Drone imaging-based wall-to-wall processing pipelines for individual tree level inventory in boreal forest plots

Precise individual tree data are essential for forest management, strategic planning, efficient commercial forestry, and accurate carbon stock assessments. In this study, a wall-to-wall drone-imaging-based forest inventory processing pipeline was developed and assessed. Different cameras and data analysis methods were assessed for individual tree detection and attribute estimation at the tree and plot levels. The experiment was conducted in Finland in six boreal forest study areas, with three major tree species: Scots pine (Pinus sylvestris), Norway spruce (Picea abies), and birch (Betula pendula and Betula pubescens). RGB and multispectral (MS) cameras provided single-sensor solutions for the forest inventory pipeline, whereas a hyperspectral (HS) camera was used in combination with the RGB camera to enhance species classification. High-quality RGB data performed better than MS data for tree detection and attribute estimation. The best tree detection rates were 56–84 % in areas with mostly dominant and co-dominant trees. The two evaluated tree detection methods (local maximum and segmentation) provided similar tree detection rates and tree attribute estimation accuracies. Tree level attributes were estimated with root mean square errors (RMSEs) of 0.97 m (5.1 %) for tree height, 3.1 cm (14 %) for diameter at breast height (DBH), 129.6 cm² (25 %) for the basal area, and 0.13 m³ (23 %) for the volume. The HS camera yielded the highest tree species classification performance, with maximum f-scores of 0.81 for RGB, 0.88 for MS, and 0.89 for combined HS + RGB data. At the plot level, RMSEs for stem density, basal area, and volume were 855.7 ha^-1 (74.6 %), 6.9 m² ha⁻¹ (24.2 %), and 48.6 m³ ha⁻¹ (17.6 %), respectively. This study was the first to assess entire inventory pipelines with a comprehensive camera setup and proved that low-cost RGB and MS cameras provide acceptable performance for tree inventories in boreal forests. These results can guide the implementation of low-cost forest inventory processes.