Abstract:
Individual tree crown detection (ITCD) and tree species classification are critical for forest
inventory, species-specific monitoring, and ecological studies. However, accurately detecting
tree crowns and identifying species in structurally complex forests with overlapping
canopies remains challenging. This study was conducted in a complex mixed conifer–
broadleaf forest in northern Japan, aiming to improve ITCD and species classification by
employing two machine learning models and different combinations of metrics derived
from very high-resolution (2.5 cm) UAV red–green–blue (RGB) and multispectral (MS)
imagery. We first enhanced ITCD by integrating different combinations of metrics into
multiresolution segmentation (MRS) and DeepForest (DF) models. ITCD accuracy was
evaluated across dominant forest types and tree density classes. Next, nine tree species
were classified using the ITCD outputs from both MRS and DF approaches, applying Random
Forest and DF models, respectively. Incorporating structural, textural, and spectral
metrics improved MRS-based ITCD, achieving F-scores of 0.44–0.58. The DF model, which
used only structural and spectral metrics, achieved higher F-scores of 0.62–0.79. For species
classification, the Random Forest model achieved a Kappa value of 0.81, while the DF
model attained a higher Kappa value of 0.91. These findings demonstrate the effectiveness
of integrating UAV-derived metrics and advanced modeling approaches for accurate
ITCD and species classification in heterogeneous forest environments. The proposed
methodology offers a scalable and cost-efficient solution for detailed forest monitoring and
species-level assessment.
