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Early thinnings are laborious and costly. Thus forest companies are searching for cost and time efficient ways to carry out this task. The studyâs purpose was to determine the productivity of the EF28 accumulating energy wood harvesting head in harvesting small-diameter hornbeam ( L.) undergrowth trees and evaluate the effect of its multi-tree handling (MTH) capacity on time consumption. The harvester was a wheeled, three-axle Komatsu 911. A time study of 7.1 hours on 19 plots, with a total area of 0.76 ha was conducted. On average, the harvested tree volume was 8 dm³ and the stand density was 2666 trees/ha. The productivity was modelled with MTH conduction, mean diameter at breast height and the number of trees handled per cycle as independent variables. On average, MTH took 27% longer per cycle, increased extracted volume per cycle by 33% and consequently increased productivity with 5.0%. In 71.9% of the cycles more than one tree was handled and if so, dimensions were smaller than in single-tree handling (5.8 cm vs. 12.0 cm). Maximum felling diameter of 23 cm was about 15% smaller than in softwood (according to the manufacturerâs specifications) and the driver didnât exploit the EF28âs theoretical potential in terms of trees handled per cycle. It can be concluded that the head could significantly improve productivity in small-diameter wood procurement.Carpinus betulus

Determining the moisture content of naturally dried fuel stock without frequent measuring is a problem still unsolved. Modelling moisture content based on automatically captured meteorological data could provide a solution. An accurate model would allow the drying period and the point of chipping to be optimised. For the experimental study, a metal frame supported by load sensors and loaded with 17 tons of logwood was set up next to a meteorological station. A multiple linear regression model was used to link meteorological and load data to provide a formula for determining the moisture content. The pile dried for a period of 14 months (average temperature of 7.3 °C, a humidity of 81%, and 777 mm of rainfall). The overall moisture content dropped from 50.1% to 32.2%. The regression model, which based on daily means and sums of meteorological parameters, provided a mean deviance from the observed curve of â0.51%±0.71% within the period of investigation. Relative humidity was found to be most important parameter in drying. Increased moisture content resulting from rainfall greater than 30 mm per day reverted back to pre-rainfall values within two to three days, if no other rainfall events followed. Covering the pile would have a positive effect on the drying performance. In terms of economic benefit it could be shown that natural drying is beneficial. Overall this study shows that meteorological data used in site specific drying models can adequately predict the moisture content of naturally dried logwood.

The promotion of electric energy production from solid biomass by the Austrian government has lead to a boom in the construction of new combined heat and power plants. The current total demand for wood chips in the research area for energy purposes is 70 400 m of loose volume chips per year. The expected increase in demand due to these new plants is more than 4 times greater than current demand: up to 302 700 m of loose volume per year. Even if the energy wood feedstock potential is satisfactory, the design of the supply chain is still unresolved. The aim of this study is to give decision-makers a base for further development. To accomplish this, we designed and tested four different supply scenarios: one for 9 plants and one for 16 plants. The scenarios were developed using a combination of geographic information systems (GIS) and linear programming methods. The results indicate that direct transport of solid fuel wood as round wood and chipping at the plant is the cheapest supply system with a resulting cost of 5.6â6.6 EUR/m loose. Using harvesting residues can only be recommended for large plants because of poor fuel quality. In this case, residues would be chipped at or near the landing, piled and transported via self-loading trucks at a cost between 8.4 and 9.1 EUR/m loose. In order to meet increasing demand and to ensure a continuous supply, especially during the winter and spring seasons it is necessary to optimize the supply chain by including storage terminals. However, using terminals and increased demand both lead to higher logistical costs. For example, if the total volume is handled via terminals, the average supply costs including storage will increase by 26%. Higher demand increases the costs by 24%.3333

A novel Bayesian spatial distributional regression model is presented to predict forest structural diversity in terms of the distributions of the stem diameter at breast height (DBH) in the protection forests in Ebensee, Austria. The distributional regression approach overcomes the limitations and uncertainties of traditional regression modeling, in which the conditional mean of the response is regressed against explanatory variables. The distributional regression addresses the complete conditional response distribution, instead. In total 36,338 sample trees were measured via a handheld mobile personal laser scanning system (PLS) on 273 sample plots each having a 20 m radius. Recent airborne laser scanning (ALS) data were used to derive regression covariates from the normalized digital vegetation height model (DVHM) and the digital terrain model (DTM). Candidate models were constructed that differed in their linear predictors of the two gamma distribution parameters. In the distributional regression approach, covariates can enter the model in a flexible form, such as via nonlinear smooth curves, cyclic smooths, or spatial effects. Supported by Bayesian diagnostics DIC and WAIC, nonlinear smoothing splines outperformed linear parametric slope coefficients, and the best implementation of spatial structured effects was achieved by a Gaussian process smooth. Model fitting and posterior parameter inference was achieved by using full Bayesian methodology and MCMC sampling algorithms implemented in the R-package BAMLSS. With BAMLSS, spatial interval predictions of the DBH distribution at any new geo-locations were enabled via straightforward access to the posterior predictive distributions of the model terms and by offering simple plug-in solutions for new covariate values. A cross-validation analysis validated the robustness of the proposed method's parameter estimation and out-of-sample prediction. Spatial predictions of stem count proportions per DBH classes revealed that regeneration of smaller trees was lacking in certain areas of the protection forest landscape. Therefore, the intensity of final felling needs to be increased to reduce shading from the dense, overmature shelter trees and to promote sunlight for the young regeneration trees. [ABSTRACT FROM AUTHOR]

Forestry work, which is considered one of the most demanding and dangerous professions in the world, is claiming more and more lives. In a country as small as Austria, more than 50 forestry workers are killed in accidents every year, and the number is increasing rapidly. This serves as a catalyst for us to implement more stringent measures for workplace safety in order to achieve the sustainability objective of SDG 3, which focuses on health and well-being. This study contributes to the analysis of occupational accidents and focuses on two large real-world datasets from both the Austrian Federal Forests (ÖBf) and the Austrian Workers' Compensation Board (AUVA). Decision trees, random forests, and fully connected neural networks are used for the analysis. By exploring different interpretation methods, this study sheds light on the decision-making processes ranging from basic association to causal inference and emphasizes the importance of causal inference in providing actionable insights for accident prevention. This paper contributes to the topic of explainable AI, specifically in its application to occupational safety in forestry. As a result, it introduces novel aspects to decision support systems in this application domain. [ABSTRACT FROM AUTHOR]

Smart forestry, an innovative approach leveraging artificial intelligence (AI), aims to enhance forest management while minimizing the environmental impact. The efficacy of AI in this domain is contingent upon the availability of extensive, high-quality data, underscoring the pivotal role of sensor-based data acquisition in the digital transformation of forestry. However, the complexity and challenging conditions of forest environments often impede data collection efforts. Achieving the full potential of smart forestry necessitates a comprehensive integration of sensor technologies throughout the process chain, ensuring the production of standardized, high-quality data essential for AI applications. This paper highlights the symbiotic relationship between human expertise and the digital transformation in forestry, particularly under challenging conditions. We emphasize the human-in-the-loop approach, which allows experts to directly influence data generation, enhancing adaptability and effectiveness in diverse scenarios. A critical aspect of this integration is the deployment of autonomous robotic systems in forests, functioning both as data collectors and processing hubs. These systems are instrumental in facilitating sensor integration and generating substantial volumes of quality data. We present our universal sensor platform, detailing our experiences and the critical importance of the initial phase in digital transformation—the generation of comprehensive, high-quality data. The selection of appropriate sensors is a key factor in this process, and our findings underscore its significance in advancing smart forestry. [ABSTRACT FROM AUTHOR]