Abstract:
Accurate maps of tree species distributions are necessary for the sustainable management of
forests with desired ecological functions. However, image classification methods to produce species
distribution maps for supporting sustainable forest management are still lacking in the Miombo
woodland ecoregion. This study used multi-date multispectral Unmanned Aerial Systems (UAS)
imagery collected at key phenological stages (leaf maturity, transition to senescence, and leaf flushing)
to classify five dominant canopy species of the wet Miombo woodlands in the Copperbelt Province of
Zambia. Object-based image analysis (OBIA) with a random forest algorithm was used on single date,
multi-date, and multi-feature UAS imagery for classifying the dominant canopy tree species of the
wet Miombo woodlands. It was found that classification accuracy varies both with dates and features
used. For example, the August image yielded the best single date overall accuracy (OA, 80.12%,
0.68 kappa), compared to October (73.25% OA, 0.59 kappa) and May (76.64% OA, 0.63 kappa). The use
of a three-date image combination improved the classification accuracy to 84.25% OA and 0.72 kappa.
After adding spectral indices to multi-date image combination, the accuracy was further improved to
87.07% and 0.83 kappa. The results highlight the potential of using multispectral UAS imagery and
phenology in mapping individual tree species in the Miombo ecoregion. It also provides guidance
for future studies using multispectral UAS for sustainable management of Miombo tree species.
