Forests

Mania, Przemysław;Kupfernagel, Carlo;Curling, Simon

doi: 10.3390/f15060892pmid: N/A

The densification of solid wood is a well-studied technique that aims to increase the strength and hardness of the material by permanently compressing the wood tissue. To optimise the densification process in this study, a pre-treatment with sodium sulphite was used (delignification). With delignification prior to densification, one achieves higher compression ratios and better mechanical properties compared to densification without pre-treatment. The reactivity of syringyl (dominant in hardwoods) and guaiacyl (dominant in softwoods) lignin towards delignification is different. The influences of this difference on the delignification and densification of softwoods and hardwoods need to be investigated. This study aimed to densify wood after delignification and investigate how variations in chemical composition between coniferous and deciduous species affect the densification process. Scots pine and Eurasian aspen specimens with a similar initial density were investigated to study the influence of the different lignin chemistry in softwoods and hardwoods on the densification process. Both timbers were delignified with sodium sulphite and sodium hydroxide and subsequently densified. While the delignification was twice as efficient in aspen than in pine, the compression ratios were almost identical in both species. The Brinell hardness and compressive strength showed a more significant increase in aspen than in Scots pine; however, one exception was the compressive strength in a radial direction, which increased more effectively in Scots pine. Scanning electron microscopy (SEM) revealed the microstructure of densified aspen and Scots pine, showing the crushing and collapse of the cells.

Zhang, Shuting;Wang, Hongtao;Wang, Cheng;Wang, Yingchen;Wang, Shaohui;Yang, Zhenqi

doi: 10.3390/f15060893pmid: N/A

Simultaneous Localization and Mapping (SLAM) using LiDAR technology can acquire the point cloud below the tree canopy efficiently in real time, and the Unmanned Aerial Vehicle LiDAR (UAV-LiDAR) can derive the point cloud of the tree canopy. By registering them, the complete 3D structural information of the trees can be obtained for the forest inventory. To this end, an improved RANSAC-ICP algorithm for registration of SLAM and UAV-LiDAR point cloud at plot scale is proposed in this study. Firstly, the point cloud features are extracted and transformed into 33-dimensional feature vectors by using the feature descriptor FPFH, and the corresponding point pairs are determined by bidirectional feature matching. Then, the RANSAC algorithm is employed to compute the transformation matrix based on the reduced set of feature points for coarse registration of the point cloud. Finally, the iterative closest point algorithm is used to iterate the transformation matrix to achieve precise registration of the SLAM and UAV-LiDAR point cloud. The proposed algorithm is validated on both coniferous and broadleaf forest datasets, with an average mean absolute distance (MAD) of 11.332 cm for the broadleaf forest dataset and 6.150 cm for the coniferous forest dataset. The experimental results show that the proposed method in this study can be effectively applied to the forest plot scale for the precise alignment of multi-platform point clouds.

Wang, Ziyi;Su, Xiyou;Mao, Shiwei

doi: 10.3390/f15060894pmid: N/A

Accurate and rapid localization and identification of tree leaves are of significant importance for urban forest planning and environmental protection. Existing object detection neural networks are complex and often large, which hinders their deployment on mobile devices and compromises their efficiency in detecting plant leaves, especially against complex backgrounds. To address this issue, we collected eight common types of tree leaves against complex urban backgrounds to create a single-species leaf dataset. Each image in this dataset contains only one type of tree but may include multiple leaves. These leaves share similar shapes and textures and resemble various real-world background colors, making them difficult to distinguish and accurately identify, thereby posing challenges to model precision in localization and recognition. We propose a lightweight single-species leaf detection model, SinL-YOLOv5, which is only 15.7 MB. First, we integrated an SE module into the backbone to adaptively adjust the channel weights of feature maps, enhancing the expression of critical features such as the contours and textures of the leaves. Then, we developed an adaptive weighted bi-directional feature pyramid network, SE-BiFPN, utilizing the SE module within the backbone. This approach enhances the information transfer capabilities between the deep semantic features and shallow contour texture features of the network, thereby accelerating detection speed and improving detection accuracy. Finally, to enhance model stability during learning, we introduced an angle cost-based bounding box regression loss function (SIoU), which integrates directional information between ground-truth boxes and predicted boxes. This allows for more effective learning of the positioning and size of leaf edges and enhances the model’s accuracy in detecting leaf locations. We validated the improved model on the single-species leaf dataset. The results showed that compared to YOLOv5s, SinL-YOLOv5 exhibited a notable performance improvement. Specifically, SinL-YOLOv5 achieved an increase of nearly 4.7 percentage points in the [email protected] and processed an additional 20 frames per second. These enhancements significantly enhanced both the accuracy and speed of localization and recognition. With this improved model, we achieved accurate and rapid detection of eight common types of single-species tree leaves against complex urban backgrounds, providing technical support for urban forest surveys, urban forestry planning, and urban environmental conservation.

Hu, Miao;Wang, Yiping;Li, Huihu;Hu, Liping;Liu, Qiaoli;Zhou, Fan;Yang, Aihong;Yu, Faxin;Ouyang, Xunzhi

doi: 10.3390/f15060895pmid: N/A

Afforestation plays a critical role in ecosystem restoration and carbon sequestration. However, there continues to be insufficient knowledge about the long-term effects of different tree species on the forest soil in central subtropical China. In this study, five indigenous afforestation tree species commonly used in the region, including Bretschneidera sinensis, Liriodendron chinense, Schima superba, Phoebe bournei, and Cunninghamia lanceolata, were selected to explore their long-term effects on the forest soil. The soil’s physicochemical properties, organic carbon content, enzyme activity, and respiration were investigated. Our results revealed significant differences in the soil physicochemical properties, enzyme activity, organic carbon content, and soil respiration among the different tree species even with the same tree species types. Broad-leaved species, particularly L. chinense and P. bournei, exhibited superior soil physicochemical properties, higher amounts of organic carbon contents, enzyme activity, and soil respiration compared to coniferous species C. lanceolata. Notably, for the two studied evergreen tree species, P. bournei seemed to improve the forest soil quality more than S. superba. Hence, increasing the proportion of broad-leaved tree species may have a beneficial effect on the soil’s physicochemical properties and microecology. Furthermore, considering tree species’ compositions in afforestation will help to optimize soil quality and ecosystem health.

Horváth, Bálint;Tóth, Viktória;Bolla, Bence;Szabóky, Csaba;Eötvös, Csaba Béla

doi: 10.3390/f15060896pmid: N/A

The many publications on forests and moth communities accomplished in different sampling regions and habitat types have produced diverse results and conclusions. The multiplicity of outcomes requires regional or local investigations on forest traits and herbivores to determine optimal management methods to maintain biodiversity and ecological stability in woodlands. Our study focused on sessile oak–hornbeam forests, which are economically and ecologically significant in many European countries. Samplings were performed in 2011–2012 using portable light traps in a highly forested area in western Hungary. We used 16 variables for PCA from the sampling of vascular plants and the local forest management plan document. These newly created variables (i.e., principal components) were related (used generalized linear models) to different groups of sampled moth communities: Macrolepidoptera, Microlepidoptera, and ecological groups (according to the host vegetation layer). Based on these significant relations, thinning activity may have various effects on moth communities, through the changed light regime and microclimate conditions. Temperature growth in the gaps could lead to the increasing abundance of heat-preferred Lepidoptera species; however, the decreasing species richness of trees (as a result of thinning) is less favourable for moth assemblages. Increasing herb coverage supports moth communities in the investigated forest stands, which may also be induced by the lower canopy closure. Besides the increasing coverage in the lower vegetation layers, plant species richness is also an important element for moth communities; this was demonstrated by the negative relation between the PC4, PC6 (weighted toward coverage), and Lepidoptera groups. Our results supported the fact that a single study on forest management practice or on vegetation traits is not sufficient to indicate their exact effect on moth communities, because their influence is complex. In order to halt the loss in diversity of the examined forest type, we suggest an overall approach to define the optimal forest management practice and tree mixture rate, regarding a larger area.

Gong, Wenfeng;Wei, Liping;Liu, Jinliang

doi: 10.3390/f15060897pmid: N/A

Cupressus gigantea is an endangered species mainly distributed on beach land, down-slope, and middle-slope positions along the Yarlung Zangbo River on the Tibet Plateau of China, with an altitude ranging from 3000 to 3400 m. We investigated the rhizosphere and fine root microbiomes of C. gigantea at these three slope positions through metagenomic analysis. Slope positions had a greater influence on microbiome composition in the rhizosphere than that in the fine roots. Down- and middle-slope positions presented higher microbial richness indeces and community similarity, while a more complex co-occurrence network was observed in the beach land samples. Rhizosphere bacterial community assembly was determined via deterministic processes in the beach land and via stochastic processes in the down- and middle-slope positions. Archaeal and fungal community assemblies were both dominated by stochastic processes in the rhizosphere and fine roots at the three slope positions. Nitrogen (N) functional genes were more sensitive to changes in slope positions than phosphorus (N) functional genes. Soil properties explained more than 60% and 34% of the variations in the N and P functional genes and more than 30% and 10% of the variations in the microbiomes in the rhizosphere and fine roots, respectively. Variation in the microbiome was significantly driven by total nirtogen, total potassium, pH, and soil moisture in rhizosphere, and by pH and soil moisture in fine roots. Our observations suggest that the effect of slope position on the microbiomes of C. gigantea was greater for the rhizosphere than the fine roots, with down- and middle-slope positions presenting higher community similarity.

Wu, Yangyang;Yang, Jinli;Li, Siliang;Yu, Honggang;Luo, Guangjie;Yang, Xiaodong;Yue, Fujun;Guo, Chunzi;Zhang, Ying;Gu, Lei;Wu, Haobiao;Yuan, Panli

doi: 10.3390/f15060898pmid: N/A

Studying the spatio-temporal changes and driving mechanisms of vegetation’s net primary productivity (NPP) is critical for achieving green and low-carbon development, as well as the carbon peaking and carbon neutrality goals. This article employs various analytical approaches, including the Carnegie–Ames–Stanford approach (CASA) model, Theil–Sen median estimator, coefficient of variation, Hurst index, and land-use and land-cover change (LUCC) transition matrix, to conduct a thorough study of NPP variations in the Shandong Hilly Plain (SDHP) region. Furthermore, the geographic detector method was used to investigate the synergistic effects of meteorological changes and human activities on NPP in this region. Between 2000 and 2020, the vegetation NPP in the SDHP exhibited an average increase rate of 0.537 g C·m−2·a−1. However, the fluctuation in mean annual NPP, ranging from 203 to 230 g C·m−2·a−1, underscores an uneven growth pattern. Significant regional disparities are evident in vegetation NPP, gradually ascending from the southeast to the northwest and from the coastal areas to inland regions. The average Hurst index for the entire study area stands at 0.556, indicating an overall sustained growth trend in the time series of SDHP vegetation NPP. The vegetation NPP changes in SDHP can be well explained by climate variables (mean annual temperature, mean annual precipitation) and human activities (LUCC, night light index); of these, LUCC (q = 0.684) has the highest explanatory power on the impact of NPP and is a major influencing factor. This study deepens the understanding of the driving factors and patterns of vegetation’s dynamic response to climate change and human activities in the SDHP region. At the same time, it provides valuable scientific insights for improving ecosystem quality and promoting the carbon peaking and carbon neutrality goals.

Watt, Michael S.;Jayathunga, Sadeepa;Hartley, Robin J. L.;Pearse, Grant D.;Massam, Peter D.;Cajes, David;Steer, Benjamin S. C.;Estarija, Honey Jane C.

doi: 10.3390/f15060899pmid: N/A

The management of plantation forests using precision forestry requires advanced inventory methods. Unmanned aerial vehicle laser scanning (ULS) offers a cost-effective approach to accurately estimate forest structural attributes at both plot and individual tree levels. We examined the utility of ULS data collected from a radiata pine stand for tree detection and prediction of diameter at breast height (DBH) and stem volume, using data thinned to 13-point densities (ranging from 10–12,200 points/m2). These datasets were created using a DTM with the highest pulse density and DTMs that used the native decimated point clouds. Models of DBH were constructed using partial least squares (PLS) and random forest (RF) from seven classes of metrics that characterized the horizontal and vertical structure of the canopy. Individual tree segmentation was consistently accurate across the 13-point densities and was insensitive to DTM type (F1 scores > 0.96). Predictions of DBH using PLS models were consistently more accurate than RF models and accuracy was insensitive to the DTM type. Using data from the native DTMs, DBH estimation using PLS had the lowest RMSE of 1.624 cm (R2 of 0.756) at a point density of 12,200 points/m2. Stem volume predictions made using PLS predictions of DBH and height from the ULS had the lowest RMSE of 0.0418 m3 (R2 of 0.792) at 12,200 points/m2. The RMSE values for DBH and volume remained relatively stable from 12,200 to between 750 and 400 points/m2, with reductions in accuracy occurring as point density declined below this threshold. Overall, these findings have significant implications, particularly for the precise estimation of DBH and stem volume at the individual tree level. They demonstrate the potential of cost-effective ULS sensors for rapid and frequent plantation forest assessment, thereby enhancing the application of light detection and ranging (LiDAR) technology in plantation forest management.

Fu, Yanglimin;Tan, Hongjian;Kou, Weili;Xu, Weiheng;Wang, Huan;Lu, Ning

doi: 10.3390/f15060900pmid: N/A

The rapid, accurate, and non-destructive estimation of rubber plantation aboveground biomass (AGB) is essential for producers to forecast rubber yield and carbon storage. To enhance the estimation accuracy, an increasing number of remote sensing variables are incorporated into the development of multi-parameter models, which makes its practical application and the potential impact on predictive precision challenging due to the inclusion of non-essential or redundant variables. Therefore, this study systematically evaluated the performance of different parameter combinations derived from Sentinel-2 imagery, using variable optimization approaches with four machine learning algorithms (Random Forest Regression, RF; XGBoost Regression, XGBR; K Nearest Neighbor Regression, KNNR; and Support Vector Regression, SVR) for the estimation of the AGB of rubber plantations. The results indicate that RF achieved the best estimation accuracy (R2 = 0.86, RMSE = 15.77 Mg/ha) for predicting rubber plantation AGB when combined with Boruta-selected variables, outperforming other combinations (variable combinations obtained based on importance ranking, univariate combinations, and multivariate combinations). Our research findings suggest that the consideration of parameter-optimized remote sensing variables is advantageous for improving the estimation accuracy of forest biophysical parameters, when utilizing a large number of parameters for estimation.

Du, Shanfeng;Xie, Deyu;Liu, Shenglong;Liu, Lingjuan;Jiang, Jiang

doi: 10.3390/f15060901pmid: N/A

The aim of our study is to understand the patterns of variation in water use efficiency (WUE) in coniferous and broad-leaved mixed forest ecosystems across multiple scales and to identify its main controlling factors. We employ the eddy covariance method to gather data from 2017, 2018, and 2020, which we use to calculate the gross primary productivity and evapotranspiration of these forests in East China and to determine WUE at the ecosystem level. The mean daily variation in WUE ranges from 4.84 to 7.88 gC kg−1 H2O, with a mean value of 6.12 gC kg−1 H2O. We use ridge regression analysis to ascertain the independent effect of environmental factors on WUE variation. We find that WUE responds differently to environmental factors at different time scales. In mixed conifer ecosystems, temperature and relative humidity emerge as the most significant environmental factors influencing WUE variability. Especially at the seasonal scale, temperature and relative humidity can explain more than 51% of the WUE variation. Our results underscore the varied effects of environmental factors on WUE variation across different time scales and aid in predicting the response of WUE to climate change in coniferous and broad-leaved mixed forest ecosystems.