Conference Agenda

Light Detection and Ranging (lidar) has become an essential tool to quantify forest structure in three dimensions, allowing extraction of tree-level metrics such as height, crown volume, diameter at breast height (DBH), and biomass. Accurate forest structure quantification supports applications such as wildfire management, biodiversity assessment, forest health monitoring, and timber management. This is particularly urgent in countries such as Canada, where wildfires pose a significant challenge to forest management due to their increasing frequency and severity; advanced fire behavior models aid wildfire preparedness by predicting fire behaviour at fine-scale in 3D but require detailed 3D fuel information including canopy and ladder fuels.

Terrestrial Laser Scanning (TLS) and Uncrewed Aerial Vehicle (UAV) lidar provide dense point clouds that allow highly accurate characterization of individual trees, critical for assessing forest attributes and wildfire fuel characteristics. However, their limited spatial coverage makes them neither time- nor cost-effective for mapping extensive forested regions. Airborne Laser Scanning (ALS), in contrast, covers broad areas efficiently by collecting data from higher altitudes, but at the cost of lower point densities (typically 1–100 points/m²), insufficient for precise individual tree characterization.

To address this challenge, this study reconstructs densified tree point clouds from low-resolution ALS data using an upsampling framework based on a deep generative network trained on real and synthetic datasets. This approach bridges the gap between ALS’s extensive coverage and the detailed structural information provided by TLS and UAV lidar, enabling accurate, large-scale quantification of forest structure for applications such as wildfire management and monitoring.

Accurate tree positions play a vital role in precision forestry and environmental sciences. In this study, we propose an accurate, efficient, and adaptable method for tree localization by integrating heading, diameter at breast height (DBH), and ultra-wideband technology. The proposed method is simple to implement in different forest environments and can determine the position of each tree within a few seconds. Compared with traditional field measures, such as laser rangefinders and inclinometers, the proposed approach is more efficient. In comparison with commonly used measures, such as terrestrial laser scanning (TLS) and mobile laser scanning (MLS), the proposed method is more cost-effective and easier to implement, making it particularly suitable for natural forests that are remote from roads yet require accurate measurements. Field experiments were conducted in a managed boreal forest in southern Finland, characterized by minimal understory vegetation and good visibility, where a total of 50 trees were mapped. Experimental results indicate that the proposed method can accurately determine tree positions with an RMSE of 0.12 m and an MAE of 0.11 m.

Plant phenotyping has become a fundamental tool in modern agronomic research, enabling quantitative analysis of morphological characteristics that can be collected in three dimensions using photogrammetric techniques or point clouds obtained by LiDAR systems. However, automatic segmentation of plants, especially the main stem and its branches, still poses a challenge for certain crops. This work proposes a non-destructive, geometry-based methodology for morphological phenotyping of tomato plants (Solanum lycopersicum) using photogrammetric point clouds. The proposed methodology consists of the following steps: stratification of the plant into horizontal sections; clustering of each stratum using the DBSCAN algorithm; selection of clusters based on the linearity tensor derived from eigenvalue analysis; and the fitting of a 3D cylinder to the linear clusters to approximate the main stem. The method was validated using manually labeled point clouds from nine tomato cultivars, achieving accuracy between 88% and 97%, with average F1-scores of 63.6% for the stem and 96% for the branches

Linking remotely sensed forest backscatter with fine-scale tree crown structural dynamics provides insights into tree growth strategies under varying conditions. In this study, we investigate whether branch-scale tree growth allocation dynamics, derived from multi-temporal TreeQSM models, are reflected in SAR and ALS observations. We analyzed branch organization dynamics of silver birch (Betula pendula) using terrestrial laser scanning data from 2021, 2023 and 2025 at a boreal forest site in southern Finland. Branch allocation metrics, including volume-weighted mean diameter (VWMD), small branch fraction (SBF), distal volume fraction, relative branch height, and top canopy volume, were quantified to capture shifts between structural reinforcement and exploratory growth. These metrics were compared with Sentinel-1 SAR features (α, entropy, C11, C22) and ALS-derived canopy metrics (plant area index, vertical complexity index) alongside local structural variables. Results show a consistent trade-off between coarse and fine branching, with strong negative correlations between ΔVWMD and ΔSBF across both periods (ρ = –0.92). SAR-derived α exhibits strong associations with these allocation shifts during 2021–2023 (ρ = –0.81 with ΔVWMD; ρ = 0.75 with ΔSBF), indicating sensitivity to internal redistribution of branch material. ALS metrics from 2021 reflect initial canopy structure and are associated with subsequent allocation shifts. Despite the small magnitude of observed changes, consistent monotonic relationships across datasets suggest that subtle within-crown branch allocation is detectable from satellite and aerial observations, reflecting the surrounding canopy context. However, weakened correlations in 2023–2025 highlight the influence of external factors on SAR signals.

Tree instance segmentation of airborne laser scanning (ALS) data is of utmost importance for forest monitoring but remains challenging due to variations in the data caused by factors such as sensor resolution, vegetation state at acquisition time, terrain characteristics, etc. Moreover, obtaining a sufficient amount of precisely labeled data to train fully supervised instance segmentation methods is expensive. To address these challenges, we propose a weakly supervised approach where labels of an initial segmentation result obtained either by a non-finetuned model or a closed-form algorithm are rated by a human operator. The labels produced during the quality assessment are then used to train a rating model, whose task is to classify a segmentation output into the same classes as specified by the human operator. Finally, the segmentation model is finetuned using feedback from the rating model. This in turn improves the original segmentation model by 34% in terms of correctly identified tree instances while considerably reducing the number of non-tree instances predicted.

Trees play a key role in our planet. They regulate climate, preserve biodiversity, and contribute to human well-being. Each species has different contributions to our globe and a specific carbon storage potential. Identify tree species enable better measurement of global carbone, help authorities for better manage forests and green spaces. Unmanned Aerial System (UAS) LiDAR has become a powerful source of 3D point cloud for vegetation analysis, given its ability to captured large area in a short time and its capacity to penetrate canopy layers. Deep learning methods extract discriminative features directly from raw point clouds and generalize well to unseen datasets. This study optimises PointNet++ deep learning architecture for tree species classification by analysing the influence of sampling configurations on the performance of model detection, by using an open-source dataset “FOR-species20K”.Three-point cloud sampling configurations (4 096, 8 192, and 16 384 points per tree) were tested with three random seeds (0,42 and 123) to assess their impact on classification accuracy and ensure stability of prediction. Results on a separate test set of 508 trees show a consistent improvement in performance of PointNet++ with a sampling configuration of 8 192 points per tree, reaching a macro-average F1-score of 89.65%, surpassing the 74.9 % reported by (Puliti et al., 2025) for evaluating the same architecture. Dominant species such as Fagus sylvatica, Picea abies, and Pinus sylvestris achieve F1-scores exceeding 90%, indicating high model robustness.