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The traditional visual tree assessment method is subjective in evaluating tree risks and therefore not effective in precisely detecting internal decay in tree trunk and root systems. To improve the accuracy of street tree risk assessment, a new nondestructive testing method was proposed. This new tree risk assessment method combines different non-destructive testing technologies, such as sonic tomography and ground-penetrating radar, which could significantly increase the accuracy of risk assessment in tree trunks and roots. The method was applied to evaluate the risk of 1,001 street trees in Shanghai's historical feature protection area. The results revealed that despite most street trees having low branch and trunk risk levels, more than one-third had high root risk levels. The risk factors of street trees were mainly in the trunk and root system, with a significant correlation between the street tree risk level and tree cavities, diseases, and insect pests, as well as the depth and range of the root distribution, leaning, and internal decay in trunks. With the help of non-destructive testing and risk assessment analysis, as well as targeted prevention measures, the possibility of street risk damage was largely reduced, including street trees tilting and collapsing during typhoons, etc. [ABSTRACT FROM AUTHOR]

Cable yarding remains an important option in steep terrain timber harvesting, a reason for which new or improved operational efficiency models are required to support science and practice. Developed traditionally, these models are known to require many resources, a reason for which new approaches to the problem were researched lately, mainly by the use of Global Navigation Satellite System (GNSS) data, spatial and statistical inference systems. This study evaluates the possibility of using GNSS data and machine learning techniques to classify important cable yarding events in the time domain. Three classes were assumed by the study as being relevant for cable yarding operational setup, namely carriage moving in the uphill (MU) and downhill (MD) directions, as well as carriage stopped (S). Data collected by a consumer-grade GNSS unit was processed to extract some differential parameters which were coupled with GNSS motorial and geometric features to feed a Multi-Layer Perceptron Neural Network with Back propagation (MLPNNB) in a pre-evaluation phase which aimed at mining the data structure as a strategy to develop the best MLPNNB configuration for training and testing. Leg distance, difference in elevation, speed of the carriage, and difference in heading were used together and interchangeably in this phase, based on logical assumptions. As a result of pre-evaluation, a MLPNNB using all these datasets was found to be the best scenario. Based on this outcome, the data was split into a training (70%) and a testing (30%) subset, then the MLPNNB was used to learn and generalize on these subsets. The main results indicate that the MLPNNB had an excellent performance, with a classification accuracy of 98.7, 98.4, and 98.8% in the pre-evaluation, training, and testing phases, respectively. Log-loss errors were also found to be very low (5, 5.9 and 4.1%, respectively), indicating a high generalization capability of the MLPNNB model. Based on the results, the main conclusion of the study is that original and derived GNSS data coupled with machine learning techniques could prove to be an important tool for operational monitoring and cable yarding efficiency model development, mainly due to the possibility of working with large amounts of data. [ABSTRACT FROM AUTHOR]

Research and development efforts are currently underway worldwide to examine the potential use of a wide range of non-destructive technologies (NDT) for evaluating wood and wood-based materials, from the assessment of standing trees to in-place structures. For this purpose, acoustic velocity by the Fakopp time of flight (TOF) tool was used to estimate the influence of four thinning treatments performed in Southern Italy. The objective of the study was to determine if the effects of silvicultural practices on wood quality can be identified using acoustic measurement to assess the MOEd of standing trees with non-destructive method in Calabrian pine (Pinus nigra Arnold subsp. calabrica). Four hundred and fifty standing trees from four sites were nondestructively tested using a time-of-flight acoustic wave technique. The thinning trials were conducted on 60-year-old plantations of Calabrian pine in four plots under different treatments: Control (T), light thinning (A), intermediate thinning (B) and heavy thinning (C). Statistical analysis demonstrated significant stress wave time differences between the stands with moderate thinning (A and B) and those with heavy thinning (C). The results showed that tree diameter has significant influence on acoustic wave measurements and a valid relationship exists between diameter at breast height and tree velocity. The results of these studies proved that the stress wave technique can be successfully applied on standing trees. [ABSTRACT FROM AUTHOR]

The production of wood pellets has grown considerably in the last decades. Besides woody biomass, other feedstocks can be used for pellet production. Among these, miscanthus presents some advantages because, even if specifically cultivated, it requires low inputs such as fertilisers and pesticides and shows high biomass yield (up to 28 tons of dry matter ha−1 in Europe). Even if in the last years some studies evaluated the environmental impact of woody pellet production, there is no information about the environmental performances of miscanthus pellet production. In this study, the environmental impact of miscanthus pellet was evaluated using the Life Cycle Assessment approach with a cradle-to plant gate perspective. Primary data were collected in a small-medium size pelletizing plant located in Northern Italy where miscanthus is cultivated to be directly processed. The results highlight how the miscanthus pellet shows lower environmental impact compared to woody pellet, mainly due to the lower energy consumption during pelletizing. The possibility to pelletize the miscanthus biomass without any drying offsets the environmental impact related to the miscanthus cultivation for all the evaluated impact categories (except for Marine eutrophication). In detail, for global warming potential, 1 ton of miscanthus pellet shows an impact of 121.6 kg CO2 eq. (about 8% lower respect to woody pellet) while for the other evaluated impact categories the impact reduction ranges from 4 to 59%. Harvesting, which unlike the other field operations is carried out every year, is by far the main contributor to the impacts of the cultivation phase while electricity is the main contributor to the pelletizing phase. [ABSTRACT FROM AUTHOR]

In the middle of XIX century, Calabrian pine was planted in southern Italy to increase the forest cover in mountainous areas. Many of these forest stands were never managed, since they were considered non-profitable for wood production. Therefore, in order to promote timber value, it is fundamental to study, more deeply, the characteristics and management options for this species. The acoustic technologies applied to predict the mechanical and physical properties of timber are well-established practices in forest research. In this study, we hypothesized that the tree stand density could influence the dynamic modulus of elasticity (MOEd) and, therefore, the future wood quality. We specifically aimed to verify if different management options, when applied, could influence the timber quality of Calabrian pine growing in similar environmental conditions. The study was conducted in the Aspromonte National Park (Calabria, Southern Italy). We derived the MOEd values from data obtained by the acoustic velocity measured through the TreeSonic™ timer. Calabrian pine trees were selected in stands where different intensities of thinning were applied eleven years before this study began (no thinning, thinning 25%, thinning 50%, and thinning 75%). The percentage refers to the number of trees cut with respect to the total number of occurring trees. The analyses were conducted on a total of 804 trees (201 trees for each intensity of thinning). A strong positive correlation was observed between the acoustic velocity, the thinning treatments and diameter at breast height (DBH). The thinning realized at 25% induced better tree wood quality. We also analyzed the best predictors for MOEd estimation, using variables easily measurable in the field, such as tree diameter, tree height, or their transformations (number of trees per hectare, basal area per hectare). We provide, here, a useful tool for predicting the wood stiffness in relation to stand parameters easily measurable in forest inventories. [ABSTRACT FROM AUTHOR]