Articles, Conference and Workshop Papers Collection
Permanent URI for this collectionhttp://10.10.97.169:4000/handle/123456789/62
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Browsing Articles, Conference and Workshop Papers Collection by Author "Bollandsås, Ole Martin"
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Item Above- and belowground tree biomass models for three mangrove species in Tanzania: a nonlinear mixed effects modelling approach(Springer, 2015-10) Njana, Marco Andrew; Bollandsås, Ole Martin; Eid, Tron; Malimbwi, Rogers Ernest; Zahabu, Eliakimu& Key message Tested on data from Tanzania, both existing species-specific and common biomass models developed elsewhere revealed statistically significant large prediction errors. Species-specific and common above- and below- ground biomass models for three mangrove species were therefore developed. The species-specific models fitted bet- ter to data than the common models. The former models are recommended for accurate estimation of biomass stored in mangrove forests of Tanzania. & Context Mangroves are essential for climate change mitiga- tion through carbon storage and sequestration. Biomass models are important tools for quantifying biomass and car- bon stock. While numerous aboveground biomass models exist, very few studies have focused on belowground biomass, and among these, mangroves of Africa are hardly or not represented.Item Modelling aboveground forest biomass using airborne laser scanner data in the miombo woodlands of Tanzania(Springer, 2015) Mauya, Ernest William; Ene, Liviu Theodor; Bollandsås, Ole Martin; Gobakken, Terje; Næsset, Erik; Malimbwi, Rogers Ernest; Zahabu, EliakimuBackground: 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 fre- quent than studies reported in temperate and boreal forests. We compared parametric and non-parametric methods, specifically linear mixed effects model (LMM) and k-nearest neighbor (k-NN). Results: The results showed that the prediction accuracy obtained when using LMM was slightly better than when using the k-NN approach. Relative root mean square errors from the cross validation was 46.8 % for the LMM and 58.1 % for the k-NN. Post-stratification according to vegetation types improved the prediction accuracy of LMM more as compared to post-stratification by using land use types. Conclusion: 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 accu- racy are recommended. Keywords: Parametric models, Prediction accuracy, Non-parametric models, LMM, k-NN, Sampling design