Skip to main content
SpringerLink
Log in

Tree Species Recognition Based on Airborne Laser Scanning and Complementary Data Sources

  • Chapter
  • First Online:
Forestry Applications of Airborne Laser Scanning

Part of the book series: Managing Forest Ecosystems ((MAFE,volume 27))

Abstract

Species-specific information is important for many tasks related to forest management. We review the use of airborne laser scanning (ALS) and complementary data for providing this information. The main ALS-based information is related to structural features, intensity of the echoes, and waveform parameters, whereas spectral information may be provided by fusing data from different sensors. Various types of classifiers are applied, the current emphasis being in non-linear or otherwise complex techniques. The results are successful with respect to the main species, whereas the overall accuracy depends on the desired level of detail in the classification. We expect fusion approaches combining ALS and especially hyperspectral data to become more common and further improvements by the development of advanced sensor technology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Ahokas E, Kaasalainen S, Hyyppä J, Suomalainen J (2006) Calibration of the Optech ALTM 3100 laser scanner intensity data using brightness targets. Int Arch Photogramm Remote Sens Spat Inf Sci XXXVI, 7 p

    Google Scholar 

  • Asner GP (2013) Biological diversity mapping comes of age. Editorial to the special issue “Remote Sensing of Biological Diversity”. Remote Sens 5:374–376

    Article  Google Scholar 

  • Asner GP, Knapp DE, Kennedy-Bowdoin T, Jones MO, Martin RE, Boardman J, Field CB (2007) Carnegie Airborne Observatory: in-flight fusion of hyperspectral imaging and waveform light detection and ranging for three-dimensional studies of ecosystems. J Appl Remote Sens 1:1–21

    Google Scholar 

  • Asner GP, Knapp DE, Boardman J, Green RO, Kennedy-Bowdoin T, Eastwood M, Martin RE, Anderson C, Field CB (2012) Carnegie Airborne Observatory-2: increasing science data dimensionality via high-fidelity multi-sensor fusion. Remote Sens Environ 124:454–465

    Article  Google Scholar 

  • Barilotti A, Crosilla F, Sepic F (2009) Curvature analysis of lidar data for single tree species classification in alpine latitude forests. Laser scanning 2009. Int Arch Photogramm Remote Sens XXXVIII:129–134

    Google Scholar 

  • Bishop CM (2006) Pattern recognition and machine learning. Springer, New York. ISBN 0-387-31073-8

    Google Scholar 

  • Brandtberg T (2007) Classifying individual tree species under leaf-off and leaf-on conditions using airborne lidar. ISPRS J Photogramm Remote Sens 61:325–340

    Article  Google Scholar 

  • Brandtberg T, Warner TA, Landenberger RE, McGraw JB (2003) Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density lidar data from the eastern deciduous forest in North America. Remote Sens Environ 85:290–303

    Article  Google Scholar 

  • Chen CH, Peter Ho PG (2008) Statistical pattern recognition in remote sensing. Pattern Recognit 41:2731–2741

    Article  Google Scholar 

  • Cho MA, Skidmore AK (2006) A new technique for extracting the red edge position from hyperspectral data: the linear extrapolation method. Remote Sens Environ 101:181–193

    Article  Google Scholar 

  • Collings S, Caccetta P, Campbell N, Wu XL (2011) Empirical models for radiometric calibration of digital aerial frame mosaics. IEEE Trans Geosci Remote Sens 49:2573–2588

    Article  Google Scholar 

  • Coren F, Sterzai P (2006) Radiometric correction in laser scanning. Int J Remote Sens 27:3097–3104

    Article  Google Scholar 

  • Dalponte M, Bruzzone L, Gianelle D (2008) Fusion of hyperspectral and LIDAR remote sensing data for the classification of complex forest areas. IEEE Trans Geosci Remote Sens 46:1416–1427

    Article  Google Scholar 

  • Dalponte M, Bruzzone L, Gianelle D (2012) Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data. Remote Sens Environ 123:258–270

    Article  Google Scholar 

  • Dalponte M, Ørka HO, Gobakken T, Gianelle D, Næsset E (2013) Tree species classification in boreal forests with hyperspectral data. IEEE Trans Geosci Remote Sens 51:2632–2645

    Article  Google Scholar 

  • Dalponte M, Ørka HO, Ene LT, Gobakken T, Næsset E (2014) Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data. Remote Sens Environ 140:306–317

    Article  Google Scholar 

  • Donoghue DNM, Watt PJ, Cox NJ, Wilson J (2007) Remote sensing of species mixtures in conifer plantations using LiDAR height and intensity data. Remote Sens Environ 110:509–522

    Article  Google Scholar 

  • Edelsbrunner H, Mücke EP (1994) Three-dimensional alpha shapes. ACM Trans Graph 13:43–72

    Article  Google Scholar 

  • Fukunaga K (1990) Introduction to statistical pattern recognition, 2nd edn. Academic, San Diego

    Google Scholar 

  • Gokcen I, Peng J (2002) Comparing linear discriminant analysis and support vector machines. Lect Notes Comput Sci 2457:104–113

    Article  Google Scholar 

  • Haralick RM, Shanmugam K, Dinstein J (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 3:610–621

    Article  Google Scholar 

  • Heikkinen V, Tokola T, Parkkinen J, Korpela I, Jääskelainen T (2010) Simulated multispectral imagery for tree species classification using support vector machines. IEEE Trans Geosci Remote Sens 48:1355–1364

    Article  Google Scholar 

  • Heinzel J, Koch B (2011) Exploring full-waveform LiDAR parameters for tree species classification. Int J Appl Earth Obs Geoinf 13:152–160

    Article  Google Scholar 

  • Heinzel J, Koch B (2012) Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation. Int J Appl Earth Obs Geoinf 18:101–110

    Article  Google Scholar 

  • Heinzel J, Ronneberger O, Koch B (2010) A comparison of support vector and linear classification of tree species. In: Silvilaser 2010, Freiburg, Freiburg, Germany, pp 377–384

    Google Scholar 

  • Hill RA, Thomson AG (2005) Mapping woodland species composition and structure using airborne spectral and LiDAR data. Int J Remote Sens 26:3763–3779

    Article  Google Scholar 

  • Höfle B, Pfeifer N (2007) Correction of laser scanning intensity data: data and model-driven approaches. ISPRS J Photogramm Remote Sens 62:415–433

    Article  Google Scholar 

  • Hollaus M, Mücke W, Höfle B, Dorigo W, Pfeifer N, Wagner W, Bauerhansl C, Regner B (2009) Tree species classification based on full-waveform airborne laser scanning data. In: Silvilaser 2009, College Station, TX, USA, pp 54–62

    Google Scholar 

  • Holmgren J, Persson Å (2004) Identifying species of individual trees using airborne laser scanner. Remote Sens Environ 90:415–423

    Article  Google Scholar 

  • Holmgren J, Persson Å, Söderman U (2008) Species identification of individual trees by combining high resolution LIDAR data with multi-spectral images. Int J Remote Sens 29:1537–1552

    Article  Google Scholar 

  • Hudak AT, Crookston NL, Evans JS, Hall DE, Falkowski MJ (2008) Nearest neighbor imputation of species-level, plot-scale forest structure attributes from LiDAR data. Remote Sens Environ 112:2232–2245

    Article  Google Scholar 

  • Hughes JS, Evans DL, Burns PY, Hill JM (1986) Identification of 2 southern pine species in high-resolution aerial MSS data. Photogramm Eng Remote Sens 52:1175–1180

    Google Scholar 

  • Jones TG, Coops NC, Sharma T (2010) Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada. Remote Sens Environ 114:2841–2852

    Article  Google Scholar 

  • Kim S, McGaughey RJ, Andersen HE, Schreuder G (2009) Tree species differentiation using intensity data derived from leaf-on and leaf-off airborne laser scanner data. Remote Sens Environ 113:1575–1586

    Article  Google Scholar 

  • Korpela IS (2008) Mapping of understory lichens with airborne discrete-return LiDAR data. Remote Sens Environ 112:3891–3897

    Article  Google Scholar 

  • Korpela I, Tokola T (2006) Potential of aerial image-based monoscopic and multiview single-tree forest inventory: a simulation approach. For Sci 52:136–147

    Google Scholar 

  • Korpela I, Ørka HO, Hyyppä J, Heikkinen V, Tokola T (2010a) Range and AGC normalization in airborne discrete-return LiDAR intensity data for forest canopies. ISPRS J Photogramm Remote Sens 65:369–379

    Article  Google Scholar 

  • Korpela I, Ørka HO, Maltamo M, Tokola T, Hyyppä J (2010b) Tree species classification using airborne LiDAR – effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type. Silva Fenn 44:319–339

    Article  Google Scholar 

  • Koukoulas S, Blackburn GA (2005) Mapping individual tree location, height and species in broadleaved deciduous forest using airborne LIDAR and multi-spectral remotely sensed data. Int J Remote Sens 26:431–455

    Article  Google Scholar 

  • Liang X, Hyyppä J, Matikainen L (2007) Deciduous-coniferous tree classification using difference between first and last pulse laser signatures. Int Arch Photogramm Remote Sens XXXVI:253–257

    Google Scholar 

  • Maltamo M, Packalén P, Kallio E, Kangas J, Uuttera J, Heikkilä J (2011) Airborne laser scanning based stand level management inventory in Finland. In: SilviLaser 2011, Hobart, Australia

    Google Scholar 

  • Moffiet T, Mengersen K, Witte C, King R, Denham R (2005) Airborne laser scanning: exploratory data analysis indicates potential variables for classification of individual trees or forest stands according to species. ISPRS J Photogramm Remote Sens 59:289–309

    Article  Google Scholar 

  • Morsdorf F, Nichol C, Malthus T, Woodhouse IH (2009) Assessing forest structural and physiological information content of multi-spectral LiDAR waveforms by radiative transfer modelling. Remote Sens Environ 113:2152–2163

    Article  Google Scholar 

  • Næsset E (2002) Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens Environ 80:88–99

    Article  Google Scholar 

  • Naidoo L, Cho M, Mathieu R, Asner G (2012) Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment. ISPRS J Photogramm Remote Sens 69:167–179

    Article  Google Scholar 

  • Niccolai A, Niccolai M, Oliver CD (2010) Point set topology extraction for branch and crown-level species classification. Photogramm Eng Remote Sens 76:319–330

    Article  Google Scholar 

  • Niska H, Skön JP, Packalén P, Tokola T, Maltamo M, Kolehmainen M (2010) Neural networks for the prediction of species-specific stem volumes using airborne laser scanning and aerial photographs. IEEE Trans Geosci Remote Sens 48:1076–1085

    Article  Google Scholar 

  • Olofsson K, Wallerman J, Holmgren J, Olsson H (2006) Tree species discrimination using Z/I DMC imagery and template matching of single trees. Scand J For Res 21:106–110

    Article  Google Scholar 

  • Ørka HO, Næsset E, Bollandsås OM (2009a) Classifying species of individual trees by intensity and structure features derived from airborne laser scanner data. Remote Sens Environ 113:1163–1174

    Article  Google Scholar 

  • Ørka HO, Næsset E, Bollandsås OM (2009b) Comparing classification strategies for tree species recognition using airborne laser scanner data. In: Silvilaser 2009, College Station, TX, USA, pp 46–53

    Google Scholar 

  • Ørka HO, Næsset E, Bollandsås OM (2010) Effects of different sensors and leaf-on and leaf-off canopy conditions on echo distributions and individual tree properties derived from airborne laser scanning. Remote Sens Environ 114:1445–1461

    Article  Google Scholar 

  • Ørka HO, Gobakken T, Næsset E, Ene L, Lien V (2012) Simultaneously acquired airborne laser scanning and multispectral imagery for individual tree species identification. Can J Remote Sens 38:125–138

    Article  Google Scholar 

  • Ørka HO, Dalponte M, Gobakken T, Næsset E, Ene LT (2013) Characterizing forest species composition using multiple remote sensing data sources and inventory approaches. Scand J For Res 28:677–688

    Article  Google Scholar 

  • Packalén P, Maltamo M (2006) Predicting the plot volume by tree species using airborne laser scanning and aerial photographs. For Sci 52:611–622

    Google Scholar 

  • Packalén P, Maltamo M (2007) The k-MSN method for the prediction of species specific stand attributes using airborne laser scanning and aerial photographs. Remote Sens Environ 109:328–341

    Article  Google Scholar 

  • Packalén P, Maltamo M (2008) Estimation of species-specific diameter distributions using airborne laser scanning and aerial photographs. Can J For Res 38:1750–1760

    Article  Google Scholar 

  • Packalén P, Suvanto A, Maltamo M (2009) A two stage method to estimate species-specific growing stock. Photogramm Eng Remote Sens 75:1451–1460

    Article  Google Scholar 

  • Persson Å, Holmgren J, Söderman U, Olsson H (2004) Tree species classification of individual trees in Sweden by combining high resolution laser data with high resolution near-infrared digital images. ISPRS working group VIII/2, pp 204–207

    Google Scholar 

  • Puttonen E, Suomalainen J, Hakala T, Räikkonen E, Kaartinen H, Kaasalainen S, Litkey P (2010) Tree species classification from fused active hyperspectral reflectance and LIDAR measurements. For Ecol Manage 260:1843–1852

    Article  Google Scholar 

  • Reitberger J, Krzystek P, Stilla U (2006) Analysis of full waveform lidar data for tree species classification. Int Arch Photogramm Remote Sens XXXVI/3, 6 p

    Google Scholar 

  • Reitberger J, Krzystek P, Stilla U (2008) Analysis of full waveform LIDAR data for the classification of deciduous and coniferous trees. Int J Remote Sens 29:1407–1431

    Article  Google Scholar 

  • Sarrazin MJD, van Aardt JAN, Asner GP, McGlinchy J, Messinger DW, Wu J (2012) Fusing small-footprint waveform LiDAR and hyperspectral data for canopy-level species classification and herbaceous biomass modeling in savanna ecosystems. Can J Remote Sens 37:653–665

    Article  Google Scholar 

  • Sato M, Koike T (2003) Classification of tree types by polarimetric Pi-SAR. IEEE Int Geosci Remote Sens Symp 1:431–433

    Google Scholar 

  • Säynäjoki R, Packalén P, Maltamo M, Vehmas M, Eerikäinen K (2008) Detection of aspens using high resolution aerial laser scanning data and digital aerial images. Sensors 8:5037–5054

    Article  PubMed Central  Google Scholar 

  • Schreier H, Lougheed J, Tucker C, Leckie D (1985) Automated measurements of terrain reflection and height variations using an airborne infrared laser system. Int J Remote Sens 6:101–113

    Article  Google Scholar 

  • Suratno A, Seielstad C, Queen L (2009) Tree species identification in mixed coniferous forest using airborne laser scanning. ISPRS J Photogramm Remote Sens 64:683–693

    Article  Google Scholar 

  • Tan SX, Narayanan RM (2004) Design and performance of a multiwavelength airborne polarimetric lidar for vegetation remote sensing. Appl Opt 43:2360–2368

    Article  PubMed  Google Scholar 

  • Theodoridis S, Koutroumbas K (2009) Pattern recognition, 4th edn. Academic, Burlington. ISBN 978-1-59749-272-0

    Google Scholar 

  • Törmä M (2000) Estimation of tree species proportions of forest stands using laser scanning. Int Arch Photogramm Remote Sens XXXIII:1524–1531

    Google Scholar 

  • van Aardt JAN, Wynne RH, Scrivani JA (2008) Lidar-based mapping of forest volume and biomass by taxonomic group using structurally homogenous segments. Photogramm Eng Remote Sens 74:1033–1044

    Article  Google Scholar 

  • Vaughn NR, Moskal LM, Turnblom EC (2012) Tree species detection accuracies using discrete point lidar and airborne waveform lidar. Remote Sens 4:377–403

    Article  Google Scholar 

  • Vauhkonen J, Tokola T, Maltamo M, Packalén P (2008) Effects of pulse density on predicting characteristics of individual trees of Scandinavian commercial species using alpha shape metrics based on airborne laser scanning data. Can J Remote Sens 34:S441–S459

    Article  Google Scholar 

  • Vauhkonen J, Tokola T, Packalén P, Maltamo M (2009) Identification of Scandinavian commercial species of individual trees from airborne laser scanning data using alpha shape metrics. For Sci 55:37–47

    Google Scholar 

  • Vauhkonen J, Korpela I, Maltamo M, Tokola T (2010) Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics. Remote Sens Environ 114:1263–1276

    Article  Google Scholar 

  • Vauhkonen J, Packalén P, Pitkänen J (2011) Airborne laser scanning-based stem volume imputation in a managed, boreal forest area: a comparison of estimation units. In: SilviLaser 2011, Hobart, Australia

    Google Scholar 

  • Vauhkonen J, Seppänen A, Packalén P, Tokola T (2012) Improving species-specific plot volume estimates based on airborne laser scanning and image data using alpha shape metrics and balanced field data. Remote Sens Environ 124:534–541

    Article  Google Scholar 

  • Vauhkonen J, Hakala T, Suomalainen J, Kaasalainen S, Nevalainen O, Vastaranta M, Holopainen M, Hyyppä J (2013) Classification of spruce and pine trees using active hyperspectral LiDAR. IEEE Geosci Remote Sens Lett 10:1138–1141

    Article  Google Scholar 

  • Vauhkonen J, Packalen P, Malinen J, Pitkänen J, Maltamo M (2014) Airborne laser scanning based decision support for wood procurement planning. Scand J For Res. doi:10.1080/02827581.2013.813063

    Google Scholar 

  • Villikka M, Packalén P, Maltamo M (2012) The suitability of leaf-off airborne laser scanning data in an area-based forest inventory of coniferous and deciduous trees. Silva Fenn 46:99–110

    Article  Google Scholar 

  • Wagner W, Ullrich A, Ducic V, Melzer T, Studnicka N (2006) Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner. ISPRS J Photogramm Remote Sens 60:100–112

    Article  Google Scholar 

  • Wehr A, Lohr U (1999) Airborne laser scanning – an introduction and overview. ISPRS J Photogramm Remote Sens 54:68–82

    Article  Google Scholar 

  • Wolf JA, Fricker GA, Meyer V, Hubbell SP, Gillespie TW, Saatchi SS (2012) Plant species richness is associated with canopy height and topography in a neotropical forest. Remote Sens 4:4010–4021

    Article  Google Scholar 

  • Yao W, Krzystek P, Heurich M (2012) Tree species classification and estimation of stem volume and DBH based on single tree extraction by exploiting airborne full-waveform LiDAR data. Remote Sens Environ 123:368–380

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Forest Sciences, University of Helsinki, Helsinki, Finland

    Jari Vauhkonen

  2. Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway

    Hans Ole Ørka

  3. Department of Forest Resource Management, Swedish University of Agricultural Sciences, Umeå, Sweden

    Johan Holmgren

  4. Department of Sustainable Agro-Ecosystems and Bioresources, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all’Adige, Italy

    Michele Dalponte

  5. Department of Remote Sensing and Landscape Information Systems, University of Freiburg, Freiburg, Germany

    Johannes Heinzel & Barbara Koch

  6. European Commission – Joint Research Centre, Institute for the Protection and Security of the Citizen, Global Security and Crisis Management, Ispra, Italy

    Johannes Heinzel

Authors
  1. Jari Vauhkonen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hans Ole Ørka
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Johan Holmgren
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michele Dalponte
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Johannes Heinzel
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Barbara Koch
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jari Vauhkonen .

Editor information

Editors and Affiliations

  1. School of Forest Sciences, University of Eastern Finland, Joensuu, Finland

    Matti Maltamo

  2. Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, Ås, Norway

    Erik Næsset

  3. Department of Forest Sciences, University of Helsinki, Helsinki, Finland

    Jari Vauhkonen

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Vauhkonen, J., Ørka, H.O., Holmgren, J., Dalponte, M., Heinzel, J., Koch, B. (2014). Tree Species Recognition Based on Airborne Laser Scanning and Complementary Data Sources. In: Maltamo, M., Næsset, E., Vauhkonen, J. (eds) Forestry Applications of Airborne Laser Scanning. Managing Forest Ecosystems, vol 27. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8663-8_7

Download citation

Publish with us

Policies and ethics

Search

Navigation