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.