Comparing nearest neighbor configurations in the prediction of species-specific diameter distributions

Original Paper

Abstract

Key message

We examine how the configurations in nearest neighbor imputation affect the performance of predicted species-specific diameter distributions. The simultaneous nearest neighbor imputation for all tree species and separate imputation by tree species are evaluated with total volume calibration as a prediction method for diameter distributions.

Context

This study considers the predictions of species-specific diameter distributions in Finnish boreal forests by means of airborne laser scanning (ALS) data and aerial images.

Aims

The aim was to investigate different configurations in non-parametric nearest neighbor (NN) imputation and to determine how changes in configurations affect prediction error rates for timber assortment volumes and the error indices of the diameter distributions.

Methods

Non-parametric NN imputation was used as a modeling method and was applied in two different ways: (1) diameter distributions were predicted at the same time for all tree species by simultaneous NN imputation, and (2) diameter distributions were predicted for one tree species at a time by separate NN imputation. Calibration to a regression-based total volume prediction was applied in both cases.

Results

The results indicated that significant changes in the volume prediction error rates for timber assortment and for error indices can be achieved by the selection of responses, calibration to total volume, and separate NN imputation by tree species.

Conclusion

Overall, the selection of response variables in NN imputation and calibration to total volume improved the predicted diameter distribution error rates. The most successful prediction performance of diameter distribution was achieved by separate NN imputation by tree species.

Keywords

NN imputation Area-based approach Airborne laser scanning Diameter distribution 

Notes

Acknowledgements

We would like to thank Prof. Heli Peltola and Prof. Jyrki Kangas for the acquisition of the financial support for the field measurements.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Axelsson P (2000) DEM generation from laser scanner data using adaptive TIN models. IAPRS, Amsterdam, The Netherlands 33:111–118Google Scholar
  2. Bollandsås O, Maltamo M, Gobakken T, Næsset E (2013) Comparing parametric and non-parametric modelling of diameter distributions on independent data using airborne laser scanning in a boreal conifer forest. Forestry 86:493–501.  https://doi.org/10.1093/forestry/cpt020 CrossRefGoogle Scholar
  3. Budei BC, St-Onge B, Hopkinson C, Audet F (2017) Identifying the genus or species of individual trees using a three-wavelength airborne lidar system. Remote Sens Environ 204:632–647.  https://doi.org/10.1016/j.rse.2017.09.037 CrossRefGoogle Scholar
  4. Breidenbach J, Gläser C, Schmidt M (2008) Estimation of diameter distributions by means of airborne laser scanner data. Can J For Res 38:1611–1620.  https://doi.org/10.1139/x07-237 CrossRefGoogle Scholar
  5. Gobakken T, Næsset E (2004) Estimation of diameter and basal area distributions in coniferous forest by means of airborne laser scanner data. Scan J For Res 19:529–542.  https://doi.org/10.1080/02827580410019454 CrossRefGoogle Scholar
  6. Gorgoso J, Álvarez González J, Rojo A, Grandas-Arias J (2007) Modelling diameter distributions of Betula alba L. stands in northwest Spain with the two-parameter Weibull function. Forest Syst 16:113–123.  https://doi.org/10.5424/srf/2007162-01002 Google Scholar
  7. Haara A, Korhonen K (2004) Kuvioittaisen arvioinnin luotettavuus. Metsätieteen aikakauskirja 2004.  https://doi.org/10.14214/ma.5667
  8. Hou Z, Xu Q, Vauhkonen J, Maltamo M, Tokola T (2016) Species-specific combination and calibration between area-based and tree-based diameter distributions using airborne laser scanning. Can J For Res 46(6):753–765CrossRefGoogle Scholar
  9. Kangas A, Maltamo M (2000) Performance of percentile based diameter distribution prediction and Weibull method in independent data sets. Silva Fenn 34:381–398.  https://doi.org/10.14214/sf.620 Google Scholar
  10. Kirkpatrick S, Gelatt C, Vecchi M (1983) Optimization by simulated annealing. Science 220:671–680CrossRefPubMedGoogle Scholar
  11. Koivuniemi J, Korhonen KT (2006) Inventory by compartments. For Invent:271–278.  https://doi.org/10.1007/1-4020-4381-3_16
  12. Korpela I, Ørka H, Maltamo M, Tokola T, Hyyppä J (2010) 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.  https://doi.org/10.14214/sf.156 CrossRefGoogle Scholar
  13. Laasasenaho J (1982) Taper curve and volume functions for pine, spruce and birch. Commun Inst For Fenn 108:1–74Google Scholar
  14. Lamb SM, MacLean DA, Hennigar CR, Pitt DG (2017) Imputing tree lists for New Brunswick spruce plantations through nearest-neighbor matching of airborne laser scan and inventory plot data. Can J Rem Sens 43:269–285.  https://doi.org/10.1080/07038992.2017.1324288 CrossRefGoogle Scholar
  15. Lappi J (1993) Metsäbiometrian menetelmiä. University of Joensuu, Faculty of Forest SciencesGoogle Scholar
  16. 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. IJFE 12:33–41Google Scholar
  17. Maltamo M, Malinen J, Kangas A, Härkönen S, Pasanen A (2003) Most similar neighbour-based stand variable estimation for use in inventory by compartments in Finland. Forestry 76:449–464.  https://doi.org/10.1093/forestry/76.4.449 CrossRefGoogle Scholar
  18. Maltamo M, Eerikäinen K, Packalén P, Hyyppä J (2006) Estimation of stem volume using laser scanning-based canopy height metrics. Forestry 79:217–229.  https://doi.org/10.1093/forestry/cpl007 CrossRefGoogle Scholar
  19. 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 247:26–34.  https://doi.org/10.1016/j.foreco.2007.04.031 CrossRefGoogle Scholar
  20. Maltamo M, Næsset E, Bollandsås OM, Gobakken T, Packalén P (2009) Non-parametric prediction of diameter distributions using airborne laser scanner data. Scand J For Res 24:541–553.  https://doi.org/10.1080/02827580903362497 CrossRefGoogle Scholar
  21. Maltamo M, Packalen P (2014) Species-specific management inventory in Finland. In: Maltamo M, Næsset E, Vauhkonen J (eds) Forestry applications of airborne laser scanning. Springer Netherlands, Dordrecht, pp 241–252CrossRefGoogle Scholar
  22. Maltamo M, Mehtätalo L, Valbuena R, Vauhkonen J, Packalen P (2017) Airborne laser scanning for tree diameter distribution modelling: a comparison of different modelling alternatives in a tropical single-species plantation. Forestry, pp 1-11.  https://doi.org/10.1093/forestry/cpx041
  23. Moeur M, Stage AR (1995) Most similar neighbor: an improved sampling inference procedure for natural resource planning. For Sci 41:337–359Google Scholar
  24. Næsset E (1997) Estimating timber volume of forest stands using airborne laser scanner data. Remote Sens.Environ 61:246–253.  https://doi.org/10.1016/S0034-4257(97)00041-2 CrossRefGoogle Scholar
  25. Næsset E (2004) Practical large-scale forest stand inventory using a small-footprint airborne scanning laser. Scand J For Res 19:164–179.  https://doi.org/10.1080/02827580310019257 CrossRefGoogle Scholar
  26. Næsset E (2014) Area-based inventory in Norway—from innovation to an operational reality. In: Maltamo M, Næsset E, Vauhkonen J (eds) Forestry applications of airborne laser scanning. Springer Netherlands, Dordrecht, pp 215–240CrossRefGoogle Scholar
  27. 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.  https://doi.org/10.1016/j.rse.2007.01.005 CrossRefGoogle Scholar
  28. Packalén P, Maltamo M (2008) Estimation of species-specific diameter distributions using airborne laser scanning and aerial photographs. Can J Res 38:1750–1760.  https://doi.org/10.1139/X08-037 CrossRefGoogle Scholar
  29. Packalén P, Suvanto A, Maltamo M (2009) A two stage method to estimate species-specific growing stock. Photogramm Eng Remote Sens 75:1451–1460.  https://doi.org/10.14358/pers.75.12.1451 CrossRefGoogle Scholar
  30. Packalén P, Temesgen H, Maltamo M (2012) Variable selection strategies for nearest neighbor imputation methods used in remote sensing based forest inventory. Can J Remote Sens 38:557–569.  https://doi.org/10.5589/m12-046 CrossRefGoogle Scholar
  31. 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 Fenn 42.  https://doi.org/10.14214/sf.237
  32. Poudel KP, Cao QV (2013) Evaluation of methods to predict Weibull parameters for characterizing diameter distributions. For Sci 59:243–252.  https://doi.org/10.5849/forsci.12-001 Google Scholar
  33. R Core Team (2017) R: a language and environment for statistical computing. R Foundation for Statistical ComputingGoogle Scholar
  34. Reynolds M, Burk T, Huang W (1988) Goodness-of-fit tests and model selection procedures for diameter distribution models. For Sci 34:373–399Google Scholar
  35. Saad R, Wallerman J, Lämås T (2015) Estimating stem diameter distributions from airborne laser scanning data and their effects on long term forest management planning. Scand J For Res 30:186–196.  https://doi.org/10.1080/02827581.2014.978888 CrossRefGoogle Scholar
  36. Shang C, Treitz P, Caspersen J, Jones T (2017) Estimating stem diameter distributions in a management context for a tolerant hardwood Forest using ALS height and intensity data. Can J remote Sens 43:79–94.  https://doi.org/10.1080/07038992.2017.1263152 CrossRefGoogle Scholar
  37. Siipilehto J (1999) Improving the accuracy of predicted basal-area diameter distribution in advanced stands by determining stem number. Silva Fenn 33.  https://doi.org/10.14214/sf.650
  38. Strunk JL, Gould PJ, Packalen P, Poudel KP, Andersen H, Temesgen H (2017) An examination of diameter density prediction with k-NN and airborne Lidar. Forests 8:444.  https://doi.org/10.3390/f8110444 CrossRefGoogle Scholar
  39. Thomas V, Oliver RD, Lim K, & Woods M (2008). LiDAR and Weibull modeling of diameter and basal area. For Chron, 84(6), 866–875.  https://doi.org/10.5558/tfc84866-6 CrossRefGoogle Scholar
  40. Tuominen S, Haapanen R (2013) Estimation of forest biomass by means of genetic algorithm-based optimization of airborne laser scanning and digital aerial photograph features. Silva Fenn 47.  https://doi.org/10.14214/sf.902
  41. Vauhkonen J, Tokola T, Packalen 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.  https://doi.org/10.5589/m08-052 Google Scholar
  42. 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.  https://doi.org/10.14214/sf.68 CrossRefGoogle Scholar
  43. White JC, Wulder MA, Varhola A, Vastaranta M, Coops NC, Cook BD, Pitt D, Woods M (2013) A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach. For Chron 89:722–723.  https://doi.org/10.5558/tfc2013-132 CrossRefGoogle Scholar
  44. Yu X, Hyyppä J, Litkey P, Kaartinen H, Vastaranta M, Holopainen M (2017) Single-sensor solution to tree species classification using multispectral airborne laser scanning. Remote Sens 9:108.  https://doi.org/10.3390/rs9020108 CrossRefGoogle Scholar

Copyright information

© INRA and Springer-Verlag France SAS, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of ForestryUniversity of Eastern FinlandJoensuuFinland

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