Detailed up-to-date ground reference data have become increasingly important in quantitative forest inventories. Field reference data are conventionally collected at the sample plot level by means of manual measurements, which are both labor-intensive and time-consuming. In addition, the number of attributes collected from the tree stem is limited. More recently, terrestrial laser scanning (TLS), using both single-scan and multi-scan techniques, has proven to be a promising solution for efficient stem mapping at the plot level. In the single-scan method, the laser scanner is placed at the center of the plot, creating only one scan, and all trees are mapped from the single-scan point cloud. Consequently, the occlusion of stems increases as the range of the scanner increases, depending on the forest’s attributes. In the conventional multi-scan method, several scans are made simultaneously inside and outside of the plot to collect point clouds representing all trees within the plot, and these scans are accurately co-registered by using artificial reference targets manually placed throughout the plot. The additional difficulty of applying the multi-scan method is due to the point-cloud registration of several scans not being fully automated yet. This paper proposes a multi-single-scan (MSS) method to map the sample plot. The method does not require artificial reference targets placed on the plot or point-level registration. The MSS method is based on the fully automated processing of each scan independently and on the merging of the stem positions automatically detected from multiple scans to accurately map the sample plot. The proposed MSS method was tested on five dense forest plots. The results show that the MSS method significantly improves the stem-detection accuracy compared with the single-scan approach and achieves a mapping accuracy similar to that achieved with the multi-scan method, without the need for the point-level registration.
References
[1]
Laasasenaho, J. Taper curve and volume functions for pine, spruce and birch. Commun. Inst. For. Fenn. 1982, 108, 1–74.
[2]
Yu, X.; Hyypp？, J.; Holopainen, M.; Vastaranta, M. Comparison of area-based and individual tree-based methods for predicting plot-level forest attributes. Remote Sens. 2010, 2, 1481–1495.
[3]
Chiba, Y. Modelling stem breakage caused by typhoons in plantation cryptomeria japonica forests. For. Ecol. Manag. 2000, 135, 123–131.
[4]
Pouliot, D.A.; King, D.J.; Bell, F.W.; Pitt, D.G. Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration. Remote Sens. Environ. 2002, 82, 322–334.
[5]
Holmstr？m, H.; Kallur, H.; St？hl, G. Cost-plus-loss analyses of forest inventory strategies based on kNN-assigned reference sample plot data. Silva Fenn. 2003, 37, 381–398.
[6]
Simonse, M.; Aschoff, T.; Spiecker, H.; Thies, M. Automatic Determination of Forest Inventory Parameters Using Terrestrial Laser Scanning. Proceedings of ScandLaser Scientific Workshop Airborne Laser Scanning Forests, Umea, Sweden, 3–4 September 2003; pp. 1–7.
[7]
Aschoff, T.; Spiecker, H. Algorithms for the automatic detection of trees in laser scanner data. Int. Arch. Photogramm. Remote Sens. Spat. Inform. Sci. 2004, 36, 71–75.
[8]
Pfeifer, N.; Winterhalder, D. Modelling of tree cross sections from terrestrial laser-scanning data with free-form curves. Int. Arch. Photogramm. Remote Sens. Spat. Inform. Sci. 2004, 36, 76–81.
[9]
Hopkinson, C.; Chasmer, L.; Young-Pow, C.; Treitz, P. Assessing forest metrics with a ground-based scanning lidar. Can. J. For. Res. 2004, 34, 573–583.
[10]
Henning, J.G.; Radtke, P.J. Detailed stem measurements of standing trees from ground-based scanning lidar. For. Sci. 2006, 52, 67–80.
[11]
Liang, X.; Hyypp？, J.; Kaartinen, H.; Holopainen, M.; Melkas, T. Detecting changes in forest structure over time with bi-temporal terrestrial laser scanning data. ISPRS Int. J. Geo-Inf. 2012, 1, 242–255.
[12]
Brolly, G.; Kiraly, G. Algorithms for stem mapping by means of terrestrial laser scanning. Acta Silvatica Lignaria Hungarica 2009, 5, 119–130.
[13]
Lovell, J.L.; Jupp, D.L.B.; Newnham, G.J.; Culvenor, D.S. 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.
[14]
Murphy, G.; Acuna, M.; Dumbrell, I. Tree value and log product yield determination in radiata pine (pinus radiata) plantations in australia: Comparisons of terrestrial laser scanning with a forest inventory system and manual measurements. Can. J. Forest Res. 2010, 40, 2223–2233.
[15]
Liang, X.; Litkey, P.; Hyypp？, J.; Kaartinen, H.; Vastaranta, M.; Holopainen, M. Automatic stem mapping using single-scan terrestrial laser scanning. IEEE Trans. Geosci. Remote Sens. 2012, 50, 661–670.
[16]
Watt, P.J.; Donoghue, D.N.M. Measuring forest structure with terrestrial laser scanning. Int. J. Remote Sens. 2005, 26, 1437–1446.
[17]
Chang, S.H.; Cheng, F.H.; Hsu, W.H.; Wu, G.Z. Fast algorithm for point pattern matching: Invariant to translations, rotations and scale changes. Patt. Recogn. 1997, 30, 311–320.
[18]
Thies, M.; Spiecker, H. Evaluation and future prospects of terrestrial laser-scanning for standardized forest inventories. Int. Arch. Photogramm. Remote Sens. Spat. Inform. Sci. 2004, 36, 192–197.
Maas, H.G.; Bienert, A.; Scheller, S.; Keane, E. Automatic forest inventory parameter determination from terrestrial laser scanner data. Int. J. Remote Sens. 2008, 29, 1579–1593.
[21]
Tansey, K.; Selmes, N.; Anstee, A.; Tate, N.J.; Denniss, A. Estimating tree and stand variables in a corsican pine woodland from terrestrial laser scanner data. Int. J. Remote Sens. 2009, 30, 5195–5209.
[22]
Huang, H.; Li, Z.; Gong, P.; Cheng, X.; Clinton, N.; Cao, C.; Ni, W.; Wang, L. Automated methods for measuring DBH and tree heights with a commercial scanning lidar. Photogramm. Eng. Remote Sens. 2011, 77, 219–227.
[23]
Bienert, A.; Scheller, S.; Keane, E.; Mohan, F.; Nugent, C. Tree detection and diameter estimations by analysis of forest terrestrial laserscanner point clouds. Int. Arch. Photogramm. Remote Sens. 2007, 36, 50–55.
[24]
Liang, X.; Litkey, P.; Hyypp？, J.; Kaartinen, H.; Kukko, A.; Holopainen, M. Automatic plot-wise tree location mapping using single-scan terrestrial laser scanning. Photogramm. J. Finland 2011, 22, 37–48.
[25]
Strahler, A.H.; Jupp, D.L.B.; Woodcock, C.E.; Schaaf, C.B.; Yao, T.; Zhao, F.; Yang, X.Y.; Lovell, J.; Culvenor, D.; et al. Retrieval of forest structural parameters using a ground-based lidar instrument (Echidna (R)). Can. J. Remote Sens. 2008, 34, S426–S440.
[26]
Lefsky, M.; McHale, M.R. Volume estimates of trees with complex architecture from terrestrial laser scanning. J. Appl. Remote Sens. 2008, doi:10.1117/1.2939008.
[27]
Liang, X.; Hyypp？, J.; Kankare, V.; Holopainen, M. Stem Curve Measurement Using Terrestrial Laser Scanning. Proceedings of 11th International Conference on LiDAR Applications for Assessing Forest Ecosystems, Tasmania, Australia, 16–20 October 2011; pp. 1–6.
[28]
Seidel, D.; Albert, K.; Fehrmann, L.; Ammer, C. The potential of terrestrial laser scanning for the estimation of understory biomass in coppice-with-standard systems. Biomass Bioenergy 2012, 47, 20–25.
[29]
Pirotti, F.; Guarnieri, A.; Vettore, A. Ground filtering and vegetation mapping using multi-return terrestrial laser scanning. ISPRS J. Photogramm. Remote Sens. 2012. in press.
[30]
Yu, X.; Liang, X.; Hyypp？, J.; Kankare, V.; Vastaranta, M.; Holopainen, M. Stem biomass estimation based on stem reconstruction from terrestrial laser scanning point clouds. Remote Sens. Lett. 2013, 4, 344–353.
[31]
Cote, J.; Widlowski, J.; Fournier, R.; Verstraete, M. The structural and radiative consistency of three-dimensional tree reconstructions from terrestrial lidar. Remote Sens. Environ. 2009, 113, 1067–1081.
[32]
Henning, J.G.; Radtke, P.J. Multiview range-image registration for forested scenes using explicitly-matched tie points estimated from natural surfaces. ISPRS J. Photogramm. Remote Sens. 2008, 63, 68–83.
