Skip to main content
SpringerLink
Log in

Species-Specific Management Inventory in Finland

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

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

Abstract

A new remote sensing based stand management inventory system was developed and adopted to operational forestry in Finland during the years 2005–2010. The inventory is based on wall-to-wall mapping of the inventory area. The outcome of the inventory is species-specific stand attributes which are estimated with the help of ALS, aerial images and field sample plots. The new inventory system has been successful and within a few years all the actors of the practical forestry have updated their inventory and planning systems to support the new method. The new inventory system is now applied for almost 3,000,000 ha annually. This chapter presents the main properties of the system.

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

  • Ahokas E, Kaartinen H, Hyyppä J (2008) On the quality checking of the airborne laser scanning-based nationwide elevation model in Finland. 21st ISPRS Congress Beijing 2008. Int Arch Photogramm Remote Sens Spat Inf Sci 37(B1/I):267–270

    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 

  • Cajander AK (1909) Ueber die Waldtypen. Acta For Fenn 1:1–175

    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 

  • Haara A, Korhonen KT (2004) Kuvioittaisen arvioinnin luotettavuus. Metsätieteen aikakauskirja 4/2004:489–508 (in Finnish)

    Google Scholar 

  • Hollaus M, Wagner W, Maier B, Schadauer K (2007) Airborne laser scanning of forest stem volume in a mountainous environment. Sensors 7:1559–1577

    Article  PubMed Central  Google Scholar 

  • Holmgren J (2004) Prediction of tree height, basal area and stem volume using airborne laser scanning. Scand J For Res 19:543–553

    Article  Google Scholar 

  • Hudak AT, Crookston NL, Evans JS, Falkowski MJ, Smith AMS, Gessler PE, Morgan P (2006) Regression modeling and mapping of coniferous forest basal area and tree density from discrete-return lidar and multispectral satellite data. Can J Remote Sens 32:126–138

    Article  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 

  • Hyyppä J, Hyyppä H, Inkinen M, Engdahl M, Linko S, Zhu YH (2000) Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. For Ecol Manage 128:109–120

    Article  Google Scholar 

  • Jensen JLR, Humes KS, Conner T, Williams CJ, DeGroot J (2006) Estimation of biophysical characteristics for highly variable mixed-conifer stands using small-footprint lidar. Can J For Res 36:1129–1138

    Article  Google Scholar 

  • Junttila V, Maltamo M, Kauranne T (2008) Sparse Bayesian estimation of forest stand characteristics from ALS. For Sci 54:543–552

    Google Scholar 

  • 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 34:916–930

    Article  Google Scholar 

  • Kilkki P, Päivinen R (1986) Weibull function in the estimation of the basal area DBH-distribution. Silva Fenn 20:149–156

    Article  Google Scholar 

  • Koivuniemi J, Korhonen KT (2006) Inventory by compartments. In: Kangas A, Maltamo M (eds) Forest inventory. Methodology and applications, vol 10, Managing forest ecosystems. Springer, Dordrecht

    Google Scholar 

  • Korhonen L, Pippuri I, Packalen P, Heikkinen V, Maltamo M, Heikkilä J (2013) Detection of the need for seedling stand tending using high-resolution remote sensing data. Silva Fenn 47. Article 952. http://dx.doi.org/10.14214/sf.952

    Google Scholar 

  • Latifi H, Nothdurft A, Koch B (2010) Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical/LiDAR-derived predictors. Forestry 83:395–407

    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, Packalén P, Suvanto A, Korhonen KT, Mehtätalo L, Hyvönen P (2009) Combining ALS and NFI training data for forest management planning – a case study in Kuortane, Western Finland. Eur J For Res 128:305–317

    Article  Google Scholar 

  • Mustonen J, Packalén P, Kangas A (2008) Automatic segmentation of forest stands using canopy height model and aerial photography. Scand J For Res 23:534–545

    Article  Google Scholar 

  • Næsset E (1997) Estimating timber volume of forest stands using airborne laser scanner data. Remote Sens Environ 51:246–253

    Article  Google Scholar 

  • Næsset E (2002) Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data. Remote Sens Environ 80:88–99

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Næsset E (2005) Assessing sensor effects and effects of leaf-off and leaf-on canopy conditions on biophysical stand properties derived from small-footprint airborne laser data. Remote Sens Environ 98:356–370

    Article  Google Scholar 

  • Næsset E (2007) Airborne laser scanning as a method in operational forest inventory: status of accuracy assessments accomplished in Scandinavia. Scand J For Res 22:433–442

    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 

  • Packalén P, Maltamo M (2006) Predicting the volume by tree species using airborne laser scanning and aerial photographs. For Sci 52:611–622

    Google Scholar 

  • 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 109:328–341

    Article  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, Suvanto A, Maltamo M (2009) A two stage method to estimate species-specific growing stock by combining ALS data and aerial photographs of known orientation parameters. Photogramm Eng Remote Sens 75:1451–1460

    Article  Google Scholar 

  • Päivinen R, Pussinen R, Tomppo E (1993) Assessment of boreal forest stands using field assessment and remote sensing. In: Operalization of remote sensing. Proceedings of Earsel 1993 Conference, 19–23 April 1993. ITC Enshedene, the Netherlands, 8 p

    Google Scholar 

  • Poso S (1994) Metsätalouden suunnittelu uusiin puihin. Voidaanko silmävaraisesta kuvioittaisesta arvioinnista luopua? Metsätieteeen Aikakauskirja 1/1994:85–89 (in Finnish)

    Google Scholar 

  • Rombouts J, Ferguson IS, Leech JW (2008) Variability of LiDAR volume prediction models for productivity assessment of radiata pine plantations in South Australia. In: Hill R, Rosette J, Suárez J (eds) Proceedings of SilviLaser 2008, 8th international conference on LiDAR applications in forest assessment and inventory, 17–19 September 2008. Heriot-Watt University, Edinburgh, UK, pp 39–49

    Google Scholar 

  • Suvanto A, Maltamo M, Packalén P, Kangas J (2005) Kuviokohtaisten puustotunnusten ennustaminen laserkeilauksella. Metsätieteen aikakauskirja 4/2005:413–428 (in Finnish)

    Google Scholar 

  • Tomppo E (2006) The Finnish multi-source National Forest Inventory – small area estimation and map production. In: Kangas A, Maltamo M (eds) Forest inventory. Methodology and applications, vol 10, Managing forest ecosystems. Springer, Dordrecht

    Google Scholar 

  • Uuttera J, Hiltunen J, Rissanen P, Anttila P, Hyvönen P (2002) Uudet kuvioittaisen arvioinnin menetelmät – arvio soveltuvuudesta yksityismaiden metsäsuunnitteluun. Metsätieteen aikakauskirja 3/2002:523–531 (in Finnish)

    Google Scholar 

  • Uuttera J, Anttila P, Suvanto A, Maltamo M (2006) Yksityismetsien metsävaratiedon keruuseen soveltuvilla kaukokartoitusmenetelmillä estimoitujen puustotunnusten luotettavuus. Metsätieteen aikakauskirja 4/2006:507–519 (in Finnish)

    Google Scholar 

  • Varjo J (2002) Metsäsuunnittelun tietohuollon järjestäminen tulevaisuudessa. Metsätieteen aikakauskirja 3/2002:537–540 (in Finnish)

    Google Scholar 

  • Villikka M, Packalén P, Maltamo M (2012) The suitability of leaf-off airborne laser scanner data for forest inventory. Silva Fenn 46:99–110

    Article  Google Scholar 

  • Wallenius T, Laamanen R, Peuhkurinen J, Mehtätalo L, Kangas A (2012) Analysing the agreement between an airborne laser scanning based forest inventory and a control inventory – a case study in the state owned forests in Finland. Silva Fenn 46:111–129

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Forest Sciences, University of Eastern Finland, Joensuu, Finland

    Matti Maltamo & Petteri Packalen

Authors
  1. Matti Maltamo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Petteri Packalen
    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., Packalen, P. (2014). Species-Specific Management Inventory in Finland. 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_12

Download citation

Publish with us

Policies and ethics

Search

Navigation