Domain-Adapted Supervised Learning for Tree Species Mapping Using UAV Multispectral Data

Individual tree species classification is essential for detailed forest inventories, ecosystem monitoring, and biodiversity assessment. While UAV-acquired RGB and multispectral (MS) imagery have advanced tree species mapping, most studies focus on a single sensor type. In practice, UAV platforms carry diverse sensors with varying spatial resolutions, spectral bands, radiometric responses, and noise characteristics, introducing domain shifts that limit model generalization across datasets. To overcome these challenges, we propose a supervised cross-sensor transfer learning approach, leveraging a DenseNet-121 model pretrained on high-resolution UAV RGB imagery to improve classification on lower-resolution multispectral imagery with limited labelled data. The adapted model achieved 75% overall accuracy and a macro-F1 score of 0.706, significantly improving over models trained from scratch. Its performance was further evaluated on downsampled UAV MS imagery simulating conventional airborne multispectral photographs, demonstrating robustness and practical applicability for regional-scale forest inventories. This study highlights cross-domain transfer learning as a pathway toward sensor-independent, efficient, and operationally scalable tree species classification.