3D modelling of individual trees using a handheld camera: Accuracy of height, diameter and volume estimates

Urban Forestry and Urban Greening

Volumn 14, Issue 4, 2015, Pages 932-940

  Miller, Jordan ^a   Morgenroth, Justin ^a   Gomez, Christopher ^a  

^a UNIVERSITY OF CANTERBURY   (New Zealand)

Author keywords

3D modelling; Forest inventory; Laser scanning; LiDAR; PhotoScan; Terrestrial laser scanning (TLS)

Indexed keywords

ACCURACY ASSESSMENT; COMPUTER VISION; DISPLACEMENT; ECOSYSTEM MODELING; FOREST INVENTORY; HEIGHT; LIDAR; MEASUREMENT METHOD; PHOTOGRAMMETRY; REMOTE SENSING; RESEARCH WORK; TERRESTRIAL ECOSYSTEM; THREE-DIMENSIONAL MODELING;

EID: 84951808500     PISSN: 16188667     EISSN: 16108167     Source Type: Journal    
DOI: 10.1016/j.ufug.2015.09.001     Document Type: Article

References (35)

1. Bemis S.P., Micklethwaite S., Turner D., James M.R., Akciz S., Thiele S.T., Bangash H.A. Ground-based and UAV-based photogrammetry: a multi-scale, high-resolution mapping tool for structural geology and paleoseismology. J. Struct. Geol. 2014, 69:163-178.
2. Bohlin J., Wallerman J., Fransson J.E.S. Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high-resolution DEM. Scand. J. For. Res. 2012, 27:692-699.
3. Bragg D.C. An improved tree height measurement technique tested on mature southern pines. South. J. Appl. For. 2008, 32:38-43.
4. Dandois J.P., Ellis E.C. Remote sensing of vegetation structure using computer vision. Remote Sens. 2010, 2:1157.
5. Dandois J.P., Ellis E.C. High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision. Remote Sens. Environ. 2013, 136:259-276.
6. 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.
7. Dassot M., Constant T., Fournier M. The use of terrestrial LiDAR technology in forest science: application fields, benefits and challenges. Ann. For. Sci. 2011, 68:959-974.
8. Eitel J.U., Vierling L.A., Magney T.S. A lightweight, low cost autonomously operating terrestrial laser scanner for quantifying and monitoring ecosystem structural dynamics. Agric. For. Meteorol. 2013, 180:86-96.
9. Fernández-Sarría A., Velázquez-Martí B., Sajdak M., Martínez L., Estornell J. Residual biomass calculation from individual tree architecture using terrestrial laser scanner and ground-level measurements. Comput. Electron. Agric. 2013, 93:90-97.
10. Fritz A., Kattenborn T., Koch B. UAV-based photogrammetric point clouds - tree stem mapping in open stands in comparison to terrestrial laser scanner point clouds. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. ISPRS Arch. 2013, XL-1/W2:141-146.
11. Gienko G.A., Terry J.P. Three-dimensional modeling of coastal boulders using multi-view image measurements. Earth Surf. Process. Landf. 2014, 39:853-864.
12. Henning J.G., Radtke P.J. Detailed stem measurements of standing trees from ground-based scanning LiDAR. For. Sci. 2006, 52:67-80.
13. 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.
14. Houghton R. Aboveground forest biomass and the global carbon balance. Glob. Change Biol. 2005, 11:945-958.
15. James M., Robson S. Straightforward reconstruction of 3D surfaces and topography with a camera: accuracy and geoscience application. J. Geophys. Res. Earth Surf. 2012, 2003-2012:117.
16. Järnstedt J., Pekkarinen A., Tuominen S., Ginzler C., Holopainen M., Viitala R. Forest variable estimation using a high-resolution digital surface model. ISPRS J. Photogramm. Remote Sens. 2012, 74:78-84.
17. Kankare V., Holopainen M., Vastaranta M., Puttonen E., Yu X., Hyyppä J., Vaaja M., Hyyppä H., Alho P. Individual tree biomass estimation using terrestrial laser scanning. ISPRS J. Photogramm. Remote Sens. 2013, 75:64-75.
18. Kitahara F., Mizoue N., Yoshida S. Effects of training for inexperienced surveyors on data quality of tree diameter and height measurements. Silva Fennica 2010, 44:657-667.
19. Liang X., Jaakkola A., Wang Y., Hyyppä J., Honkavaara E., Liu J., Kaartinen H. The use of a hand-held camera for individual tree 3D mapping in forest sample plots. Remote Sens. 2014, 6:6587.
20. Liang X., Kankare V., Yu X., Hyyppa J., Holopainen M. Automated stem curve measurement using terrestrial laser scanning. IEEE Trans. Geosci. Remote Sens. 2014, 52:1739-1748.
21. 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.
22. Miller J. Estimation of Individual Tree Metrics Using Structure-From-Motion Photogrammetry 2015, University of Canterbury, Christchurch, New Zealand.
23. Moorthy I., Miller J.R., Berni J.A.J., Zarco-Tejada P., Hu B., Chen J. Field characterization of olive (Olea europaea L.) tree crown architecture using terrestrial laser scanning data. Agric. For. Meteorol. 2011, 151:204-214.
24. Morgenroth J., Gomez C. Assessment of tree structure using a 3D image analysis technique - a proof of concept. Urban For. Urban Green. 2014.
25. Moskal L.M., Zheng G. Retrieving forest inventory variables with terrestrial laser scanning (TLS) in urban heterogeneous forest. Remote Sens. 2011, 4:1-20.
26. Næsset E. Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens. Environ. 2002, 80:88-99.
27. Nowak D.J., Greenfield E.J., Hoehn R.E., Lapoint E. Carbon storage and sequestration by trees in urban and community areas of the United States. Environ. Pollut. 2013, 178:229-236.
28. 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.
29. Özçelik R., Wiant H.V., Brooks J.R. Accuracy using xylometry of log volume estimates for two tree species in Turkey. Scand. J. For. Res. 2008, 23:272-277.
30. R Core Team R: A Language and Environment for Statistical Computing 2014, R Foundation for Statistical Computing, Vienna, Austria.
31. Rosell J.R., Llorens J., Sanz R., Arno J., Ribes-Dasi M., Masip J., Escolà A., Camp F., Solanelles F., Gràcia F. Obtaining the three-dimensional structure of tree orchards from remote 2D terrestrial LiDAR scanning. Agric. For. Meteorol. 2009, 149:1505-1515.
32. Tanhuanpää T., Vastaranta M., Kankare V., Holopainen M., Hyyppä J., Hyyppä H., Alho P., Raisio J. Mapping of urban roadside trees - a case study in the tree register update process in Helsinki City. Urban For. Urban Green. 2014, 13:562-570.
33. Westoby M., Brasington J., Glasser N., Hambrey M., Reynolds J. 'Structure-from-Motion' photogrammetry: a low-cost, effective tool for geoscience applications. Geomorphology 2012, 179:300-314.
34. Xiao Q., McPherson E.G. Rainfall interception of three trees in Oakland, California. Urban Ecosyst. 2011, 14:755-769.
35. Xu C., Morgenroth J., Manley B. Integrating data from discrete return airborne LiDAR and optical sensors to enhance the accuracy of forest description: a review. Curr. For. Rep. 2015, 1-14.