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Monitoring early-successional trees for tropical forest restoration using low-cost UAV-based species classification
Logged forests cover four million square kilometres of the tropics, capturing carbon more rapidly than temperate
forests and harbouring rich biodiversity. Restoring these forests is essential to help avoid the worst impacts of
climate change. Yet monitoring tropical forest recovery is challenging. We track the abundance of early-successional
species in a forest restoration concession in Indonesia. If the species are carefully chosen, they can be used as an
indicator of restoration progress. We present SLIC-UAV, a new pipeline for processing Unoccupied Aerial Vehicle
(UAV) imagery using simple linear iterative clustering (SLIC)to map early-successional species in tropical forests.
The pipeline comprises: (a) a field verified approach for manually labelling species; (b) automatic segmentation of
imagery into ’superpixels’ and (c) machine learning classification of species based on both spectral and textural
features. Creating superpixels massively reduces the dataset’s dimensionality and enables the use of textural
features, which improve classification accuracy. In addition, this approach is flexible with regards to the spatial
distribution of training data. This allowed us to be flexible in the field and collect high-quality training data with
the help of local experts. The accuracy ranged from from 74.3% for a four-species classification task to 91.7% when
focusing only on the key early-succesional species. We then extended these models across 100 hectares of forest,
mapping species dominance and forest condition across the entire restoration project.NE/N008952/