Automatic mapping of forest stands based on three-dimensional point clouds derived from terrestrial laser-scanning

Forests

Volumn 8, Issue 8, 2017, Pages

  Ritter, Tim ^a   Schwarz, Marcel ^a   Tockner, Andreas ^a   Leisch, Friedrich ^a   Nothdurft, Arne ^a  

^a UNIVERSITY OF NATURAL RESOURCES AND LIFE SCIENCES   (Austria)

Author keywords

Density based clustering; Forest inventory; Terrestrial laser scanning

Indexed keywords

FORESTRY; MAPPING; SCANNING;

DENSITY CLUSTERING; DENSITY-BASED CLUSTERING; FOREST INVENTORY; INTENSIVE RESEARCH; OMISSION AND COMMISSION ERRORS; OVERALL ACCURACIES; TERRESTRIAL LASER SCANNING; THREE-DIMENSIONAL POINT CLOUDS;

SURVEYING INSTRUMENTS;

ALGORITHM; FOREST INVENTORY; LASER; STAND DYNAMICS; TERRESTRIAL ENVIRONMENT; THREE-DIMENSIONAL MODELING; VEGETATION MAPPING;

DENSITY; FORESTS; THREE DIMENSIONAL DESIGN;

EID: 85026362951     PISSN: None     EISSN: 19994907     Source Type: Journal    
DOI: 10.3390/f8080265     Document Type: Article

References (57)

1. Köhl, M.; Magnussen, S.S.; Marchetti, M. Sampling Methods, Remote Sensing and GIS Multiresource Forest Inventory; Springer-Verlag: Berlin/Heidelberg, Germany, 2006; ISBN 978-3-540-32572-7.
2. MCPFE. Background information for improved Pan-European indicators for sustainable forest management. In Proceedings of the Ministerial Conference on the Protection of Forests in Europe (MCPFE) Liaison Unit, Vienna, Austria, 7-8 October 2002.
3. MCPFE. State of Europe's Forests 2003: The MCPFE report on sustainable forest management in Europe. In Proceedings of the Ministerial Conference on the Protection of Forests in Europe (MCPFE) Liaison Unit, Vienna, Austria, 28-30 April 2003.
4. IUFRO. Guidelines for Designing Multipurpose Resource Inventories: A Project of IUFRO Research Group 4.02.02; Lund, H.G., Ed.; IUFROWorld Series: Vienna, Austria, 1998; Volume 8.
5. Kenning, R.S.; Ducey, M.J.; Brissette, J.C.; Gove, J.H. Field efficiency and bias of snag inventory methods. Can. J. For. Res. 2005, 35, 2900-2910.
6. 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.
7. Maltamo, M.; Bollandsås, O.M.; Gobakken, T.; Næsset, E. Large-scale prediction of aboveground biomass in heterogeneous mountain forests by means of airborne laser scanning. Can. J. For. Res. 2016, 1144, 1138-1144.
8. De Vries, W.; Vel, E.; Reinds, G.J.; Deelstra, H.; Klap, J.M.; Leeters, E.E.J.M.; Hendriks, C.M.A.; Kerkvoorden, M.; Landmann, G.; Herkendell, J.; et al. Intensive monitoring of forest ecosystems in Europe 1. Objectives, set-up and evaluation strategy. For. Ecol. Manag. 2003, 174, 77-95.
9. Mellert, K.H.; Deffner, V.; Küchenhoff, H.; Kölling, C. Modeling sensitivity to climate change and estimating the uncertainty of its impact: A probabilistic concept for risk assessment in forestry. Ecol. Modell. 2015, 316, 211-216.
10. IFER. Field-Map-Tool Designed for Computer Aided Field Data Collection. Available online: http://www. fieldmap.cz/ (accessed on 28 February 2017).
11. Field, H.L. Landscape Surveying; Cengage Learning: Delmar, CA, USA, 2012; ISBN 1111310602.
12. Culvenor, D.S. TIDA: An algorithm for the delineation of tree crowns in high spatial resolution remotely sensed imagery. Comput. Geosci. 2002, 28, 33-44.
13. Pouliot, D.; King, D.; Bell, F.; Pitt, D. Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration. Remote Sens. Environ. 2002, 82, 322-334.
14. Ke, Y.; Quackenbush, L.J. A review of methods for automatic individual tree-crown detection and delineation from passive remote sensing. Int. J. Remote Sens. 2011, 32, 4725-4747.
15. Van Leeuwen, M.; Nieuwenhuis, M. Retrieval of forest structural parameters using LiDAR remote sensing. Eur. J. For. Res. 2010, 129, 749-770.
16. Liang, X.; Kankare, V.; Hyyppä, J.; Wang, Y.; Kukko, A.; Haggrén, H.; Yu, X.; Kaartinen, H.; Jaakkola, A.; Guan, F.; et al. Terrestrial laser scanning in forest inventories. ISPRS J. Photogramm. Remote Sens. 2016, 115, 63-77.
17. Næsset, E.; Gobakken, T.; Holmgren, J.; Hyyppä, H.; Hyyppä, J.; Maltamo, M.; Nilsson, M.; Olsson, H.; Persson, Å.; Söderman, U. Laser scanning of forest resources: the nordic experience. Scand. J. For. Res. 2004, 19, 482-499.
18. Kwak, D.-A.; Lee, W.-K.; Lee, J.-H.; Biging, G.S.; Gong, P. Detection of individual trees and estimation of tree height using LiDAR data. J. For. Res. 2007, 12, 425-434.
19. Ferraz, A.; Bretar, F.; Jacquemoud, S.; Gonçalves, G.; Pereira, L.; Tomé, M.; Soares, P. 3-D mapping of a multi-layered Mediterranean forest using ALS data. Remote Sens. Environ. 2012, 121, 210-223.
20. Kaartinen, H.; Hyyppä, J.; Yu, X.; Vastaranta, M.; Hyyppä, H.; Kukko, A.; Holopainen, M.; Heipke, C.; Hirschmugl, M.; Morsdorf, F.; et al. An international comparison of individual tree detection and extraction using airborne laser scanning. Remote Sens. 2012, 4, 950-974.
21. Vauhkonen, J.; Ene, L.; Gupta, S.; Heinzel, J.; Holmgren, J.; Pitkanen, J.; Solberg, S.; Wang, Y.; Weinacker, H.; Hauglin, K.M.; et al. Comparative testing of single-tree detection algorithms under different types of forest. Forestry 2012, 85, 27-40.
22. Maack, J.; Lingenfelder, M.; Weinacker, H.; Koch, B. Modelling the standing timber volume of Baden-Württemberg-A large-scale approach using a fusion of Landsat, airborne LiDAR and National Forest Inventory data. Int. J. Appl. Earth Obs. Geoinf. 2016, 49, 107-116.
23. Deo, R.K.; Froese, R.E.; Falkowski, M.J.; Hudak, A.T. Optimizing variable radius plot size and LiDAR resolution to model standing volume in conifer forests. Can. J. Remote Sens. 2016, 42, 428-442.
24. Scrinzi, G.; Clementel, F.; Floris, A. Angle count sampling reliability as ground truth for area-based LiDAR applications in forest inventories. Can. J. For. Res. 2015, 45, 506-514.
25. Srinivasan, S.; Popescu, S.C.; Eriksson, M.; Sheridan, R.D.; Ku, N.-W.; Waser, L.T.; Wynne, R.H.; Thenkabail, P.S. Terrestrial laser scanning as an effective tool to retrieve tree level height, crown width, and stem diameter. Remote Sens. 2015, 7, 1877-1896.
26. Yang, B.; Dai, W.; Dong, Z.; Liu, Y. Automatic forest mapping at individual tree levels from terrestrial laser scanning point clouds with a hierarchical minimum cut method. Remote Sens. 2016, 8, 372.
27. Hager, H. Die Klimastationen im lehrforst der universität für bodenkultur. Allg. Forstzeitung 1980, 91, 54-57.
28. Glatzel, G.; Sieghardt, M. Die Böden des Lehrforstes. Allg. Forstztg. 1980, 91, 53-54.
29. Haglöf Measurement Solutions in Forest and Field. Available online: http://www.haglofcg.com/index. php/en/products/instruments/height/341-vertex-iv (accessed on 17 March 2017).
30. FARO Laser Scanner FARO Focus3D-Overview-3D Surveying. Available online: http://www.faro.com/ en-us/products/3d-surveying/faro-focus3d/overview (accessed on 28 February 2017).
31. FARO FARO Laser Scanner Software-SCENE-Overview. Available online: http://www.faro.com/en-us/ products/faro-software/scene/overview (accessed on 28 February 2017).
32. Isenburg, M. LAStools-Efficient LiDAR Processing Software (version 160429, Academic). Available online: https://rapidlasso.com/lastools/ (accessed on 28 February 2017).
33. R Development Core Team. R: A Language and Environment for Statistical Computing, R Version 3.3.2; R Foundation for Statistical Computing: Vienna, Austria, 2016.
34. Rodriguez, A.; Laio, A. Clustering by fast search and find of density peaks. Science 2014, 344, 1492-1496.
35. Pedersen, T.L.; Hughes, S. Densityclust: Clustering by Fast Search and Find of Density Peaks, R package version 0.2.1; 2016. Available online: https://cran.r-project.org/package=densityClust (accessed on 15 June 2017).
36. Baddeley, A.; Rubak, E.; Turner, R. Spatial Point Patterns: Methodology and Applications with R; Chapman and Hall/CRC Press: London, UK, 2015.
37. Edelsbrunner, H.; Kirkpatrick, D.; Seidel, R. On the shape of a set of points in the plane. IEEE Trans. Inf. Theory 1983, 29, 551-559.
38. Pateiro-Lopez, B.; Rodriguez-Casal, A. Alphahull: Generalization of the Convex Hull of a Sample of Points in the Plane, R package version 2.1; 2016. Available online: https://cran.r-project.org/package=alphahull (accessed on 15 June 2017).
39. Müller, C.H.; Garlipp, T. Simple consistent cluster methods based on redescending M-estimators with an application to edge identification in images. J. Multivar. Anal. 2005, 92, 359-385.
40. Garlipp, T. Edci: Edge Detection and Clustering in Images. 2016. Available online: https://CRAN.R-project. org/package=edci (accessed on 17 June 2017).
41. Chernov, N. Circular and Linear Regression: Fitting Circles and Lines by Least Squares; CRC Press: Taylor & Francis Group, Boca Raton, Florida, USA, 2011; ISBN 9781439835906.
42. Gama, J.; Chernov, N. Conicfit: Algorithms for Fitting Circles, Ellipses and Conics Based on theWork by Prof. Nikolai Chernov, R package version 1.0.4; 2015. Available online: https://CRAN.R-project.org/package=conicfit (accessed on 17 June 2017).
43. Fitzgibbon, A.W.; Pilu, M.; Fisher, R.B. Direct least squares fitting of ellipses. IEEE Trans. Pattern Anal. Mach. Intell. 1996, 21, 476-480.
44. 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, 1593-1879.
45. Xia, S.;Wang, C.; Pan, F.; Xi, X.; Zeng, H.; Liu, H. Detecting stems in dense and homogeneous forest using single-scan TLS. Forests 2015, 6, 3923-3945.
46. Astrup, R.; Ducey, M.J.; Granhus, A.; Ritter, T.; von Lüpke, N. Approaches for estimating stand-level volume using terrestrial laser scanning in a single-scan mode. Can. J. For. Res. 2014, 44, 666-676.
47. Ducey, M.J.; Astrup, R. Adjusting for nondetection in forest inventories derived from terrestrial laser scanning. Can. J. Remote Sens. 2013, 39, 410-425.
48. 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.
49. Thies, M.; Spiecker, H. Evaluation and future prospects of terrestrial laser scanning for standardized forest inventories. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2004, 36, 192-197.
50. Strahler, A.H.; Jupp, D.L.;Woodcock, C.E.; Schaaf, C.B.; Yao, T.; Zhao, F.; Yang, X.; Lovell, J.; Culvenor, D.; Newnham, G.; et al. Retrieval of forest structural parameters using a ground-based lidar instrument (Echidna®). Can. J. Remote Sens. 2008, 34, 426-440.
51. Brolly, G.; Kiraly, G. Algorithms for stem mapping by means of terrestrial laser scanning. Acta Silv. Lign. Hung 2009, 5, 119-130.
52. Murphy, G.E.; Acuna, M.A.; 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. For. Res. 2010, 40, 2223-2233.
53. 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.
54. Liang, X.; Litkey, P.; Hyyppa, 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.
55. Liang, X.; Hyyppä, J. Automatic stem mapping by merging several terrestrial laser scans at the feature and decision levels. Sensors 2013, 13, 1614-1634.
56. Eysn, L.; Hollaus, M.; Lindberg, E.; Berger, F.; Monnet, J.-M.; Dalponte, M.; Kobal, M.; Pellegrini, M.; Lingua, E.; Mongus, D.; et al. A benchmark of lidar-based single tree detection methods using heterogeneous forest data from the alpine space. Forests 2015, 6, 1721-1747.
57. Dalponte, M.; Reyes, F.; Kandarre, K.; Gianelle, D. Delineation of individual tree crowns from ALS and hyperspectral data: A comparison among four methods. Eur. J. Remote Sens. 2015, 48, 365-382.