Search Results (730)

A comprehensive understanding of the pattern of genetic variation among populations and adaptations to environmental heterogeneity is very important for conservation and genetic improvement. Forest tree species are ideal resources for understanding population genetic differentiation and detecting signatures of selection due to their adaptations to heterogeneous landscapes. Ulmus lamellosa is a tree species that is endemic to northern China. In this study, using restriction-site-associated DNA sequencing (RAD-seq) data, 12,179 single-nucleotide polymorphisms were identified across 51 individuals from seven populations. There was a high level of genetic diversity and population differentiation in U. lamellosa. Population genetic structure analyses revealed a significant genetic structure related to the configuration of the mountains. Additionally, we found that the isolation-by-distance pattern explained the genetic differentiation best, and environmental heterogeneity also played a role in shaping the landscape genetics of this species inhabiting mountain ecosystems. The F_ST-based outlier and genotype–environment association approaches were used to explore the genomic signatures of selection and local adaptation and detected 317 candidate outlier loci. Precipitation seasonality (coefficient of variation), precipitation in the driest month, and enhanced vegetation index were important determinants of adaptive genetic variation. This study provides abundant genetic resources for U. lamellosa and insights into the genetic variation patterns among populations. Full article

The combination of UAV-LiDAR and LiDAR-SLAM (Simultaneous Localization and Mapping) technology can overcome the scanning limitations of different platforms and obtain comprehensive 3D structural information of forest stands. To address the challenges of the traditional registration algorithms, such as high initial value requirements and susceptibility to local optima, in this paper, we propose a high-precision, robust, NDT-VGICP registration method that integrates voxel features to register UAV-LiDAR and LiDAR-SLAM point clouds at the forest stand scale. First, the point clouds are voxelized, and their normal vectors and normal distribution models are computed, then the initial transformation matrix is quickly estimated based on the point pair distribution characteristics to achieve preliminary alignment. Second, high-dimensional feature weighting is introduced, and the iterative closest point (ICP) algorithm is used to optimize the distance between the matching point pairs, adjusting the transformation matrix to reduce the registration errors iteratively. Finally, the algorithm converges when the iterative conditions are met, yielding an optimal transformation matrix and achieving precise point cloud registration. The results show that the algorithm performs well in Chinese fir forest stands of different age groups (average RMSE—horizontal: 4.27 cm; vertical: 3.86 cm) and achieves high accuracy in single-tree crown vertex detection and tree height estimation (average F-score: 0.90; R² for tree height estimation: 0.88). This study demonstrates that the NDT-VGICP algorithm can effectively fuse and collaboratively apply multi-platform LiDAR data, providing a methodological reference for accurately quantifying individual tree parameters and efficiently monitoring 3D forest stand structures. Full article

Forests are critical for providing ecosystem services and contributing to human well-being, but their health and extent are threatened by climate change, requiring effective monitoring systems. Traditional field-based methods are often labour-intensive, costly, and logistically challenging, limiting their use for large-scale applications. Drones offer advantages such as low operating costs, versatility, and rapid data collection. However, challenges remain in optimising data processing and methods to effectively integrate the acquired data for forest monitoring. This study addresses this challenge by integrating drone-based LiDAR and multispectral data for forest species classification and health monitoring. We developed the methodology in Ticino Park (Italy), where intensive field campaigns were conducted in 2022 to collect tree species compositions, the leaf area index (LAI), canopy chlorophyll content (CCC), and drone data. Individual trees were first extracted from LiDAR data and classified using spectral and textural features derived from the multispectral data, achieving an accuracy of 84%. Key forest traits were then retrieved from the multispectral data using machine learning regression algorithms, which showed satisfactory performance in estimating the LAI (R² = 0.83, RMSE = 0.44 m² m⁻²) and CCC (R² = 0.80, RMSE = 0.33 g m⁻²). The retrieved traits were used to track species-specific changes related to drought. The results obtained highlight the potential of integrating drone-based LiDAR and multispectral data for cost-effective and accurate forest health monitoring and change detection. Full article

The tree-associated microbiome is vital for both individual trees and the forest ecosystem. The microbiome is dynamic; however, it is influenced by the developmental stages and environmental stresses experienced by host trees. Chinese fir (Cunninghamia lanceolata) is an economically important tree species in the subtropical regions of China. This study investigated the diversity of microbial communities, including bacteria and fungi, in the roots and bulk soil of young (2 years old) and old (46 years old) Chinese fir. It specifically examined the functional characteristics of these microbial communities. Through a non-metric multidimensional scaling (NMDS) analysis, we examined differences in microbial community structures among root and soil samples of Chinese fir. Evaluations using α-diversity metrics (Chao1, Shannon, Pielou, etc.) confirmed significant differences in diversity and structure between soil and root samples but high similarity between young and old tree samples. A network analysis identified key bacterial and fungal genera, such as Burkholderia and Russula, which play pivotal roles in the microbiome structure. We also demonstrated significant variations in microbial metabolic functions, such as dioxin and benzoic acid degradation metabolic pathways, which might relate to stress alleviation for tree fitness. Additionally, for the detection of endophytic microorganisms in Chinese fir seeds, only small amounts (less than 10%) of fungal endophytes and bare bacterial endophytes were identified. In summary, this study revealed that the stable structure of the rhizosphere microbiome was established in the early stage of tree life in Chinese fir, which mostly originated from surrounding soil rather than seed endophytes. The associated microbial metabolic activity naturally decreased with tree aging, implicating the tree microbial dynamics and the need for the addition of an actively functional synthetic community for tree fitness. Full article

Natural secondary forests play a crucial role in global ecological security, climate change mitigation, and biodiversity conservation. However, accurately delineating individual tree crowns and identifying tree species in dense natural secondary forests remains a challenge. This study combines deep learning with traditional image segmentation methods to improve individual tree crown detection and species classification. The approach utilizes hyperspectral, unmanned aerial vehicle laser scanning data, and ground survey data from Maoershan Forest Farm in Heilongjiang Province, China. The study consists of two main processes: (1) combining semantic segmentation algorithms (U-Net and Deeplab V3 Plus) with watershed transform (WTS) for tree crown detection (U-WTS and D-WTS algorithms); (2) resampling the original images to different pixel densities (16 × 16, 32 × 32, and 64 × 64 pixels) and inputting them into five 3D-CNN models (ResNet10, ResNet18, ResNet34, ResNet50, VGG16). For tree species classification, the MSFB combined with the CNN models were used. The results show that the U-WTS algorithm achieved a recall of 0.809, precision of 0.885, and an F-score of 0.845. ResNet18 with a pixel density of 64 × 64 pixels achieved the highest overall accuracy (OA) of 0.916, an improvement of 0.049 over the original images. After incorporating MSFB, the OA improved by approximately 0.04 across all models, with only a 6% increase in model parameters. Notably, the floating-point operations (FLOPs) of ResNet18 + MSFB were only one-eighth of those of ResNet18 with 64 × 64 pixels, while achieving similar accuracy (OA: 0.912 vs. 0.916). This framework offers a scalable solution for large-scale tree species distribution mapping and forest resource inventories. Full article

Silvicultural operations such as planting, pruning, and thinning are vital for the forest value chain, requiring efficient monitoring to prevent value loss. While effective, traditional field plots are time-consuming, costly, spatially limited, and rely on assumptions that they adequately represent a wider area. Alternatively, unmanned aerial vehicles (UAVs) can cover large areas while keeping operators safe from hazards including steep terrain. Despite their utility, optimal flight parameters to ensure flight efficiency and data quality remain under-researched. This study evaluated the impact of forward and side overlap and flight altitude on the quality of two- and three-dimensional spatial data products from UAV photogrammetry (UAV-SfM) for assessing stand density in a recently thinned Pinus radiata D. Don plantation. A contemporaneously acquired UAV laser scanner (ULS) point cloud provided reference data. The results indicate that the optimal UAV-SfM flight parameters are 90% forward and 85% side overlap at a 120 m altitude. Flights at an 80 m altitude offered marginal resolution improvement (2.2 cm compared to 3.2 cm ground sample distance/GSD) but took longer and were more error-prone. Individual tree detection (ITD) for stand density assessment was then applied to both UAV-SfM and ULS canopy height models (CHMs). Manual cleaning of the detected ULS tree peaks provided ground truth for both methods. UAV-SfM had a lower recall (0.85 vs. 0.94) but a higher precision (0.97 vs. 0.95) compared to ULS. Overall, the F-score indicated no significant difference between a prosumer-grade photogrammetric UAV and an industrial-grade ULS for stand density assessments, demonstrating the efficacy of affordable, off-the-shelf UAV technology for forest managers. Furthermore, in addressing the knowledge gap regarding optimal UAV flight parameters for conducting operational forestry assessments, this study provides valuable insights into the importance of side overlap for orthomosaic quality in forest environments. Full article

Orf (ORF) is an acute disease caused by the Orf virus (ORFV), and poses a certain threat to animal and human health. Live attenuated vaccines play an important role in the prevention and control of ORF. The effectiveness of the live attenuated Orf virus vaccine is influenced by several factors, including the genomic match between the vaccine strain and circulating epidemic strains. Genomic differences between an ORFV epidemic strain (ORFV-2W) and a vaccine strain (ORFV-1V) were identified in this study via analysis of multiple sequence alignments, phylogenetic trees, and single nucleotide polymorphisms. Phylogenetic analysis revealed that ORFV-2W and ORFV-1V were closely related, with a whole genome homology of 99.8%. Furthermore, a deletion in the non-coding region at the end of the whole genome of ORFV-1V was detected. Such non-essential genes in the terminal regions are usually unnecessary for virus replication but may play important roles in pathogenicity, host and tissue tropism. Single nucleotide polymorphism analysis identified three missense mutations in ORF067, ORF072, and the terminal non-coding region of ORFV-1V. Moreover, a frameshift mutation in ORF102 of ORFV-1V was detected. Mutations in individual genes and deletion of terminal non-coding regions may be related to the attenuation of the vaccine strain. These results provide useful context for improving ORFV vaccines. Full article

Sedentary behaviors, including poor postures, are significantly detrimental to health, particularly for individuals losing motion ability. This study presents a posture detection system utilizing four force-sensitive resistors (FSRs) and two triaxial accelerometers selected after rigorous assessment for consistency and linearity. We compared various machine learning algorithms based on classification accuracy and computational efficiency. The k-nearest neighbor (KNN) algorithm demonstrated superior performance over Decision Tree, Discriminant Analysis, Naive Bayes, and Support Vector Machine (SVM). Further analysis of KNN hyperparameters revealed that the city block metric with K = 3 yielded optimal classification results. Triaxial accelerometers exhibited higher accuracy in both training (99.4%) and testing (99.0%) phases compared to FSRs (96.6% and 95.4%, respectively), with slightly reduced processing times (0.83 s vs. 0.85 s for training; 0.51 s vs. 0.54 s for testing). These findings suggest that, apart from being cost-effective and compact, triaxial accelerometers are more effective than FSRs for posture detection. Full article

Sugarcane is the primary crop in the global sugar industry, yet it remains highly susceptible to a wide range of diseases that significantly impact its yield and quality. An effective solution is required to address the issues caused by the manual identification of plant diseases, which is time-consuming and has low detection accuracy. This paper proposes the development of a robust Deep Ensemble Convolutional Neural Network (DECNN) model for the accurate detection of sugarcane leaf diseases. Initially, several transfer learning (TL) models, including EfficientNetB0, MobileNetV2, DenseNet121, NASNetMobile, and EfficientNetV2B0, were enhanced through the addition of specific layers. A comparative analysis was then conducted on the enlarged dataset of sugarcane leaf diseases, which was divided into six categories and 4800 images. The application of data augmentation, along with the addition of dense layers, batch normalization layers, and dropout layers, led to improved detection accuracy, precision, recall, and F1 scores for each model. Among the five enhanced transfer learning models, the modified EfficientNetB0 model demonstrated the highest detection accuracy, ranging from 97.08% to 98.54%. In conclusion, the DECNN model was developed by integrating the modified EfficientNetB0, MobileNetV2, and DenseNet121 models using a distinctive performance-based custom-weighted ensemble method, with weight optimization carried out using the Tree-structured Parzen Estimator (TPE) technique. This resulted in a model that achieved a detection accuracy of 99.17%, which outperformed the individual performance of the modified EfficientNetB0, MobileNetV2, and DenseNet121 models in detecting sugarcane leaf diseases. Full article

Sophora japonica is an important native tree species in northern China, with high ornamental, medicinal, and ecological value. In order to elucidate the genetic resources of ancient S. japonica, 16 simple sequence repeat (SSR) markers were used to evaluate its genetic diversity and population structure and build a core collection of 416 germplasms from the Shandong, Shanxi, and Hebei provinces. A total of 160 alleles were detected, the mean major allele frequency (MAF)was 0.39, and the mean effective number of alleles (Ne) was 4.08. Shannon’s information index (I), the observed heterozygosity (Ho), the expected heterozygosity (He), and the polymorphism information content (PIC) were 1.58, 0.64, 0.74, and 0.70, respectively, indicating relatively high genetic diversity in ancient S. japonica germplasms. Low genetic differentiation coefficient (Fst = 0.04) and frequent gene flow (Nm = 9.74) were found in the tested S. japonica populations, and an analysis of molecular variance (AMOVA) indicated that the genetic variation mainly came from within individuals (84%). A genetic structure and cluster analysis indicated that 416 ancient S. japonica germplasms could be divided into five subgroups, and there were obvious genetic exchanges among different subgroups. A core collection consisting of 104 (25% of the original collection) germplasms was constructed using the R language package Genetic Subsetter version 0.8 based on the stepwise regression method. The retention rates of the number of alleles (Na), Ne, I, He, and PIC were 87.50%, 106.24%, 103.02%, 102.50%, and 102.74%, respectively. The t-test analysis suggested that there were no significant differences between the core collection and the original collection. The principal coordinate analysis (PCoA) showed that the core collection was uniformly distributed within the initial collection and was able to fully represent the genetic diversity of the original collection. These results provide a scientific basis for the conservation and utilization of ancient S. japonica germplasms. Full article

Individual tree data could offer potential uses for both forestry and landscape visualization but has not yet been realized on a large scale. Relying on 5 points/m² Finnish national laser scanning, we present the design and implementation of a system for producing, storing, distributing, querying, and viewing individual tree data, both in a web browser and in a game engine-mediated interactive 3D visualization, “virtual forest”. In our experiment, 3896 km² of airborne laser scanning point clouds were processed for individual tree detection, resulting in over 100 million trees detected, but the developed technical infrastructure allows for containing 10+ billion trees (a rough number of log-sized trees in Finland) to be visualized in the same system. About 92% of trees wider than 20 cm in diameter at breast height (corresponding to industrial log-size trees) were detected using national laser scanning data. Obtained relative RMSE for height, diameter, volume, and biomass (stored above-ground carbon) at individual tree levels were 4.5%, 16.9%, 30.2%, and 29.0%, respectively. The obtained RMSE and bias are low enough for operational forestry and add value over current area-based inventories. By combining the single-tree data with open GIS datasets, a 3D virtual forest was produced automatically. A comparison against georeferenced panoramic images was performed to assess the verisimilitude of the virtual scenes, with the best results obtained from sparse grown forests on sites with clear landmarks. Both the online viewer and 3D virtual forest can be used for improved decision-making in multifunctional forestry. Based on the work, individual tree inventory is expected to become operational in Finland in 2026 as part of the third national laser scanning program. Full article

Forest plot surveys are vital for monitoring forest resource growth, contributing to their sustainable development. The accuracy and efficiency of these surveys are paramount, making technological advancements such as Simultaneous Localization and Mapping (SLAM) crucial. This study investigates the application of SLAM technology, utilizing LiDAR (Light Detection and Ranging) and monocular cameras, to enhance forestry plot surveys. Conducted in three 32 × 32 m plots within the Tibet Autonomous Region of China, the research compares the efficacy of LiDAR-based and visual SLAM algorithms in estimating tree parameters such as diameter at breast height (DBH), tree height, and position, alongside their adaptability to forest environments. The findings revealed that both types of algorithms achieved high precision in DBH estimation, with LiDAR SLAM presenting a root mean square error (RMSE) range of 1.4 to 1.96 cm and visual SLAM showing a slightly higher precision, with an RMSE of 0.72 to 0.85 cm. In terms of tree position accuracy, the three methods can achieve tree location measurements. LiDAR SLAM accurately represents the relative positions of trees, while the traditional and visual SLAM systems exhibit slight positional offsets for individual trees. However, discrepancies arose in tree height estimation accuracy, where visual SLAM exhibited a bias range from −0.55 to 0.19 m and an RMSE of 1.36 to 2.34 m, while LiDAR SLAM had a broader bias range and higher RMSE, especially for trees over 25 m, attributed to scanning angle limitations and branch occlusion. Moreover, the study highlights the comprehensive point cloud data generated by LiDAR SLAM, useful for calculating extensive tree parameters such as volume and carbon storage and Tree Information Modeling (TIM) through digital twin technology. In contrast, the sparser data from visual SLAM limits its use to basic parameter estimation. These insights underscore the effectiveness and precision of SLAM-based approaches in forestry plot surveys while also indicating distinct advantages and suitability of each method to different forest environments. The findings advocate for tailored survey strategies, aligning with specific forest conditions and requirements, enhancing the application of SLAM technology in forestry management and conservation efforts. Full article

Polarimetric measurement has been proven to be of great importance in various applications, including remote sensing in agriculture and forest. Polarimetric full waveform LiDAR is a relatively new yet valuable active remote sensing tool. This instrument offers the full waveform data and polarimetric information simultaneously. Current studies have primarily used commercial non-polarimetric LiDAR for tree species classification, either at the dominant species level or at the individual tree level. Many classification approaches combine multiple features, such as tree height, stand width, and crown shape, without utilizing polarimetric information. In this work, a customized Multiwavelength Airborne Polarimetric LiDAR (MAPL) system was developed for field tree measurements. The MAPL is a unique system with unparalleled capabilities in vegetation remote sensing. It features four receiving channels at dual wavelengths and dual polarization: near infrared (NIR) co-polarization, NIR cross-polarization, green (GN) co-polarization, and GN cross-polarization, respectively. Data were collected from several tree species, including coniferous trees (blue spruce, ponderosa pine, and Austrian pine) and deciduous trees (ash and maple). The goal was to improve the target identification ability and detection accuracy. A machine learning (ML) approach, specifically a decision tree, was developed to classify tree species based on the peak reflectance values of the MAPL waveforms. The results indicate a re-substitution error of 3.23% and a k-fold loss error of 5.03% for the 2106 tree samples used in this study. The decision tree method proved to be both accurate and effective, and the classification of new observation data can be performed using the previously trained decision tree, as suggested by both error values. Future research will focus on incorporating additional LiDAR data features, exploring more advanced ML methods, and expanding to other vegetation classification applications. Furthermore, the MAPL data can be fused with data from other sensors to provide augmented reality applications, such as Simultaneous Localization and Mapping (SLAM) and Bird’s Eye View (BEV). Its polarimetric capability will enable target characterization beyond shape and distance. Full article

Increasingly, humans are exposed to different activities at work, at home, and in general in their daily lives that generate episodes of stress. In many cases, these episodes could produce disorders in their health and reduce their quality of life. For this reason, it is crucial to implement mechanisms that can detect stress in individuals and develop applications that provide feedback through various activities to help reduce stress levels. Physiological parameters, such as galvanic skin response (GSR) and heart rate (HR) are indicative of stress-related changes. There exist methodologies that use wearable sensors to measure these stress levels. In this study, a sensor of blood volume pulse (BVP) and an electrocardiography (ECG) sensor were utilized to obtain metrics like heart rate variability (HRV) and pulse arrival time (PAT). Their features were extracted, processed, and analyzed for anxiety detection. The classification performance was evaluated using decision trees, a support vector machine (SVM), and meta-classifiers to accurately distinguish between “stressed” and “non-stressed” states. We obtained the best results with the SVM classifier using all the features. Additionally, we found that the ECG AD8232 sensor provided more reliable data compared to the photoplethysmography (PPG) signal obtained from the MAX30100 sensor. Therefore, the ECG is a more accurate tool for assessing emotional states related to stress and anxiety. Full article

Characterizing Management Units (MUs) with tree-level data is instrumental for a comprehensive understanding of forest structure and for providing information needed to support forest management decision-making. Airborne Laser Scanning (ALS) data may enhance this characterization. While some studies rely on Individual Tree Detection (ITD) methods using ALS data to estimate tree diameters within stands, these methods often face challenges when the goal is to characterize MUs in dense forests. This study proposes a methodology that simulates diameter distributions from LiDAR data using an Area-Based Approach (ABA) to overcome these limitations. Focusing on maritime pine (Pinus pinaster Ait.) MUs within a forest intervention zone in northern Portugal, the research initially assesses the suitability of two highly flexible Probability Density Functions (PDFs), Johnson’s SB and Weibull, for simulating diameter distribution in maritime pine stands in Portugal using the PINASTER database. The selected PDF is then used in conjunction with ABA to derive the variables needed for parameter recovery, enabling the simulation of diameter distributions within each MU. Monte Carlo Simulation (MCS) is applied to generate a sample list of tree diameters from the simulated distributions. The results indicate that this methodology is appropriate to estimate diameter distributions within maritime pine MUs by using ABA combined with Johnson’s SB and Weibull PDFs. Full article