Modelling and predicting measures of tree species diversity using airborne laser scanning data in miombo woodlands of Tanzania
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Date
2021
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Publisher
Tanzania Journal of Forestry and Nature Conservation
Abstract
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.
Description
Journal Article
Keywords
Airborne laser scanning, Liwale-Tanzania, k- NN, Biodiversity, Miombo woodland, Tree species diversity