Suchergebnisse

This study was set up to find out how Finnish timber transport entrepreneurs perceive their operating environment in terms of using larger and heavier vehicles (LHVs and HCVs), with the main aim of providing guidance for developing sustainable policies that would improve timber transportation. A total of 100 entrepreneurs responded to a questionnaire survey which contained three sections administrated by an online service. Five-point Likert scales were used to collect perceptions and, in addition to statistical descriptors, the Kruskal Wallis and Mann-Whitney U test was used to compare the responses of entrepreneurs. They felt that the 76 t vehicle combination is best suited for transporting timber when transport starts from the forest. When the analysis considered transport from terminals, 76 t combinations remained the most popular, but the popularity of >76 t combinations increased substantially. The farther the LHVs operate from the highways, the less suitable the road network was perceived for timber transport. The results also show that driving and rest time regulations as parts of the working time legislation were perceived as the most disruptive. Further, the availability, responsiveness of technical support, and long delays in solving the queries of drivers were critical problems for the efficient use of in-vehicle ICTapplications. The profitability over the 2014–2018 period has declined more for entrepreneurs using <76 t vehicle combinations. When looking at the development of profitability over the 2020–2024 period, the responses changed in a more positive direction. However, the current skilled workers are retiring and there are too few educated ones to replace them. This is expected to increase wages, which may be reflected in a decreased profitability in the timber transport sector in the future.

In recent decades, Zagros forests from western Iran have experienced dramatic changes in cover and structure. Conservation policies, on the other hand, have existed or are being implemented in these forests since 2002 to prevent deforestation. There is, however, the question on how effective were the conservation policies in preventing forest loss. The goal of this study was to analyze the effect of conservation policies in preventing forest loss, as well as to forecast their future effectiveness. Since the spatio-temporal changes in forest cover, land-use and its patterns occur in a non-linear way, this study was based on the use of Land Transformation Model (LTM). Using geographic information systems (GIS) and artificial neural networks (ANNs), this model forecasts future forest changes for the next 30 years. Three scenarios were used for this purpose, in which the input patterns included the years 2002-2012, 2002-2022, and 2012-2022. Based on these, deforestation was predicted for the next three decades using 14 variables. Assuming no changes in the implementation of conservation policies in the Zagros forests, the model was characterized by a consistent accuracy and indicated a projected pattern of increased deforestation over the next years in the region. In other words, by the ongoing conservation policies, the net deforestation overtakes net reforestation. It appears that to stop further forest degradation, Iran's Forestry Service decision-makers must implement improved forest conservation policies.

Implementing a scheduled and reliable estimation of forest characteristics is important for the sustainable management of forests. This study aimed at evaluating the capability of Sentinel-2 satellite data to estimate above-ground biomass (AGB) in coppice forests of Persian oak (Quercus brantii var. persica) located in Western Iran. To estimate the AGB, field data collection was implemented in 80 square plots (40×40 m, area of 1600 m2). Two diameters of the crown were measured and used to calculate the AGB of each tree based on allometric equations. Then, the performance of satellite data in estimating the AGB was evaluated for the area of study using the field-based AGB (dependent variable) as well as the spectral band values, spectrally-derived vegetation indices (independent variables) and four machine learning (ML) algorithms: Multi-Layer Perceptron Artificial Neural Network (MLPNN), k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Regression (SVR). A five-fold cross-validation was used to verify the effectiveness of models. Examination of the Pearson's correlation coefficient between AGB and the extracted ‎values showed that IPVI and NDVI vegetation indices had the highest correlation with AGB (r = 0.897). The results indicated that the MLPNN algorithm was the best ML option (RMSE = 1.71 t ha-1; MAE = 1.37 t ha-1; relative RMSE = 24.75%; R2 = 0.87) in estimating the AGB, providing new insights on the capability of remotely sensed-based AGB modeling of sparse Mediterranean forest ecosystems in an area with limited number of field sample plots.

Biomass for energy production and other bioproducts may be procured from various sources including willow short-rotation coppice (WSRC). Management of WSRCs involves several operations, including harvesting, which accounts for the greatest cost share and, if conducted motor-manually, it can expose the workers to noise, uncomfortable work postures and high cardiovascular loads. In this study, we evaluated the productivity, physical strain, exposure to noise, and postural risk index of workers operating in motor-manual felling of WSRC using a set of automatic dataloggers. Productivity of felling operations was rated at 0.07 ha/h, which is in line with the results reported by other studies. Cardiovascular load was rated at cca. 35% of the HRR, indicating a medium to heavy work experienced by the feller, with a greater contribution of tasks involving movement. Exposure to noise (LEX,8h = 95.19) exceeded the limit value set by the European legislation (87 dBA) and it could increase as a function of the engine utilization rate, which was 68% in this study, advocating for mandatory wearing of protective equipment. Postural risk index was evaluated at 191.11% for the worker handling the brush cutter and at 192.02% for the manual assistant indicating rather reduced risks, but also the need to evaluate how the dynamic work of the upper limbs would affect the workers' health. While this work stands for a preliminary case study, the procedures described may be successfully used to easily collect long-term data in such operations.