acta Silvatica et Lignaria Hung 5: 119-130.

Calders K, Newnham G, Burt A, Murphy S, Raumonen P, Herold M, Culvenor D, Avitabile L, Disney M, Armston J, Kaasalainen M, 2015. Nondestructive estimates of above-ground biomass using terrestrial laser scanning. Meth Ecol Evol 89: 86-93.

Chai T, Draxler RR, 2014. Root mean square error (RMSE) or mean absolute error (MAE)? - Arguments against avoiding RMSE in the literature. Geosci Model Dev 7: 1247-1250.

Côté JF, Fournier RA, Egli R, 2011. An architectural model of trees to estimate forest structural attributes using terrestrial LiDAR. Environ Model Softw 26: 761-777.

Côté JF, Fournier RA, Frazer GW, Niemann KO, 2012. A fine-scale architectural model of trees to enhance LiDAR-derived measurements of forest canopy structure. Agr Forest Meteorol 166: 72-85.

Dassot M, Colin A, Santenoise P, Fournier M, Constant T, 2012. Terrestrial laser scanning for measuring the solid wood volume, including branches, of adult standing trees in the forest environment. Comp Electron Agr 89: 86-93.

Enquist BJ, West GB, Brown JH, 2009. Extensions and evaluations of a general quantitative theory of forest structure and dynamics. P Nat Acad Sci USA 106 (17): 7046-7051.

Fischler MA, Bolles RC, 1981. Random sample consensus: A paradigm for model fitting with application to image analysis and automated cartography. Commun ACM 24 (6): 381-395.

Gatteschi P, Meli R, 1996. I rimboschimenti di Monte Morello 85 anni dopo (1909-1994) [Monte Morello reforestation 85 years later (1909-1994)]. L'Italia Forestale e Montana 4: 231-249.

Gorte B, Pfeifer N, 2004. Structuring laser scanned trees using 3D mathematical morphology. Int Archiv Photogramm Remote Sens 35 (B5): 929-933.

Hackenberg J, Morhart C, Sheppard J, Spiecker H, Disney M, 2014. Highly accurate tree models derived from terrestrial laser scan data: a method description. Forests 5: 1069-1105.

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

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 (4): 1274-1300.

Henning JG, Radtke PJ, 2006. Detailed stem measurements of standing trees from ground-based scanning Lidar. Forest Sci 52 (1): 67-80.

Holopainen M, Vastaranta M, Kankare V, Räty M, Vaaja M ,Liang X, Yu X, Hyyppä J, Hyyppä H, Viitala R, Kaasalainen S, 2011. Biomass estimation of individual trees using stem and crown diameter TLS measurements. Int Archiv Photogramm Remote Sens Spat Inf Sci - ISPRS Archiv 38 (5W12): 91-95.

Hopkinson C, Chasmer L, Young-Pow C, Treitz P, 2004. Assessing forest metrics with a ground-based scanning lidar. Can J Forest Res 34 (3): 573-583.

Hosoi F, Nakai Y, Omasa K, 2013. 3-D voxel-based solid modeling of a broad-leaved tree for accurate volume estimation using portable scanning lidar. ISPRS J Photogramm Remote Sens 82: 41-48.

Ishak NI, Abu Bakar MA, Abdul Rahman MZA, Rasib AW, Kanniah KD, Meng Shin AL, Razak KA, 2015. Estimating single tree stem and branch biomass using terrestrial laser scanning. Jurnal Teknologi 77 (26): 59-67.

Kunz M, Hess C, Raumonen P, Bienert A, Hackenberg J, Maas HG, Härdtle W, Fichtner A, Von Oheimb G, 2017. Comparison of wood volume estimates of young trees from terrestrial laser scan data. iForest 10: 451-458.

Larjavaara M, Muller-Landau HC, 2013. Measuring tree height: a quantitative comparison of two common field methods in a moist tropical forest. Meth Ecol Evol 4 (9): 793-801.

Liang X, Hyyppä J, 2013. Automatic stem mapping by merging several terrestrial laser scans at the feature and decision levels. Sensors 13: 1614-1634.

Liang X, Kankare V, Yu X, Hyyppa J, Holopainen M, 2014. Automated stem curve measurement using terrestrial laser scanning. IEEE Trans Geosci Remote Sens 52: 1739-1748.

Liang X, Kankare V, Hyyppä J, Wang Y, Kukko A, Haggrén H, Yu X, Kaartinen H, Jaakkola A, Guan F, et al., 2016. Terrestrial laser scanning in forest inventories. ISPRS J Photogramm Remote Sens 115: 63-77.

Livny Y, Yan F, Olson M, Chen B, Zhang H, El-Sana J, 2010. Automatic reconstruction of tree skeletal structures from point clouds. ACM Trans on Graphics 29 (6): 151.

Maas HG, Bienert A, Scheller S, Keane E, 2008. Automatic forest inventory parameter determination from terrestrial laser scanner data. Int J Remote Sens 9 (5): 1579-1593.

Maetzke F, 2002. I rimboschimenti di Monte Morello: analisi e indirizzi di un progetto aperto per la loro rinaturalizzazione [The reforestations of Monte Morello: analysis and addresses of an open project for their renaturalisation] L'Italia Forestale e Montana 2: 125-138.

Mengesha T, Hawkins M, Nieuwenhuis M, 2015. Validation of terrestrial laser scanning data using conventional forest inventory methods. Eur J Forest Res 134: 211-222.

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

Mozaffar MH, Varshosaz M, 2017. Optimal placement of a terrestrial laser scanner with an emphasis on reducing occlusions. The Photogrammetric Record 31 (156): 374-393.

Newnham GJ, Armston JD, Calders K, Disney MI, Lovell JL, Schaaf CB, Strahler AH, Danson FM, 2015. Terrestrial laser scanning for plot-scale forest measurements. Curr Forest Rep 1: 239-251.

Olofsson K, Holmgren J, Olsson H, 2014. Tree stem and height measurements using terrestrial laser scanning and the RANSAC algorithm. Remote Sens 6: 4323-4344.

Othmani A, Piboule A, Krebs M, Stolz C, Lew Yan Voon LFC, 2011. Towards automated and operational forest inventories with T-Lidar. 11th Int Conf on LiDAR Applications for Assessing Forest Ecosystems (SilviLaser 2011), Oct 2011, Hobart, Australia.

Potapov I, Järvenpää M, Åkerblom M, Raumonen P, Kaasalainen M, 2016. Data-based stochastic modeling of tree growth and structure formation. Silva Fennica 50 (1): 1-11.

R Core Team, 2013. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://wwwR-projectorg/.

Rahman MZA, Bakar MAA, Razak KA, Rasib AW, Kanniah KD, Kadir WHW, Omar H, Faidi A, Kassim AR, Latif ZA, 2017. Non-destructive, laser-based individual tree aboveground biomass estimation in a tropical rainforest. Forests 8 (86): 1-22.

Raumonen P, Kaasalainen M, Akerblom M, Kaasalainen S, Kaartinen H, Vastaranta M, Holopainen M, Disney M, Lewis P, 2013. Fast automatic precision tree models from terrestrial laser scanner data. Remote Sens 5 (2): 491-520.

Raumonen P, Casella E, Calders K, Murphy S, Åkerblom M, Kaasalainen M, 2015. Massive-scale tree modelling from TLS Data. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-3/W4, 2015, PIA15+HRIGI15 - Joint ISPRS Conf 2015, 25-27 March, Munich.

Seidel D, 2017. A holistic approach to determine tree structural complexity based on laser scanning data and fractal analysis. Ecol Evol 8 (1): 128-134.

Simonse M, Aschoff T, Spiecker H, 2003. Automatic determination of forest inventory parameters using terrestrial laser scanning. Proc Scan Laser Scientific Workshop on Airborne Laser Scanning of Forests, Umea, Sweden. pp: 251-257.

Srinivasan S, Popescu SC, Eriksson M, Sheridan RD, Ku NW, 2015. Terrestrial laser scanning as an effective tool to retrieve tree level height, crown width, and stem diameter. Remote Sens 7: 1877-1896.

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

Tabacchi G, Di Cosmo L, Patrizia G, 2011. Aboveground tree volume and phytomass prediction equations for forest species in Italy. Eur J Forest Res 130 (6): 911-934.

Thies M, Spiecker H, 2004. Evaluation and future prospects of terrestrial laser scanning for standardized forest inventories. Int Archiv Photogramm Remote Sens Spat Inf Sci XXXVI - 8/W2.

Thies M, Pfeifer N, Winterhalder D, Gorte BGH, 2004. Three-dimensional reconstruction of stems for assessment of taper, sweep and lean based on laser scanning of standing trees. Scand J Forest Res 19 (6): 571-581.

Torr PHS, Zisserman A, 2000. MLESAC: A new robust estimator with application to estimating image geometry. Comp Vision Image Underst 78 (1): 138-156.

Trochta J, Král K, Janík D, Adam D, 2013. Arrangement of terrestrial laser scanner positions for area-wide stem mapping of natural forests. Can J For Res 43: 355-363.

Trochta J, Krůček M, Vrška T, Král K, 2017. 3D Forest: An application for descriptions of three-dimensional forest structures using terrestrial LiDAR. PLoS ONE 12 (5): e0176871

Vasilescu MM, 2013. Standard error of tree height using Vertex III. Bull Transilvania Univ of Braşov Series II: Forest Wood Indus Agr Food Eng 6 (55-2): 2013.

Vonderach C, Voegtle T, Adler, P, 2012. Voxel-based approach for estimating urban tree volume from terrestrial laser scanning data. Int Arch Photogramm Remote Sens Spatial Inf Sci XXXIX-B8: 451-456.

Watt PJ, Donoghue DNM, 2005. Measuring forest structure with terrestrial laser scanning. Int J Remote Sens 26 (7): 1437-1446.

West GB, Brown JH, Enquist BJ, 1997. A general model for the origin of allometric scaling laws in biology. Science 276 (5309): 122-126.

Willmott CJ, Matsuura K, 2005. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 30: 79-82.

Wilson AD, 2000. New methods, algorithms, and software for rapid mapping of tree positions in coordinate forest plots. Res Pap SRS-19. Asheville, NC: USDA For Serv, South Res Stat. 31 p.

Xu H, Gosset N, Chen B, 2007. Knowledge and heuristic-based modeling of laser scanned trees. ACM Trans Graphics 26(4): 19.

DOI: 10.5424/fs/2018271-12658