Forest management requires balancing ecological, economic, and social objectives, often involving complex optimisation problems. Traditional mathematical methods struggle with these challenges, leading to the adoption of metaheuristic approaches like the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). This paper introduces a custom NSGA-II algorithm, incorporating a specialised mutation operator to enhance solution generation for multi-objective forest planning. The custom NSGA-II is compared to the standard NSGA-II in a scenario aiming to maximise timber harvest volume and minimise its standard deviation, with a minimum volume constraint. Key performance metrics include non-dominated solutions, spacing, computational cost, and hypervolume. The results demonstrate that the custom NSGA-II provides more valid solutions and better explores the solution space. This approach offers a user-friendly and efficient tool for forest managers, integrating well with Web-based systems for modern, sustainability-oriented forest planning.

Measuring biometric tree characteristics to estimate the volume of wood in a forest area is a time consuming task. It is usually performed by a team of two or more people, who measure the diameter and height of several trees in sampling plots. The results are then extrapolated for the forest stand. The present paper describes a method which facilitates estimating tree biometric parameters using computational techniques. A camera takes two pictures of each sample tree, with an especially designed target placed close to the tree, to facilitate image processing and camera calibration steps. Taking advantage of the trees’ natural shape and assuming a symmetric stem, the diameter and height of the tree stems are estimated from the images and the volumes of the tree stems are calculated. Experimental trials show promising results, exhibiting errors similar to the traditional methods used currently, in the range of 10%, showing that the method is suitable for forest inventory.

Cork is a versatile natural material. It can be used as an insulator in construction, among many other applications. For good forest management of cork oaks, forest owners need to calculate the volume of cork periodically. This will allow them to choose the right time to harvest the cork. The traditional method is laborious and time consuming. The present work aims to automate the process of calculating the trunk area of a cork oak from which cork is extracted. Through this calculation, it will be possible to estimate the volume of cork produced before the stripping process. A deep neural network, Mask R-CNN, and a machine learning algorithm are used. A dataset of images of cork oaks was created, where targets of known dimensions were fixed on the trunks. The Mask R-CNN was trained to recognize targets cork regions, and so the area of cork was estimated based on the target dimensions. Preliminary results show that the model presents a good performance in the recognition of targets and trunks, registering a mAP@0.7 of 0.96. After obtaining the mask results, three machine learning models were trained to estimate the cork volume based on the area and biometric parameters of the tree. The results showed that a support vector machine produced an average error of 8.75%, which is within the error margins obtained using traditional methods.

Pinus pinaster, commonly called the maritime pine, is a vital species in Mediterranean forests. Its ability to thrive in the local climate and rapid growth make it an essential resource for wood production and reforestation efforts. Accurately estimating the volume of wood within a pine forest is of great significance to the wood industry. The traditional process is either a rough estimation without measurements or a time-consuming process based on manual measurements and calculations. This article presents a method for determining a tree’s diameter, total height, and volume based on a photograph. The method involves placing reference targets of known dimensions on the trees. A deep learning neural network is used to extract the tree trunk and the targets from the background, and the dimensions of the trunk are estimated based on the dimensions of the targets. The results indicate less than 10% estimation errors for diameter, height, and volume in general. The proposed methodology automates the estimation of the dendrometric characteristics of trees, reducing field time consumed in a forest inventory and without the need to use nonprofessional instruments.

In Portugal, increasing wildfire frequency and severe storm events have intensified the need for advanced monitoring tools to assess forest damage and recovery efficiently. This study explores the application of deep learning neural network techniques, specifically the Mask R-CNN architecture, for the automatic detection of trees in Pinus pinaster stands using RGB and multispectral imagery captured by a drone. The research addresses two distinct forest scenarios, resulting from disturbances intensified by climate change. The first concerns the detection of fallen trees following an extreme weather event to support damage assessment and inform post-disturbance forest management. The second focuses on segmenting individual trees in a newly established plantation after wildfire to evaluate the effectiveness of ecological restoration efforts. The collected images were processed to generate high-resolution orthophotos and orthomosaics, which were used as input for tree detection using Mask R-CNN. Results showed that integrating drone-based imagery with deep learning models can significantly enhance the efficiency of forest assessments, reducing the need for fieldwork effort and increasing the reliability of the collected data. Results demonstrated high performance, with average precision scores of 90% for fallen trees and 75% for recently planted trees, while also enabling the extraction of spatial metrics relevant to forest monitoring. Overall, the proposed methodology shows strong potential for rapid response in post-disturbance environments and for monitoring the early development of forest plantations.

Forests play a critical role in biodiversity and in the conservation of ecosystems. Reliable and efficient forest inventories are therefore essential for sustainable forest management. Handheld mobile laser scanning (HMLS) is operationally attractive for forest inventory but has rarely been validated against a destructive reference in Mediterranean Pinus pinaster stands. We propose an open-source five-phase HMLS pipeline comprising PDAL pre-processing with CSF ground classification, TLS2trees segmentation, CSF-anchored per-tree setup, slice-wise stem reconstruction with a sequential RANSAC prior, a one-parameter species-specific shape model, and analytical volume integration with a form-factor sanity clamp. The pipeline is calibrated against a destructive reference of 45 Pinus pinaster trees across four plots in central Portugal. Leave-one-plot-out cross-validation produces a volume MAPE of 9.9%, a height MAPE of 4.4% and a DBH MAPE of 3.9%. On the matched sub-sample for which the FJD Trion S1 manufacturer software and a manual CloudCompare workflow also have outputs, the proposed pipeline is competitive with the manual workflow on volume and delivers the lowest median DBH error of the three methods, while remaining fully open-source and reporting per-tree quality and uncertainty diagnostics alongside every estimate.