Skip to main content

Integrating Airborne Laser Scanning with Data from Global Navigation Satellite Systems and Optical Sensors

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

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

Abstract

Most forestry applications of airborne laser scanning (ALS) require simultaneous use of various data sources. This chapter covers a number of common issues that practitioners face when dealing with data fusion schemes. The first subsection points out the objectives that may be pursued when integrating different data sources, and the benefits that can be obtained from using diverse remote sensors onboard differing platforms. The next subsections are devoted to the two data sources that usually pose most problems in their spatial co-registration with ALS datasets: field inventory and aerial photographs. All data sources ultimately rely on global navigation satellite systems (GNSS) which are especially error-prone when operating under forest canopies. Positioning methods and spatial accuracy assessment applied to forest plot and individual tree surveying are presented, also including terrestrial laser scanning (TLS). Furthermore, procedures for digital elevation model (DEM) generation are reviewed in the context of their use in orthorectification, which is the most widespread method for fusion of ALS with optical sensors. Drawbacks of using orthophotos are identified, therefore suggesting alternatives: true-orthorectification, back-projecting ALS and image matching.

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

Access this chapter

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

Notes

  1. 1.

    More precisely denominated camera constant as most cameras consist of a system of compound lenses, though the collinearity model simplifies it to a pin-point exposure of a single lens with equivalent focal distance.

References

  • Andersen H, Clarkin T, Winterberger K, Strunk J (2009) An accuracy assessment of positions obtained using survey- and recreational-grade global positioning system receivers across a range of forest conditions within the Tanana valley of interior Alaska. West J Appl For 24:128–136

    Google Scholar 

  • Andersen H, Strunk J, Temesgen H, Atwood D, Winterberger K (2011) Using multilevel remote sensing and ground data to estimate forest biomass resources in remote regions: a case study in the boreal forests of interior Alaska. Can J Remote Sens 37:596–611

    Article  Google Scholar 

  • Armenakis C, Gao Y, Sohn G (2010) Co-registration of lidar and photogrammetric data for updating building databases. ISPRS Arch 36, Part 4–8–2–W9:96–100

    Google Scholar 

  • Arslan N, Demirel H (2008) The impact of temporal ionospheric gradients in Northern Europe on relative GPS positioning. J Atmos Sol-Terr Phys 70:1382–1400

    Article  Google Scholar 

  • Asner GP, Mascaro J, Anderson C, Knapp DE, Martin RE, Kennedy-Bowdoin T, van Breugel M, Davies S, Hall JS, Muller-Landau HC, Potvin C, Sousa W, Wright J, Bermingham E (2013) High-fidelity national carbon mapping for resource management and REDD+. Carbon Balance Manage 8(1):art.7

    Google Scholar 

  • Avitabile V, Baccini A, Friedl MA, Schmullius C (2012) Capabilities and limitations of Landsat and land cover data for aboveground woody biomass estimation of Uganda. Remote Sens Environ 117:366–380

    Article  Google Scholar 

  • Axelsson P (1999) Processing of laser scanner data—algorithms and applications. ISPRS J Photogramm Remote Sens 54:138–147

    Article  Google Scholar 

  • Axelsson P (2000) DEM generation from laser scanner data using adaptive TIN models. Int Arch Photogramm Remote Sens 33(Part B4):110–117

    Google Scholar 

  • Baltsavias EP (1999) A comparison between photogrammetry and laser scanning. ISPRS J Photogramm Remote Sens 54:83–94

    Article  Google Scholar 

  • Baltsavias EP, Käser C (1998) DTM and orthoimage generation—a thorough analysis and comparison of four digital photogrammetric systems. Int Arch Photogramm Remote Sens 32:42–51

    Google Scholar 

  • Bright BC, Hicke JA, Hudak AT (2012) Estimating aboveground carbon stocks of a forest affected by mountain pine beetle in Idaho using lidar and multispectral imagery. Remote Sens Environ 124:270–281

    Article  Google Scholar 

  • Deckert C, Bolstad PV (1996) Forest canopy, terrain, and distance effects on global positioning system point accuracy. Photogramm Eng Remote Sens 62:317–321

    Google Scholar 

  • Cohen WB, Yang Z, Kennedy R (2010) Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync – Tools for calibration and validation. Remote Sens Environ 114:2911–2924

    Article  Google Scholar 

  • d’Oliveira MVN, Reutebuch SE, McGaughey RJ, Andersen H (2012) Estimating forest biomass and identifying low-intensity logging areas using airborne scanning lidar in Antimary State Forest, Acre State, Western Brazilian Amazon. Remote Sens Environ 124:479–491

    Article  Google Scholar 

  • Dorigo W, Hollaus M, Wagner W, Schadauer K (2010) An application-oriented automated approach for co-registration of forest inventory and airborne laser scanning data. Int J Remote Sens 31:1133–1153

    Article  Google Scholar 

  • Elmstrom MD, Smith PW, Abidi MA (1998) Stereo-based registration of Ladar and color imagery. P Soc Photo-Opt Ins, pp 343–354

    Google Scholar 

  • Forkuo EK, King B (2005) Automatic fusion of photogrammetric imagery and laser scanner point clouds. Int Arch Photogramm Remote Sens 35:921–926

    Google Scholar 

  • Forsman M, Börlin N, Holmgren J (2012) Estimation of tree stem attributes using terrestrial photogrammetry. Int Arch Photogramm Remote Sens Spat Inf Sci XXXIX-B5:261–265

    Article  Google Scholar 

  • Frazer GW, Magnussen S, Wulder MA, Niemann KO (2011) Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR-derived estimates of forest stand biomass. Remote Sens Environ 115:636–649

    Article  Google Scholar 

  • Fritz A, Weinacker H, Koch B (2011) A method for linking TLS- and ALS-derived trees. SilviLaser 2011, University of Tasmania, Hobart, Australia

    Google Scholar 

  • Gatziolis D, Fried JS, Monleon VS (2010) Challenges to estimating tree height via lidar in closed-canopy forests: a parable from Western Oregon. For Sci 56:139–155

    Google Scholar 

  • Gobakken T, Næsset E (2009) Assessing effects of positioning errors and sample plot size on biophysical stand properties derived from airborne laser scanner data. Can J For Res 39:1036–1052

    Article  Google Scholar 

  • Habrich H, Gurtner W, Rothacher M (1999) Processing of GLONASS and combined GLONASS/GPS observations. Adv Space Res 23:655–658

    Article  Google Scholar 

  • Hasegawa H, Yoshimura T (2007) Estimation of GPS positional accuracy under different forest conditions using signal interruption probability. J For Res 12:1–7

    Article  Google Scholar 

  • Henning JG, Radtke PJ (2006) Detailed stem measurements of standing trees from ground-based scanning lidar. For Sci 52(1):67–80

    Google Scholar 

  • Hilker T, van Leeuwen M, Coops N, Wulder M, Newnham G, Jupp D, Culvenor D (2010) Comparing canopy metrics derived from terrestrial and airborne laser scanning in a Douglas-fir dominated forest stand. Trees-Struct Func 24:819–832

    Article  Google Scholar 

  • Hilker T, Coops NC, Culvenor DS, Newnham G, Wulder MA, Bater CW, Siggins A (2012) A simple technique for co-registration of terrestrial LiDAR observations for forestry applications. Remote Sens Lett 3:239–247

    Article  Google Scholar 

  • Hodgson ME, Bresnahan P (2004) Accuracy of airborne lidar-derived elevation: empirical assessment and error budget. Photogramm Eng Remote Sens 70:331–339

    Article  Google Scholar 

  • Hollaus M, Wagner W, Eberhöfer C, Karel W (2006) Accuracy of large-scale canopy heights derived from LiDAR data under operational constraints in a complex alpine environment. ISPRS J Photogramm Remote Sens 60:323–338

    Article  Google Scholar 

  • Holmgren J, Persson A, Soderman 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 

  • Holmgren J, Barth A, Larsson H, Olsson H (2012) Prediction of stem attributes by combining airborne laser scanning and measurements from harvesters. Silva Fenn 46(2):227–239

    Article  Google Scholar 

  • Honkavaara E, Ahokas E, Hyyppä J, Jaakkola J, Kaartinen H, Kuittinen R, Markelin L, Nurminen K (2006) Geometric test field calibration of digital photogrammetric sensors. ISPRS J Photogramm Remote Sens 60:387–399

    Article  Google Scholar 

  • Korpela I, Tuomola T, Välimäki E (2007) Mapping forest plots: an efficient method combining photogrammetry and field triangulation. Silva Fenn 41:457–469

    Article  Google Scholar 

  • Kraus K, Pfeifer N (1998) Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS J Photogramm Remote Sens 53:193–203

    Article  Google Scholar 

  • Küchler M, Ecker K, Feldmeyer-Christe E, Graf U, Küchler H, Waser LT (2004) Combining remotely sensed spectral data and digital surface models for fine-scale modelling of mire ecosystems. Comm Ecol 5:55–68

    Article  Google Scholar 

  • Lämås T (2010) The Haglöf PosTex ultrasound instrument for the positioning of objects on forest sample plots. SLU Arbetsrapport 296, Skoglig resurshushållning. Umeå. Sweden

    Google Scholar 

  • Leckie DG, Gougeon FA, Tinis S, Nelson T, Burnett CN, Paradine D (2005) Automated tree recognition in old growth conifer stands with high resolution digital imagery. Remote Sens Environ 94:311–326

    Article  Google Scholar 

  • Leppänen VJ, Tokola T, Maltamo M, Mehtätalo L, Pusa T, Mustonen J (2008) Automatic delineation of forest stands from lidar data. In: Hay GJ, Blaschke T, Marceau D (eds) GEOBIA 2008 – Pixels, Objects, Intelligence. GEOgraphic Object Based Image Analysis for the 21st century, Calgary, Alberta, Canada

    Google Scholar 

  • Lindberg E, Holmgren J, Olofsson K, Olsson H (2012) Estimation of stem attributes using a combination of terrestrial and airborne laser scanning. Eur J For Res 131:1–15

    Google Scholar 

  • Liu CJ, Brantigan RD (1995) Using differential GPS for forest traverse surveys. Can J For Res 25:1795–1805

    Article  Google Scholar 

  • Liu X, Zhang Z, Peterson J, Chandra S (2007) LiDAR-derived high quality ground control information and DEM for image orthorectification. Geoinformatica 11:37–53

    Article  Google Scholar 

  • Lovell JL, Jupp DLB, Culvenor DS, Coops NC (2003) Using airborne and ground-based ranging lidar to measure canopy structure in Australian forests. Can J Remote Sens 29:607–622

    Article  Google Scholar 

  • Maas HG, Bienert A, Scheller S, Keane E (2008) Automatic forest inventory parameter determination from terrestrial laser scanner data. Int J Remote Sens 29:1579–1593

    Article  Google Scholar 

  • Mauro F, Valbuena R, Manzanera JA, García-Abril A (2011) Influence of Global Navigation Satellite System errors in positioning inventory plots for tree-height distribution studies. Can J For Res 41:11–23

    Article  Google Scholar 

  • McInerney DO, Suárez-Mínguez J, Valbuena R, Nieuwenhuis M (2010) Forest canopy height retrieval using LiDAR data, medium-resolution satellite imagery and kNN estimation in Aberfoyle, Scotland. Forestry 83:195–206

    Article  Google Scholar 

  • Næsset E (1999) Point accuracy of combined pseudorange and carrier phase differential GPS under forest canopy. Can J For Res 29:547–553

    Article  Google Scholar 

  • Næsset E (2001) Effects of differential single- and dual-frequency GPS and GLONASS observations on point accuracy under forest canopies. Photogramm Eng Remote Sens 67:1021–1026

    Google Scholar 

  • Næsset E (2004) Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scand J For Res 19:164–179

    Article  Google Scholar 

  • Næsset E (2009) Effects of different sensors, flying altitudes, and pulse repetition frequencies on forest canopy metrics and biophysical stand properties derived from small-footprint airborne laser data. Remote Sens Environ 113:148–159

    Article  Google Scholar 

  • Næsset E, Gjevestad JG (2008) Performance of GPS precise point positioning under conifer forest canopies. Photogramm Eng Remote Sens 74:661–668

    Article  Google Scholar 

  • Næsset E, Jonmeister T (2002) Assessing point accuracy of DGPS under forest canopy before data acquisition, in the field and after postprocessing. Scand J For Res 17:351–358

    Article  Google Scholar 

  • Næsset E, Bjerke T, Ovstedal O, Ryan LH (2000) Contributions of differential GPS and GLONASS observations to point accuracy under forest canopies. Photogramm Eng Remote Sens 66:403–407

    Google Scholar 

  • Olofsson K, Lindberg E, Holmgren J (2008) A method for linking field-surveyed and aerial-detected single trees using cross correlation of position images and the optimization of weighted tree list graphs. SilviLaser 2008, Edinburgh, UK

    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 

  • 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, 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 A, 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. Int Arch Photogramm Remote Sens XXXVI(W2):204–207

    Google Scholar 

  • Reutebuch SE, McGaughey RJ, Andersen HE, Carson WW (2003) Accuracy of a high-resolution LIDAR terrain model under a conifer forest canopy. Can J Remote Sens 29:527–535

    Article  Google Scholar 

  • Schenk T (1999) Digital photogrammetry. TerraScience, Laurelville

    Google Scholar 

  • Schickler W, Thorpe A (1998) Operational procedure for automatic true orthophoto generation. Int Arch Photogramm Remote Sens 32:527–532

    Google Scholar 

  • Sigrist P, Coppin P, Hermy M (1999) Impact of forest canopy on quality anti accuracy of GPS measurements. Int J Remote Sens 20:3595–3610

    Article  Google Scholar 

  • Sithole G, Vosselman G (2004) Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds. ISPRS J Photogramm Remote Sens 59:85–101

    Article  Google Scholar 

  • Solberg S, Næsset E, Lange H, Bollandsås OM (2004) Remote sensing of forest health. ISPRS Arch XXXVI, 8–W2:161

    Google Scholar 

  • St-Onge BA (2008) Methods for improving the quality of a true orthomosaic of Vexcel UltraCam images created using a lidar digital surface model. SilviLaser 2008, Edinburgh, UK

    Google Scholar 

  • St-Onge A, Achaichia N (2001) Measuring forest canopy height using a combination of LIDAR and aerial photography data. Int Arch Photogramm Remote Sens 34:131–137

    Google Scholar 

  • Tachiki Y, Yoshimura T, Hasegawa H, Mita T, Sakai T, Nakamura F (2005) Effects of polyline simplification of dynamic GPS data under forest canopy on area and perimeter estimations. J For Res 10:419–427

    Article  Google Scholar 

  • Tuominen S, Pekkarinen A (2004) Local radiometric correction of digital aerial photographs for multi source forest inventory. Remote Sens Environ 89:72–82

    Article  Google Scholar 

  • Valbuena R, Fernández de Sevilla T, Mauro F, Pascual C, García-Abril A, Martín-Fernández S, Manzanera JA (2008) Lidar and true-orthorectification of infrared aerial imagery of high Pinus sylvestris forest in mountainous relief. SilviLaser 2008, Edinburgh, UK

    Google Scholar 

  • Valbuena R, Mauro F, Rodríguez-Solano R, Manzanera JA (2010) Accuracy and precision of GPS receivers under forest canopies in a mountainous environment. Span J Agric Res 8:1047–1057

    Article  Google Scholar 

  • Valbuena R, Mauro F, Arjonilla FJ, Manzanera JA (2011) Comparing airborne laser scanning-imagery fusion methods based on geometric accuracy in forested areas. Remote Sens Environ 115:1942–1954

    Article  Google Scholar 

  • Valbuena R, Mauro F, Rodriguez-Solano R, Manzanera JA (2012) Partial least squares for discriminating variance components in global navigation satellite systems accuracy obtained under Scots pine canopies. For Sci 58:139–153

    Google Scholar 

  • Valbuena R, De Blas A, Martín Fernández S, Maltamo M, Nabuurs GJ, Manzanera JA (2013) Within-species benefits of back-projecting laser scanner and multispectral sensors in monospecific Pinus sylvestris forests. Eur J Remote Sens 46:401–416

    Google Scholar 

  • Vauhkonen J, Ene I, Gupta S, Heinzel J, Holmgren J, Pitkänen J, Solberg S, Wang Y, Weinacker H, Hauglin KM, Lien V, Packalén P, Gobakken T, Koch B, Næsset E, Tokola T, Maltamo M (2012) Comparative testing of single-tree detection algorithms under different types of forest. Forestry 85:27–40

    Article  Google Scholar 

  • Waser LT, Baltsavias EP, Ecker K, Eisenbeiss H, Feldmeyer-Christe E, Ginzler C, Küchler M, Zhang L (2008) Assessing changes of forest area and shrub encroachment in a mire ecosystem using digital surface models and CIR aerial images. Remote Sens Environ 112:1956–1968

    Article  Google Scholar 

  • Wolf PR (1983) Elements of photogrammetry. McGraw-Hill, New York

    Google Scholar 

  • Wolf PR, Brinker RC (1994) Elementary surveying. Harper Collins, New York, pp 248

    Google Scholar 

  • Zengin H, Yeşil A (2006) Comparing the performances of real-time kinematic GPS and a handheld GPS receiver under forest cover. Turk J Agric For 30:101–110

    Google Scholar 

  • Zhang L, Gruen A (2004) Automatic DSM generation from linear array imagery data. Int Arch Photogramm Remote Sens 35:128–133

    Google Scholar 

Download references

Acknowledgments

The author thanks José Antonio Manzanera and Susana Martín (Technical University of Madrid), Petteri Packalén (University of Eastern Finland) and the editors for their revision and useful comments, and Niina Valbuena (European Forest Institute) for language revision. Rubén Valbuena’s work is funded by Metsähallitus (Finnish Forest Service) Grant awarded by the Foundation for European Forest Research (FEFR).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rubén Valbuena .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Valbuena, R. (2014). Integrating Airborne Laser Scanning with Data from Global Navigation Satellite Systems and Optical Sensors. 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_4

Download citation

Publish with us

Policies and ethics