Ebinur Lake Wetland National Nature Research Articles

Monitoring and evaluation of vegetation restoration in the Ebinur Lake Wetland National Nature Reserve under lockdown protection.

For a long time, human activities have been prohibited in ecologically protected areas in the Ebinur Lake Wetland National Nature Reserve (ELWNNR). The implementation of total closure is one of the main methods for ecological protection. For arid zones, there is a lack of in-depth research on whether this measure contributes to ecological restoration in the reserve. The Normalized Difference Vegetation Index (NDVI) is considered to be the best indicator for ecological monitoring and has a key role to play in assessing the ecological impacts of total closure. In this study, we used Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data to select optimal data and utilized Sen slope estimation, Mann-Kendall statistical tests, and the geographical detector model to quantitatively analyze the normalized difference vegetation index (NDVI) dynamics and its driving factors. Results were as follows: (1) The vegetation distribution of the Ebinur Lake Wetland National Nature Reserve (ELWNNR) had obvious spatial heterogeneity, showing low distribution in the middle and high distribution in the surroundings. The correlation coefficients of Landsat-8 and MODIS, Sentinel-2 and MODIS, and Sentinel-2 and Landsat-8 were 0.952, 0.842, and 0.861, respectively. The NDVI calculated from MODIS remote sensing data was higher than the value calculated by Landsat-8 and Sentinel-2 remote sensing images, and Landsat-8 remote sensing data were the most suitable data. (2) NDVI indicated more degraded areas on the whole, but the ecological recovery was obvious in the localized areas where anthropogenic closure was implemented. The ecological environment change was the result of the joint action of man and nature. Man-made intervention will change the local ecological environment, but the overall ecological environment change was still dominated by natural environmental factors. (3) Factors affecting the distribution of NDVI in descending order were as follows: precipitation > evapotranspiration > land use type > elevation > vegetation type > soil type > soil erosion > slope > temperature > slope direction. Precipitation was the main driver of vegetation change in ELWNNR. The synergistic effect of the factors showed two-factor enhancement and nonlinear enhancement, and the combined effect of the driving factors would increase the influence on NDVI.

Growth characteristics of Suaeda salsa under different soil salinity gradients in controlled experiments

Xinjiang is the province with the largest saline-alkali land in China. The growth of halophytes will be stressed when their salt content reaches the threshold. In this study, the pot experiments were conducted and Suaeda salsa, a typical halophyte, was selected as the object to explore the relationship between growth characteristics and salt content under different soil salinity gradients. Salinity gradients were set according to the maximum soil salinity in the current years of Ebinur Lake Wetland National Nature Reserve (ELWNNR). They were classified using the following threshold values: ≤2 g/kg (Control Group), 10 g/kg, 20 g/kg, 30 g/kg, and 40 g/kg. The original spectrum, trilateral position and growth characteristics were used as indexes for analysis. With the increase of soil salt content, the red edge shift occurs first, followed by the blue edge shift. The position of yellow edge, green peak and blue edge were not sensitive with the change of soil salinity. This indicates that the red edge is the best indicator for evaluating plant growth. Compared with the Control Group, when the salt content is less than 10 g/kg, the growth status of plant seems not significantly affected. However, plant growth begins to be stressed when the salt content increases to 20 g/kg, which is a turning point for plant health. The increase of soil salt content can inhibit chlorophyll synthesis and plant growth. Plants begin to die when the salt content reaches the 40 g/kg. Therefore, 40 g/kg could be regarded as the critical point of salt content which inhibits plant growth. The study also shows that the relationship between plant-height change rate and soil salt content is the most significant, indicating that the plant-height change rate is significantly impacted by soil salt content. Keywords: Suaeda salsa, soil salt content, gradient, spectral, growth characteristics DOI: 10.25165/j.ijabe.20211405.5871 Citation: Zhang F, Chen Y, Yuan J, Ma L G, Liu C J. Growth characteristics of Suaeda salsa under different soil salinity gradients in controlled experiments. Int J Agric & Biol Eng, 2021; 14(5): 142–148.

Growth characteristics of Suaeda salsa under different soil salinity gradients in controlled experiments

Xinjiang is the province with the largest saline-alkali land in China. The growth of halophytes will be stressed when their salt content reaches the threshold. In this study, the pot experiments were conducted and Suaeda salsa, a typical halophyte, was selected as the object to explore the relationship between growth characteristics and salt content under different soil salinity gradients. Salinity gradients were set according to the maximum soil salinity in the current years of Ebinur Lake Wetland National Nature Reserve (ELWNNR). They were classified using the following threshold values: ≤2 g/kg (Control Group), 10 g/kg, 20 g/kg, 30 g/kg, and 40 g/kg. The original spectrum, trilateral position and growth characteristics were used as indexes for analysis. With the increase of soil salt content, the red edge shift occurs first, followed by the blue edge shift. The position of yellow edge, green peak and blue edge were not sensitive with the change of soil salinity. This indicates that the red edge is the best indicator for evaluating plant growth. Compared with the Control Group, when the salt content is less than 10 g/kg, the growth status of plant seems not significantly affected. However, plant growth begins to be stressed when the salt content increases to 20 g/kg, which is a turning point for plant health. The increase of soil salt content can inhibit chlorophyll synthesis and plant growth. Plants begin to die when the salt content reaches the 40 g/kg. Therefore, 40 g/kg could be regarded as the critical point of salt content which inhibits plant growth. The study also shows that the relationship between plant-height change rate and soil salt content is the most significant, indicating that the plant-height change rate is significantly impacted by soil salt content. Keywords: Suaeda salsa, soil salt content, gradient, spectral, growth characteristics DOI: 10.25165/j.ijabe.20211405.5871 Citation: Zhang F, Chen Y, Yuan J, Ma L G, Liu C J. Growth characteristics of Suaeda salsa under different soil salinity gradients in controlled experiments. Int J Agric & Biol Eng, 2021; 14(5): 142–148.

Estimation of soil salt content using machine learning techniques based on remote-sensing fractional derivatives, a case study in the Ebinur Lake Wetland National Nature Reserve, Northwest China

AbstractSoil salinity is a common global environmental problem that severely restricts industrial, agricultural and human development. In Northwest China, soil salinity is a problem affecting the Lake Ebinur area and needs to be monitored and addressed. The use of optical remote sensing technology to for timely and accurate soil salinity monitoring has great potentials and can be crucial for industrial and agricultural development. Optical remote sensing technology is an important data source for monitoring of soil salinization because of its rich spectral information and high-resolution. Based on the HJ-1/HSI data of fractional derivative transformation, this paper calculated the parameters, such as the difference index (DI), normalized difference index (NDI), ratio index (RI) and salt index (SI), that can invert the spatial distribution of salinization and then calculates a particle swarm optimization support vector machine (PSO-SVM). These index parameters were used to invert the spatial distribution information of salinized soil to explore the influence of fractional derivatives on the estimation accuracy of the spatial distribution of salinized soil. The results showed the following: (1) The optimal model based on different fractional derivatives and exponential transformations was the DI transformation under the fractional derivative of 1.2-order. R2, RMSE and RPD were 0.66, 13.81 g/kg and 2.59, respectively. These data show that the model had a good prediction effect. (2) The application of fractional derivatives in the processing of hyperspectral images could highlight the spectral details of the images and enrich their pretreatment methods, which is of great significance for the inversion of the distribution of salinized soil by hyperspectral images. (3) In the lakeside area of Lake Ebinur, the soil is mainly saline to severely saline. In the northern mountainous region of Lake Ebinur, the soil is mainly non-saline soil to mildly saline soil. Finally, in the Kuitun riverbank area in the southeast of Lake Ebinur, the soils are mainly non-saline soils to mildly saline soils. The results of this study demonstrate the potential of spectral index optimization and prediction models to monitor soil salinization based on fractional derivatives. This technique highlights the spectral detail information and enriches the preprocessing methods of hyperspectral imagery, resulting in the mapping of the spatial distribution of the degree of soil salinization. This has practical importance for the prevention and control of soil salinization and its spatial extent in arid and semi-arid regions.

Quantitative Evaluation of Spatial and Temporal Variation of Soil Salinization Risk Using GIS-Based Geostatistical Method

Soil salinization is one of the environmental threats affecting the sustainable development of arid oases in the northwest of China. Thus, it is necessary to assess the risk of soil salinity and analyze spatial and temporal changes. The objective of this paper is to develop a temporal and spatial soil salinity risk assessment method based on an integrated scoring method by combining the advantages of remote sensing and GIS technology. Based on correlation coefficient analysis to determine the weights of risk evaluation factors, a comprehensive scoring system for the risk of salinity in the dry and wet seasons was constructed for the Ebinur Lake Wetland National Nature Reserve (ELWNNR), and the risk of spatial variation of soil salinity in the study area was analyzed in the dry and wet seasons. The results show the following: (1) The risk of soil salinity during the wet season is mainly influenced by the plant senescence reflectance index (PSRI), deep soil water content (D_wat), and the effect of shallow soil salinity (SH_sal). The risk of soil salinity during the dry season is mainly influenced by shallow soil salinity (SH_sal), land use and land cover change (LUCC), and deep soil moisture content (D_wat). (2) The wet season was found to have a high risk of salinization, which is mainly characterized by moderate, high, and very high risks. However, in the dry season, the risk of salinity is mainly characterized by low and moderate risk of salinity. (3) In the ELWNNR, as the wet season changes to dry season (from May to August), moderate-risk area in the wet season easily shifts to low risk and risk-free, and the area of high risk in the wet season easily shifts to moderate risk. In general, the overall change in salinity risk of the ELWNNR showed a significant relationship with changes in lake water volume, indicating that changes in water volume play an important role in the risk of soil salinity occurrence. Ideally, the quantitative analysis of salinity risk proposed in this study, which takes into account temporal and spatial variations, can help decision makers to propose more targeted soil management options.

Machine learning-based detection of soil salinity in an arid desert region, Northwest China: A comparison between Landsat-8 OLI and Sentinel-2 MSI

Accurate assessment of soil salinization is considered as one of the most important steps in combating global climate change, especially in arid and semi-arid regions. Multi-spectral remote sensing (RS) data including Landsat series provides the potential for frequent surveys for soil salinization at various scales and resolutions. Additionally, the recently launched Sentinel-2 satellite constellation has temporal revisiting frequency of 5 days, which has been proven to be an ideal approach to assess soil salinity. Yet, studies on detailed comparison in soil salinity tracking between Landsat-8 OLI and Sentinel-2 MSI remain limited. For this purpose, we collected a total of 64 topsoil samples in an arid desert region, the Ebinur Lake Wetland National Nature Reserve (ELWNNR) to compare the monitoring accuracy between Landsat-8 OLI and Sentinel-2 MSI. In this study, the Cubist model was trained using RS-derived covariates (spectral bands, Tasseled Cap transformation-derived wetness (TCW), and satellite salinity indices) and laboratory measured electrical conductivity of 1:5 soil:water extract (EC). The results showed that the measured soil salinity had a significant correlation with surface soil moisture (Pearson's r = 0.75). The introduction of TCW generated satisfactory estimating performance. Compared with OLI dataset, the combination of MSI dataset and Cubist model yielded overall better model performance and accuracy measures (R2 = 0.912, RMSE = 6.462 dS m−1, NRMSE = 9.226%, RPD = 3.400 and RPIQ = 6.824, respectively). The differences between Landsat-8 OLI and Sentinel-2 MSI were distinguishable. In conclusion, MSI image with finer spatial resolution performed better than OLI. Combining RS data sets and their derived TCW within a Cubist framework yielded accurate regional salinity map. The increased temporal revisiting frequency and spectral resolution of MSI data are expected to be positive enhancements to the acquisition of high-quality soil salinity information of desert soils.

Assessment of spatial and temporal variation of ecological environment quality in Ebinur Lake Wetland National Nature Reserve, Xinjiang, China

Abstract Remote sensing technology can objectively and quantitatively evaluate spatial-temporal change of ecological environmental quality. This paper uses images from Landsat5 Thematic Mapper (TM) in 2007 and Landsat8 operational land imager (OLI) in 2013 and 2016 to extract indicators such as greenness, wetness, heat, and dryness that reflect the ecological environment quality of arid area. The principal component analysis (PCA) used the evaluating ecological environment quality of the Ebinur Lake Wetland National Nature Reserve to explore the characteristics of spatial differentiation for ecological quality in environment as indicated in the remote sensing ecological index (RSEI). The results showed that the greenness and wetness have positive effects on the ecological environment while the heat and dryness have negative effects. Wetness has a greater impact on the ecological environment than the other three indicators. The proportion of RSEI of degeneration, invariability, and improvement were 20.57%, 42.98%, 36.44%. Thus, the ecological environment had improved. The improved area in 2016 was once located in bad level in 2007. The ecological environment had degraded in the southern part of the lake. The value of Moran's I showed an increased trend first and then decreased. The value of Moran's I increased in 2013, in comparison with 2007, indicating a strong positive correlation and certain internal connection between the ecological environment qualities in the study area. The spatial distribution was clustered rather than random. In comparison with 2013, the auto-correlation in 2016 was relatively weak, an indication that the spatial distribution was random and disperse. The area of High-High (HH) mostly reached to the significant level of 0.05 in 2007 and then to 0.01 in 2013 and 2016. This study offered important results and information for ecological environment protection and region planning.

Assessing and predicting changes of the ecosystem service values based on land use/cover change in Ebinur Lake Wetland National Nature Reserve, Xinjiang, China

Ecosystem service value (ESV) included some contributions of ecological environments to human health and living, and provide cultural and economic benefits for sustaining and fulfilling living organisms. The author selected the Ebinur lake Wetland Nature Reserve as our study site to analyze the spatial distribution and dynamics of ecosystem services, and we used 1972, 1998, 2006, 2014 and 2016 images to study land use/cover change (LUCC) over these periods. LUCC is one of the key drivers that affect ecosystem services. Firstly, in 1972–2030, land use/cover (LULC) significantly changed, with all LUCC practices increasing except for the categories of water area, bare land area and barren land/other. Secondly, changes in ESV were significant during the last 60 years. The total annual ESV in the study area decreased by 50.89% between 1972 and 2030, mostly attributable to the 6.82% growth of saline land, 0.09% growth of desert, 1% decrease of wetlands and 0.95% decrease of water bodies. Thirdly, the spatial-temporal variation of ESV was prominent. Spatial correlation was significant, and the degree of spatial clustering was relatively high in the study area, which was mainly distributed in the water body and wetland regions, and these high-value regions exhibited a decreasing trend. Low values occurred around the lake areas, which increased over time.

Machine-learning-based quantitative estimation of soil organic carbon content by VIS/NIR spectroscopy.

Soil organic carbon (SOC) is an important soil property that has profound impact on soil quality and plant growth. With 140 soil samples collected from Ebinur Lake Wetland National Nature Reserve, Xinjiang Uyghur Autonomous Region of China, this research evaluated the feasibility of visible/near infrared (VIS/NIR) spectroscopy data (350–2,500 nm) and simulated EO-1 Hyperion data to estimate SOC in arid wetland regions. Three machine learning algorithms including Ant Colony Optimization-interval Partial Least Squares (ACO-iPLS), Recursive Feature Elimination-Support Vector Machine (RF-SVM), and Random Forest (RF) were employed to select spectral features and further estimate SOC. Results indicated that the feature wavelengths pertaining to SOC were mainly within the ranges of 745–910 nm and 1,911–2,254 nm. The combination of RF-SVM and first derivative pre-processing produced the highest estimation accuracy with the optimal values of Rt (correlation coefficient of testing set), RMSEt and RPD of 0.91, 0.27% and 2.41, respectively. The simulated EO-1 Hyperion data combined with Support Vector Machine (SVM) based recursive feature elimination algorithm produced the most accurate estimate of SOC content. For the testing set, Rt was 0.79, RMSEt was 0.19%, and RPD was 1.61. This practice provides an efficient, low-cost approach with potentially high accuracy to estimate SOC contents and hence supports better management and protection strategies for desert wetland ecosystems.

New methods for improving the remote sensing estimation of soil organic matter content (SOMC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in northwest China

This study aimed to improve the potential of Analytical Spectral Devices (ASD) hyperspectral and Landsat Operational Land Imager (OLI) data in predicting soil organic matter content (SOMC) in the bare topsoil of the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in northwest China. The results indicated that the correlation of coefficients (R) between SOMCs and hyperspectral data processed by fractional derivative were significant at the 0.01 level; the number of wave bands increased initially and then decreased when the order increased. The correlation of coefficient peak appeared at the 1.2 order with a value of 0.52. The correlation of coefficients (R) between SOMCs and the optimal remote sensing indexes (the ratio index, RI; difference index, DI; and the normalized difference index, NDI) of peaked at the 1.2 order, with correlation of coefficients (R) values of 0.81, 0.86 and 0.82, respectively. Six SOMC estimation models were created by means of a single band and optimal remote sensing indexes using Gray Relational Analysis-BP Neural Network (GRA-BPNN). This study found that the optimal model was a 1.2 order derivative model, where the lowest root mean square error (RMSE) was 3.26 g/kg, the highest was 0.92, and the residual prediction deviation (RPD) was 2.26. To complete the high accuracy retrieval of SOMCs, based on Landsat OLI operational land images data, more ‘hidden’ information from the Landsat OLI images were obtained by employing the subsection of spectral band method and the fractional derivative algorithm. Accuracy of the SOMC map was attained by the optimal model of the ground hyperspectral data and the Landsat OLI data, which had low RMSE values of 4.21 g/kg and 4.16 g/kg, respectively. Therefore, we conclude that the SOMC can be estimated and retrieved using a fractional derivative algorithm, the subsection of spectral band method, and the optimal remote sensing index.

Spatial–temporal variation of ecosystem service values in Ebinur Lake Wetland National Natural Reserve from 1972 to 2016, Xinjiang, arid region of China

National nature reserves in China protect ecosystem and well-being in its region while benefiting humans by providing ecosystem services. Land-use/cover changes result in changes in the landscape pattern and affect the provision of ecosystem service value (ESV). The monetary evaluation of spatial–temporal changes in landscape patterns and ecosystem services in the arid region of China has been inadequately studied. Therefore, the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in the arid region of China was chosen as a study object to evaluate the spatial–temporal variation in ecosystem service values (ESVs) under changes in land use/cover. The research analysed land-use/cover change in ELWNNR using a Sankey diagram, the spatial–temporal variation in ELWNNR ESVs using ArcGIS. Meanwhile, it also analysed the landscape patterns and total ESVs of the core area, buffer area and experimental area of ELWNNR at a regional scale. Four Landsat data sets from 1972, 1998, 2007 and 2016 were chosen to estimate the changes in land-use/cover types and ESV in ELWNNR. Meanwhile, the sensitivity of ESV was also analysed to validate the estimation of ESV in ELWNNR. The results showed that the total ESV of ELWNNR was 3.12 × 109 CNY (USD$ 4.88 × 108) in 1972 and dropped to 2.71 × 109 CNY (USD$ 4.24 × 108) in 2016. The provision of total ESV in ELWNNR decreased by approximately 0.41 × 109 CNY (USD$ 6.42 × 107) from 1972 to 2016, although there were fluctuations in 1998 and 2007. The core area possessed the highest ESV among all the areas, showing an increase in the provision of ESV in ELWNNR. All the areas of ELWNNR became highly fragmented, and the landscape patterns became more complex during this time period. The results of the study provide sufficient information about the ecosystem services in the region in the absence of complete data sets and contribute to conservation planning of national nature reserves.

Estimation of soil salt content (SSC) in the Ebinur Lake Wetland National Nature Reserve (ELWNNR), Northwest China, based on a Bootstrap-BP neural network model and optimal spectral indices

Soil salinity is recognized worldwide as a major threat to agriculture, particularly in arid regions. Producers and decision-makers thus require updated and accurate maps of salinity in agronomical and environmentally relevant regions. The goals of this study were to test various regression models for estimating soil salt content based on hyperspectral data, HJ-CCD images, and Landsat OLI data using; develop optimal band Difference Index (DI), Ratio Index (RI), and Normalization Index (NDI) algorithms for monitoring soil salt content using image and spectral data; and to compare the performances of the proposed models using a Bootstrap-BP neural network model (Bootstrap-BPNN) from different data sources. The results showed that previously published optimal remote sensing parameters can be applied to estimate the soil salt content in the Ebinur Lake Wetland National Nature Reserve (ELWNNR). Optimal band combination indices based on DI, RI, and NDI were developed for different data sources. Then, the Bootstrap-BP neural network model was built using 1000 groups of Bootstrap samples of remote sensing indices (DI, RI and NDI) and soil salt content. When verifying the accuracy of hyperspectral data, the model yields an R2 value of 0.95, a root mean square error (RMSE) of 4.38g/kg, and a residual predictive deviation (RPD) of 3.36. The optimal model for remote sensing images was the first derivative model of Landsat OLI, which yielded R2 value of 0.91, RMSE of 4.82g/kg, and RPD of 3.32; these data indicated that this model has a high predictive ability. When comparing the salinization monitoring accuracy of satellite images to that of ground hyperspectral data, the accuracy of the first derivative of the Landsat OLI model was close to that of the hyperspectral parameter model. Soil salt content was inverted using the first derivative of the Landsat OLI model in the study area.

Spectral response characteristics and identification of typical plant species in Ebinur lake wetland national nature reserve (ELWNNR) under a water and salinity gradient

The spectral characteristics of variable selection are particularly important for wetland vegetation mapping. In the present study, we combined soil salt and water content with spectral data collected in the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in Western China to understand the effects of soil salt and water content on plant spectra. The results showed the following: (1) the distribution of plants reflect the macroscopic response characteristics of plants on water and salt environment; (2) a certain response rule exists between the spectra of different plants under a water and salt gradient, e.g., with increase in water and salt gradient, the spectral reflectivity of salt-dilution plant decreases, and salt-exclusion plant increases; (3) a response pattern is formed between the “trilateral” characteristics of plant spectrum and water salt gradient. With the increase of salinity gradient, the “red edge”, “blue edge”, and “yellow edge” shows the most obvious changes in the 0.8 order derivatives, e.g., when the soil salt content was range from 4.2 to 8.8g/kg, the spectral characteristics of the plants were the most obvious; (4) Fisher linear discriminant analysis found that during fractional order to integer promotion, classification accuracy of the 0.8 order derivative was higher than the integer order derivatives. Therefore, the “trilateral” characteristics of plants spectra in the 0.8 order derivatives were more accurate than the first derivative. The 0.8 order derivative was more advantageous to distinguishing plants, with a classification accuracy of 89.37%, indicating the potential of 0.8-order derivative for hyperspectral remote sensing of plants. This study introduced a fractional order derivative to hyperspectral remote sensing for the quantitative analysis of differences in the vegetation spectrum, provided new insights to the research and application of vegetation remote sensing.

Assessment of Land-Cover/Land-Use Change and Landscape Patterns in the Two National Nature Reserves of Ebinur Lake Watershed, Xinjiang, China

Land-cover and land-use change (LCLUC) alters landscape patterns and affects regional ecosystems. The objective of this study was to examine LCLUC and landscape patterns in Ebinur Lake Wetland National Nature Reserve (ELWNNR) and Ganjia Lake Haloxylon Forest National Nature Reserve (GLHFNNR), two biodiversity-rich national nature reserves in the Ebinur Lake Watershed (ELW), Xinjiang, China. Landsat satellite images from 1972, 1998, 2007 and 2013 were used to calculate the dynamics of a land-cover and land-use (LCLU) transition matrix and landscape pattern index using ENVI 5.1 and FRAGSTATS 3.3. The results showed drastic land use modifications have occurred in ELWNNR during the past four decades. Between 1972 and 1998, 1998 and 2007, and 2007 and 2013, approximately 251.50 km2 (7.93%), 122.70 km2 (3.87%), and 195.40 km2 (6.16%) of wetland were turned into salinized land. In GLHFNNR both low and medium density Haloxylon forest area declined while high density Haloxylon forest area increased. This contribution presents a method for characterizing LCLUC using one or more cross-tabulation matrices based on Sankey diagrams, demonstrating the depiction of flows of energy or materials through ecosystem network. The ecological landscape index displayed that a unique landscape patches have shrunk in size, scattered, and fragmented. It becomes a more diverse landscape. Human activities like farming were negatively correlated with the landscape diversity of wetlands. Furthermore, evidence of degraded wetlands caused by air temperature and annual precipitation, was also observed. We conclude that national and regional policies related to agriculture and water use have significantly contributed to the extensive changes; the ELWNNR and GLHFNNR are highly susceptible to LCLUC in the surrounding Ebinur Lake Watershed.

Analysis of land cover and landscape change patterns in Ebinur Lake Wetland National Nature Reserve, China from 1972 to 2013

For effective monitoring and protection, researchers need to analyze the trends and causes of landscape pattern change in wetlands. The present study used Geographic Information System and remote sensing technology to analyze temporal and spatial dynamics of landscape pattern and related driving forces in the Ebinur Lake Wetland National Nature Reserve (ELWNNR) in China. We used Landsat images from 1972, 1998, 2007 and 2013, and classified the landscape into six types of land cover: water body, wetland, vegetation, salinized land, desert, and other objects (i.e., Gobi and Mountain). Landscape transition matrices and change pattern analysis were used understand wetland dynamics. Our results showed that land cover had changed dramatically during 1972–2013. The spatial extent of much of the wetland areas in the ELWNNR had reduced gradually from 1972 to 2007, and wetland loss occurred more rapidly after 2007. From 1972 to 2013, 415.8 km2 of wetland area had been lost and only 250.1 km2 wetland area had expanded, which represents a 22% decrease in wetland area in the ELWNNR. At the same time, the landscape pattern experienced diverse changes in the Nature Reserve. The high-density human population of the region has an intensive effect on the region. Wetlands in the nature reserve have also changed with recent rapid development in the region. This study may provide scientific information for developing effective and sustainable conservation plans for the ELWNNR.

Response of vegetative growth and biomass allocation of Lappula semiglabra seedlings to dew gradient.

Three dew amount gradients,i.e., few amount of dew (W0), natural amount of dew (W1) and the doubled amount of natural dew (W2) were set to study the response strategies of the morphology, physiology and the dry matter allocation of Lappula semiglabra seedlings on dew amount in Ebinur Lake Wetland National Nature Reserve in Xinjiang, China. The results showed that plant traits in connection with absorbing water by plant leaves, involving in relative content of chlorophyll, leaf water potential, plant height, crown breadth, stem mass and leaf mass ratio increased significantly with the amount of dew. On the contrary, the main stem diameter, root length and root diameter which associated with water absorption and transportation by root had no significant change with the amount of dew. Plant height and the relative content of chlorophyll were the most obvious traits in response to dew amount among all leaf absorption water traits. The proportion of stem biomass had no significant difference along the dew amount gradient. However, the root mass ratio declined gradually along the growth of seedlings, and seedlings in W0 had the smallest decline rather than W1 and W2. In conclusion, L. semiglabra seedlings changed aboveground traits in response to the change of dew amount. Specifically, it increased its photosynthetic capacity and ratio of leaf dry matter to adapt to the variation on dew amount.

荒漠区垂直河岸带植物多样性格局及其成因

PDF HTML阅读 XML下载 导出引用 引用提醒 荒漠区垂直河岸带植物多样性格局及其成因 DOI: 10.5846/stxb201401160128 作者: 作者单位: 新疆大学资源与环境科学学院,新疆大学资源与环境科学学院,新疆大学资源与环境科学学院,新疆大学资源与环境科学学院,新疆大学资源与环境科学学院,新疆大学资源与环境科学学院,新疆大学资源与环境科学学院,新疆大学资源与环境科学学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学重点基金项目(41130531);教育部创新团队项目(IRT1180);绿洲生态教育部重点实验室开放课题(XJDX0201-2013-08);国家自然科学基金(31060061) Patterns and mechanisms influencing plant diversity in an arid desert region riparian zone of the Aqikesu River, Xinjiang, China Author: Affiliation: College of Resources and Environment Science,Xinjiang University,College of Resources and Environment Science,Xinjiang University,College of Resources and Environment Science,Xinjiang University,College of Resources and Environment Science,Xinjiang University,College of Resources and Environment Science,Xinjiang University,College of Resources and Environment Science,Xinjiang University,College of Resources and Environment Science,Xinjiang University,College of Resources and Environment Science,Xinjiang University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:荒漠区河岸植物多样性格局特征和影响机制研究可为河岸带生物多样性保护和管理提供依据。艾比湖荒漠区垂直河岸样带的植物多样性格局及其土壤影响机制的研究表明:(1)距河2.0-3.0 km (T3.0)和0.9-1.5 km (T1.5)的样带植物本质多样性、α多样性整体高于距河0.1-0.2 km (T0.2)和0.2-0.4 km (T0.4)的样带(P <0.05);(2) T0.4样带多样性最低,物种相似性最高,且植物生活型和土壤属性均较T0.2有较大的变化,反映了距河0.2-0.4 km区域群落性质的转变,0.2-0.4 km可作为确定河岸带宽度的参考样带;(3) 影响植物多样性的土壤因素和途径沿T0.2-T3.0样带趋于简单化,即由近河带(T0.2)贫营养(碳磷比(C/P)、土壤有机质(SOM))和高土壤水分(SWC)、盐分(TS)的限制作用,到远离河流旱胁迫加剧时对植物多样性影响逐渐突出的土壤全磷(P)和SWC;(4) 根据T1.5、T3.0植物多样性特征,研究区土壤水分在7.0-7.5%间, C/P在26.1-30.2之间以及盐分低于1.0%时能维持较高的多样性。最后对保护区河岸带和缓冲带宽度的确定、河岸带管理、植被资源保护以及生态系统恢复等提出针对性建议,以期为制定有效的河岸带生物多样性维护和资源管理对策提供理论参考和科学依据。 Abstract:Plant diversity in riparian areas is an important topic in restoration ecology and biodiversity conservation science. The Aqikesu River in Xinjiang Uygur Autonomous Region, China, a typical inland river in this arid desert region, is the only river located in the Ebinur Lake Wetland National Nature Reserve and thus plays a crucial role in maintaining balanced and sustainable development of the local ecosystem. Exploration of plant diversity patterns and influencing mechanisms in the riparian zone in this desert area provides scientific baseline data related to the conservation of riparian biodiversity and scientific management of this reserve. However, the patterns of biodiversity and influencing mechanisms in the riparian zone along this river remain unclear. This study used four transects established perpendicular to the Aqikesu River in north and south of Dongdaqiao Station. The four transects were located 0.1-0.2 km, 0.2-0.4 km, 0.9-1.5 km, and 2-3 km from the river and named T0.2, T0.4, T1.5, and T3.0, respectively. Plots were established in each transect based on the appearance and characteristics of the plant communities, that is 10 m × 10 m for trees, 5 m × 5 m for shrubs, and 1 m × 1 m for herbs. Specifically, T0.2 included nine plots (three, four and two for trees, shrubs and herbs, respectively) and T0.4 had six plots (two plots each for trees, shrubs and herbs). T1.5 had eight plots (two plots each for trees and herbs, and four for shrubs). T3.0 had eight plots (two plots for trees and three plots each for shrubs and herbs). Geographic data were recorded in each plot; abundance, height, and diameter at breast height of trees were surveyed and recorded along with the abundance of shrubs and herbs. Soil samples were collected in each plot and soil water content (SWC), total salt (TS), pH, soil organic matter (SOM), and soil phosphorus (P) were measured in the lab. First, based on field and experimental data, the intrinsic and α diversity indices of T1.5 and T3.0 transects were all higher than those of T0.2 and T0.4 transects. Secondly, T0.4 transects exhibited the lowest diversity; the Cody and Wilson index of T0.4 was significantly lower than in other transects (P <0.05), and the Jaccard and Sørenson indices of T0.4 were significantly higher than that of other transects (P < 0.05). Compared with T0.2, the T0.4 transects had greatly reduced herbaceous flora, SWC, TS, and soil organic matter, which indicated the transitional nature of the community. Therefore, the T0.4 transect could function as a reference for determining the width of the riparian zone. Third, as distance from the river increases as one proceeds from T0.2 to T3.0, the effects and influence of soil conditions on plant diversity gradually become simpler. In the nearest distance away from the river (T0.2), plant diversity is constrained by poor nutrition (low carbon/ phosphorus ratio (C/P) and low SOM), high soil moisture, and high salt content. Farther from the river in the T0.4 and T3.0 area, only soil P and SWC were observed to gradually and prominently affect plant diversity more and more as drought stress increases with distance from the river. Fourth, based on plant diversity traits of T1.5 and T3.0, the highest plant diversity occurs when soil conditions in the study area have a an SWC between 7.0% and 7.5%, soil C/P between 26.1 and 30.2, and soil salt content of less than 1.0%. Finally, specific suggestions for determining the appropriate width of riparian and buffer zones and suggestions for riparian zone management, vegetation resource protection, and ecosystem restoration were proposed to provide a theoretical and scientific basis for effective biodiversity maintenance, resource management, and habitat damage mitigation in riparian zones. 参考文献 相似文献 引证文献

艾比湖荒漠-湿地生态系统非生长季碳通量数据特征

PDF HTML阅读 XML下载 导出引用 引用提醒 艾比湖荒漠-湿地生态系统非生长季碳通量数据特征 DOI: 10.5846/stxb201401130092 作者: 作者单位: 新疆绿洲生态教育部重点实验室,新疆绿洲生态教育部重点实验室;新疆大学资源与环境科学学院,新疆绿洲生态教育部重点实验室,新疆绿洲生态教育部重点实验室,新疆绿洲生态教育部重点实验室 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然基金重点项目(41130531);教育部创新团队(IRT1180) Research on data characteristics during non-growing season of desert-wetland ecosystem in Ebinur Lake Author: Affiliation: Xinjiang Key Laboratory of Oasis Ecology,Xinjiang Key Laboratory of Oasis Ecology;China;College of Resources and Environment Science,Xinjiang University,Urumqi,;China,Xinjiang Key Laboratory of Oasis Ecology,Xinjiang Key Laboratory of Oasis Ecology,Xinjiang Key Laboratory of Oasis Ecology Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:选取新疆艾比湖湿地国家级自然保护区非生长季(2012年1月15日-3月15日、11月1日-12月14日、2013年11月1日-11月16日)通量观测数据,参考中国通量数据处理标准开展荒漠-湿地生态系统非生长季碳通量数据特征研究.结果表明:研究区非生长季碳通量观测数据存在大量"野点",占数据总量的37.39%,有效通量变化范围为-0.197-0.283 mg m-2 s-1;平面拟合旋转(PF)校正具有较好的结果(R2=0.9349,P < 0.01),频率响应校正引起的碳通量增量为7.55%,水热校正影响较小;碳通量数据在不同质量等级分布较平均,大气湍流发展的充分性处于中等水平,夜间摩擦风速可划分为3级,在0 m/s < u* < 0.30 m/s内进行选择性剔除;传感器状态异常、检验分析和阈值分析剔除数据比例分别为26.34%、2.48%和8.57%;碳通量与5 cm土壤温度和太阳辐射不存在显著相关,采用线性内插和平均每日变异法可实现缺失数据的插补. Abstract:With the global climate change and the resulting frequent regional natural disasters becoming increasingly real, CO2, H2O, and energy transport and transformation processes of the surface atmosphere are becoming important concerns for the international community. Carbon flux in the non-growing season (Jan. 15 to Mar. 15, Nov. 1 to Dec. 14 in 2012; Nov. 1 to 16 in 2013) was measured in the Ebinur Lake Wetland National Nature Reserve of Xinjiang using the eddy covariance technique. Then, research was conducted on the carbon flux data processing method after consulting the flux data processing standards in China, and the data were further analyzed to understand the carbon flux data characteristics in the non-growing season, and to attempt to reveal the controlling factors and change law. The results indicated that there were a large number of "wild" non-growing season carbon flux observation data in the study area, which accounted for 37.39% of the total. The effective carbon flux was in the range from -0.197 mg m-2 s-1 to 0.283 mg m-2 s-1, and the average atmospheric CO2 concentration and carbon flux were 766.905 mg/m3 and 0.018 mg m-2 s-1, respectively, which showed weak carbon sources. Axis-rotation correction results showed TR < DR < PF, and the planer fit (PF) correction showed better accuracy (R2 = 0.9349, P < 0.01). The frequency-response correction increased carbon flux by 7.55%, but the carbon flux was less affected by the hydrothermal correction. The energy closure throughout the non-growing season in the study area was 0.62, indicating that 38% of the energy is not closed in the non-growing season. The proportion of IST classification results of carbon flux data in the desert wetland in quality levels 1-6 was 92.03%, indicating that the distribution in different quality grades of carbon flux data in the desert-wetland ecosystem was relatively uniform. The percentage of ITC classification results in the five quality levels was 91.65%, illustrating that the development of atmospheric turbulence was at a middle level of adequacy in this period. When the friction velocity was 0-0.15, 0.15-0.30, and more than 0.30 m/s, respectively, the carbon flux showed grade variation characteristics; therefore, frictional wind speed at night can be divided into the three levels, and data within the 0 m/s and 0.30 m/s can be selectively weeded out. The proportions of data eliminated through the methods of sensor abnormal state, check analysis, and threshold analysis were 26.34%, 2.48%, and 8.57%, respectively, and the vast majority showed intermittent loss characteristics. There were no significant correlations between carbon flux and solar radiation or 5 cm soil temperature. Use of the linear interpolation and average daily variation methods could realize interpolation of missing data, but these could not reflect the true flux variations in a better manner. Conducting research on carbon flux in the non-growing season in a unique ecosystem in a desert-wetland environment surrounded by arid land and probing the carbon flux data characteristics and peculiarities could help provide the needed carbon flux research data and also a basis for improving research on carbon flux in China. 参考文献 相似文献 引证文献

基于盐分梯度的荒漠植物多样性与群落、种间联接响应

PDF HTML阅读 XML下载 导出引用 引用提醒 基于盐分梯度的荒漠植物多样性与群落、种间联接响应 DOI: 10.5846/stxb201306071403 作者: 作者单位: 新疆大学资源与环境科学学院,新疆大学资源与环境科学学院,华东师范大学环境科学系,新疆大学资源与环境科学学院,新疆大学资源与环境科学学院,新疆大学资源与环境科学学院,新疆大学资源与环境科学学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学重点基金项目(41130531) Responses of desert plant diversity, community and interspecific association to soil salinity gradient Author: Affiliation: College of Resources and Environment Science,Xinjiang University,College of Resources and Environment Science,Xinjiang University,Department of Environment Science,East China Normal University Shanghai,College of Resources and Environment Science,Xinjiang University,College of Resources and Environment Science,Xinjiang University,College of Resources and Environment Science,Xinjiang University,College of Resources and Environment Science,Xinjiang University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:土壤盐分是影响干旱区荒漠植物群落动态的决定因素之一。基于样方调查和不同土壤盐分梯度下植物多样性指数及群落与种间关联的计算结果,分析干旱区荒漠群落植物多样性、群落联接性和种间关联对土壤盐分梯度的响应动态。结果表明,在土壤盐含量为0.03%-0.55% (S1)、0.61%-1.24% (S2)和1.41%-1.79% (S3)的盐分梯度上,(1)随土壤盐含量升高,群落生活型结构改变,草本比例减少,乔木比例增加;(2)植物多样性指数随土壤盐分增加而下降,低盐分梯度下,二者极显著正相关(P<0.01),中盐梯度下二者间呈部分显著负相关(P<0.05),高盐梯度下则转为以正相关为主(P>0.05);(3)群落联接性随土壤盐分梯度转变,在0.61%-1.24%的中度盐含量时正联接性最强(VR=1.89),高盐度含量下群落转为不显著的负联接,稳定性降低;(4)沿盐分升高梯度,种间的负相关种对数增加,正相关种对数减少,种间关联(相关)强度提高,正负相关种对比(PNR)与多样性指数呈显著正相关(P<0.05)。综上可知,干旱区荒漠植物多样性在土壤盐含量达到1.41%-1.79%水平时总体显著降低;土壤盐分水平显著影响植物群落和种间联接性,种间互利性随盐分梯度增加下降,物种趋于独立分布,并最终导致荒漠植物多样性降低。 Abstract:Dynamic response of plant diversity, community and interspecific association along environmental gradients is one of the basic issues in community ecology and biodiversity science. Soil salinity is one of the most decisive factors that govern desert plant community dynamics. The Ebinur Lake wetland national nature reserve is located in typical arid desert area. Previous related studies about the reserve had point out that soil salinity influences the desert plant community characteristic significantly, but the specific role of soil salinity in plant community dynamics is still unknown. Based on this, the objective of this study is focused on the responses of desert plant diversity, community overall association and interspecific association to soil salinity gradient.In this study, altogether 74 sampling plots were investigated; including 22 plots for trees (10 m×10 m), 35 plots for shrubs (5 m×5 m) and 17 plots for herbs (1 m×1 m). At each plot, abundance, height and diameter at breast height of trees, abundance of shrubs and herbs were surveyed. According to soil salinity of the corresponding plot, all plots were classified to 3 salinity gradients by cluster analysis, with the salt contents of low (S1), middle (S2) and high (S3) defined as 0.03%-0.55% (S1), 0.61%-1.24% (S2) and 1.41%-1.79% (S3) respectively. A range of different diversity indices including Shannon-Wiener, Simpson, Pielou, Fisher-α of logarithmic series distribution and Margalef were calculated to examine the plant diversity under each salinity gradient. Variance ratio (VR), χ2 test and Spearman rank correlation were employed to quantitatively analyze the community overall association and interspecific association under different salinity gradient. VR value, proportion of species pairs with positive and negative correlation, Dice index and Spearman rank correlation coefficient were used in exploring response of overall association and interspecific association to different soil salinity. The results indicated that: 42 species (which belong to 39 Genus of 18 Family) were identified in the Ebinur Lake wetland national nature reserve. Along the soil salinity gradient (S1-S3), (1) Life form structure in community was changed, more specifically, proportion of herbaceous decreased while tree proportion increased as salinity increases, and shrubs proportion decreased slightly. (2) Plant diversity indices decreased with increased soil salinity, and showed a very significant positive correlation in low salinity (P<0.01), then the correlation turned into partially significant negative (P<0.05) in middle salinity and showed positive mainly in high salinity gradient (P>0.05). (3) Community overall associations was significant positive in low (VR=1.42) and middle (VR=1.89) salinity, but converted to negative (VR=0.62) in high salinity, which means that high soil salinity lead to the decrease of community stability. (4) Along rising gradient of soil salinity, number of species pairs with a negative correlation increased, while number of species pairs with a positive correlation decreased; both of positive and negative correlation strength (coefficients) was enhanced as soil salinity increases; ratio of positive species pairs to negative species pairs showed a significantly positive correlation with diversity (P<0.05). In conclusion, reduced desert plant diversity and shifted community and interspecific association under high salinity threatened plant community stability of arid area, and may lead to retrogressive succession of community. Results of the present study provide a scientific basis for arid area in vegetation management, vegetation restoration and soil salinization prevention. 参考文献 相似文献 引证文献