title: Modelling aboveground forest biomass using airborne laser scanner data in the miombo woodlands of Tanzania.
abstract: BACKGROUND
  Airborne laser scanning (ALS) has emerged as one of the most promising remote sensing technologies for estimating aboveground biomass (AGB) in forests. Use of ALS data in area-based forest inventories relies on the development of statistical models that relate AGB and metrics derived from ALS. Such models are firstly calibrated on a sample of corresponding field- and ALS observations, and then used to predict AGB over the entire area covered by ALS data. Several statistical methods, both parametric and non-parametric, have been applied in ALS-based forest inventories, but studies that compare different methods in tropical forests in particular are few in number and less frequent than studies reported in temperate and boreal forests. We compared parametric and non-parametric methods, specifically linear mixed effects model (LMM) andk-nearest neighbor (k-NN).
  
  
  RESULTS
  The results showed that the prediction accuracy obtained when using LMM was slightly better than when using thek-NN approach. Relative root mean square errors from the cross validation was 46.8 % for the LMM and 58.1 % for thek-NN. Post-stratification according to vegetation types improved the prediction accuracy of LMM more as compared to post-stratification by using land use types.
  
  
  CONCLUSIONS
  Although there were differences in prediction accuracy between the two methods, their accuracies indicated that both of methods have potentials to be used for estimation of AGB using ALS data in the miombo woodlands. Future studies on effects of field plot size and the errors due to allometric models on the prediction accuracy are recommended.
authors.affiliation: Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norway.
authors.forename: Ernest William
authors.initials: EW
authors.lastname: Mauya
authors.affiliation: Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norway.
authors.forename: Liviu Theodor
authors.initials: LT
authors.lastname: Ene
authors.affiliation: Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norway.
authors.forename: Ole Martin
authors.initials: OM
authors.lastname: Bollandsås
authors.affiliation: Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norway.
authors.forename: Terje
authors.initials: T
authors.lastname: Gobakken
authors.affiliation: Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, 1432 Ås, Norway.
authors.forename: Erik
authors.initials: E
authors.lastname: Næsset
authors.affiliation: Department of Forest Mensuration and Management, Sokoine University of Agriculture, P.O. Box 3013, Morogoro, Tanzania.
authors.forename: Rogers Ernest
authors.initials: RE
authors.lastname: Malimbwi
authors.affiliation: Department of Forest Mensuration and Management, Sokoine University of Agriculture, P.O. Box 3013, Morogoro, Tanzania.
authors.forename: Eliakimu
authors.initials: E
authors.lastname: Zahabu
keywords: LMM; Non-parametric models; Parametric models; Prediction accuracy; Sampling design; k-NN