Search Results (10)

Nitrogen inputs exert significant impacts on plant species composition and ecosystem stability within alpine grasslands. The exploration of leaf functional traits holds great potential in uncovering plants’ adaptive strategies and competitive edges, and is pivotal in comprehending the ramifications of nitrogen inputs on biodiversity. In this study, the Bayanbulak grassland was selected as the research subject to investigate the impact of nitrogen addition on leaf functional traits of different plant functional groups. Specifically, various gradients of nitrogen addition were established to observe changes in leaf dry matter content (LDMC) and leaf area (LA) among three distinct plant functional groups. Furthermore, structural equation modeling (SEM) was employed to analyze the pathways through which nitrogen addition influenced the LDMC of these plant functional groups. The results were as follows: (1) LA and leaf length (LL) of Poaceae changed significantly (p < 0.05) under different N addition gradients, and leaf nutrient contents of Poaceae, Rosaceae and Fabaceae showed significant changes under different N addition gradients. (2) Pearson correlation analyses showed that total nitrogen (TN), total carbon (TOC) and leaf width (LW) of Rosaceae leaves had a significant positive correlation, and the TOC and total phosphorus (TP) of Fabaceae leaves showed a significant negative correlation. (3) SEM of the three plant functional groups showed direct and indirect effects of N addition on leaf dry matter content of Poaceae and Rosaceae, and only indirect effects on Fabaceae. Full article

Vegetation diversity is a crucial indicator for evaluating grassland ecosystems. Remote sensing technology has great potential in assessing grassland vegetation diversity. In this study, the relationship between remote sensing indices and species diversity was investigated at varying spatial and temporal scales in Bayanbulak Grassland National Nature Reserve, China. Spectral variation, defined as the coefficient of variation in vegetation indices, was used as a proxy for species diversity, which was quantified using species diversity indices. The “spectral diversity-species diversity” relationship was validated across diverse spatial scales and between different years using Sentinel-2 images and ground investigation data. This study found that Kendall’s τ coefficients showed the best performance in evaluating the relationship between the coefficient of variation in VIs (CV_VIs) and species diversity index. The highest τ value was observed for CV_NDVI in 2017 (τ = 0.660, p < 0.01), followed by the Shannon index in 2018 (τ = 0.451, p < 0.01). In addition, CV_EVI demonstrated a significant positive correlation with the Shannon-Wiener Index at the 50 m scale (τ = 0.542), and the highest relationship τ between CV_NDVI and the Shannon-Wiener Index was observed at the 100 m scale (τ = 0.660). The Shannon-Wiener Index in relation to CV_VIs performs better in representing changes in grassland vegetation. Spatial scales and vegetation indices influence the assessment of grassland vegetation diversity. These findings underscore the critical role of remote sensing technology in assessing grassland vegetation diversity across various scales, offering valuable support tools for measuring regional grassland vegetation diversity. Full article

The rapid spread of invasive plants presents significant challenges for the management of grasslands. Uncrewed aerial vehicles (UAVs) offer a promising solution for fast and efficient monitoring, although the optimal methodologies require further refinement. The objective of this research was to establish a rapid, repeatable, and cost-effective computer-assisted method for extracting Pedicularis kansuensis (P. kansuensis), an invasive plant species. To achieve this goal, an investigation was conducted into how different backgrounds (swamp meadow, alpine steppe, land cover) impact the detection of plant invaders in the Bayanbuluk grassland in Xinjiang using Random Forest (RF), Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost) with three feature combinations: spectral band, vegetation index (VI), and spectral band + VI. The results indicate that all three feature combinations achieved an overall accuracy ranging from 0.77 to 0.95. Among the three models, XGBoost demonstrates the highest accuracy, followed by Random Forest (RF), while Support Vector Machine (SVM) exhibits the lowest accuracy. The most significant feature bands for the three field plots, as well as the invasive species and land cover, were concentrated at 750 nm, 550 nm, and 660 nm. It was found that the green band proved to be the most influential for improving invasive plant extraction while the red edge 750 nm band ranked highest for overall classification accuracy among these feature combinations. The results demonstrate that P. kansuensis is highly distinguishable from co-occurring native grass species, with accuracies ranging from 0.9 to 1, except for SVM with six spectral bands, indicating high spectral variability between its flowers and those of co-occurring native background species. Full article

To better understand the phenotypic plasticity of the highly invasive native weed, Pedicularis kansuensis, we investigated and compared phenotypes (morphology, biomass, and nutrient composition) at different levels of invasion (low: 0 < cover ≤ 30%; medium: 30% < cover ≤ 70%; and high: cover > 70%). With the increase in invasion level, the plasticity of inflorescence length, single-leaf thickness, and specific leaf area increased, while the plasticity of single-leaf area and crown width decreased. During the invasion process, we observed significant density-dependent effects, including changed morphological characteristics, increased total aboveground biomass, and decreased plant height, inflorescence length, root length, crown width, single-leaf area, structure biomass of structures (root, stem, inflorescence), and individual biomass (p < 0.05). During the reproductive period of P. kansuensis, the resource allocation (C, N, and P content, total biomass, biomass allocation) to inflorescence was significantly higher than to root and stem, while the elemental ratios (C:N, C:P, N:P) of inflorescences were significantly lower than those of roots and stems (p < 0.05). When the invasion level increased, the ratio of inflorescence C:N and biomass allocation to roots increased significantly; conversely, inflorescence N and biomass allocation to inflorescences and stems decreased significantly (p < 0.05). This led to a decrease in resource allocation to aboveground parts and more resources allocated to the roots, significantly increasing the root-to-shoot ratio (p < 0.05). Based on the phenotypic differences among different invasion levels, we suggest that P. kansuensis adapted to a competitive environment by regulating morphology, biomass, and nutrient allocation, thereby enhancing the potential of invasion and spread. Full article

In order to study the changes in invasive plant population characteristics under different nutrient addition treatments, this study used the native invasive species Pedicularis kansuensis, which is spreading in the Bayabulak alpine grassland, as the research object and conducted two consecutive years of field studies in which nutrients were added to plots. Changes in the P. kansuensis population’s invasive characteristics were monitored in 2020 and 2021 in four different nutrient-addition treatments, namely no-nutrients (control), low-nitrogen, high-nitrogen, and phosphorus treatments. The result showed that (1) nutrient addition had significant effects on P. kansuensis height and root/shoot ratio (p < 0.05); the time effect had significant effects on P. kansuensis height, coverage, abundance, aboveground biomass, and belowground biomass (p < 0.01), and the interaction between nutrient addition and time had a significant effect on P. kansuensis height (p < 0.01). (2) Nitrogen addition effectively inhibited the growth and the development of P. kansuensis, especially under high-nitrogen conditions in the second growing season, where the effect of height (2.50 cm), coverage (0.13%), richness (3 strains), aboveground biomass (0.21 g m⁻²), and belowground biomass (0.03 g m⁻²) was significant, with the P. kansuensis population almost disappearing by the end of the trial. (3) Phosphorus addition had no significant effect on the P. kansuensis population’s invasive characteristics. These results indicate that higher nitrogen addition could effectively slow the invasion of the P. kansuensis population, and the findings of this study could provide certain baseline data and scientific guidance for the effective control of the P. kansuensis invasion of the Bayabulak alpine grassland in the future as well as identify certain theoretical bases for the effect of nutrient addition on invasive plants overall. Full article

Chlorophyll-a(chl-a) has been used as an important indicator of water quality. Great efforts have been invested to develop remote-sensing-based chl-a retrieval models. However, due to the spatial difference in chl-a concentration, a single model usually cannot accurately predict the whole range of chl-a concentration. To test the performance of precedent chl-a models, we carried out an experiment along the upper and middle reaches of the Kaidu River and around some small ponds in the Bayanbulak Wetland. We measured water surface reflectance in the field and analyzed the chl-a concentration in the laboratory. Initially, we performed a sensitivity analysis of the spectrum band to chl-a concentration with the aim of identifying the most suitable bands for various chl-a models. We found that the water samples could be divided into two groups with a threshold of 4.50 mg/m³. Then, we tested the performance of 11 precedent chl-a retrieval models and 7 spectral index-based regression models from this study for all the sample datasets and the two separate datasets with relatively high and low chl-a concentrations. Through a complete comparison of the performance of these models, we selected the D3B model for water bodies with high chl-a concentration and OC2 model (ocean color 2) for low chl-a concentration waters, resulting in the hierarchical and piecewise retrieval algorithm OC2-D3B. The chl-a concentration of 4.50 mg/m³ corresponded to the D3B value of −0.051; therefore, we used −0.051 as the threshold value of the OC2-D3B model. The result of the OC2-D3B model showed a better performance than the other algorithms. Finally, we mapped the spatial distribution and seasonal pattern of chl-a concentration in Bayanbulak Wetland using Sentinel-2 images from 2016 to 2019. The results indicated that the chl-a concentration in the riparian ponds was generally in the range of 8–10 mg/m³, which was higher than that in rivers with a range of 2–4 mg/m³. The highest chl-a concentration usually appears in summer, followed by spring and autumn, and the lowest in winter. The correlation between meteorological data and chl-a concentration showed that temperature is the dominant factor for chl-a concentration changes. Our analytical framework could provide a better way to accurately map the spatial distribution of chl-a concentration in complex river systems. Full article

Climate is a key element in building design. The determination of adequate climate zoning is crucial for achieving building energy efficiency and reduced carbon emissions. In this study, a cluster analysis method was applied to develop new thermal climate zones for building envelope thermal design in China. In total, three different cluster analysis methods, including k-means, average-linkage, and Ward’s clustering, were considered. The analysis indicated that the average-linkage clustering was more appropriate for this study, and the elbow method could not accurately determine the best number of categories of average-linkage clustering. Further analysis showed that the unsupervised cluster processes may generate an unavoidable redundancy category and, to obtain precise results, supervision may be necessary in some contexts. Finally, China was classified into 10 climate zones. The North China plain and Qinghai–Tibet plateau are classified into two independent climate zones, and Turpan and Bayanbulak were classified into two newly defined climate zones different from their surrounding area in the new definition. Quantitative analysis indicated that the new zones were more precise when compared to the current ones, which can provide more precise climate information and contribute to formulating more precise standards and policies related to the thermal design of building envelopes in the future. Full article

The pro-environmental behaviour intentions (PEBIs) of tourists is a popular topic in tourism geography research. Visitors are important stakeholders in the development and conservation of World Natural Heritage sites (WNHs). Based on the perspective of the Mehrabian–Russell (M-R) theory, to advance our understanding of the transmission mechanism and mediation effect of the “perception–emotion–behaviour” chain of visitors at World Natural Heritage sites, we introduced two variables, namely heritage genes perception (HGP) and environmental knowledge perception (EKP), combined with place attachment (PA) and pro-environmental behaviour intentions (PEBIs), and scientifically constructed the conceptual model of the “EHPP model”, consisting of EKP, HGP, PA and PEBIs. Taking the Bayanbulak Heritage Site as an example, the EHPP model was fitted and tested using the structural equation model (SEM). The results show that: (1) the EHPP model is applied to fit the “cognitive–emotional–behaviour intentions” chain of visitors in WNHs and passed the empirical test; (2) there were positive and significant effects of EKP on HGP, and EKP indirectly affects PEBIs via HGP and PA; (3) place dependence (PD) had a significant and positive influence on place identity (PI); and (4) compliance with pro-environmental behaviour intentions (CPEBIs) had a direct positive influence on pro-environmental behaviour intentions (PPEBIs). The findings of this study provide empirical references for stimulating the pro-environmental behaviour intentions of tourists at World Natural Heritage sites. Full article

Monitoring the ecosystem health for world natural heritage sites is essential for protecting them and benefits the formulation of more targeted protection policies. This study used Bayanbulak world natural heritage site as a case, established a framework for assessing the ecosystem health through remote sensing based on the parameters of ecosystem vigour, organization, resilience, and services. Then, we verified the obtained results through field sampling. The results show that the ecosystem health in the overall study area had declined over time, however, the health within the property zone remained at high levels and stable. The area proportion of low health was low and primarily distributed in the buffer zone. Thus, in general, the ecosystem in the study area was healthy. Besides, the ecosystem health exhibited distinct spatial agglomeration characteristics, and the degree of agglomeration enhanced over time. In addition, the field vegetation samplings were consistent with the changes in the ecosystem health levels, therefore, the result of RS monitoring of ecosystem health were credible. Thus, this study provides a scientific basis for heritage managers to formulate suitable ecological protection policies and should aid further research on the ecological monitoring of heritage sites. Full article

Snow depth is a general input variable in many models of agriculture, hydrology, climate, and ecology. However, there are some uncertainties in the retrieval of snow depth by remote sensing. Errors occurred in snow depth evaluation under the D-InSAR methods will affect the accuracy of snow depth inversion to a certain extent. This study proposes a scheme to estimate spatial snow depth that combines remote sensing with site observation. On the one hand, this scheme adopts the Sentinel-1 C-band of the European Space Agency (ESA), making use of the two-pass method of differential interferometry for inversion of spatial snow depth. On the other hand, the 3DVAR (three dimensional variational) fusion algorithm is used to integrate actual snow depth data of virtual stations and real-world observation stations into the snow depth inversion results. Thus, the accuracy of snow inversion will be improved. This scheme is applied in the study area of Bayanbulak Basin, which is located in the central hinterland of Tianshan Mountains in Xinjiang, China. Observation data from stations in different altitudes are selected to test the fusion method. According to the results, most of the obtained snow depth values using interferometry are lower than the observed ones. However, after the fusion using the 3DVAR algorithm, the snow depth accuracy is slightly higher than it was in the inversion results (R² = 0.31 vs. R² = 0.50, RMSE = 2.51 cm vs. RMSE = 1.96 cm; R² = 0.27 vs. R² = 0.46, RMSE = 4.04 cm vs. RMSE = 3.65 cm). When compared with the inversion results, the relative error (RE) improved by 6.97% and 3.59%, respectively. This study shows that the scheme can effectively improve the accuracy of regional snow depth estimation. Therefore, its future application is of great potential. Full article