Statistical Methods & Applications

, Volume 22, Issue 1, pp 113–129

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


  • Ole Martin Bollandsås
    • Department of Ecology and Natural Resource ManagementNorwegian University of Life Sciences
    • School of Forestry and Environmental StudiesYale University
  • Erik Næsset
    • Department of Ecology and Natural Resource ManagementNorwegian University of Life Sciences
  • Bernt-Håvard Øyen
    • Norwegian Forest and Landscape InstituteDistrict Office Western Norway

DOI: 10.1007/s10260-012-0220-5

Cite this article as:
Bollandsås, O.M., Gregoire, T.G., Næsset, E. et al. Stat Methods Appl (2013) 22: 113. doi:10.1007/s10260-012-0220-5


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 changeAirborne laser scanningMultitemporal data

Copyright information

© Springer-Verlag Berlin Heidelberg 2012