Forest plantations are globally important for the economy and are significant for carbon sequestration. Properly managing plantations requires accurate information about stand timber stocks. In this study, we used the area (ABA) and individual tree (ITD) based approaches for estimating stem volume in fast-growing Eucalyptus spp forest plantations. Herein, we propose a new method to improve individual tree detection (ITD) in dense canopy homogeneous forests and assess the effects of stand age, slope and scan angle on ITD accuracy. Field and Light Detection and Ranging (LiDAR) data were collected in Eucalyptus urophylla x Eucalyptus grandis even-aged forest stands located in the mountainous region of the Rio Doce Valley, southeastern Brazil. We tested five methods to estimate volume from LiDAR-derived metrics using ABA: Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM), and linear and Gompertz models. LiDAR-derived canopy metrics were selected using the Recursive Feature Elimination algorithm and Spearman’s correlation, for nonparametric and parametric methods, respectively. For the ITD, we tested three ITD methods: two local maxima filters and the watershed method. All methods were tested adding our proposed procedure of Tree Buffer Exclusion (TBE), resulting in 35 possibilities for treetop detection. Stem volume for this approach was estimated using the Schumacher and Hall model. Estimated volumes in both ABA and ITD approaches were compared to the field observed values using the F-test. Overall, the ABA with ANN was found to be better for stand volume estimation ( $r_{y\widehat{y}}$ = 0.95 and RMSE = 14.4%). Although the ITD results showed similar precision ( $r_{y\widehat{y}}$ = 0.94 and RMSE = 16.4%) to the ABA, the results underestimated stem volume in younger stands and in gently sloping terrain (<25%). Stem volume maps also differed between the approaches; ITD represented the stand variability better. In addition, we discuss the importance of LiDAR metrics as input variables for stem volume estimation methods and the possible issues related to the ABA and ITD performance.

Precision forestry seeks to optimize forest management by using site-specific information at fine spatial scales, often supported by remote sensing. Implementing this framework requires detailed ground-truth data of forest attributes and conditions, but the collection of such information across large areas is limited by cost and scalability. Complementary data sources can help address this demand by expanding the availability of tree-level information. This thesis explores how different data sources, namely close-range laser scanning, airborne laser scanning, harvester production reports, and synthetic point clouds, can expand data availability for precision forestry. Paper I develops and evaluates a method for deriving stem attributes such as diameter, taper, and volume from car-mounted mobile laser scanning acquired along forest roads, demonstrating its potential as an efficient source of tree-level reference data. Paper II assesses the suitability of mobile laser scanning as an alternative to conventional field plots for training airborne laser scanning-based models, showing that both sources can support tree-level modelling of diameter and volume. Paper III presents a pipeline for automatic tree species annotation by linking airborne laser scanning data with harvester production reports, demonstrating that data derived from forest operations can reduce the need for field surveys or manual labelling in species classification tasks. Finally, Paper IV introduces a semi-empirical simulation framework for generating synthetic stem defects in terrestrial laser scanning point clouds, using them to train a convolutional neural network for crook detection and discussing the implications of simulation. Taken together, the four studies show that complementary data sources have the potential to serve as ground-truth information in forest assessments, providing data of sufficient quality and quantity to support remote sensing-based forest inventories at tree-level. This thesis underlines both opportunities and limitations of these approaches, highlighting their relevance for integration into inventory frameworks.

Stem crooks are critical stem defects that reduce timber quality and value. This study proposes a simulation pipeline for generating synthetic crooks in Terrestrial Laser Scanning (TLS) point clouds and evaluates the performance of a Convolutional Neural Network (CNN) trained on this dataset for detecting crooks in real-world TLS data. We introduced parameterized crook deformations into TLS-derived stems, producing 14 000 synthetic examples based on Scots pine (Pinus sylvestris L.) trees. The simulated crooks were represented in two patterns: single-and double-directional bends. Each stem was rasterized into 2D images from multiple vertical sections and viewpoints. We trained a YOLOv8s model exclusively on these synthetic images and tested its performance on an independently collected real-world TLS dataset containing 310 stems and 65 visually annotated crooks. The detector achieved F1-scores above 0.6 for low Intersection over Union (IoU) thresholds (<0.3), demonstrating its ability to identify crooks despite domain differences between simulated and real point clouds. However, localization accuracy declined under stricter IoU criteria, with a mean Average Precision (mAP@50–95) of 0.24. Error patterns revealed more commission errors in upper stem regions and more omissions near the butt logs, reflecting branch complexity, point cloud sparsity, and limitations in lower-stem simulations. These results show that synthetic crook data can provide valuable training material for defect detection while also highlighting the challenges associated with using such synthetic data for model training.

Simulates parameterized single- and double-directional stem deformations in tree point clouds derived from terrestrial or mobile laser scanning, enabling the generation of realistic synthetic datasets for training and validating machine learning models in wood defect detection, quality assessment, and precision forestry. For more details see Pires (2025) .