Modelling aboveground forest biomass using airborne laser scanner data in the miombo woodlands of Tanzania

dc.contributor.authorMauya, Ernest William
dc.contributor.authorEne, Liviu Theodor
dc.contributor.authorBollandsås, Ole Martin
dc.contributor.authorGobakken, Terje
dc.contributor.authorNæsset, Erik
dc.contributor.authorMalimbwi, Rogers Ernest
dc.contributor.authorZahabu, Eliakimu
dc.date.accessioned2022-05-07T10:55:41Z
dc.date.available2022-05-07T10:55:41Z
dc.date.issued2015
dc.description.abstractBackground: 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 designen_US
dc.identifier.urihttps://www.suaire.sua.ac.tz/handle/123456789/4088
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectParametric modelsen_US
dc.subjectPrediction accuracyen_US
dc.subjectNon-parametric modelsen_US
dc.subjectSampling designen_US
dc.titleModelling aboveground forest biomass using airborne laser scanner data in the miombo woodlands of Tanzaniaen_US
dc.typeArticleen_US
dc.url10.1186/s13021-015-0037-2en_US

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