Skip to main content
SpringerLink
Log in

Detection of biomass change in a Norwegian mountain forest area using small footprint airborne laser scanner data

  • Published:
Statistical Methods & Applications Aims and scope Submit manuscript

Abstract

Different approaches for estimation of change in biomass between two points in time by means of airborne laser scanner data were tested. Both field and laser data were collected at two occasions on 52 sample plots in a mountain forest in southeastern Norway. In the first approach, biomass change was estimated as the difference between predicted biomass for the two measurement occasions. Joint models for the biomass at both occasions were fitted using different height and density variables from laser data as explanatory variables. The second approach modelled the observed change directly using the change in different variables extracted from the laser data as explanatory variables. In the third approach we modelled the relative change in biomass. The explanatory variables were also expressed as relative change between measurement occasions. In all approaches we allowed spline terms to be entered. We also investigated the aptness of models for which the residual variance was modeled by allowing it to be proportional to the area of the plot on which biomass was assessed. All alternative models were initially assessed by AIC. All models were also evaluated by estimating biomass change on the model development data. This evaluation indicated that the two direct approaches (approach 2 and 3) were better than relying on modeling biomass at both occasions and taking change as the difference between biomass estimates. Approach 2 seemed to be slightly better than approach 3 based on assessments of bias in the evaluation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Akaike H (1974) A new look at the statistical model identification. IEEE Trans Auto Control 19: 716–723

    Article  MathSciNet  MATH  Google Scholar 

  • Anon (2005) TerraScan user’s guide. Terrasolid Ltd., Jyvaskyla. http://www.terrasolid.fi.Accessed2Oct2006

  • Axelsson P (2000) Dem generation from laser scanner data using adaptive tin models. Int Arch Photogramm Remote Sen 33: 110–117

    Google Scholar 

  • Braastad H (1966) Volume tables for birch (in Norwegian with English summary). Commun Nor For Res Inst 21: 265–365

    Google Scholar 

  • Braastad H (1980) Tilvekstmodellprogram for furu (growth model computer program for pinus sylvestris) (in Norwegian with English summary). Technical report 5, Norwegian Forest Research Institute

  • Brantseg A (1967) Volume functions and tables for scots pine. South norway (in Norwegian with English summary). Commun Nor For Res Inst 22: 695–739

    Google Scholar 

  • Fisher RA (1921) Some remarks on the methods formulated in a recent article on “The quantitative analysis of plant growth”. Ann Appl Biol 7: 367–372

    Article  Google Scholar 

  • Gobakken T, Næsset E (2004) Effects of forest growth on laser derived canopy metrics. In: International archives of photogrammetry, remote sensing and spatial information sciences, Freiburg, vol XXXVI, Part 8/W2, pp 224–227

  • Harrell FE (2001) Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. Springer, New York

    MATH  Google Scholar 

  • Höhne N, Wartmann S, Herold A, Freibauer A (2007) The rules for land use, land use change and forestry under the Kyoto Protocol—lessons learned for the future climate negotiations. Environ Sci Policy 10: 353–369

    Article  Google Scholar 

  • Holtmeier F, Broll G (2007) Treeline advance-driving processes and adverse factors. Landsc Online 1: 1–33

    Article  Google Scholar 

  • Hopkinson C, Chasmer L, Hall RJ (2008) The uncertainty in conifer plantation growth prediction from multi-temporal lidar datasets. Remote Sens Environ 112: 1168–1180

    Article  Google Scholar 

  • Kupfer JA, Cairns DM (1996) The suitability of montane ecotones as indicators of global climatic change. Prog Phys Geogr 20: 253–272

    Article  Google Scholar 

  • Marklund L (1988) Biomass functions for pine, spruce and birch in sweden (in Swedish with English summary). Technical report Rapport 45, Swedish University of Agricultural Sciences, Department of Forest Survey, Umeå

  • 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 (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 

  • Næsset E, Gobakken T (2005) Estimating forest growth using canopy metrics derived from airborne laser scanner data. Remote Sens Environ 96: 453–465

    Article  Google Scholar 

  • Næsset E, Nelson R (2007) Using airborne laser scanning to monitor tree migration in the boreal-alpine transition zone. Remote Sens Environ 110: 357–369

    Article  Google Scholar 

  • Nilsson M (1996) Estimation of tree heights and stand volume using an airborne lidar system. Remote Sens Environ 56: 1–7

    Article  MathSciNet  Google Scholar 

  • Opdam P, Wascher D (2004) Climate change meets habitat fragmentation: linking landscapes and biogeographical scale levels in research and conservation. Biol Conserv 117: 285–297

    Article  Google Scholar 

  • Pinheiro J, Bates D, DebRoy S, Sarkar D (2009) nlme: linear and nonlinear mixed effects models. R package version 3, technical report, pp 1–96

  • R Core Team (2012) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. http://www.R-project.org, ISBN 3-900051-07-0

  • Solberg S, Næsset E, Hanssen KH, Christiansen E (2006) Mapping defoliation during a severe insect attack on scots pine using airborne laser scanning. Remote Sens Environ 102: 364–376

    Article  Google Scholar 

  • St-Onge B, Vepakomma U (2004) Assessing forest gap dynamics and growth using multi-temporal laser-scanner data. In: Thies M, Koch B, Spiecker H, Weinacker H (eds) Laser-scanners for forest and landscape assessment. Proceedings of the ISPRS working group VIII/2. international archives of photogrammetry, remote sensing and spatial information sciences, vol XXXVI, Part 8/W2. University of Freiburg, Germany, pp 173–178

  • Stenseth NC, Mysterud A, Ottersen G, Hurrell JW, Chan KS, Lima M (2002) Ecological effects of climate fluctuations. Science 297: 1292–1296

    Article  Google Scholar 

  • Thieme N, Bollandsås OM, Gobakken T, Næsset E (2011) Detection of small single trees in the forest-tundra ecotone using height values from airborne laser scanning. Can J Remote Sens 37: 264–274

    Article  Google Scholar 

  • Tveite B (1977) Site index curves for norway spruce (picea abies (l.) Karst.). Technical report 33, Norwegian Forest Research Institute

  • Vepakomma U, St-Onge B, Kneeshaw D (2008) Spatially explicit characterization og boreal forest gap dynamics using multi-temporal lidar data. Remote Sens Environ 112: 2326–2340

    Article  Google Scholar 

  • Vepakomma U, Kneeshaw D, St-Onge B (2010) Interactions of multiple disturbances in shaping boreal forest dynamics: a spatially explicit analysis using multi-temporal lidar data and high-resolution imagery. J Ecol 98: 526–539

    Article  Google Scholar 

  • Vestjordet E (1967) Functions and tables for volume of standing trees. Norway spruce (in Norwegian with English summary). Commun Nor For Res Inst 22: 543–574

    Google Scholar 

  • Winjum JK, Dixon RK, Schroeder PE (1993) Forest management and carbon storage: an analysis of 12 key forest nations. Water Air Soil Pollut 70: 239–257

    Article  Google Scholar 

  • Woodall CW, Oswalt CM, Westfall JA, Perry CH, Nelson MD, Finley AO (2009) An indicator of tree migration in forests of the eastern united states. For Ecol Manag 257: 1434–1444

    Article  Google Scholar 

  • Yu X, Hyyppä J, Rönnholm P, Kaartinen H, Maltamo M, Hyyppä H (2003) Detection of harvested trees and estimation of forest growth using laser scanning. In: Proceedings of the scandLaser scientific workshop on airborne laser scanning of forests, pp 115–124

  • 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: 451–462

    Article  Google Scholar 

  • Yu X, Hyyppä J, Kaartinen H, Hyyppä H, Maltamo M, Rönnholm P (2005) Measuring the growth of individual trees using multi-temporal airborne laser scanning point clouds. In: Proceedings of the ISPRS workshop laser scanning 2005, vol 36, Enschede, pp 204–208

  • 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: 1339–1348

    Google Scholar 

  • Yu X, Hyyppä J, Kaartinen H, Maltamo M, Hyyppä H (2008) Obtaining plotwise mean height and volume growth in boreal forests using multi-temporal laser surveys and various change detection techniques. Int J Remote Sens 29: 1367–1386

    Article  Google Scholar 

  • Zheng D, Freeman M, Bergh J, Røsberg I, Nilsen P (2002) Production of picea abies in south-east norway in response to climate change: a case study using process-based model simulation with field validation. Scand J For Res 17: 35–46

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432, Ås, Norway

    Ole Martin Bollandsås & Erik Næsset

  2. School of Forestry and Environmental Studies, Yale University, 360 Prospect Street, New Haven, CT, 06511-2104, USA

    Timothy G. Gregoire

  3. Norwegian Forest and Landscape Institute, District Office Western Norway, Fanaflaten 4, 5244, Fana, Norway

    Bernt-Håvard Øyen

Authors
  1. Ole Martin Bollandsås
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Timothy G. Gregoire
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Erik Næsset
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bernt-Håvard Øyen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Timothy G. Gregoire.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bollandsås, O.M., Gregoire, T.G., Næsset, E. et al. Detection of biomass change in a Norwegian mountain forest area using small footprint airborne laser scanner data. Stat Methods Appl 22, 113–129 (2013). https://doi.org/10.1007/s10260-012-0220-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10260-012-0220-5

Keywords

Search

Navigation