Search Results (9)

Accurately measuring the moisture content (MC) of square timber is crucial for ensuring the quality and performance of wood products in wood processing. Traditional MC detection methods have certain limitations. Therefore, this study developed a one-dimensional convolutional neural network (1D-CNN) model based on the first 8 nanoseconds of ground-penetrating radar (GPR) signals to predict the MC of square timber. The study found that the mixed-species model exhibited effective predictive performance (R² = 0.9864, RMSE = 0.0393) across the tree species red spruce, Dahurian larch, European white birch, and Manchurian ash (MC range 0%–133.1%), while single-species models showed even higher accuracy (R² ≥ 0.9876, RMSE ≤ 0.0358). Additionally, the 1D-CNN model outperformed other algorithms in automatically capturing complex patterns in GPR full-waveform amplitude data. Moreover, the algorithms based on full-waveform amplitude data demonstrated significant advantages in detecting wood MC compared to those based on a traditional time–frequency feature parameter. These results indicate that the 1D-CNN model can be used to optimize the drying process and detect the MC of load-bearing timber in construction and bridge engineering. Future work will focus on expanding the dataset, further optimizing the algorithm, and validating the models in industrial applications to enhance their reliability and applicability. Full article

Accurate and reliable information on tree species composition and distribution is crucial in operational and sustainable forest management. Developing a high-precision tree species map based on time series satellite data is an effective and cost-efficient approach. However, we do not quantitatively know how the time scale of data acquisitions contributes to complex tree species mapping. This study aimed to produce a detailed tree species map in a typical forest zone of the Changbai Mountains by incorporating Sentinel-2 images, topography data, and machine learning algorithms. We focused on exploring the effects of the three-year time series of Sentinel-2 within monthly, seasonal, and yearly time scales on the classification of ten dominant tree species. A random forest (RF) and support vector machine (SVM) were compared and employed to map continuous tree species. The results showed that classification with monthly datasets (overall accuracy (OA): 83.38–87.45%) outperformed that with seasonal and yearly datasets (OA:72.38–85.91%), and the RF (OA: 81.70–87.45%) was better than the SVM (OA: 72.38–83.38%) at processing the same datasets. Short-wave infrared, the normalized vegetation index, and elevation were the most important variables for tree species classification. The highest classification accuracy of 87.45% was achieved by combining RF, monthly datasets, and topography information. In terms of single species’ accuracy, the F1 scores of the ten tree species ranged from 62.99% (Manchurian ash) to 97.04% (Mongolian Oak), and eight of them obtained high F1 scores greater than 87%. This study confirmed that monthly Sentinel-2 datasets, topography data, and machine learning algorithms have great potential for accurate tree species mapping in mountainous regions. Full article

The accurate classification of single tree species in forests is important for assessing species diversity and estimating forest productivity. However, few studies have explored the influence of canopy morphological characteristics on the classification of tree species. Therefore, based on UAV LiDAR and hyperspectral data, in this study, we designed various classification schemes for the main tree species in the study area, i.e., birch, Manchurian ash, larch, Ulmus, and mongolica, in order to explore the effects of different data sources, classifiers, and canopy morphological features on the classification of a single tree species. The results showed that the classification accuracy of a single tree species using multisource remote sensing data was greater than that based on a single data source. The classification results of three different classifiers were compared, and the random forest and support vector machine classifiers exhibited similar classification accuracies, with overall accuracies above 78%. The BP neural network classifier had the lowest classification accuracy of 75.8%. The classification accuracy of all three classifiers for tree species was slightly improved when UAV LiDAR-extracted canopy morphological features were added to the classifier, indicating that the addition of canopy morphological features has a certain relevance for the classification of single tree species. Full article

The long coexistence of various floral elements, landscape diversity, and island isolation led to the formation of the richest Eurasian hemiboreal liverwort flora in the southern Kurils. This land that covers less than 5000 square kilometres and houses 242 species and two varieties of liverworts and hornworts. The flora ‘core’ is represented by hemiboreal East Asian and boreal circumpolar taxa. Other elements that have noticeable input in the flora formation are cool-temperate East Asian hypoarctomontane circumpolar and arctomontane. The distribution of some species is restricted to the thermal pools near active or dormant volcanoes or volcanic ash deposits; such species generally provide specificity to the flora. Despite the territorial proximity, the climate of each considered island is characterized by features that, in the vast majority of cases, distinguish it from the climate of the neighbouring island. The last circumstance may inspire the difference in the liverwort taxonomic composition of each of the islands. The comparison of the taxonomic composition of district floras in the Amphi-Pacific hemiarctic, boreal, and cool-temperate Asia revealed four main focal centres: East Kamchatka Peninsula and Sakhalin Island, the southern Sikhote-Alin and the East Manchurian Mountains, the mountains of the southern part of the Korean Peninsula, and the South Kurils plus northern Hokkaido. The remaining floras involved in the comparison occupy an intermediate position between these four centres. Full article

Some common ash trees (Fraxinus excelsior) show tolerance towards shoot dieback caused by the invasive ascomycete Hymenoscyphus fraxineus. Leaf petioles are considered to serve as a pathogen colonization route to the shoots. We compared four common ash clones with variation in disease tolerance, and included the native host, Manchurian ash (Fraxinus mandshurica), as a reference. Tissue colonization, following rachis inoculation by H. fraxineus, was monitored by histochemical observations and a quantitative polymerase chain reaction (qPCR) assay specific to H. fraxineus. Axial spread of the pathogen towards the petiole base occurred primarily within the phloem and parenchyma, tissues rich in starch in healthy petioles. In inoculated petioles, a high content of phenolics surrounded the hyphae, presumably a host defense response. There was a relationship between field performance and susceptibility to leaf infection in three of the four studied common ash clones, i.e., good field performance was associated with a low petiole colonization level and vice versa. Low susceptibility to leaf infection may counteract leaf-to-shoot spread of the pathogen in common ash, but the limited number of clones studied warrants caution and a larger study. The Manchurian ash clone had the highest petiole colonization level, which may suggest that this native host has evolved additional mechanisms to avoid shoot infection. Full article

The mixture of tree species has gradually become the focus of forest research, especially native species mixing. Mixed-species plantations of Manchurian ash (Fraxinus mandshurica Rupr.) and Changbai larch (Larix olgensis Henry) have successfully been cultivated in Northeast China. Height–diameter (H–D) models were found to be effective in designing the silvicultural planning for mixed-species plantations. Thus, this study aimed to develop a new system of H–D models for juvenile ash and larch mixed-species plantations, based on competition information and tree and stand attributes. The leave-one-out cross-validation was utilized for model validation. The result showed that the H–D relationship was affected not only by the tree attributes (i.e., tree size and competition information) but also by stand characteristics, such as site quality and species proportion of basal area. The best model explained more than 80% and 85% variation of the tree height of ash and larch, respectively. Moreover, model validation also confirmed the high accuracy of the newly developed model’s predictions. We also found that, in terms of total tree height, ash in middle rows were higher than those in side rows, while larch in the middle rows were higher in the early growth period but then became lower than those in the side rows, as the diameter increased. The newly established H–D models would be useful for forestry inventory practice and have the potential to aid decisions in mixed-species plantations of ash and larch. Full article

All-wood biocomposites were prepared with an efficient method. The ionic liquid of 1-butyl-3-methylimidazolium chloride (BMIMCl) was used to impregnate manchurian ash (MA) before hot-pressing, and the all-wood biocomposites were prepared by controllable dissolving and regenerating the cellulose in MA. The Fourier transform infrared analysis suggested that all the components of MA remained unchanged during the preparation. X-ray diffraction, thermogravimetric and scanning electron microscope analysis were carried out to study the process parameters of hot-pressing pressure and time on the crystallinity, thermal properties and microstructure of the all-wood biocomposites. The tensile strength of the prepared all-wood biocomposites reached its highest at 212.6 MPa and was increased by 239% compared with that of the original MA sample. The thermogravimetric analysis indicated that as the thermo-stability of the all-wood biocomposites increased, the mass of the residual carbon increased from 19.7% to 22.7% under a hot-pressing pressure of 10 MPa. This work provides a simple and promising pathway for the industrial application of high-performance and environmentally friendly all-wood biocomposites. Full article

The Emerald Ash Borer (EAB), Agrilus planipennis, Fairmaire, an Asian invasive alien buprestid has devastated tens of millions of ash trees (Fraxinus spp.) in North America. Foliar phytochemicals of the genus Fraxinus (Oleaceae): Fraxinus pennsylvanica (Green ash), F. americana (White ash), F. profunda (Bush) Bush. (Pumpkin ash), F. quadrangulata Michx. (Blue ash), F. nigra Marsh. (Black ash) and F. mandshurica (Manchurian ash) were investigated using HPLC-MS/MS and untargeted metabolomics. HPLC-MS/MS help identified 26 compounds, including phenolics, flavonoids and coumarins in varying amounts. Hydroxycoumarins, esculetin, esculin, fraxetin, fraxin, fraxidin and scopoletin were isolated from blue, black and Manchurian ashes. High-throughput metabolomics revealed 35 metabolites, including terpenes, secoiridoids and lignans. Metabolomic profiling indicated several upregulated putative compounds from Manchurian ash, especially fraxinol, ligstroside, oleuropin, matairesinol, pinoresinol glucoside, 8-hydroxypinoresinol-4-glucoside, verbenalin, hydroxytyrosol-1-O-glucoside, totarol and ar-artemisene. Further, dicyclomine, aphidicolin, parthenolide, famciclovir, ar-turmerone and myriocin were identified upregulated in blue ash. Principal component analysis demonstrated a clear separation between Manchurian and blue ashes from black, green, white and pumpkin ashes. The presence of defensive compounds upregulated in Manchurian ash, suggests their potential role in providing constitutive resistance to EAB, and reflects its co-evolutionary history with A. planipennis, where they appear to coexist in their native habitats. Full article

Manchurian ash (Fraxinus mandshurica Rupr.) is a valuable hardwood species in Northeast China. In cultures of F. mandshurica, somatic embryos were produced mainly on browned explants. Therefore, we studied the mechanism of explant browning and its relationship with somatic embryogenesis (SE). We used explants derived from F. mandshurica immature zygotic embryo cotyledons as materials. Proteins were extracted from browned embryogenic explants, browned non-embryogenic explants, and non-brown explants, and then separated by 2-dimensional electrophoresis. Differentially and specifically expressed proteins were analyzed by mass spectrometry to identify proteins involved in the browning of explants and SE. Some stress response and defense proteins such as chitinases, peroxidases, aspartic proteinases, and an osmotin-like protein played important roles during SE of F. mandshurica. Our results indicated that explant browning might not be caused by the accumulation and oxidation of polyphenols only, but also by some stress-related processes, which were involved in programmed cell death (PCD), and then induced SE. Full article