Department of Forest Technology and Wood Sciences

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Browsing Department of Forest Technology and Wood Sciences by Subject "Airborne laser scanning"

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    Methods for estimating volume, biomass and tree species diversity using field inventory and airborne laser scanning in the tropical forests of Tanzania.
    (Norwegian University of Life Sciences, 2015) Mauya, Ernest William
    Deforestation and forest degradation in the tropical countries have reduced the extent of forest and woodlands, which conserve biodiversity, provide essential resources to people and help in mitigating climate change through carbon sequestration. Forest conservation projects need methods for estimating tree species diversity to effectively generate information necessary for implementing biodiversity management plans, while greenhouse gas reduction programmes such REDD* (Reducing Emissions from Deforestation and Forest Degradation) require robust methods to estimate volume and aboveground biomass (AGB). Such methods are also needed in the context of general forest management planning. The four papers included in this thesis are aimed to test and evaluate methods for estimating volume. AGB. and tree species diversity using field and remotely sensed data in the tropical forests and woodlands of Tanzania. In paper 1. tree models for estimating total, merchantable stem, and branch volume applicable for the entire miombo woodlands of Tanzania were developed. In Paper II. Ill. and IV the potential of airborne laser scanning (AI.S) data for predicting AGB and measures of tree species diversity was tested and evaluated. The results have shown that ALS data can be used for predicting AGB with reasonable accuracy by using both parametric and nonparametric approaches. Effects of plot size on the AGB estimates were investigated and the results indicated that the prediction accuracy of AGB in ALS-assisted inventories improved as the plot size increased. Finally, the results showed that measures of tree species diversity and particularly tree species richness and Shannon diversity index, can potentially be predicted by using ALS data.
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    Modelling and predicting measures of tree species diversity using airborne laser scanning data in miombo woodlands of Tanzania
    (Tanzania Journal of Forestry and Nature Conservation, 2021) Mauya, Ernest William
    In the recent decade, remote sensing techniques had emerged as one among the best options for quantification of measures of tree species diversity. In this study, potential of using remotely sensed data derived from airborne laser scanning (ALS) for predicting tree species richness and Shannon diversity index was evaluated. Two modelling approaches were tested: linear mixed effects modelling (LMM), by which each of the measures was modelled separately, and the k-nearest neighbour technique (k-NN), by which both measures were jointly modelled (multivariate approach). For both methods, the effect of vegetation type on the prediction accuracies of tree species richness and Shannon diversity index was tested. Separate predictions for richness and Shannon diversity index using LMM resulted in relative root mean square errors (RMSEcv) of 40.7%, and 39.1%, while for the k-NN they were 41.4% and 39.1%, respectively. Inclusion of dummy variables representing vegetation types to the LMM improved the prediction accuracies of tree species richness (RMSEcv = 40.2%) and Shannon diversity index (RMSEcv = 38.0%). The study concluded that ALS data has a potential for modelling and predicting measures of tree species diversity in the miombo woodlands of Tanzania.