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Tree height–diameter allometry is crucial in regulating forest ecosystem structure and estimating biomass and carbon storage. Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) is the most widely planted tree species across subtropical China. Yet our understanding of how height–diameter allometry of Chinese fir relates to climate over different sites is limited. Remeasured data of 60 plots were used to explore height–diameter allometry of Chinese fir in relation to climate using a three-level NLME approach. The data consisted of five initial planting densities distributed in Fujian, Jiangxi, Guangxi and Sichuan provinces (sites) in subtropical China. Results showed that tree heights increased with increasing mean annual temperature (MAT) and mean warmest month temperature (MWMT) and decreased with increasing annual heat–moisture index (AHM) and summer mean maximum temperature (SMMT). The contribution of MAT to height–diameter allometry was larger than AHM, SMMT and MWMT, which suggested that temperature was a key climate factor that shapes height–diameter allometry of Chinese fir in subtropical China. Trees planted at a higher density level of 6667–10 000 trees ha−1 were slenderer than those planted at a lower density level of 1667–5000 trees ha−1. The scale of height–diameter allometry of Chinese fir nearly held constant in the four sites and was close to 0.5 predicted by the stress-similarity model. Our results will help further understanding of the climate sensitivity affecting the height–diameter allometry of Chinese fir plantations. [ABSTRACT FROM AUTHOR]

How trees allocate photosynthetic products to primary height growth and secondary radial growth reflects their capacity to best use environmental resources. Despite substantial efforts to explore tree height–diameter relationship empirically and through theoretical modeling, our understanding of the biological mechanisms that govern this phenomenon is still limited. By thinking of stem woody biomass production as an ecological system of apical and lateral growth components, we implement game theory to model and discern how these two components cooperate symbiotically with each other or compete for resources to determine the size of a tree stem. This resulting allometry game theory is further embedded within a genetic mapping and association paradigm, allowing the genetic loci mediating the carbon allocation of stemwood growth to be characterized and mapped throughout the genome. Allometry game theory was validated by analyzing a mapping data of stem height and diameter growth over perennial seasons in a poplar tree. Several key quantitative trait loci were found to interpret the process and pattern of stemwood growth through regulating the ecological interactions of stem apical and lateral growth. The application of allometry game theory enables the prediction of the situations in which the cooperation, competition or altruism is an optimal decision of a tree to fully use the environmental resources it owns. [ABSTRACT FROM AUTHOR]

Adaptive cluster sampling (ACS) has been widely used for data collection of environment and natural resources. However, the randomness of its final sample size often impedes the use of this method. To control the final sample sizes, in this study, a -step ACS based on Horvitz-Thompson (HT) estimator was developed and an unbiased estimator was derived. The -step ACS-HT was assessed first using a simulated example and then using a real survey for numbers of plants for three species that were characterized by clustered and patchily spatial distributions. The effectiveness of this sampling design method was assessed in comparison with ACS Hansen-Hurwitz (ACS-HH) and ACS-HT estimators, and -step ACS-HT estimator. The effectiveness of using different -step sizes was also compared. The results showed that -step ACS-HT estimator was most effective and ACS-HH was the least. Moreover, stable sample mean and variance estimates could be obtained after a certain number of steps, but depending on plant species. -step ACS without replacement was slightly more effective than that with replacement. In -step ACS, the variance estimate of one-step ACS is much larger than other -step ACS (), but it is smaller than ACS. This implies that -step ACS is more effective than traditional ACS, besides, the final sample size can be controlled easily in population with big clusters. [ABSTRACT FROM AUTHOR]

Crown width is a tree variable that is commonly used as an important predictor in forest growth and yield models that serve as decision-support tools in forestry. Here, we developed a generalized interregional nonlinear mixed-effects individual tree crown width model using data from 3369 Prince Rupprecht larch ( Larix principis - rupprechtii Mayr.) trees on 116 sample plots that were distributed across the two most important regions in northern China. Because measurements from the same sample plot were highly correlated with each other, random effects at the levels of both sample plots and stands with different site conditions (blocks) were used to develop a two-level nonlinear mixed-effects crown width model. To describe the interregional variability of crown width between the regions, a dummy variable, which accounts for region-specific differences, was introduced into the model. The results showed that the two-level interregional nonlinear mixed-effects crown width model accurately described the regional variability of crown width for Prince Rupprecht larch in northern China. Modeling the random effects at the block level alone led to significantly high correlations among the residuals. However, these correlations decreased significantly when the random effects were modeled at both the block and sample plot levels. Measuring the crown width of four randomly selected Prince Rupprecht larch trees per sample plot is recommended for localizing the mixed-effects crown width model and precisely predicting the crown widths of the remaining trees on each plot. [ABSTRACT FROM AUTHOR]

Proximal support vector machine via generalized eigenvalues (GEPSVM) is one of the most successful methods for classification problems. However, GEPSVM is vulnerable to outliers since it learns classifiers based on the squared L 2 -norm distance without a specific strategy to deal with the outliers. Motivated by existing studies that improve the robustness of GEPSVM via the L 1 -norm distance or not-squared L 2 -norm distance formulation, a novel GEPSVM formulation that minimizes the p -order of L 2 -norm distance is proposed, namely, L 2,p -GEPSVM. This formulation weakens the negative effects of both light and heavy outliers in the data. An iterative algorithm is designed to solve the general L 2,p -norm distance minimization problems and rigorously prove its convergence. In addition, we adjust the parameters of L 2,p -GEPSVM to balance the accuracy and training time. This is especially useful for larger datasets. Extensive results indicate that the L 2,p -GEPSVM improves the classification performance and robustness in various experimental settings. [ABSTRACT FROM AUTHOR]

Drill feed speed significantly affects the measurement accuracy and efficiency of the micro drill resistance instrument. Generally, different speeds are set according to the density of the wood to be drilled. It is necessary to convert the resistance data measured at different drill feed speeds into resistance data at a uniform speed to properly analyze resistance data. In this study, drill resistance data for a Paulownia wood block was measured with a self-made micro drill resistance instrument at different drill feed speeds. We found that the drill resistance is positively correlated with changes in its feed speed and the natural logarithm of change times for feed speed. We developed a conversion algorithm for the micro drill resistance instrument measurements at different feed speeds. The accuracy of the conversion algorithm is up to 95%. The algorithm can be used to convert the drill resistance of Paulownia wood measured at different drill feed speeds; it has reference value for the mutual conversion of other tree species' drill resistance data measured at different feed speeds. This work may provide a feasible solution to improving micro drill resistance instrument accuracy when drill feed speed error is high. [ABSTRACT FROM AUTHOR]

Microclimate ecology is attracting renewed attention because of its fundamental importance in understanding how organisms respond to climate change. Many hot issues can be investigated in desert ecosystems, including the relationship between species distribution and environmental gradients (e.g., elevation, slope, topographic convergence index, and solar insolation). Species Distribution Models (SDMs) can be used to understand these relationships. We used data acquired from the important desert plant Nitraria tangutorum Bobr. communities and desert topographic factors extracted from LiDAR (Light Detection and Ranging) data of one square kilometer in the inner Mongolia region of China to develop SDMs. We evaluated the performance of SDMs developed with a variety of both the parametric and nonparametric algorithms (Bioclimatic Modelling (BIOCLIM), Domain, Mahalanobi, Generalized Linear Model, Generalized Additive Model, Random Forest (RF), and Support Vector Machine). The area under the receiver operating characteristic curve was used to evaluate these algorithms. The SDMs developed with RF showed the best performance based on the area under curve (0.7733). We also produced the Nitraria tangutorum Bobr. distribution maps with the best SDM and suitable habitat area of the Domain model. Based on the suitability map, we conclude that Nitraria tangutorum Bobr. is more suited to southern part with 0–20 degree slopes at an elevation of approximately 1010 m. This is the first attempt of modelling the effects of topographic heterogeneity on the desert species distribution on a small scale. The presented SDMs can have important applications for predicting species distribution and will be useful for preparing conservation and management strategies for desert ecosystems on a small scale. [ABSTRACT FROM AUTHOR]

Distance Metric Learning for Large Margin Nearest Neighbor (LMNN), as a classic distance metric learning (DML) method, has attracted much attention among researchers. However, it, like most of the existing DML methods, cannot be guaranteed to achieve independent and shared feature subspaces from multiple sources or different feature subsets, such that much statistical feature information is ignored in model learning. In this paper, we propose a novel DML model, called Multi-view DML Based on Independent and Shared Feature Subspace (MVML-ISFS), which learns multiple distance metrics to unify the information from multiple views. The proposed method finds a distance metric for each view in an independent feature space to preserve its specific property as well as a sparse representation related to the distance metrics from distinct views in a shared feature space to remain their common properties. The objective problem of MVML-ISFS is formulated based on LMNN, thus encouraging a large margin for each view that makes the distance between each of the same class pairs of samples be smaller than that between each of the different class pairs of samples. The proposed model in MVML-ISFS involves multivariate variables, which are optimized by a gradient descent strategy. The experimental results show the effectiveness of our MVML-ISFS on remote sensing, face, forest fire, and UCI datasets. [ABSTRACT FROM AUTHOR]

Accurate and real-time prediction of short-term traffic states is a crucial research topic in modern intelligent transportation systems. However, effectively modeling traffic state predictions is difficult due to the complicated characteristics of stochastic and dynamic traffic processes. In addition, collected traffic data are typically influenced by external factors (e.g. , weather, traffic jams and accidents), leading to errors and missing data. This increases the difficulty in selecting an effective method for predicting traffic conditions over time. To improve the traffic state prediction performance and alleviate the negative effect of traffic data with outliers, a novel multiclass classification least squares twin support vector machine model based on the robust L 2,p -norm (0 < p ≤ 2) distance, known as PLSTSVM, was proposed. We adjusted the PLSTSVM parameters to balance the prediction accuracy and training time. To solve the optimization problem of the PLSTSVM, an iterative algorithm was developed, which has great potential for solving other optimization problems. In addition, an integrated classification indicator system based on speed, traffic volume, occupancy rate and ample degree was used, increasing the feasibility of the traffic state analysis. To improve the learning and generalization abilities of the nonlinear PLSTSVM, we combined the polynomial kernel function and the radial basis function to construct a hybrid kernel function. The results on two real traffic datasets demonstrate that our model yields better prediction performance and robustness than other competitors, which make unsatisfactory predictions. • A robust multi-class SVM is proposed to alleviate the effect of traffic data with outliers. • An iterative algorithm is designed to solve non-smooth L 2,p -norm optimization problem. • A novel classification indicator (ample degree) is utilized to forecast the traffic state. • A hybrid kernel function is built by combining polynomial and Gaussian kernel for multi-class SVM. [ABSTRACT FROM AUTHOR]

Zero-Shot Learning (ZSL) aims to employ seen images and their related semantics to identify unseen images through knowledge transfer. Among past numerous methods, the generative methods are more prominent and achieve better results than other methods. However, we find the input for generating samples is too monotonous, there are only semantics of each class and artificially defined noise, which makes the generated visual features non-discriminative and the classifier cannot effectively distinguish them. In order to solve this problem, we propose a novel approach with cascade Generative Adversarial Network (GAN) to generate discriminative and representative features. In this method, we define a latent space where the features from different categories are orthogonal to each other and the generator for this latent space is learned with a Wasserstein GAN. In addition, in order to make up for the deficiency that the features in this latent space cannot accurately simulate the true distribution of species, we utilize another Wasserstein GAN or Cramér GAN cascaded with the previous one to generate more discriminative and representative visual features. In this way, we can not only expand the content used as input in the generation process, but also make the final generated visual features clear and separable under the influence of latent spatial orthogonality. Extensive experiments on five benchmark datasets, i.e. , AWA1, AWA2, CUB, SUN and APY, demonstrate that our proposed method can outperform most of the state-of-the-art methods on both conventional and generalized zero-shot learning settings. • Generating more representative latent and visual features to alleviate domain shift. • Features of different categories are more discriminative through orthogonal projection. • It can outperform the SOTA methods on five popular datasets. [ABSTRACT FROM AUTHOR]

Using unmanned aerial vehicles (UAV) as platforms for light detection and ranging (LiDAR) sensors offers the efficient operation and advantages of active remote sensing; hence, UAV-LiDAR plays an important role in forest resource investigations. However, high-precision individual tree segmentation, in which the most appropriate individual tree segmentation method and the optimal algorithm parameter settings must be determined, remains highly challenging when applied to multiple forest types. This article compared the applicability of methods based on a canopy height model (CHM) and a normalized point cloud (NPC) obtained from UAV-LiDAR point cloud data. The watershed algorithm, local maximum method, point cloud-based cluster segmentation, and layer stacking were used to segment individual trees and extract the tree height parameters from nine plots of three forest types. The individual tree segmentation results were evaluated based on experimental field data, and the sensitivity of the parameter settings in the segmentation methods was analyzed. Among all plots, the overall accuracy F of individual tree segmentation was between 0.621 and 1, the average RMSE of tree height extraction was 1.175 m, and the RMSE% was 12.54%. The results indicated that compared with the CHM-based methods, the NPC-based methods exhibited better performance in individual tree segmentation; additionally, the type and complexity of a forest influence the accuracy of individual tree segmentation, and point cloud-based cluster segmentation is the preferred scheme for individual tree segmentation, while layer stacking should be used as a supplement in multilayer forests and extremely complex heterogeneous forests. This research provides important guidance for the use of UAV-LiDAR to accurately obtain forest structure parameters and perform forest resource investigations. In addition, the methods compared in this paper can be employed to extract vegetation indices, such as the canopy height, leaf area index, and vegetation coverage. [ABSTRACT FROM AUTHOR]

In the reaction of catalytic oxidation of CH4, CO2 with O2 to synthesis gas, carbon-deposition is an important factor for deactivation. By adding different oxides to Ni/Al2O3 catalyst, its resistance to carbon-deposition was improved. The experimental results indicate that the order of resistance to carbon-deposition is as follows: Ni/CaO-Al2O3>Ni/MgO-Al2O3>Ni/ TiO2-Al2O3 > Ni/CeO2-Al2O3>Ni/La2O3-Al2O3>Ni/Y2O3-Al2O3>Ni/Fe2O3-Al2O3>Ni/Al2O3. The catalysts were characterized by CO2-TPD, O2-TPD and XPS methods. Here the relation between the order of resistance to carbon-deposition and performance of catalyst is discussed. [ABSTRACT FROM AUTHOR]

Multiview Generalized Eigenvalue Proximal Support Vector Machines (MvGSVMs) is an effective multi-view classification algorithm, which effectively combines multi-view learning and classification. Then it was found that in the classification learning task, the classifier combined with multi-view learning has a better classification effect than considering only a single view. In order to utilize the multi-view learning framework more fully and accurately, we further research this. We explore the internal relationship between different views between samples to replace the method of connecting different views through distance combinations. We propose a new method named Multi-view Generalized Support Vector Machine via Mining the Inherent Relationship between Views (MRMvGSVM). At the same time, we use the L2,1-norm constraint relationship matrix as a multi-view regularization term to select the most relevant sample data from different views. It not only helps to improve the accuracy of classification but also reduces the influence of extraneous factors to a certain extent and improves the robustness of the algorithm. The effectiveness of the algorithm is proved by theory and experiments on UCI, and Face and Fire Smoke image datasets. [ABSTRACT FROM AUTHOR]

Canopy volume is a crucial biological parameter for assessing tree growth, accurately estimating forest Above-Ground Biomass (AGB), and evaluating ecosystem stability. Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) are advanced precision mapping technologies that capture highly accurate point clouds for forest digitization studies. Despite advances in calculating canopy volume, challenges remain in accurately extracting the canopy and removing gaps. This study proposes a canopy volume extraction method based on an improved PointNeXt model, fusing ALS and TLS point cloud data. In this work, improved PointNeXt is first utilized to extract the canopy, enhancing extraction accuracy and mitigating under-segmentation and over-segmentation issues. To effectively calculate canopy volume, the canopy is divided into multiple levels, each projected into the xOy plane. Then, an improved Mean Shift algorithm, combined with KdTree, is employed to remove gaps and obtain parts of the real canopy. Subsequently, a convex hull algorithm is utilized to calculate the area of each part, and the sum of the areas of all parts multiplied by their heights yields the canopy volume. The proposed method's performance is tested on a dataset comprising poplar, willow, and cherry trees. As a result, the improved PointNeXt model achieves a mean intersection over union (mIoU) of 98.19% on the test set, outperforming the original PointNeXt by 1%. Regarding canopy volume, the algorithm's Root Mean Square Error (RMSE) is 0.18 m3, and a high correlation is observed between predicted canopy volumes, with an R-Square (R2) value of 0.92. Therefore, the proposed method effectively and efficiently acquires canopy volume, providing a stable and accurate technical reference for forest biomass statistics. [ABSTRACT FROM AUTHOR]

Background: Fast and accurate forest aboveground biomass (AGB) estimation and mapping is the basic work of forest management and ecosystem dynamic investigation, which is of great significance to evaluate forest quality, resource assessment, and carbon cycle and management. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), as one of the latest launched spaceborne light detection and ranging (LiDAR) sensors, can penetrate the forest canopy and has the potential to obtain accurate forest vertical structure parameters on a large scale. However, the along-track segments of canopy height provided by ICESat-2 cannot be used to obtain comprehensive AGB spatial distribution. To make up for the deficiency of spaceborne LiDAR, the Sentinel-2 images provided by google earth engine (GEE) were used as the medium to integrate with ICESat-2 for continuous AGB mapping in our study. Ensemble learning can summarize the advantages of estimation models and achieve better estimation results. A stacking algorithm consisting of four non-parametric base models which are the backpropagation (BP) neural network, k-nearest neighbor (kNN), support vector machine (SVM), and random forest (RF) was proposed for AGB modeling and estimating in Saihanba forest farm, northern China. Results: The results show that stacking achieved the best AGB estimation accuracy among the models, with an R2 of 0.71 and a root mean square error (RMSE) of 45.67 Mg/ha. The stacking resulted in the lowest estimation error with the decreases of RMSE by 22.6%, 27.7%, 23.4%, and 19.0% compared with those from the BP, kNN, SVM, and RF, respectively. Conclusion: Compared with using Sentinel-2 alone, the estimation errors of all models have been significantly reduced after adding the LiDAR variables of ICESat-2 in AGB estimation. The research demonstrated that ICESat-2 has the potential to improve the accuracy of AGB estimation and provides a reference for dynamic forest resources management and monitoring. [ABSTRACT FROM AUTHOR]

[Display omitted] • Proposed a novel method termed SPV to screen strongly collinear ALS variables. • SPV-based AGB models exhibited better performance than SFS-based models. • SPV-based AGB models exhibited better performance than LASSO-based models. • Canopy height metrics were the most important variables in estimating AGB. • Canopy density metric had a significant effect on AGB of coniferous forests. Airborne laser scanning technique (ALS) is the most appealing remote sensing technique for the precise estimation of forest above-ground biomass (AGB). Significantly strong correlations (collinearity) among the independent variables derived from ALS data decrease the accuracy of developed AGB models. To address this issue, we propose a novel variable selection algorithm-an improved sure independence screening (SPV), which integrates the Pearson correlation coefficient, threshold μ , and variance inflation factor. We further compared the performance of SPV-based, stepwise feature selection (SFS)-based, and least absolute shrinkage and selection operator (LASSO)-based AGB models developed with different regression approaches that were sensitive to strong collinearity. Field-measured data and corresponding ALS data, acquired from 1002 sample plots distributed across four distinct forest types within the Guangxi Zhuang Autonomous Region in Southern China, were used to evaluate variable selection techniques and develop AGB models. Results indicated that ALS variables selected by SPV exhibited weaker collinearity compared to those selected by SFS and LASSO. SPV-based AGB models outperformed SFS-based AGB models with higher leave-one-out cross-validation adjusted R2 (LOOCV R adj 2) by 0.1% − 27.8%. SPV-based AGB models outperformed LASSO-based AGB models with higher LOOCV R adj 2 by 0.4% − 16.3%. Hence, for variable selection in constructing AGB models (linear regression model, log–log regression model, and generalized additive model (GAM)) based on strongly collinear ALS variables, SPV is most preferred, followed by SFS and LASSO. The smooth curves from our GAMs developed using SPV-selected variables revealed that five canopy height variables (whp40 , hmean , hp25 , hp70, hskew), one canopy density variable (whp85), three density-related variables (nVegPts, bcc , and mcc) , and one vertical structural variable (LADmean) were positively correlated with AGB. The canopy height variables (whp40 , hmean , hp25 , and hp70) were identified as the most important variables in estimating AGB for four forest types. The canopy density variable wdp85 showed a strong effect on AGB of the coniferous forests, whereas it had almost no effect on the AGB of broadleaved forests. Overall, this manuscript proposes a novel variable selection algorithm named SPV, aimed at addressing collinearity of variables derived from ALS data, which has significant implications for the application of ALS in forest inventory and forest modeling. [ABSTRACT FROM AUTHOR]