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.

Due to the challenges brought by field measurements to estimate the aboveground biomass (AGB), such as the remote locations and difficulties in walking in these areas, more accurate and cost-effective methods are required, by the use of remote sensing. In this study, Sentinel-2 data were used for estimating the AGB in pure stands of Carpinus betulus (L., common hornbeam) located in the Hyrcanian forests, northern Iran. For this purpose, the diameter at breast height (DBH) of all trees thicker than 7.5 cm was measured in 55 square plots (45 × 45 m). In situ AGB was estimated using a local volume table and the specific density of wood. To estimate the AGB from remotely sensed data, parametric and nonparametric methods, including Multiple Regression (MR), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), and Random Forest (RF), were applied to a single image of the Sentinel-2, having as a reference the estimations produced by in situ measurements and their corresponding spectral values of the original spectral (B2, B3, B4, B5, B6, B7, B8, B8a, B11, and B12) and derived synthetic (IPVI, IRECI, GEMI, GNDVI, NDVI, DVI, PSSRA, and RVI) bands. Band 6 located in the red-edge region (0.740 nm) showed the highest correlation with AGB (r = −0.723). A comparison of the machine learning methods indicated that the ANN algorithm returned the best ABG-estimating performance (%RMSE = 19.9). This study demonstrates that simple vegetation indices extracted from Sentinel-2 multispectral imagery can provide good results in the AGB estimation of C. betulus trees of the Hyrcanian forests. The approach used in this study may be extended to similar areas located in temperate forests.

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.

The forestry and timber industry are strong sectors in the economies of European countries. The current trend of introducing forestry management that respects the various functions of the forest has created new challenges. However, forestry itself, as well as those challenges, varies in different regions in Europe. The aim of this review paper was to describe forest resources and their potential as well to define challenges in forestry and forest engineering in regions of East Europe. Case studies were selected from four countries: Croatia, Latvia, Poland and Romania. The background data and information of the forest-based sector included: forest resources and forest productivity, forest utilisation, development of forest operations and difficulties in forest management. In the analysed countries, state-owned forestry was represented by at least 45%. Forestry is an important sector in all four countries and future challenges are observed in forest management and forest engineering mainly including: an increase in timber resources, improvement in species composition for better productivity and the introduction of effective mechanised forest operations in pre-commercial thinning. Further improvement of harvester heads is expected for the harvesting of broadleaved species and for young stands. Issues linked to the environment were also recognised as challenging factors: mild winters make it difficult to use CTL technology on wet and sensitive sites. Additionally, dry seasons have a high impact on forest fire frequency, but this can be controlled by effective monitoring systems. Improvement in IT systems used in forest operations should limit the carbon footprint by optimising transport, machine use and limiting fuel use. Finally, innovations are recognised as key issues in the improvement of forest management and forest engineering; therefore, special budgets have been allocated to support science and development.