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European Journal of Forest Research

, Volume 129, Issue 6, pp 1131–1142 | Cite as

Uncertainty in timber assortment estimates predicted from forest inventory data

  • Markus Holopainen
  • Mikko Vastaranta
  • Jussi Rasinmäki
  • Jouni Kalliovirta
  • Antti Mäkinen
  • Reija Haapanen
  • Timo Melkas
  • Xiaowei Yu
  • Juha Hyyppä
Original Paper

Abstract

Uncertainty factors related to inventory methodologies and forest-planning simulation computings in the estimation of logging outturn assortment volumes and values were examined. The uncertainty factors investigated were (1) forest inventory errors, (2) errors in generated stem distribution, (3) effects of generated stem distribution errors on the simulation of thinnings and (iv) errors related to the prediction of stem form and simulation of bucking. Regarding inventory errors, standwise field inventory (SWFI) was compared with area-based airborne laser scanning (ALS) and aerial photography inventorying. Our research area, Evo, is located in southern Finland. In all, 31 logging sites (12 clear-cutting and 19 thinning sites) measured by logging machine in winter 2008 were used as field reference data. The results showed that the most significant source of error in the prediction of clear-cutting assortment outturns was inventory error. Errors related to stem-form prediction and simulated bucking as well as generation of stem distributions also cause uncertainty. The bias and root-mean-squared error (RMSE) of inventory errors varied between −11.4 and 21.6 m3/ha and 6.8 and 40.5 m3/ha, respectively, depending on the assortment and inventory methodology. The effect of forest inventory errors on the value of logging outturn in clear-cuttings was 29.1% (SWFI) and 24.7% (ALS). The respective RMSE values related to thinnings were 41.1 and 42%. The generation of stem distribution series using mean characteristics led to an RMSE of 1.3 to 2.7 m3/ha and a bias of −1.2 to 0.6 m3/ha in the volume of all assortments. Prediction of stem form and simulation of bucking led to a relative bias of −0.28 to 0.00 m3 in predicted sawtimber volume. Errors related to pulpwood volumes were −0.4 m3 to 0.21 m3.

Keywords

Low-pulse ALS Timber assortment estimates Stem distributions Stem form Forest-planning simulations Stock value 

Notes

Acknowledgments

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).

References

  1. Eid T (2000) Use of uncertain inventory data in forestry scenario models and consequential incorrect harvest decisions. Silva Fennica 34:89–100Google Scholar
  2. 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
  3. Gobakken T, Næsset E (2004) Estimation of diameter and basal area distributions in coniferous forest by means of airborne laser scanner data. Scand J For Res 19:529–542CrossRefGoogle Scholar
  4. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Publishing Company, Reading, p 412Google Scholar
  5. Haara A (2005) The uncertainty of forest management planning data in Finnish non-industrial private forestry. Doctoral thesis. Dissertationes Forestales 8, 34 pGoogle Scholar
  6. Haara A, Korhonen K (2004) Kuvioittaisen arvioinnin luotettavuus. Metsätieteen aikakauskirja 4:489–508 (in Finnish)Google Scholar
  7. Haralick R (1979) Statistical and structural approaches to texture. Proceedings of the IEEE 67(5):786–804CrossRefGoogle Scholar
  8. Haralick RM, Shanmugan K, Dinstein I (1973) Textural features for image classification. IEEE Transactions on Systems. Man and Cybernetics 3(6):610–621CrossRefGoogle Scholar
  9. Holmgren J (2003) Estimation of forest variables using airborne laser scanning. Ph.D. Thesis. Acta Universitatis Agriculturae Sueciae, Silvestria 278, Swedish University of Agricultural Sciences, UmeåGoogle Scholar
  10. Holopainen M, Talvitie T (2006) Effects of data acquisition accuracy on timing of stand harvests and expected net present value. Silva Fennica 40(3):531–543Google Scholar
  11. Holopainen M, Haapanen R, Tuominen S, Viitala R (2008) Performance of airborne laser scanning- and aerial photograph-based statistical and textural features in forest variable estimation. In: Hill R, Rossette J, Suárez J (eds) Silvilaser 2008 Proceedings, pp 105–112Google Scholar
  12. Holopainen M, Mäkinen A, Rasinmäki J, Hyyppä J, Hyyppä H, Kaartinen H, Viitala R, Vastaranta M, Kangas A (2009) Effect of tree level airborne laser scanning accuracy on the timing and expected value of harvest decisions. Euro J For Res (in press)Google Scholar
  13. Hyyppä J, Inkinen M (1999) Detecting and estimating attributes for single trees using laser scanner. The Photogrammetric Journal of Finland 16:27–42Google Scholar
  14. Kangas A, Maltamo M (2000) Performance of percentile based diameter distribution prediction and Weibull method in independent data sets. Silva Fennica 34:381–398Google Scholar
  15. Kilkki P, Päivinen R (1987) Reference sample plots to combine field measurements and satellite data in forest inventory. Department of Forest Mensuration and Management, University of Helsinki. Research notes 19:210–215Google Scholar
  16. Kilkki P, Maltamo M, Mykkänen R, Päivinen R (1989) Use of the Wiebull function in estimating the basal-area diameter distribution. Silva Fennica 23:311–318Google Scholar
  17. Koskela L, Nummi T, Wenzel S, Kivinen V-P (2006) On the analyses of cubic smoothing spline-based stem curve prediction for forest harvesters. Can J For Res 36:2909–2919CrossRefGoogle Scholar
  18. Laasasenaho J (1982) Taper curve and volume functions for pine, spruce and birch. Communicationes. Institute Forestalis Fenniae 108, 74 pGoogle Scholar
  19. Lappi J (1986) Mixed linear models for analyzing and predicting stem form variation of Scots pine. Seloste: Männyn runkomuodon analysointi ja ennustaminen lineaaristen sekamallien avulla. CF 134, 69 pGoogle Scholar
  20. 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
  21. 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
  22. Malinen J, Maltamo M, Harstela P (2001) Application of most similar neighbor inference for estimating marked stand characteristics using harvester and inventory generated stem databases. International Journal of Forest Engineering 12:33–41Google Scholar
  23. Maltamo M, Kangas A (1998) Methods based on k-nearest neighbour regression in the prediction of basal area diameter distribution. Can J For Res 28:1107–1115CrossRefGoogle Scholar
  24. 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
  25. Maltamo M, Eerikäinen K, Packalén P, Hyyppä J (2006) Estimation of stem volume using laser scanning based canopy height metrics. Forestry 79:217–229CrossRefGoogle Scholar
  26. Maltamo M, Suvanto A, Packalén P (2007) Comparison of basal area and stem frequency diameter distribution modelling using airborne laser scanner data and calibration estimation. For Ecol Manag 247:26–34CrossRefGoogle Scholar
  27. Mehtätalo L (2002) MELA2002 ja uudet tukkivähennysmallit. In Nuutinen T, Kiiskinen A (toim.). MELA2002 ja käyttöpuun kuvaus. MELA-käyttäjäpäivä 7.5.2002 Joensuu. Metsäntutkimuslaitoksen tiedonantoja 865:32–46Google Scholar
  28. MetInfo (2008) http://www.metla.fi/metinfo/ (April 2008)
  29. Muinonen E, Tokola T (1990) An application of remote sensing for communal forest inventory. Proceedings from SNS/IUFRO workshop: the usability of remote sensing for forest inventory and planning, 26–28 February 1990, Umeå, Sweden. Remote Sensing Laboratory, Swedish University of Agricultural Sciences, Report 4, 35–42Google Scholar
  30. Mykkänen R (1986) Weibull-funktion käyttö puuston läpimittajakauman estimoinnissa. M. Sc. thesis. University of Joensuu, Faculty of Forestry, 80 p (In Finnish)Google Scholar
  31. Næsset E (1997) Estimating timber volume of forest stands using airborne laser scanner data. Remote Sens Environ 61:246–253CrossRefGoogle Scholar
  32. Næsset E (2002) Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens Environ 80:88–99CrossRefGoogle Scholar
  33. Näsberg M (1985) Mathematical programming models for optimal log bucking. Linko¨ping studies in science and technology. Dissertation No. 132, 200 pp. ISBN 91-7372- 932-9Google Scholar
  34. Ojansuu R (1993) Prediction of Scots pine increment using a multivariate variance component model. Tiivistelmä: Männyn kasvun ennustaminen monimuuttuja- ja varianssikomponenttimallilla. Acta Forestalia Fennica 239:72 (In Finnish)Google Scholar
  35. Ojansuu R, Halinen M, Härkönen K (2002) Metsätalouden suunnittelujärjestelmän virhelähteet männyn esiharvennuskypsyyden määrittämisessä. Metsätieteen aikakauskirja 3:441–457 (In Finnish)Google Scholar
  36. Oksanen-Peltola L, Paananen R, Schneider H, Ärölä E (1997) Solmu, Metsäsuunnittelun maastotyöopas. Metsätalouden kehittämiskeskus Tapio, 81 p (in Finnish)Google Scholar
  37. Packalén P, Maltamo M (2006) Predicting the plot volume by tree species using airborne laser scanning and aerial photographs. Forest Science 56:611–622Google Scholar
  38. 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 Sensing of Environment 109:328–341CrossRefGoogle Scholar
  39. Packalén P, Maltamo M (2008) Estimation of species-specific diameter distributions using airborne laser scanning and aerial photographs. Can J For Res 38:1750–1760CrossRefGoogle Scholar
  40. Persson Å, Holmgren J, Söderman U (2002) Detecting and measuring individual trees using an airborne laser scanner. Photogrammetric Engineering and Remote Sensing 68:925–932Google Scholar
  41. Peuhkurinen J, Maltamo M, Malinen J (2008) Estimating species-specific diameter, distributions and saw log recoveries of boreal forests from airborne laser scanning data and aerial photographs: a distribution-based approach. Silva Fennica 42:625–641Google Scholar
  42. 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
  43. Poso S (1983) Basic features of inventory by compartments. Silva Fennica 17:313–349 (in Finnish)Google Scholar
  44. Rasinmäki J, Kalliovirta J, Mäkinen A (2009) SIMO: an adaptable simulation framework for multiscale forest resource data. Comput Electron Agric 66:76–84CrossRefGoogle Scholar
  45. Saari A, Kangas A (2005) Kuvioittaisen arvioinnin harhan muodostuminen. Metsätieteen aikakauskirja 1:5–18Google Scholar
  46. Siipilehto J (1999) Improving the accuracy of predicted basal-area diameter distribution in advanced stands by determining stem number. Silva Fennica 34:331–349Google Scholar
  47. StanFord (2009) Standard for forestry data and communication. SkogForsk http://www.skogforsk.se/
  48. Tokola T (1990) Satelliittikuvan ja VMI-koealatiedon käyttö metsätalousalueen puuston inventoinnissa. Joensuun yliopisto, metsätieteellinen tiedekunta. Lisensiaattitutkimus. 53sGoogle Scholar
  49. Tomppo E (1991) Satellite image-based national forest inventory of Finland. International Archives of Photogrammetry and Remote Sensing 28:419–424Google Scholar
  50. Uusitalo J, Kokko S, Kivinen V-P (2004) The effect of two bucking methods on Scots pine lumber quality. Silva Fennica 38(3):291–303Google Scholar
  51. Varjo J (1995) Latvan hukkaosan pituusmallit männylle, kuuselle ja koivulle metsurimittausta varten. Puutavaran mittauksen kehittämistutkimuksia 1989–1993, Verkasalo, E (toim.), Finnish Forest Research Institute Research Papers 558, pp 21–23Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Markus Holopainen
    • 1
  • Mikko Vastaranta
    • 1
  • Jussi Rasinmäki
    • 1
  • Jouni Kalliovirta
    • 1
  • Antti Mäkinen
    • 1
  • Reija Haapanen
    • 2
  • Timo Melkas
    • 3
  • Xiaowei Yu
    • 4
  • Juha Hyyppä
    • 4
  1. 1.Department of Forest SciencesUniversity of HelsinkiHelsinkiFinland
  2. 2.Haapanen Forest ConsultingVanhakyläFinland
  3. 3.Metsäteho LtdHelsinkiFinland
  4. 4.Finnish Geodetic InstituteKirkkonummiFinland

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