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

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dc.creator Mauya, Ernest William
dc.creator Ene, Liviu Theodor
dc.creator Bollandsås, Ole Martin
dc.creator Gobakken, Terje
dc.creator Næsset, Erik
dc.creator Malimbwi, Rogers Ernest
dc.creator Zahabu, Eliakimu
dc.date 2022-05-07T10:55:41Z
dc.date 2022-05-07T10:55:41Z
dc.date 2015
dc.date.accessioned 2022-10-25T08:50:16Z
dc.date.available 2022-10-25T08:50:16Z
dc.identifier https://www.suaire.sua.ac.tz/handle/123456789/4088
dc.identifier.uri http://hdl.handle.net/123456789/89991
dc.description 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 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
dc.format application/pdf
dc.language en
dc.publisher Springer
dc.subject Parametric models
dc.subject Prediction accuracy
dc.subject Non-parametric models
dc.subject Sampling design
dc.title Modelling aboveground forest biomass using airborne laser scanner data in the miombo woodlands of Tanzania
dc.type Article


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