European Journal of Forest Research

, Volume 129, Issue 5, pp 899–907 | Cite as

Effect of tree-level airborne laser-scanning measurement accuracy on the timing and expected value of harvest decisions

  • Markus HolopainenEmail author
  • Antti Mäkinen
  • Jussi Rasinmäki
  • Juha Hyyppä
  • Hannu Hyyppä
  • Harri Kaartinen
  • Risto Viitala
  • Mikko Vastaranta
  • Annika Kangas
Original Paper


The objective was to compare tree-level airborne laser-scanning (ALS) data accuracy with standwise estimation data accuracy as input data for forest planning, using tree- and stand-level simulators. The influence of the input data accuracy was studied with respect to (1) timing of the next thinning or clear-cutting and (2) the relative variation in the predicted income of the next logging expressed as the net present value (NPV). The timing and predicted NPV of thinning and clear-cutting operations were considered separately. The research was based on Monte Carlo simulations carried out with the tree- and stand-level simulators using a simulation and optimisation (SIMO) framework. The simulations used treewise measurements taken on 270 circular plots measured at the Evo Field Station, Finland, as input data. Deviations in the tree data measured were generated according to the mean standard errors found in standwise field estimation and tree-level ALS. The accuracy factors of ALS individual tree detection were based on the EUROSDR/ISPRS Tree Extraction Project. The results show that input data accuracy significantly affects both the timing and relative NPV of loggings. Tree-level ALS produces more accurate simulation results than standwise estimation with the error levels assumed. Diameter-based characteristics are the most important input data in all simulations. Accurate tree height estimates cannot be fully utilised in current simulators.


Forest inventory Forest management planning Simulation Optimisation Airborne laser-scanning (ALS) Net present value (NPV) 



This study was made possible by financial aid from the Finnish Academy project Improving Forest Supply Chain by Means of Advanced Laser Measurements (L-Impact). The field measurements were carried out by the Forest Engineering students of the Evo Forestry Unit in Häme Polytechnic.


  1. Barth A, Ståhl G (2007) Determining sampling size in a national forest inventory by cost-plus-loss analysis. In: Barth A (ed) Spatially comprehensive data for sorestry scenario analysis—consequences of errors and methods to enhance usability. Doctoral thesis No 2007:101. Faculty of Forest sciences, SLUGoogle Scholar
  2. Brandtberg T, Warner T, Landenberger R, McGraw J (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 85:290–303CrossRefGoogle Scholar
  3. Burkhart HE, Stuck RD, Leuschner WA, Reynolds MA (1978) Allocating inventory resources for multiple-use planning. Can J For Res 8:100–110CrossRefGoogle Scholar
  4. Duvemo K, Barth A and Wallerman J (2007) Evaluating sample point imputation techniques as input in forest management planning. Can J For Res (in press)Google Scholar
  5. Eid T (2000) Use of uncertain inventory data in forestry scenario models and consequential incorrect harvest decisions. Silva Fennica 34:89–100Google Scholar
  6. Eid T, Gobakken T, Næsset E (2004) Comparing stand inventories for large areas based on photo-interpretation and laser scanning by means of cost-plus-loss analyses. Scand J For Res 19:512–523CrossRefGoogle Scholar
  7. Gobakken T, Næsset E (2005) Weibull and percentile models for lidar-based estimation of basal area distribution. Scand J For Res 20:490–502Google Scholar
  8. Haara A, Korhonen K (2004) Kuvioittaisen arvioinnin luotettavuus. Metsätieteen aikakauskirja 4:489–508 (in Finnish)Google Scholar
  9. Hamilton DA (1978) Specifying precision in natural resource inventories. In: Integrated inventories of renewable resources: proceedings of the workshop. USDA Forest Service, General technical report RM-55, pp 276–281Google Scholar
  10. Holmgren J (2003) Estimation of forest variables using airborne laser scanning. PhD Thesis. Acta Universitatis Agriculturae Sueciae, Silvestria 278. Swedish University of Agricultural Sciences, Umeå, SwedenGoogle Scholar
  11. Holmgren J, Persson Å (2004) Identifying species of individual trees using airborne laser scanning. Remote Sens Environ 90:415–423CrossRefGoogle Scholar
  12. Holmström H, Kallur H, Ståhl G (2003) Cost-plus-loss analyses of forest inventory strategies based on kNN-assigned reference sample plot data. Silva Fennica 37:381–398Google Scholar
  13. Holopainen M, Talvitie T (2006) Effects of data acquisition accuracy on timing of stand harvests and expected net present value. Silva Fennica 40:531–543Google Scholar
  14. Hynynen J, Ojansuu R, Hökkä H, Siipilehto J, Salminen H and Haapala P (2002) Models for predicting stand development in MELA system. Finn For Res Inst Res Pap 835Google Scholar
  15. Hyyppä J, Inkinen M (1999) Detecting and estimating attributes for single trees using laser scanner. Photogramm J Finl 16:27–42Google Scholar
  16. Hyyppä J, Hyyppä, H, Maltamo M, Yu X, Ahokas E and Pyysalo U (2003) Laser scanning of forest resources—some of the Finnish experience. In Proceedings of the scandlaser scientific workshop on airborne laser scanning of forests, 3–4 Sep 2003, Umeå, Sweden, pp 53–59Google Scholar
  17. Hyyppä J, Mielonen T, Hyyppä H, Maltamo M, Yu X, Honkavaara E and Kaartinen H (2005) Using individual tree crown approach for forest volume extraction with aerial images and laser point clouds. In: Proceedings of ISPRS workshop laser scanning 2005, 12–14 Sep 2005, Enschede, Netherlands. GITC bv, Netherlands, XXXVI, Part 3/W19, pp 144–149Google Scholar
  18. Hyyppä J, Yu X, Hyyppä H and Maltamo M (2006) Methods of airborne laser scanning for forest information extraction. EARSeL SIG forestry. In: International workshop 3D remote sensing in forestry proceedings, 14–15 Feb 2006, Vienna, pp 63–78Google Scholar
  19. Hyyppä J, Hyyppä H, Leckie D, Gougeon F, Yu X, Maltamo M (2008) Review of methods of small-footprint airborne laser scanning for extracting forest inventory data in boreal forests. Int J Remote Sens 29:1339–1366CrossRefGoogle Scholar
  20. Hyytiäinen K, Tahvonen O (2001) The effects of legal limits and recommendations in timber production: the case of Finland. For Sci 47:443–454Google Scholar
  21. Juntunen R (2006) Puustotiedon laadun vaikutus metsänkäsittelyn optimoinnin tuloksiin—UPM Metsän laserkeilausaineiston ja kuviotiedon vertailu. Master thesis, University of Helsinki, 76 p (in Finnish)Google Scholar
  22. Kaartinen H and Hyyppä J (2008) EuroSDR-Project Commission 2 “Tree Extraction”, Final Report. In: EuroSDR—European Spatial Data Research, Official Publication (in press)Google Scholar
  23. Kaartinen H, Hyyppä J, Liang X, Litkey P, Kukko A, Yu X, Hyyppä H, Holopainen M (2008) Accuracy of automatic tree extraction using airborne laser scanner data. In: Hill R, Rossette J, Suárez J (eds) Silvilaser 2008 proceedings, pp 467–476Google Scholar
  24. Kalliovirta J, Tokola T (2005) Functions for estimating stem diameter and tree age using tree height, crown width and existing stand database information. Silva Fennica 39(2):227–248Google Scholar
  25. Kangas A (1999) Methods for assessing the uncertainty of growth and yield predictions. Can J For Res 292:1357–1364CrossRefGoogle Scholar
  26. Kangas A, Rasinmäki J (eds) (2008) SIMO Adaptable simulation and optimization for forest management planning. Department of Forest Resource Management publications 41, 40 pGoogle Scholar
  27. Kangas A, Heikkinen E, Maltamo M (2004) Accuracy of partially visually assessed stand characteristics: a case study of Finnish forest inventory by compartments. Can J For Res 34:916–930CrossRefGoogle Scholar
  28. Klemperer WD (1996) Forest resource economics and finance. Virginia Polytechnic Institute and State University College of Forestry and Wildlife ResourcesGoogle Scholar
  29. Kraus K, Pfeifer N (1998) Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS J Photogramm Remote Sens 53:193–203CrossRefGoogle Scholar
  30. Laasasenaho J, Päivinen R (1986) Kuvioittaisen arvioinnin tarkistamisesta. Folia Forestalia 664, 19 p (in Finnish)Google Scholar
  31. Leckie D, Gougeon F, Hill D, Quinn R, Armstrong L, Shreenan R (2003) Combined high-density lidar and multispectral imagery for individual tree crown analysis. Can J For Res 29:633–649Google Scholar
  32. Lim K, Treitz P, Wulder M, St.Onge B, Flood M (2003) LIDAR remote sensing of forest structure. Prog Phys Geogr 27:88–106CrossRefGoogle Scholar
  33. Magnussen S, Eggermont P, LaRiccia VN (1999) Recovering tree heights from airborne laser scanner data. For Sci 45:407–422Google Scholar
  34. Maltamo M, Eerikäinen K, Pitkänen J, Hyyppä J, Vehmas M (2004) Estimation of timber volume and stem density based on scanning laser altimetry and expected tree size distribution functions. Remote Sens Environ 90:319–330CrossRefGoogle Scholar
  35. Mielikäinen K (1985) Koivusekoituksen vaikutus kuusikon rakenteeseen ja kehitykseen. Summary: effect of an admixture of birch on the structure and development of Norway spruce stands. Communicationes Instituti Forestalis Fenniae 133:1–79 (in Finnish with English summary)Google Scholar
  36. Næsset E (1997a) Determination of mean tree height of forest stands using airborne laser scanner data. ISPRS J Photogramm Remote Sens 52:49–56CrossRefGoogle Scholar
  37. Næsset E (1997b) Estimating timber volume of forest stands using airborne laser scanner data. Remote Sens Environ 61:246–253CrossRefGoogle Scholar
  38. Naesset E (2002) Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens Environ 80:88–99CrossRefGoogle Scholar
  39. Næsset E (2004) Practical large-scale forest stand inventory using a small footprint airborne scanning laser. Scand J For Res 19:164–179CrossRefGoogle Scholar
  40. Nilsson M, Brandtberg T, Hagner O, Holmgren J, Persson Å, Steinvall O, Sterner H, Söderman U, Olsson H (2003) Laser scanning of forest resources—the Swedish experience. In: Proceedings of the scandlaser scientific workshop on airborne laser scanning of forests, 3–4 Sep 2003, Umeå, Sweden, pp 43–52Google Scholar
  41. Oikarinen M (1983) EteläSuomen viljeltyjen rauduskoivikoiden kasvumallit. Communicationes Ins. For. Fenniae 113 (in Finnish)Google Scholar
  42. Oksanen-Peltola L, Paananen R., Schneider H, Ärölä E (1997) Solmu, Metsäsuunnittelun maastotyöopas. Metsätalouden kehittämiskeskus Tapio, 81 p (in Finnish)Google Scholar
  43. Packalén P, Maltamo M (2007) The k-MSN method in the prediction of species specific stand attributes using airborne laser scanning and aerial photographs. Remote Sens Environ 109:328–341CrossRefGoogle Scholar
  44. Persson Å, Holmgren J, Söderman U (2002) Detecting and measuring individual trees using an airborne laser scanner. Photogramm Eng Remote Sens 68:925–932Google Scholar
  45. Popescu S, Wynne R, Nelson R (2003) Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass. Can J For Res 29:564–577Google Scholar
  46. Poso S (1983) Basic features of inventory by compartments. Silva Fennica 17:313–349 (in Finnish)Google Scholar
  47. Salminen H, Lehtonen M, Hynynen J (2005) Reusing legacy FORTRAN in the MOTTI growth and yield simulator. Comput Electron Agric 49:103–113CrossRefGoogle Scholar
  48. Saramäki J (1977) Ojitettujen turvemaiden hieskoivikoiden kehitys Kainuussa ja Pohjanmaalla. Finn For Res Inst Res Pap 91(2) (in Finnish)Google Scholar
  49. Ståhl G (1994) Optimizing the utility of forest inventory activities. Report 27, Department of Biometry and Forest Management, Swedish University of Agricultural SciencesGoogle Scholar
  50. Vuokila Y, Väliaho H (1980) Viljeltyjen havumetsiköiden kasvumallit. Finn For Res Inst Res Pap 99(2) (in Finnish)Google Scholar
  51. Wulder M (2003) The current status of laser scanning of forests in Canada and Australia. In: Proceedings of the scandlaser scientific workshop on airborne laser scanning of forests, 3–4 Sep 2003, Umeå, Sweden, pp 21–33Google Scholar
  52. Yu X, Hyyppä J, Kaartinen H, Maltamo M (2004) Automatic detection of harvested trees and determination of forest growth using airborne laser scanning. Remote Sens Environ 90:451–462CrossRefGoogle Scholar
  53. Yu X, Hyyppä J, Kukko A, Maltamo M, Kaartinen H (2006) Change detection techniques for canopy height growth measurements using airborne laser scanner data. Photogramm Eng Remote Sens 72:1339–1348Google Scholar
  54. Yu X, Hyyppä J, Kaartinen H, Maltamo M, Hyyppä H (2008) Obtaining plotwise mean height and volume growth in boreal forests using multitemporal laser surveys and various change detection techniques. Int J Remote Sens 29:1367–1386CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Markus Holopainen
    • 1
    Email author
  • Antti Mäkinen
    • 1
  • Jussi Rasinmäki
    • 1
  • Juha Hyyppä
    • 2
  • Hannu Hyyppä
    • 3
  • Harri Kaartinen
    • 2
  • Risto Viitala
    • 4
  • Mikko Vastaranta
    • 1
  • Annika Kangas
    • 1
  1. 1.Department of Forest Resource ManagementUniversity of HelsinkiHelsinkiFinland
  2. 2.Finnish Geodetic InstituteMasalaFinland
  3. 3.Laboratory of Photogrammetry and Remote SensingHelsinki University of TechnologyHelsinkiFinland
  4. 4.HAMK, University of Applied SciencesEvoFinland

Personalised recommendations