MODELLING AND PREDICTING MEASURES OF TREE SPECIES DIVERSITY USING AIRBORNE LASER SCANNING DATA IN MIOMBO WOODLANDS OF TANZANIA

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.