Skip to main content
SpringerLink
Log in

Predicting Tree Diameter Distributions

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

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

Abstract

Diameter distribution of trees is an important stand attribute that describes stand structure in terms of volume, biomass, value, growth and biodiversity factors. Diameter distribution can be characterized using different approaches such as probability density functions, percentile-based distributions or nearest neighbour applications. We review the research related to airborne laser scanning (ALS)-based predictions of diameter distributions. This includes the above-mentioned plot level approaches, as well as predicting the diameter of individual trees and combinations of different approaches. Although ALS does not directly measure tree diameter, there is a strong statistical relationship between ALS metrics and the characteristics of a diameter distribution. The capability of ALS to reproduce different shapes of diameter distribution is the most notable feature of these applications.

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

  • Bailey RL, Dell TR (1973) Quantifying diameter distributions with the Weibull function. For Sci 19:97–104

    Google Scholar 

  • Bailey RL, Abernthy NC, Jones EP (1981) Diameter distributions models for repeatedly thinned slash pine plantations. In: Barnett JP (ed) Proceedings of the 1st Biennial Southern Silvicultural Research Conference, Atlanta, Georgia

    Google Scholar 

  • Bollandsås OM, Næsset E (2007) Estimating percentile-based diameter distributions in uneven-aged Norway spruce stands using airborne laser scanner data. Scand J For Res 22:33–47

    Article  Google Scholar 

  • Borders BE, Souter RA, Bailey RL, Ware KD (1987) Percentile-based distributions characterize forest stand tables. For Sci 33:570–576

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Breidenbach J, Næsset E, Lien V, Gobakken T, Solberg S (2010) Prediction of species-specific forest inventory attributes using a nonparametric semi-individual tree crown approach based on fused airborne laser scanning and multispectral data. Remote Sens Environ 114:911–924

    Article  Google Scholar 

  • Cao QV (2004) Predicting parameters of a Weibull function for modelling diameter distribution. For Sci 50:682–685

    Google Scholar 

  • Deville J-C, Särndal C-E (1992) Calibration estimators in survey sampling. J Am Stat Assoc 87:376–382

    Article  Google Scholar 

  • Dubey SD (1967) Some percentile estimators for Weibull parameters. Technometrics 9:119–129

    Article  Google Scholar 

  • Ene L, Næsset E, Gobakken T (2012) Single tree detection in heterogeneous boreal forests using airborne laser scanning and area-based stem number estimates. Int J Remote Sens 33:5171–5193

    Article  Google Scholar 

  • Eriksson LO, Sallnäs O (1987) A model for predicting log yield from stand characteristics. Scand J For Res 2:253–261

    Article  Google Scholar 

  • 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 19:529–542

    Article  Google Scholar 

  • Gobakken T, Næsset E (2005) Weibull and percentile models for LIDAR-based estimation of basal area distribution. Scand J For Res 20:490–502

    Article  Google Scholar 

  • Haara A, Maltamo M, Tokola T (1997) The k-nearest-neighbour method for estimating basal-area diameter distribution. Scand J For Res 12:200–208

    Article  Google Scholar 

  • Holopainen M, Vastaranta M, Rasinmäki J, Kalliovirta J, Mäkinen A, Haapanen R, Melkas T, Yu X, Hyyppä J (2010) Uncertainty in timber assortment predicted from forest inventory data. Eur J For Res 129:1131–1142

    Article  Google Scholar 

  • Holte A (1993) Diameter distribution functions for even-aged (Picea abies) stands. Norsk Institutt for Skogforskning, Ås

    Google Scholar 

  • Hyyppä J, Inkinen M (1999) Detecting and estimating attributes for single trees using laser scanner. Photogramm J Finl 16:27–42

    Google Scholar 

  • Kalliovirta J, Tokola T (2005) Functions for estimating stem diameter and tree age using tree height, crown width and existing stand data bank information. Silva Fenn 39:227–248

    Google Scholar 

  • Kangas A, Maltamo M (2000) Calibrating predicted diameter distribution with additional information. For Sci 46:390–396

    Google Scholar 

  • Lindberg E, Holmgren J, Olofsson K, Wallerman J, Olsson H (2010) Estimation of tree lists from airborne laser scanning by combining single-tree and area-based methods. Int J Remote Sens 31:1175–1192

    Article  Google Scholar 

  • Maltamo M (1997) Comparing basal area diameter distributions estimated by tree species and for the entire growing stock in a mixed stand. Silva Fenn 31:53–65

    Article  Google Scholar 

  • 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 90:319–330

    Article  Google Scholar 

  • Maltamo M, Eerikäinen K, Packalén P, Hyyppä J (2006) Estimation of stem volume using laser scanning based canopy height metrics. Forestry 79:217–229

    Article  Google Scholar 

  • 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 Manage 247:26–34

    Article  Google Scholar 

  • Maltamo M, Næsset E, Bollandsås OM, Gobakken T, Packalén P (2009a) Non-parametric estimation of diameter distributions by using ALS data. Scand J For Res 24:541–553

    Article  Google Scholar 

  • Maltamo M, Peuhkurinen J, Malinen J, Vauhkonen J, Packalén P, Tokola T (2009b) Predicting tree attributes and quality characteristics of Scots pine using airborne laser scanning data. Silva Fenn 43:507–521

    Article  Google Scholar 

  • Maltamo M, Mehtätalo L, Vauhkonen J, Packalén P (2012) Predicting and calibrating tree size and quality attributes by means of airborne laser scanning and field measurements. Can J For Res 42:1896–1907

    Article  Google Scholar 

  • Mehtätalo L (2006) Eliminating the effect of overlapping crowns from aerial inventory estimates. Can J For Res 36:1649–1660

    Article  Google Scholar 

  • Mehtätalo L, Maltamo M, Packalén P (2007) Recovering plot-specific diameter distribution and height-diameter curve using ALS based stand characteristics. In: Proceedings of ISPRS workshop laser scanning 2007 and Silvilaser 2007, Finland, September 12–14, 2007. Int Arch Photogramm Remote Sens Spat Inf Sci XXXVI:288–293

    Google Scholar 

  • Packalén P, Maltamo M (2008) The 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, Pitkänen J, Maltamo M (2008) Comparison of individual tree detection and canopy height distribution approaches: a case study in Finland. In: Proceedings of SilviLaser 2008, 8th international conference on LiDAR applications in forest assessment and inventory. Heriot-Watt University, Edinburgh, UK, 17–19 September 2008

    Google Scholar 

  • Packalén P, Temesgen H, Maltamo M (2012) Variable selection for nearest neighbor imputation in remote sensing based forest inventory. Can J Remote Sens 38:557–569

    Article  Google Scholar 

  • Päivinen R (1980) Puiden läpimittajakauman estimointi ja siihen perustuva puustotunnusten laskenta. (On the estimation of stem-diameter distribution and stand characteristics). Folia For 442:1–28 (In Finnish with English summary)

    Google Scholar 

  • Persson A, Holmgren J, Söderman U (2002) Detecting and measuring individual trees using an airborne laser scanner. Photogramm Eng Remote Sens 68:925–932

    Google Scholar 

  • 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(4):625–641

    Article  Google Scholar 

  • Peuhkurinen J, Mehtätalo L, Maltamo M (2011) Comparing individual tree detection and the area-based statistical approach for the retrieval of forest stand characteristics using airborne laser scanning in Scots pine stands. Can J For Res 41:583–598

    Article  Google Scholar 

  • Poudel KP, Cao QV (2013) Evaluation of methods to predict Weibull parameters for characterizing diameter distributions. For Sci 59:243–252

    Google Scholar 

  • Reynolds MR Jr, Burk TE, Huang W-C (1988) Goodness-of-fit tests and model selection procedures for diameter distribution models. For Sci 34:373–399

    Google Scholar 

  • Salas C, Ene L, Gregoire TG, Næsset E, Gobakken T (2010) Modelling tree diameter from airborne laser scanning derived variables: a comparison of spatial statistical models. Remote Sens Environ 114:1277–1285

    Article  Google Scholar 

  • Siipilehto J (1999) Improving the accuracy of predicted basal-area diameter distribution in advanced stands by determining stem number. Silva Fenn 33:281–301

    Google Scholar 

  • Temesgen H (2003) Estimating tree-lists from aerial information: a comparison of a parametric and most similar neighbor approaches. Scand J For Res 18:279–288

    Article  Google Scholar 

  • Tham Å (1988) Structure of mixed Picea abies (L.) Karst. and Betula pendula Roth and Betula pubescens Ehrh. stands in South and Middle Sweden. Scand J For Res 3:355–369

    Article  Google Scholar 

  • Thomas V, Oliver RD, Lim K, Woods M (2008) Lidar and Weibull modeling of diameter and basal area. For Chron 84(6):866–875

    Article  Google Scholar 

  • Van Deusen PC (1986) Fitting assumed distributions to horizontal point sample diameters. For Sci 32:146–148

    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 

  • Villikka M, Maltamo M, Packalén P, Vehmas M, Hyyppä J (2007) Alternatives for predicting tree-level stem volume of Norway spruce using airborne laser scanner data. Photogramm J Finl 20:33–42

    Google Scholar 

  • Yu X, Hyyppä J, Holopainen M, Vastaranta M (2010) Comparison of area based and individual tree based methods for predicting plot level attributes. Remote Sens 2:1481–1495

    Article  Google Scholar 

  • Zhang L, Gove JH, Liu C, Leak WB (2001) A finite mixture distribution for modeling the diameter distribution of rotated-sigmoid, uneven-aged stands. Can J For Res 31:1654–1659

    Article  Google Scholar 

Download references

Author information

Authors 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

    Terje Gobakken

Authors
  1. Matti Maltamo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Terje Gobakken
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Matti Maltamo .

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

Maltamo, M., Gobakken, T. (2014). Predicting Tree Diameter Distributions. 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_9

Download citation

Publish with us

Policies and ethics

Search

Navigation