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Abstract

This study explores the potential of spatially explicit Harvester Production Reports (HPRs) for automatic annotation of Aerial Laser Scanning (ALS) data at tree-level, enabling accurate tree species classification using Convolutional Neural Networks (CNNs). By integrating HPRs into the modelling process, this approach provides a practical solution for addressing challenges in remote sensing data annotation for forestry applications. The ALS data were acquired in managed Norway spruce-dominated forests in southern Sweden using a dual-wavelength system composed by two monochromatic sensors. Thus, three datasets were produced: the 905 nm miniVUX dataset (similar to 100 points/m(2)), the 1550 nm VUX dataset (similar to 875 points/m(2)), and the dual-wavelength dataset (similar to 975 points/m(2)), the last being a junction of the two first datasets. The automatic annotation was performed by matching tree records in the HPR and ALS data based on spatial proximity and height similarity, with a total of 45,516 HPR-recorded tree positions being linked to ALS-derived segments and assigned species labels based on HPR records. Then, the individual tree-level ALS point clouds were converted into 2D images from multiple viewing angles, with varying image dimensions and pixel sizes to accommodate trees of different sizes. These images served as input for CNN-based classification, enabling species identification across ALS datasets with varying spectral and spatial resolutions. The CNN models were trained and evaluated to classify trees into Norway spruce, Scots pine, Deciduous, and a "Noise" class for segmentation errors. The classification accuracy varied according to the dataset used, with the dual-wavelength dataset achieving the highest macro-F1 score (0.896), followed by the VUX dataset (0.894) and miniVUX dataset (0.835). These findings highlight spatially explicit HPRs as efficient, high-quality reference data for CNN-based tree species classification with minimal annotation effort.

Keywords

Tree species classification; Convolutional neural networks; Aerial laser scanning; Dual-wavelength; Harvester data

Published in

International Journal of Applied Earth Observation and Geoinformation
2025, volume: 140, article number: 104607
Publisher: ELSEVIER

SLU Authors

UKÄ Subject classification

Earth Observation

Publication identifier

  • DOI: https://doi.org/10.1016/j.jag.2025.104607

Permanent link to this page (URI)

https://res.slu.se/id/publ/142090