Assessment of classifiers and remote sensing features of hyperspectral imagery and stereo-photogrammetric point clouds for recognition of tree species in a forest area of high species diversity

Remote Sensing

Volumn 10, Issue 5, 2018, Pages

  Tuominen, Sakari ^a   Näsi, Roope ^b   Honkavaara, Eija ^b   Balazs, Andras ^a   Hakala, Teemu ^b   Viljanen, Niko ^b   Pölönen, Ilkka ^c   Saari, Heikki ^d   Ojanen, Harri ^d  

^a FINNISH FOREST RESEARCH INSTITUTE   (Finland)

^b FINNISH GEOSPATIAL RESEARCH INSTITUTE   (Finland)

^c UNIVERSITY OF JYVÄSKYLÄ   (Finland)

^d VTT TECHNICAL RESEARCH CENTRE OF FINLAND   (Finland)

Author keywords

Dense point cloud; Genetic algorithm; Hyperspectral imagery; Machine learning; Photogrammetry; Random forest; Reflectance calibration; Tree species recognition; UAV

Indexed keywords

AERIAL PHOTOGRAPHY; ANTENNAS; CAMERAS; DECISION TREES; FORESTRY; GENETIC ALGORITHMS; IMAGE CLASSIFICATION; INFRARED DEVICES; INFRARED RADIATION; LEARNING SYSTEMS; NEAREST NEIGHBOR SEARCH; PHOTOGRAMMETRY; REFLECTION; REMOTE SENSING; SPECTROSCOPY; UNMANNED AERIAL VEHICLES (UAV);

GEO-SPATIAL INFORMATIONS; HYPER-SPECTRAL IMAGERIES; K-NEAREST NEIGHBOR METHOD; POINT CLOUD; RANDOM FOREST ALGORITHM; RANDOM FORESTS; TREE SPECIES; TREE SPECIES COMPOSITION;

STEREO IMAGE PROCESSING;

EID: 85047571087     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs10050714     Document Type: Article

References (68)

1. Laasasenaho, J. Taper curve and volume functions for pine, spruce and birch. Commun. Inst. For. Fenn. 1982, 108, 1-74
2. Repola, J. Biomass equations for birch in Finland. Silva Fenn. 2008, 42, 605-624
3. Repola, J. Biomass equations for Scots pine and Norway spruce in Finland. Silva Fenn. 2009, 43, 625-647
4. Hynynen, J.; Ahtikoski, A.; Siitonen, J.; Sievänen, R.; Liski, J. Applying the MOTTI simulator to analyse the effects of alternative management schedules on timber and non-timber production. For. Ecol. Manag. 2005, 207, 5-18
5. Fassnacht, F.E.; Latifi, H.; Sterenczak, K.; Modzelewska, A.; Lefsky, M.; Waser, L.T.; Straub, C.; Ghosh, A. Review of studies on tree species classification from remotely sensed data. Remote Sens. Environ. 2016, 186, 64-87
6. Gibbs, J.N. Intercontinental Epidemiology of Dutch Elm Disease. Annu. Rev. Phytopathol. 1978, 16, 287-307
7. Nevalainen, S.; Pouttu, A. Metsätuhot Vuonna 2015; Luonnonvarakeskus: Helsinki, Finland, 2016; Available online: https://jukuri.luke.fi/bitstream/handle/10024/535832/luke-luobio_32_2016.pdf (accessed on 5 May 2018)
8. Ørka, H.O.; Næsset, E.; Bollandsås, O.M. Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data. Remote Sens. Environ. 2009, 113, 1163-1174
9. Koch, B.; Heyder, U.; Weinacker, H. Detection of Individual Tree Crowns in Airborne Lidar Data. Photogramm. Eng. Remote Sens. 2006, 72, 357-363
10. 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
11. Puliti, S.; Gobakken, T.; Ørka, H.O.; Næsset, E. Assessing 3D point clouds from aerial photographs for species-specific forest inventories. Scand. J. For. Res. 2017, 32, 68-79
12. Waser, L.T.; Ginzler, C.; Kuechler, M.; Baltsavias, E.; Hurni, L. Semi-automatic classification of tree species in different forest ecosystems by spectral and geometric variables derived from Airborne Digital Sensor (ADS40) and RC30 data. Remote Sens. Environ. 2011, 115, 76-85
13. Dalponte, M.; Ørka, H.O.; Ene, L.T.; Gobakken, T.; Næsset, E. Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data. Remote Sens. Environ. 2014, 140, 306-317
14. Pant, P. Optimizing Spectral Bands of Airborne Imager for Tree Species Classification Paras Pant Optimizing Spectral Bands of Airborne Imager for Tree Species Classification; University of Eastern Finland: Joensuu, Finland, 2015; Volume 177
15. Zhang, K.; Hu, B. Individual urban tree species classification using very high spatial resolution airborne multi-spectral imagery using longitudinal profiles. Remote Sens. 2012, 4, 1741-1757
16. Jackson, R.D.; Teillet, P.M.; Slater, P.N.; Fedosejevs, G.; Jasinski, M.F.; Aase, J.K.; Moran, M.S. Bidirectional measurements of surface reflectance for view angle corrections of oblique imagery. Remote Sens. Environ. 1990, 32, 189-202
17. Li, X.; Strahler, A.H. Geometric-optical bidirectional reflectance modeling of the discrete crown vegetation canopy: Effect of crown shape and mutual shadowing. IEEE Trans. Geosci. Remote Sens. 1992, 30, 276-292
18. Pellikka, P.; King, D.J.; Leblanc, S.G. Quantification and reduction of bidirectional effects in aerial cir imagery of deciduous forest using two reference land surface types. Remote Sens. Rev. 2000, 19, 259-291
19. Holopainen, M.; Wang, G. The calibration of digitized aerial photographs for forest stratification. Int. J. Remote Sens. 1998, 19, 677-696
20. King, D.J. Determination and reduction of cover type brightness variations with view angle in airborne multispectral video imagery. Photogramm. Eng. Remote Sens. 1991, 57, 1571-1577
21. Tuominen, S.; Pekkarinen, A. Local radiometric correction of digital aerial photographs for multi source forest inventory. Remote Sens. Environ. 2004, 89, 72-82
22. Nevalainen, O.; Honkavaara, E.; Tuominen, S.; Viljanen, N.; Hakala, T.; Yu, X.; Hyyppä, J.; Saari, H.; Pölönen, I.; Imai, N.; Tommaselli, A. Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging. Remote Sens. 2017, 9, 185
23. Tuominen, S.; Näsi, R.; Honkavaara, E.; Balazs, A.; Hakala, T.; Viljanen, N.; Pölönen, I.; Saari, H.; Reinikainen, J. Tree species recognition in species rich area using UAV-borne hyperspectral imagery and stereo-photogrammetric point cloud. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences-ISPRS Archives; International Society for Photogrammetry and Remote Sensing: Istanbul, Turkey, 2017; Volume 42
24. Brandtberg, T.;Walter, F. Automated delineation of individual tree crowns in high spatial resolution aerial images by multiple-scale analysis. Mach. Vis. Appl. 1998, 11, 64-73
25. Wang, L.; Gong, P.; Biging, G.S. Individual Tree-Crown Delineation and Treetop Detection in High-Spatial-Resolution Aerial Imagery. Photogramm. Eng. Remote Sens. 2004, 70, 351-357
26. Baltsavias, E.; Gruen, A.; Eisenbeiss, H.; Zhang, L.; Waser, L.T. High-quality image matching and automated generation of 3D tree models. Int. J. Remote Sens. 2008, 29, 1243-1259
27. Haala, N.; Hastedt, H.; Wolf, K.; Ressl, C.; Baltrusch, S. Digital Photogrammetric Camera Evaluation-Generation of Digital Elevation Models. Photogramm Fernerkund. Geoinf. 2010, 2010, 99-115
28. St-Onge, B.; Vega, C.; Fournier, R.A.; Hu, Y. Mapping canopy height using a combination of digital stereo-photogrammetry and LiDAR. Int. J. Remote Sens. 2008, 29, 3343-3364
29. Colomina, I.; Molina, P. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 2014, 92, 79-97
30. Büttner, A.; Röser, H.-P. Hyperspectral Remote Sensing with the UAS 'Stuttgarter Adler'-System Setup, Calibration and First Results. Photogramm. Fernerkund. Geoinf. 2014, 2014, 265-274
31. Hruska, R.; Mitchell, J.; Anderson, M.; Glenn, N.F. Radiometric and geometric analysis of hyperspectral imagery acquired from an unmanned aerial vehicle. Remote Sens. 2012, 4, 2736-2752
32. Lucieer, A.; Malenovský, Z.; Veness, T.;Wallace, L. HyperUAS-Imaging spectroscopy from a multirotor unmanned aircraft system. J. F. Robot. 2014, 31, 571-590
33. Zarco-Tejada, P.J.; González-Dugo, V.; Berni, J.A.J. Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera. Remote Sens. Environ. 2012, 117, 322-337
34. Aasen, H.; Burkart, A.; Bolten, A.; Bareth, G. Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance. ISPRS J. Photogramm. Remote Sens. 2015, 108, 245-259
35. Honkavaara, E.; Saari, H.; Kaivosoja, J.; Pölönen, I.; Hakala, T.; Litkey, P.; Mäkynen, J.; Pesonen, L. Processing and Assessment of Spectrometric, Stereoscopic Imagery Collected Using a Lightweight UAV Spectral Camera for Precision Agriculture. Remote Sens. 2013, 5, 5006-5039
36. Mäkynen, J.; Holmlund, C.; Saari, H.; Ojala, K.; Antila, T. Unmanned aerial vehicle (UAV) operated megapixel spectral camera. In Electro-Optical Remote Sensing, Photonic Technologies, and Applications V; International Society for Optics and Photonics: Prague, Czech Republic, 2011; Volume 8186, p. 81860Y
37. Saari, H.; Pellikka, I.; Pesonen, L.; Tuominen, S.; Heikkilä, J.; Holmlund, C.; Mäkynen, J.; Ojala, K.; Antila, T. Unmanned Aerial Vehicle (UAV) operated spectral camera system for forest and agriculture applications. In Remote Sensing for Agriculture, Ecosystems, and Hydrology XIII; Neale, C.M.U., Maltese, A., Eds.; International Society for Optics and Photonics: Prague, Czech Republic, 2011; Volume 8174, p. 81740H
38. Zarco-Tejada, P.J.; Diaz-Varela, R.; Angileri, V.; Loudjani, P. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. Eur. J. Agron. 2014, 55, 89-99
39. Näsi, R.; Honkavaara, E.; Lyytikäinen-Saarenmaa, P.; Blomqvist, M.; Litkey, P.; Hakala, T.; Viljanen, N.; Kantola, T.; Tanhuanpää, T.; Holopainen, M. Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level. Remote Sens. 2015, 7, 15467-15493
40. Näsi, R.; Honkavaara, E.; Tuominen, S.; Saari, H.; Pölönen, I.; Hakala, T.; Viljanen, N.; Soukkamäki, J.; Näkki, I.; Ojanen, H.; Reinikainen, J. UAS based tree species identification using the novel FPI based hyperspectral cameras in visible, NIR and SWIR spectral ranges. ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2016, 41, 1143-1148
41. Mannila, R.; Holmlund, C.; Ojanen, H.J.; Näsilä, A.; Saari, H. Short-wave infrared (SWIR) spectral imager based on Fabry-Perot interferometer for remote sensing. In Sensors, Systems, and Next-Generation Satellites XVIII; Meynart, R., Neeck, S.P., Shimoda, H., Eds.; International Society for Optics and Photonics: Prague, Czech Republic, 2014; Volume 9241, p. 92411M
42. Honkavaara, E.; Eskelinen, M.A.; Polonen, I.; Saari, H.; Ojanen, H.; Mannila, R.; Holmlund, C.; Hakala, T.; Litkey, P.; Rosnell, T.; et al. Remote sensing of 3-D geometry and surface moisture of a peat production area using hyperspectral frame cameras in visible to short-wave infrared spectral ranges onboard a small unmanned airborne vehicle (UAV). IEEE Trans. Geosci. Remote Sens. 2016, 54, 5440-5454
43. Häkli, P. Practical test on accuracy and usability of virtual reference station method in Finland. In FIG Working Week; International Federation of Surveyors: Athens, Greece, 2004; pp. 22-27
44. Honkavaara, E.; Rosnell, T.; Oliveira, R.; Tommaselli, A. Band registration of tuneable frame format hyperspectral UAV imagers in complex scenes. ISPRS J. Photogramm. Remote Sens. 2017, 134, 96-109
45. Smith, G.M.; Milton, E.J. The use of the empirical line method to calibrate remotely sensed data to reflectance. Int. J. Remote Sens. 1999, 20, 2653-2662
46. Næsset, E. Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scand. J. For. Res. 2004, 19, 164-179
47. Packalén, P.; Maltamo, M. Predicting the Plot Volume by Tree Species Using Airborne Laser Scanning and Aerial Photographs. For. Sci. 2006, 52, 611-622
48. Packalén, P.; Maltamo, M. Estimation of species-specific diameter distributions using airborne laser scanning and aerial photographs. Can. J. For. Res. 2008, 38, 1750-1760
49. Beyer, K.; Goldstein, J.; Ramakrishnan, R.; Shaft, U. When is 'nearest neighbor' meaningful? In Proceedings of the 7th International Conference on Database Theory, Jerusalem, Israel, 10-12 January 1999; Beeri, C., Buneman, P., Eds.; Springer: Jerusalem, Israel, 1999; Volume 1540, pp. 217-235
50. Hinneburg, A.; Aggarwal, C.C.; Keim, D.A. What Is the Nearest Neighbor in High Dimensional Spaces? In Proceedings of the 26th International Conference on Very Large Data Bases, Cairo, Egypt, 10-14 September 2000; El Abbadi, A., Brodie, W.L., Chakravarthy, S., Dayal, U., Kamel, N., Schlageter, G., Whang, K.-Y., Eds.; Morgan Kaufmann Publishers Inc.: San Francisco, CA, USA, 2000; pp. 506-515
51. Kilkki, P.; Päivinen, R. Reference Sample Plots to Combine Field Measurements and Satellite Data in Forest Inventory; Research Notes; Department of Forest Mensuration and Management, University of Helsinki: Helsinki, Finland, 1987
52. Muinonen, E.; Tokola, T. An application of remote sensing for communal forest inventory. In Proceedings from SNS/IUFRO Workshop; International Union of Forest Research Organizations: Umeå, Sweden, 1990; pp. 35-42
53. Tomppo, E. Satellite image-based national forest inventory of Finland. Int. Arch. Photogramm. Remote Sens. 1991, 28, 419-424
54. Campbell, J.B.; Wynne, R.H. Introduction to Remote Sensing; Guilford Press: New York, NY, USA, 1987; ISBN 9780898627763
55. R Development Core Team. R: A Language and Environment for Statistical Computing. Available online: http://www.r-project.org (accessed on 2 March 2017)
56. Willighagen, E.; Ballings, M. Genalg: R Based Genetic Algorithm. Available online: https://cran.r-project. org/web/packages/genalg/index.html (accessed on 2 March 2017)
57. Garey, M.R.; Johnson, D.S. Computers and Intractability: A Guide to the Theory of NP-Completeness; W. H. Freeman & Co.: New York, NY, USA, 1979; ISBN 0716710455
58. Broadhurst, D.; Goodacre, R.; Jones, A.; Rowland, J.J.; Kell, D.B. Genetic algorithms as a method for variable selection in multiple linear regression and partial least squares regression, with applications to pyrolysis mass spectrometry. Anal. Chim. Acta 1997, 348, 71-86
59. Moser, P.; Vibrans, A.C.; McRoberts, R.E.; Næsset, E.; Gobakken, T.; Chirici, G.; Mura, M.; Marchetti, M. Methods for variable selection in LiDAR-assisted forest inventories. Forestry 2017, 90, 112-124
60. Tuominen, S.; Balazs, A.; Honkavaara, E.; Pölönen, I.; Saari, H.; Hakala, T.; Viljanen, N. Hyperspectral UAV-Imagery and photogrammetric canopy height model in estimating forest stand variables. Silva Fenn. 2017, 51
61. Breiman, L.E.O. Random forests. Mach. Learn. 2001, 45, 5-32
62. Stephens, T. Titanic: Getting Started With R-Part 5: Random Forests. Available online: http://trevorstephens.com/kaggle-titanic-tutorial/r-part-5-random-forests/(accessed on 14 March 2017)
63. Hovi, A.; Raitio, P.; Rautiainen, M. A spectral analysis of 25 boreal tree species. Silva Fenn. 2017, 51
64. Ballanti, L.; Blesius, L.; Hines, E.; Kruse, B. Tree species classification using hyperspectral imagery: A comparison of two classifiers. Remote Sens. 2016, 8, 445
65. Kim, S.; McGaughey, R.J.; Andersen, H.-E.; Schreuder, G. Tree species differentiation using intensity data derived from leaf-on and leaf-offairborne laser scanner data. Remote Sens. Environ. 2009, 113, 1575-1586
66. Zhang, C.; Qiu, F. Mapping individual tree species in an urban forest using airborne LiDAR data and hyperspectral imagery. Photogramm. Eng. Remote Sens. 2012, 78, 1079-1087
67. Somers, B.; Asner, G.P. Hyperspectral time series analysis of native and invasive species in Hawaiian rainforests. Remote Sens. 2012, 4, 2510-2529
68. Mickelson, J.G.; Civco, D.L.; Silander, J.A. Delineating forest canopy species in the northeastern United States using multi-temporal TM imagery. Photogramm. Eng. Remote Sens. 1998, 64, 891-904