Individual tree species prediction using airborne laser scanning data and derived point-cloud metrics within a dual-stream deep learning approach

Abstract

Accurate tree species mapping is essential for effective forest management but is often constrained by manual, labour-intensive workflows that limit scalability. While airborne laser scanning (ALS) supports large-scale forest attribute prediction, species classification remains difficult in complex, multi-species forests. To address this, we propose an automated, data-driven dual-stream deep learning framework that integrates ALS data with point-cloud metrics to identify individual tree species. Our framework incorporates an automated approach to individual tree segmentation and species labelling using existing forest inventory and field data, resulting in a dataset of 16,269 labelled individual tree point-clouds of four species across a 630,000 ha boreal mixed species forest in Ontario, Canada. Our dual-stream deep learning model integrates a Point Extractor to generate feature representations from raw ALS point-clouds and a complementary Metrics Network to process the point-cloud metrics. Results, based on the split test set of 2441 trees, showed that the inclusion of the Metrics Network improved tree species classification accuracy by approximately 11 % compared to models that rely solely on the Point Extractor. A weighted F1-score of 0.70 and area under the receiver operating characteristic curve of 0.88 was achieved using this dual-stream approach, along with enhanced predictive probabilities for all species thus improving the reliability of the predicted results. This approach reduces the manual processing bottleneck of individual tree segmentation and labelling and demonstrates the value of combining raw point-clouds and point-cloud metrics into a deep learning framework, offering a scalable and operational solution for reliable species predictions.