Statistical Methods & Applications

, Volume 22, Issue 1, pp 113–129 | Cite as

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

  • Ole Martin Bollandsås
  • Timothy G. GregoireEmail author
  • Erik Næsset
  • Bernt-Håvard Øyen


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.


Biomass change Airborne laser scanning Multitemporal data 


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  1. Akaike H (1974) A new look at the statistical model identification. IEEE Trans Auto Control 19: 716–723MathSciNetzbMATHCrossRefGoogle Scholar
  2. Anon (2005) TerraScan user’s guide. Terrasolid Ltd., Jyvaskyla.
  3. Axelsson P (2000) Dem generation from laser scanner data using adaptive tin models. Int Arch Photogramm Remote Sen 33: 110–117Google Scholar
  4. Braastad H (1966) Volume tables for birch (in Norwegian with English summary). Commun Nor For Res Inst 21: 265–365Google Scholar
  5. Braastad H (1980) Tilvekstmodellprogram for furu (growth model computer program for pinus sylvestris) (in Norwegian with English summary). Technical report 5, Norwegian Forest Research InstituteGoogle Scholar
  6. Brantseg A (1967) Volume functions and tables for scots pine. South norway (in Norwegian with English summary). Commun Nor For Res Inst 22: 695–739Google Scholar
  7. Fisher RA (1921) Some remarks on the methods formulated in a recent article on “The quantitative analysis of plant growth”. Ann Appl Biol 7: 367–372CrossRefGoogle Scholar
  8. 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 224–227Google Scholar
  9. Harrell FE (2001) Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. Springer, New YorkzbMATHGoogle Scholar
  10. 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: 353–369CrossRefGoogle Scholar
  11. Holtmeier F, Broll G (2007) Treeline advance-driving processes and adverse factors. Landsc Online 1: 1–33CrossRefGoogle Scholar
  12. Hopkinson C, Chasmer L, Hall RJ (2008) The uncertainty in conifer plantation growth prediction from multi-temporal lidar datasets. Remote Sens Environ 112: 1168–1180CrossRefGoogle Scholar
  13. Kupfer JA, Cairns DM (1996) The suitability of montane ecotones as indicators of global climatic change. Prog Phys Geogr 20: 253–272CrossRefGoogle Scholar
  14. 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, UmeåGoogle Scholar
  15. 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
  16. 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: 433–442CrossRefGoogle Scholar
  17. 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: 148–159CrossRefGoogle Scholar
  18. Næsset E, Gobakken T (2005) Estimating forest growth using canopy metrics derived from airborne laser scanner data. Remote Sens Environ 96: 453–465CrossRefGoogle Scholar
  19. Næsset E, Nelson R (2007) Using airborne laser scanning to monitor tree migration in the boreal-alpine transition zone. Remote Sens Environ 110: 357–369CrossRefGoogle Scholar
  20. Nilsson M (1996) Estimation of tree heights and stand volume using an airborne lidar system. Remote Sens Environ 56: 1–7MathSciNetCrossRefGoogle Scholar
  21. Opdam P, Wascher D (2004) Climate change meets habitat fragmentation: linking landscapes and biogeographical scale levels in research and conservation. Biol Conserv 117: 285–297CrossRefGoogle Scholar
  22. Pinheiro J, Bates D, DebRoy S, Sarkar D (2009) nlme: linear and nonlinear mixed effects models. R package version 3, technical report, pp 1–96Google Scholar
  23. R Core Team (2012) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna., ISBN 3-900051-07-0
  24. 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: 364–376CrossRefGoogle Scholar
  25. 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 173–178Google Scholar
  26. Stenseth NC, Mysterud A, Ottersen G, Hurrell JW, Chan KS, Lima M (2002) Ecological effects of climate fluctuations. Science 297: 1292–1296CrossRefGoogle Scholar
  27. 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: 264–274CrossRefGoogle Scholar
  28. Tveite B (1977) Site index curves for norway spruce (picea abies (l.) Karst.). Technical report 33, Norwegian Forest Research InstituteGoogle Scholar
  29. 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: 2326–2340CrossRefGoogle Scholar
  30. 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: 526–539CrossRefGoogle Scholar
  31. Vestjordet E (1967) Functions and tables for volume of standing trees. Norway spruce (in Norwegian with English summary). Commun Nor For Res Inst 22: 543–574Google Scholar
  32. Winjum JK, Dixon RK, Schroeder PE (1993) Forest management and carbon storage: an analysis of 12 key forest nations. Water Air Soil Pollut 70: 239–257CrossRefGoogle Scholar
  33. 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: 1434–1444CrossRefGoogle Scholar
  34. 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 115–124Google Scholar
  35. 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: 451–462CrossRefGoogle Scholar
  36. 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 204–208Google Scholar
  37. 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: 1339–1348Google Scholar
  38. 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: 1367–1386CrossRefGoogle Scholar
  39. 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: 35–46CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ole Martin Bollandsås
    • 1
  • Timothy G. Gregoire
    • 2
    Email author
  • Erik Næsset
    • 1
  • Bernt-Håvard Øyen
    • 3
  1. 1.Department of Ecology and Natural Resource ManagementNorwegian University of Life SciencesÅsNorway
  2. 2.School of Forestry and Environmental StudiesYale UniversityNew HavenUSA
  3. 3.Norwegian Forest and Landscape InstituteDistrict Office Western NorwayFanaNorway

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