Assessment of handheld mobile terrestrial laser scanning for estimating tree parameters

Abstract

Sustainable forest management heavily relies on the accurate estimation of tree parameters. Among others, the diameter at breast height (DBH) is important for extracting the volume and mass of an individual tree. For systematically estimating the volume of entire plots, airborne laser scanning (ALS) data are used. The estimation model is frequently calibrated using manual DBH measurements or static terrestrial laser scans (STLS) of sample plots. Although reliable, this method is time-consuming, which greatly hampers its use. Here, a handheld mobile terrestrial laser scanning (HMTLS) was demonstrated to be a useful alternative technique to precisely and efficiently calculate DBH. Different data acquisition techniques were applied at a sample plot, then the resulting parameters were comparatively analysed. The calculated DBH values were comparable to the manual measurements for HMTLS, STLS, and ALS data sets. Given the comparability of the extracted parameters, with a reduced point density of HTMLS compared to STLS data, and the reasonable increase of performance, with a reduction of acquisition time with a factor of 5 compared to conventional STLS techniques and a factor of 3 compared to manual measurements, HMTLS is considered a useful alternative technique.

Keywords

Forest inventory / DBH / Airborne laser scanning / Terrestrial laser scanning / Handheld mobile laser scanning / Point cloud processing

References

[1] Altman DG, Bland JM. Measurement in medicine: the analysis of method comparison studies. Stat, 1983, 32: 307.

[2] Altman DG, Bland JM. Statistical methods for assessing agreement between measurement. Biochimica, 1987, 11: 399-404.

[3] Bailey T, Durrant-Whyte H. Simultaneous localization and mapping (SLAM): part II. IEEE Robot Autom Mag, 2006, 13: 108-117.

[4] Baltsavias EP. Airborne laser scanning: basic relations and formulas. ISPRS J Photogramm Remote Sens, 1999, 54: 199-214.

[5] 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.

[6] Bienert A, Maas H, Scheller S (2006) Analysis of the information content of terrestrial laserscanner point clouds for the automatic determination of forest inventory parameters. In: Workshop on 3D remote sensing in forestry. pp 1–7

[7] Bland JM, Altman DG. Comparing methods of clinical measurement - a citation-classic. Curr Contents / Clin Med, 1992, 5: 8.

[8] Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurements. Lancet, 1986, 327: 307-310.

[9] Boehler W, Marbs A. Investigating laser scanner accuracy. Int Arch Photogramm Remote Sens Spat Inf Sci, 2003, 34: 696-701.

[10] Bosse M, Zlot R, Flick P. Zebedee: design of a spring-mounted 3-D range sensor with application to mobile mapping. IEEE Trans Robot, 2012, 28: 1104-1119.

[11] Coomes DA, Dalponte M, Jucker T, Asner GP, Banin LF, Burslem DFRP, Lewis SL, Nilus R, Phillips OL, Phua MH, Qie L. Area-based vs tree-centric approaches to mapping forest carbon in Southeast Asian forests from airborne laser scanning data. Remote Sens Environ, 2017, 194: 77-88.

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

[13] Doneus M, Briese C, Fera M, Janner M. Archaeological prospection of forested areas using full-waveform airborne laser scanning. J Archaeol Sci, 2008, 35: 882-893.

[14] Faltýnová M, Matoušková E, Šedina J, Pavelka K. Building facade documentation using laser scanning and photogrammetry and data implementation into BIM. Int Arch Photogramm Remote Sens Spat Inf Sci, 2016, 41: 215-220.

[15] Fassi F, Fregonese L, Ackermann S, De Troia V. Comparison between laser scanning and automated 3D modeling techniques to resconstruct complex and extensive cultural heritage areas. Int Arch Photogramm Remote Sens Spat Inf Sci, 2013, 5(W1): 73-80.

[16] Girardeau-Montaut D (2020) CloudCompare (3D point cloud and mesh processing software Open Source Project),available at: https://www.cloudcompare.org/

[17] González-Jorge H, Rodríguez-Gonzálvez P, Shen YQ, Lagüela S, Diaz-Vilarino L, Lindenbergh R, Gonzalez-Aguilera D, Arias P. Metrological intercomparison of six terrestrial laser scanning systems. IET Sci Meas Technol, 2017, 12: 218-222.

[18] Guan HY, Li J, Cao S, Yu YT. Use of mobile LiDAR in road information inventory. Int J Image Data Fusion, 2016, 7: 219-242.

[19] Hancock S, Anderson K, Disney M, Gaston KJ. Measurement of fine-spatial-resolution 3D vegetation structure with airborne waveform lidar: calibration and validation with voxelised terrestrial lidar. Remote Sens Environ, 2017, 188: 37-50.

[20] Hauglin M, Dibdiakova J, Gobakken T, Næsset E. Estimating single-tree branch biomass of Norway spruce by airborne laser scanning. ISPRS J Photogramm Remote Sens, 2013, 79: 147-156.

[21] Jayathunga S, Owari T, Tsuyuki S. Analysis of forest structural complexity using airborne LiDAR data and aerial photography in a mixed conifer–broadleaf forest in northern Japan. J For Res, 2018, 29(2): 479-493.

[22] Jin S, Tamura M, Susaki J. A new approach to retrieve leaf normal distribution using terrestrial laser scanners. J For Res, 2016, 27(3): 631-638.

[23] Leica (2017) Leica-Geosystems: ScanStation P30/P40 specifications. https://leica-geosystems.com. Accessed 15 April 2020

[24] Leonard JJ, Durrant-Whyte HF. Mobile robot localization by tracking geometric beacons. IEEE Trans Robot Autom, 1991, 7: 376-382.

[25] Liang XL, Kankare V, Hyyppä J, Wang YS, Kukko A, Haggrén H, Yu XW, Kaartinen H, Jaakkola A, Guan FY, Holopainen M, Vastaranta M. Terrestrial laser scanning in forest inventories. ISPRS J Photogramm Remote Sens, 2016, 115: 63-77.

[26] Lovell JL, Jupp DLB, Newnham GJ, Culvenor DS. Measuring tree stem diameters using intensity profiles from ground-based scanning lidar from a fixed viewpoint. ISPRS J Photogramm Remote Sens, 2011, 66: 46-55.

[27] Maisonobe L (2007) Finding the circle that best fits a set of points. In: https://www.spaceroots.org/

[28] Melville GJ, Welsh AH, Stone C. Improving the efficiency and precision of tree counts in pine plantations using airborne LiDAR data and flexible-radius plots: model-based and design-based approaches. J Agric Biol Environ Stat, 2015, 20: 229-257.

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

[30] Othmani A, Piboule A, Krebs M, Stolz C, Lew Yan Voon LFC (2011) Towards automated and operational forest inventories with T-Lidar. 11th International conference on LiDAR applications for assessing forest ecosystems (SilviLaser 2011), Oct. 16‒19—Hobart, Australia. https://hal.archives-ouvertes.fr/hal-00646403

[31] Oude Elberink S, Vosselman G. Quality analysis on 3D building models reconstructed from airborne laser scanning data. ISPRS J Photogramm Remote Sens, 2011, 66: 157-165.

[32] Petrie G, Toth CK, Toth CK (2018) Terrestrial laser scanners. In: Topographic laser ranging and scanning. CRC Press, Second edition. Boca Raton : Taylor and Francis, CRC Press, 2018, pp 29–88

[33] Piboule A, Krebs M, Esclatine L, Hervé J (2013) Computree: a collaborative platform for use of terrestrial lidar in dendrometry. In: Proceedings of the International IUFRO Conference MeMoWood. Nancy, pp 1–4

[34] Roelens J, Dondeyne S, Van Orshoven J, Diels J. Extracting cross sections and water levels of vegetated ditches from LiDAR point clouds. Int J Appl Earth Obs Geoinf, 2016, 53: 64-75.

[35] Ryding J, Williams E, Smith M, Eichhorn M. Assessing handheld mobile laser scanners for forest surveys. Remote Sens, 2015, 7: 1095-1111.

[36] Shang C, Jones T, Treitz P (2016) Effect of size and number of calibration plots on the estimation of stem diameter distributions using airborne laser scanning. In: International geoscience and remote sensing symposium (IGARSS). Institute of Electrical and Electronics Engineers Inc., pp 1753–1756

[37] Smith R, Self M, Cheeseman P. Estimating uncertain spatial relationships in robotics. Autonomous robot vehicles, 1990, New York: Springer 167 193

[38] Stal C, Tack F, De Maeyer P, De Wulf A, Goossens R. Airborne photogrammetry and lidar for DSM extraction and 3D change detection over an urban area—a comparative study. Int J Remote Sens, 2013, 34: 1087-1110.

[39] Stovall AEL, Shugart HH. Improved biomass calibration and validation with terrestrial LiDAR: implications for future LiDAR and SAR missions. EEE J Sel Top Appl Earth Obs Remote Sens, 2018, 11: 3527-3537.

[40] Telling J, Lyda A, Hartzell P, Glennie C. Review of earth science research using terrestrial laser scanning. Earth-Science Rev, 2017, 169: 35-68.

[41] Wallace L, Lucieer A, Malenovský Z, Turner D, Vopenka P. Assessment of forest structure using two UAV techniques: a comparison of airborne laser scanning and structure from motion (SfM) point clouds. Forests, 2016 7 3 62

[42] Werbrouck I, Antrop M, Van Eetvelde V, Stal C, De Maeyer P, Bats M, Bourgeois J, Court-Picon M, Crombé P, De Reu J, De Smedt P, Finke PA, Van Meirvenne M, Verniers J, Zwertvaegher A. Digital elevation model generation for historical landscape analysis based on LiDAR data, a case study in Flanders (Belgium). Expert Syst Appl, 2011, 38: 8178-8185.

[43] Wilkes P, Jones SD, Suarez L, Haywood A, Mellor A, Woodgate W, Soto-Berelov M, Skidmore AK. Using discrete-return airborne laser scanning to quantify number of canopy strata across diverse forest types. Methods Ecol Evol, 2016, 7: 700-712.

[44] Zhao GY, Lian M, Li YY, Duan Z, Zhu SM, Mei L, Svanberg S. Mobile lidar system for environmental monitoring. Appl Opt, 2017, 56: 1506.