Acoustic Sensor-Based Soundscape Analysis and Acoustic Assessment of Bird Species Richness in Shennongjia National Park, China.

Passive acoustic sensor-based soundscape analysis has become an increasingly important ecological method for evaluation of ecosystem conditions using acoustic indices. Understanding the soundscape composition and correlations between acoustic indices and species richness of birds, the most important sound source in the ecosystem, are of great importance for measuring biodiversity and the level of anthropogenic disturbance. In this study, based on yearlong sound data obtained from five acoustic sensors deployed in Dalongtan, Shennongjia National Park, we analyzed the soundscape composition by comparing the distributions of the soundscape power in different frequency ranges, and examined the correlations between acoustic indices and bird species richness by means of the Spearman rank correlation coefficient method.

Segmentation of multi-temporal polarimetric SAR data based on mean-shift and spectral graph partitioning.

Polarimetric SAR (PolSAR) image segmentation is a key step in its interpretation. For the targets with time series changes, the single-temporal PolSAR image segmentation algorithm is difficult to provide correct segmentation results for its target recognition, time series analysis and other applications. For this, a new algorithm for multi-temporal PolSAR image segmentation is proposed in this paper.

Mass-ratio and complementarity effects simultaneously drive aboveground biomass in temperate forests through stand structure.

Forests play a key role in regulating the global carbon cycle, a substantial portion of which is stored in aboveground biomass (AGB). It is well understood that biodiversity can increase the biomass through complementarity and mass-ratio effects, and the contribution of environmental factors and stand structure attributes to AGB was also observed. However, the relative influence of these factors in determining the AGB of forests remains poorly understood.

Biodiversity increased both productivity and its spatial stability in temperate forests in northeastern China.

Although the relationship between biodiversity and ecosystem functioning has been extensively studied, it remains unclear if the relationships of biodiversity with productivity and its spatial stability vary along productivity gradients in natural ecosystems. Based on a large dataset from 2324 permanent forest inventory plots across northeastern China, we examined the intensity of species richness (SR) and tree size diversity (Hd) effects on aboveground wood productivity (AWP) and its spatial stability among different productivity levels. Structural equation modeling was applied, integrating abiotic (climate and soil) and biotic (stand density) factors.

Identifying the patterns of changes in α- and β-diversity across communities in Hainan Island, China.

Exploring vegetation distribution spatial patterns facilitates understanding how biodiversity addresses the potential threat of future climate variability, especially for highly diverse and threatened tropical plant communities, but few empirical studies have been performed. is a constructive and endangered species in the tropical mountain forests of Hainan Island, China. In this study, sixty-eight 30 m × 30 m permanent plots of were investigated, and species-based and phylogenetic-based methods were used to analyze the α- and β-diversity pattern variation and its key drivers.

A Decade's Change in Vegetation Productivity and Its Response to Climate Change over Northeast China.

In this study, we simulated vegetation net primary productivity (NPP) using the boreal ecosystem productivity simulator (BEPS) between 2003 and 2012 over Northeast China, a region that is significantly affected by climate change. The NPP was then validated against the measurements that were calculated from tree ring data, with a determination coefficient () = 0.84 and the root mean square error () = 42.

Implementing a comprehensive approach to study the causes of human-bear (Ursus arctos pruinosus) conflicts in the Sanjiangyuan region, China.

Personal injury and property loss caused by wildlife often deteriorates the relationship between humans and animals, prompting retaliatory killings that threaten species survival. Conflicts between humans and Tibetan brown bears (Ursus arctos pruinosus) (Human-Bear Conflicts, HBC) in the Sanjiangyuan region have recently dramatically increased, seriously affecting community enthusiasm for brown bears and the conservation of other species. In order to understand the driving mechanisms of HBC, we proposed six potential drivers leading to increased occurrences of HBC.

A classifier-combined method based on D-S evidence theory for the land cover classification of the Tibetan Plateau.

The Tibetan Plateau (TP) is a region with high altitudes and complicated terrain conditions. Due to the special conditions of this region, it is also regarded as the third pole of the Earth. The land cover and vegetation in this region have not been extensively studied, so this study investigated the possibility of using a combined classifier that was established based on D-S evidence theory to extract the land cover of the TP.

Improved multi-view GEPSVM via Inter-View Difference Maximization and Intra-view Agreement Minimization.

Multiview Generalized Eigenvalue Proximal Support Vector Machine (MvGEPSVM) is an effective method for multiview data classification proposed recently. However, it ignores discriminations between different views and the agreement of the same view. Moreover, there is no robustness guarantee.

Simulation of climate change and thinning effects on productivity of Larix olgensis plantations in northeast China using 3-PG model.

Understanding the effects of thinning on forest productivity under climate change is vital to adaptive forest management. In the present study, the 3-PG model was applied to simulate the thinning effects on productivity of Larix olgensis plantations under climate change using 164 sample plots collected from the 6th, 7th and 8th National Forest Inventories in Jilin Province, northeast China. Climate scenarios of RCP 4.

Spatial variability in the amount of forest litter at the local scale in northeastern China: Kriging and cokriging approaches to interpolation.

Litter is essential to promote nutrient cycling and maintain the sustainability of forest resources. However, its variability has not been sufficiently studied at the local scale. The prediction of litter amount using ordinary cokriging with Pearson correlation analysis (COK) and ordinary cokriging with principal component analysis (COK) was compared with that using ordinary kriging (OK) based on cross-validation at the local scale of a 1-ha plot over natural spruce-fir mixed forest in Jilin Province, China.

Identifying the risk regions of house break-ins caused by Tibetan brown bears () in the Sanjiangyuan region, China.

Damage to homesteads by brown bears () has become commonplace in Asia, Europe, and the Americas. Science-based solutions for preventing damages can contribute to the establishment of mechanisms that promote human-bear coexistence. We examined the spatial distribution patterns of house break-ins by Tibetan brown bears () in Zhiduo County of the Sanjiangyuan region in China.

Identifying climate refugia and its potential impact on Tibetan brown bear () in Sanjiangyuan National Park, China.

Climate change has direct impacts on wildlife and future biodiversity protection efforts. Vulnerability assessment and habitat connectivity analyses are necessary for drafting effective conservation strategies for threatened species such as the Tibetan brown bear (). We used the maximum entropy (MaxEnt) model to assess the current (1950-2000) and future (2041-2060) habitat suitability by combining bioclimatic and environmental variables, and identified potential climate refugia for Tibetan brown bears in Sanjiangyuan National Park, China.

Capturing Maize Stand Heterogeneity Across Yield-Stability Zones Using Unmanned Aerial Vehicles (UAV).

Despite the new equipment capabilities, uneven crop stands are still common occurrences in crop fields, mainly due to spatial heterogeneity in soil conditions, seedling mortality due to herbivore predation and disease, or human error. Non-uniform plant stands may reduce grain yield in crops like maize. Thus, detecting signs of variability in crop stand density early in the season provides critical information for management decisions and crop yield forecasts.

Robust auto-weighted projective low-rank and sparse recovery for visual representation.

Most existing low-rank and sparse representation models cannot preserve the local manifold structures of samples adaptively, or separate the locality preservation from the coding process, which may result in the decreased performance. In this paper, we propose an inductive Robust Auto-weighted Low-Rank and Sparse Representation (RALSR) framework by joint feature embedding for the salient feature extraction of high-dimensional data. Technically, the model of our RALSR seamlessly integrates the joint low-rank and sparse recovery with robust salient feature extraction.

[Community structure characteristics and spatial distribution of dominant species of secondary Quercus mongolica forest in Changbai Mountains, China.].

Understanding forest community structure is the basis for revealing community maintenance mechanism and succession dynamics, and the premise forest management activities. Taking two permanent 1-hm plots of Quercus mongolica broadleaved mixed forest located in Wangqing Fore-st Bureau in Jilin Province as objects, we analyzed the community structure characteristics of secondary Q. mongolica forest and spatial distribution of dominant species with the point pattern analysis method (the O-ring statistics).

Flexible non-greedy discriminant subspace feature extraction.

Recently, L-norm-based non-greedy linear discriminant analysis (NLDA-L) for feature extraction has been shown to be effective for dimensionality reduction, which obtains projection vectors by a non-greedy algorithm. However, it usually acquires unsatisfactory performances due to the utilization of L-norm distance measurement. Therefore, in this brief paper, we propose a flexible non-greedy discriminant subspace feature extraction method, which is an extension of NLDA-L by maximizing the ratio of L-norm inter-class dispersion to intra-class dispersion.

[Effects of different disturbance patterns on stand structure of infected pure Pinus massoniana plantation].

To explore the effects of different disturbance patterns on restoring the health of an infected stand, concentrated disturbance of not cutting trees before 10 years after infection, mode-rate disturbance of cutting infected pine trees, and strong distrubance of cutting infected pine trees, the neighboring trees and poorly growing pine trees were compared in a pure Pinus massomiana plantation infected by Bursaphelenchus xylophlius in Anji, Zhejiang, China. After 16 years, the importance values of P. massoniana in the three treatments were: concentrated disturbance > mode-rate disturbance > strong disturbance.

Robust capped L1-norm twin support vector machine.

Twin support vector machine (TWSVM) is a classical and effective classifier for binary classification. However, its robustness cannot be guaranteed due to the utilization of squared L2-norm distance that can usually exaggerate the influence of outliers. In this paper, we propose a new robust capped L1-norm twin support vector machine (CTWSVM), which sustains the advantages of TWSVM and promotes the robustness in solving a binary classification problem with outliers.

Modeling and estimating aboveground biomass of Dacrydium pierrei in China using machine learning with climate change.

Accurate estimations of the aboveground biomass (AGB) of rare and endangered species are particularly important for protecting forest ecosystems and endangered species and for providing useful information to analyze the influence of past and future climate change on forest AGB. We investigated the feasibility of using three developed and two widely used models, including a generalized regression neural network (GRNN), a group method of data handling (GMDH), an adaptive neuro-fuzzy inference system (ANFIS), an artificial neural network (ANN) and a support vector machine (SVM), to estimate the AGB of Dacrydium pierrei (D. pierrei) in natural forests of China.

Development of novel EST-SSR markers for (Phyllanthaceae) and cross-amplification in two related species.

Premise Of The Study: A novel set of EST-SSR markers was developed for (Phyllanthaceae) to investigate the genetic structure and gene flow, identify novel genes of interest, and develop markers for assisted breeding.

Methods And Results: Based on the transcriptome data of , 83 EST-SSR primer pairs were designed; 52 primer pairs were successfully amplified, with 20 showing polymorphisms in 90 individuals from three populations of . The number of alleles per locus varied from 11 to 44.

Preliminary research on total nitrogen content prediction of sandalwood using the error-in-variable models based on digital image processing.

This paper presents a method for predicting the total nitrogen content in sandalwood using digital image processing. The goal of this study is to provide a real-time, efficient, and highly automated nutritional diagnosis system for producers by analyzing images obtained in forests. Using images acquired from field servers, which were installed in six forest farms of different cities located in northern Hainan Province, we propose a new segmentation algorithm and define a new indicator named "growth status" (GS), which includes two varieties: GSMER (the ratio of sandalwood pixels to the minimum enclosing rectangle pixels) and GSMCC (the ratio of sandalwood pixels to minimum circumscribed circle pixels).

[Difference analysis in estimating biomass conversion and expansion factors of masson pine in Fujian Province, China based on national forest inventory data: A comparison of three decision tree models of ensemble learning.].

Biomass conversion and expansion factors (BCEFs) are important parameters for estimating carbon storage in forest biomass. Clarifying the source of differences in estimating BCEFs could reduce uncertainties in forest biomass carbon estimation. The decision tree models of ensemble learning can be used to properly figure out the source of differences in estimating BCEFs.

Lp- and Ls-Norm Distance Based Robust Linear Discriminant Analysis.

Recently, L1-norm distance measure based Linear Discriminant Analysis (LDA) techniques have been shown to be robust against outliers. However, these methods have no guarantee of obtaining a satisfactory-enough performance due to the insufficient robustness of L1-norm measure. To mitigate this problem, inspired by recent works on Lp-norm based learning, this paper proposes a new discriminant method, called Lp- and Ls-Norm Distance Based Robust Linear Discriminant Analysis (FLDA-Lsp).