Purpose of Review: Mechanized logging operations with ground-based equipment commonly represent European production forestry but are well-known to potentially cause soil impacts through various forms of soil disturbances, especially on wet soils with low bearing capacity. In times of changing climate, with shorter periods of frozen soils, heavy rain fall events in spring and autumn and frequent needs for salvage logging, forestry stakeholders face increasingly unfavourable conditions to conduct low-impact operations. Thus, more than ever, planning tools such as trafficability maps are required to ensure efficient forest operations at reduced environmental impact. This paper aims to describe the status quo of existence and implementation of such tools applied in forest operations across Europe. In addition, focus is given to the availability and accessibility of data relevant for such predictions. Recent Findings: A commonly identified method to support the planning and execution of machine-based operations is given by the prediction of areas with low bearing capacity due to wet soil conditions. Both the topographic wetness index (TWI) and the depth-to-water algorithm (DTW) are used to identify wet areas and to produce trafficability maps, based on spatial information. Summary: The required input data is commonly available among governmental institutions and in some countries already further processed to have topography-derived trafficability maps and respective enabling technologies at hand. Particularly the Nordic countries are ahead within this process and currently pave the way to further transfer static trafficability maps into dynamic ones, including additional site-specific information received from detailed forest inventories. Yet, it is hoped that a broader adoption of these information by forest managers throughout Europe will take place to enhance sustainable forest operations.

Large language models (LLMs) such as Chat Generative Pre-Trained Transformer (ChatGPT) are increasingly used across domains, yet their generic training data and propensity for hallucination limit reliability in safety-critical fields like forestry. This paper outlines the conception and prototype of ForestGPT, a domain-specialised assistant designed to support forest professionals while preserving expert oversight. It addresses two looming risks: unverified adoption of generic outputs and professional mistrust of opaque algorithms. We propose a four-level development path: (1) pre-training a transformer on curated forestry literature to create a baseline conversational tool; (2) augmenting it with Retrieval-Augmented Generation to ground answers in local and time-sensitive documents; (3) coupling growth simulators for scenario modeling; and (4) integrating continuous streams from sensors, drones and machinery for real-time decision support. A Level-1 prototype, deployed at Futa Expo 2025 via a mobile app, successfully guided multilingual visitors and demonstrated the feasibility of lightweight fine-tuning on open-weight checkpoints. We analyse technical challenges, multimodal grounding, continual learning, safety certification, and social barriers including data sovereignty, bias and change management. Results indicate that trustworthy, explainable, and accessible LLMs can accelerate the transition to Forestry 5.0, provided that human-in-the-loop guardrails remain central. Future work will extend ForestGPT with full RAG pipelines, simulator coupling and autonomous data ingestion. Whilst exemplified in forestry, a complex, safety-critical, and ecologically vital domain, the proposed architecture and development path are broadly transferable to other sectors that demand trustworthy, domain-specific language models under expert oversight.

In view of the increasing demand for forest resources in Europe, it is an option to foster the use of non-native tree species that can keep pace with the rapidly changing environmental conditions, such as Douglas-fir (Pseudotsuga menziesii). Thus, sufficient knowledge of how to manage such introduced species is highly required. In this study, we investigate theoretical silvicultural management options of Douglas-fir for forests in central Europe. We follow a three-step approach: (i) we collect the current central European management practices based on 434 Douglas-fir stands managed by 19 forest companies in Eastern Austria and Southern Germany using a survey. (ii) We calibrate and validate a Douglas-fir parameter set for the tree growth simulator MOSES so that we are able to (iii) simulate the silvicultural management options of Douglas-fir management. Our simulation results suggest: in mixed stands, Douglas-fir should be planted in mono-species patches. This leads to about six times higher productivity compared to a random arrangement. Natural regeneration is possible but requires active management at further development since the productivity might decrease up to 86% when growing in association with the highly competitive native tree species, Common beech (Fagus sylvatica). Intensive tending, as well as thinning, yields a surplus stem volume production of more than 30% in comparison with a moderate intervention. Even if our simulation results were not validated in the field, this analysis suggests that modeling as a heuristic tool is a useful instrument for forest managers in the decision-making process.

In Europe, Norway spruce forests are facing challenges due to climate change. One possible adaptive option is to enhance the resilience of forests by thinning. Usually, thinning requires two working steps: (i) tree marking by a forest manager and (ii) the harvesting operation. Since the typical tree selection procedure by forest managers is expensive and time consuming, the integration of the tree selection process in the harvesting operation is of high interest. In this paper, we examined four tree selection methods on 21 experimental plots located within eight Norway spruce stands and assessed their impact on the future stand development. The future stand development by tree selection methods was examined by means of a simulation study using the tree growth model MOSES (MOdeling Stand rESponse). The selection methods were (i) tree selection by forest managers, (ii) tree selection by the harvester operator as part of a fully mechanized harvesting system. Since we used the tree growth model MOSES, we additionally employed (iii) a random tree selection process implemented in MOSES, and (iv) a control simulation assuming no thinning. The Norway spruce stands are located in Lower Austria. The results show that 70% of the trees selected by the forest managers were identical to those chosen by the harvester operator and thus no significant differences in the key stand parameters after a 50-year simulation is detectable. Our study suggests that the tree selection for thinning by a trained harvester operator is a cost-efficient and fast method.

The goal of this study was to develop a GIS-based Decision Support Model for selecting the best timber harvesting systems on steep terrain. The model combines multiple layers, each representing an important factor in mechanized logging. These layers are used to create a final map that functions as a spatially explicit Decision Support Model that helps decide which machines are best suited for different forest areas. A key idea of this study is to consider not only operational criteria (slope, ruggedness, wetness, and road accessibility), but also a fundamental silvicultural aspect, i.e., the assessment of tree growth classes to enable the integration of silvicultural deliberations into timber harvest planning. The data used for this model come from orthophoto image and a Digital Terrain Model (DTM). The operational factors were analyzed using GIS tools, while the silvicultural aspects were assessed using the deep learning algorithm DeepForest and tree growth equations (allometric functions). The model was tested by comparing its results with field data taken in a Norway Spruce stand in South Tyrol/Italy. The findings show that the model reliably evaluates operational factors. For silvicultural aspects, it tends to underestimate the number of small trees, but provides a good representation of tree size classes within a forest stand. The innovation of this method is that it relies on low-cost, open-source tools instead of expensive 3D scanning devices.