Abstract:Motor-manual tree felling and processing (MMTFP) is among the most used options in timber harvesting operations and it is formally known to be a heavy job exposing the workers to safety hazards and harmful factors. Nevertheless, both workload and exposure depend on many operational, organizational, and worker-related parameters. Few studies have evaluated the ergonomics of such operations and fewer have been carried out using an integrated approach able to collect and interpret data for more than one ergonomic parameter. This study evaluated the ergonomic conditions of task-based MMTFP operations in flatland poplar forests by the means of workload, exposure to noise, and risk of musculoskeletal disorders. A fully-automatic approach was used to collect and pair the heart rate and noise exposure data that was complemented by video recording to collect postural data. Workload experienced by the worker was evaluated in terms of heart rate reserve (%HRR), indicating a heavy load during the productive time (%HRR = 46%); exposure to noise was calculated at the task and study level, exceeding (LAeq = 97.15 dB(A); L EX,8h = 96.18 dB(A)) the acceptable limits; and the risk of musculoskeletal disorders was evaluated using the concepts and procedures of the Ovako Working Posture Analysis System, indicating a high postural risk index (PRI = 275), which can cause musculoskeletal disorders (MSD). For more conclusive results, the research should be extended to cover the relevant variability factors.

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Accurate and real-time land use/land cover (LULC) maps are important to provide precise information for dynamic monitoring, planning, and management of the Earth. With the advent of cloud computing platforms, time series feature extraction techniques, and machine learning classifiers, new opportunities are arising in more accurate and large-scale LULC mapping. In this study, we aimed at finding out how two composition methods and spectral–temporal metrics extracted from satellite time series can affect the ability of a machine learning classifier to produce accurate LULC maps. We used the Google Earth Engine (GEE) cloud computing platform to create cloud-free Sentinel-2 (S-2) and Landsat-8 (L-8) time series over the Tehran Province (Iran) as of 2020. Two composition methods, namely, seasonal composites and percentiles metrics, were used to define four datasets based on satellite time series, vegetation indices, and topographic layers. The random forest classifier was used in LULC classification and for identifying the most important variables. Accuracy assessment results showed that the S-2 outperformed the L-8 spectral–temporal metrics at the overall and class level. Moreover, the comparison of composition methods indicated that seasonal composites outperformed percentile metrics in both S-2 and L-8 time series. At the class level, the improved performance of seasonal composites was related to their ability to provide better information about the phenological variation of different LULC classes. Finally, we conclude that this methodology can produce LULC maps based on cloud computing GEE in an accurate and fast way and can be used in large-scale LULC mapping.

Forest canopy cover (FCC) is an important ecological parameter of forest ecosystems, and is correlated with forest characteristics, including plant growth, regeneration, biodiversity, light regimes, and hydrological properties. Here, we present an approach of combining Sentinel-2 data, high-resolution aerial images, and machine learning (ML) algorithms to model FCC in the Hyrcanian mixed temperate forest, Northern Iran. Sentinel-2 multispectral bands and vegetation indices were used as variables for modeling and mapping FCC based on UAV ground truth to a wider spatial extent. Random forest (RF), support-vector machine (SVM), elastic net (ENET), and extreme gradient boosting (XGBoost) were the ML algorithms used to learn and generalize on the remotely sensed variables. Evaluation of variable importance indicated that vegetation indices including NDVI, NDVI-A, NDRE, and NDI45 were the dominant predictors in most of the models. Model accuracy estimation results showed that among the tested models, RF (R2 = 0.67, RMSE = 18.87%, MAE = 15.35%) and ENET (R2 = 0.63, RMSE = 20.04%, MAE = 16.44%) showed the best and the worst performance, respectively. In conclusion, it was possible to prove the suitability of integrating UAV-obtained RGB images, Sentinel-2 data, and ML models for the estimation of FCC, intended for precise and fast mapping at landscape-level scale.

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.

Motor-manual operations are commonly implemented in the traditional and short rotation forestry. Deep knowledge of their performance is needed for various strategic, tactical and operational decisions that rely on large amounts of data. To overcome the limitations of traditional analytical methods, Artificial Intelligence (AI) has been lately used to deal with various types of signals and problems to be solved. However, the reliability of AI models depends largely on the quality of the signals and on the sensing modalities used. Multimodal sensing was found to be suitable in developing AI models able to learn time and location-related data dependencies. For many reasons, such as the uncertainty of preserving the sensing location and the inter- and intra-variability of operational conditions and work behavior, the approach is particularly useful for monitoring motor-manual operations. The main aim of this study was to check if the use of acceleration data sensed at two locations on a brush cutter could provide a robust AI model characterized by invariance to data sensing location. As such, a Multi-Layer Perceptron (MLP) with backpropagation was developed and used to learn and classify operational events from bimodally-collected acceleration data. The data needed for training and testing was collected in the central part of Romania. Data collection modalities were treated by fusion in the training dataset, then four single-modality testing datasets were used to check the performance of the model on a binary classification problem. Fine tuning of the regularization parameters (α term) has led to acceptable testing and generalization errors of the model measured as the binary cross-entropy (log loss). Irrespective of the hyperparameters’ tunning strategy, the classification accuracy (CA) was found to be very high, in many cases approaching 100%. However, the best models were those characterized by α set at 0.0001 and 0.1, for which the CA in the test datasets ranged from 99.1% to 99.9% and from 99.5% to 99.9%, respectively. Hence, data fusion in the training set was found to be a good strategy to build a robust model, able to deal with data collected by single modalities. As such, the developed MLP model not only removes the problem of sensor placement in such applications, but also automatically classifies the events in the time domain, enabling the integration of data collection, handling and analysis in a simple less resource-demanding workflow, and making it a feasible alternative to the traditional approach to the problem.

Steep terrain harvesting can only be implemented by a limited set of operational alternatives; therefore, it is important to be efficient in such conditions, in order to avoid incurring high costs. Harvesting abiotically-disturbed forests (salvage harvests caused by wet snow), which is becoming common these days, can significantly impact the operational efficiency of extraction operations. This study was implemented in order to evaluate the performance of truck-mounted uphill cable yarding operations in salvage logging deployed in coniferous stands. A time study was used to estimate the productivity and yarding costs, and predictive models were developed in order to relate the time consumption and productivity to the relevant operational factors, including the degree of wood damage. The average operational conditions were characterized by an extraction distance of 101 m and a lateral yarding distance of 18 m, resulting in a productivity rate of 20.1 m3 h−1. In response to different kind of delays, the productivity rate decreased to 12.8 m3 h−1. Under the prevailing conditions, lateral yarding accounted for 32% of the gross work cycle time, and for 50% of the delay-free work cycle time of the machine. Decreasing the lateral yarding distance and increasing the payload volume to the maximum capacity of the machine would eventually lead to a yarding productivity of close to 30 m3 per SMH (scheduled machine hour). The calculation of the gross costs of uphill yarding showed that the labor costs (35.7%) were slightly higher than the fixed costs (32.9%), and twice as high compared to the variable costs (17.7%). The remote control of the carriage, mechanical slack-pulling mechanisms, and radio-controlled chokers are just some of the improvements that would have led to increments in operational efficiency.

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