Search

• Combining ICESat-2 and GEE can monitor water volume change in large areas precisely. • Precision of lake volume change is mainly affected by water level measurement. • ICESat-2 laser altimetry data has high accuracy in estimating water level change. • Seasonal variations in water level of natural lake and reservoir vary significantly. • Reservoirs generally show larger variations than lakes in water volume change. Monitoring the water level and volume changes of lakes and reservoirs is essential for deepening our understanding of the temporal and spatial dynamics of water resources in the Yellow River Basin, with a view to better utilizing and managing water resources. In recent years, there have been many studies on monitoring water level and volume changes in inland waters, but they were mainly focused on radar altimetry and the full waveform LiDAR ICESat, which was retired in 2010. Few studies based on the latest photon-counting LiDAR ICESat-2 have been reported. Compared with previous sensors, ICESat-2 has great advantages in footprint size, transmitting frequency, pulse number, etc, but its performance in monitoring water level and volume changes in inland waters has not been fully explored. Here we investigated the spatial distribution of water level and volume changes of 11 lakes and 8 reservoirs in the Yellow River Basin based on ICESat-2 and Google Earth Engine, and analyzed the factors affecting the measurement uncertainties. In-situ validation of lake level in Lake Qinghai indicates that the Root Mean Square Error (RMSE) of our result is only 7 cm after the reference coordinate system conversion. We found that the water level trend of the natural lake shows significant seasonal variations, while the water level trend of the reservoir shows a sharp rise and fall. In addition, precipitation plays a decisive role in the changes in natural lake levels and indirectly affects the artificial control of reservoirs' water discharges. The uncertainty of water volume change monitoring is mainly affected by water level measurement uncertainty for lakes, while for reservoirs, that is affected by the combination of water level and area measurement uncertainties. The stability of lake level measurement increases with the increase in photon counts. The introduction of ICESat-2 ATL13 Significant Wave Height might lead larger standard deviation in water level measurement. According to the law of propagation of uncertainty, the uncertainty of the water volume change estimation by the combination of ICESat-2 and GEE is less than 9 %. [ABSTRACT FROM AUTHOR]

The objective of this research was to extract β-glucan from highland barley (HBBG), illustrate its prebiotic potentials and explore the effects of co-microencapsulation of HBBG with probiotics to the survival of cells during gastrointestinal digestion. After applied HBBG by in vitro fermentation, both significant increases in gas production and OD 600 value as well as remarkable reductions in pH were obtained corresponding with abundant production of short chain fatty acids. Compared with the control microcapsule, higher encapsulation efficiencies and lower moisture contents were obtained after the addition of HBBG. During gastrointestinal digestion, the survival of cells in microcapsules with adding HBBG were significantly improved and the more of the HBBG addition, the higher of the viability after digestion, which corresponding with fewer amounts of cracked pieces. The current study demonstrates that HBBG owns prebiotic potential and co-microencapsulation of HBBG with probiotic bacteria could provide better protective effects during gastrointestinal digestion. [Display omitted] • HBBG can be degraded and fermented by intestinal microorganisms to produce SCFAs. • Incorporation of HBBG into WPI-GA matrix could significantly improves morphology and structure of probiotic microcapsules. • Addition of HBBG increased Lactobacillus survival during spray drying and in vitro digestion. • Co-microencapsulation of HBBG and probiotic could effectively improve bioavailability of cells. [ABSTRACT FROM AUTHOR]

The objective of this research was to encapsulate probiotic bacteria based on soy protein isolate (SPI) matrix using either coacervation, cross-linking modification or their combination, and investigate the influences on the survival of probiotic bacteria during spray drying, in vitro gastrointestinal digestion, heating, UV irradiation and storage. A probiotic isolate Lactobacillus plantarum 550 was spray dried in SPI solution combined with either pectin (PEC), transglutaminase (TGase) or their combination. High survival rate and low moisture content were obtained for all the samples. Microcapsules fabricated using either SPI-PEC coacervation or TGase induced cross-linking obtained well cell survival during gastric digestion, heat/UV treatment and storage, however, combination of these two modification methods generated microcapsules with lower cross-linking degree and loose package structure corresponding with significant negative effects to the cell viability during gastric digestion and storage. Among the formulations tested, SPI-PEC microparticles provided the best protection to the probiotic cells, as pectin own well thermal stability and digestion resistibility, could fill the cracks and voids existed in SPI matrix and form hydrogen bond to stabilize cell membrane during processing. In conclusion, the current study demonstrated that SPI based probiotic microcapsules with high structural density and cross-linking degree may provide better protection to adverse external environment. [Display omitted] • Probiotic microcapsules consisted by TGase induced cross-linking of SPI was successfully prepared by spray drying. • Incorporation of PEC into SPI could significantly improve the survival during gastric digestion and storage. • SPI based probiotic microcapsules with high structural density and cross-linking degree may provide better protection. [ABSTRACT FROM AUTHOR]

[Display omitted] • The influence of meteorological and land surface hydrothermal on LAI in mountainous areas was assessed. • A clear greening trend was observed in Yan Mountains, and the greening trend was the largest in summer. • LAI was negatively correlated with land surface temperature and soil moisture but positively correlated with precipitation and air temperature. • The hysteresis of LAI of different vegetation types to changes in hydrothermal conditions was explored. Changes in hydrothermal conditions have significant effects on vegetation, but there is still a lack of understanding of how vegetation responds to land surface (surface temperature and soil moisture) and meteorological (temperature and precipitation) conditions in mountain regions. This study examined the trends of leaf area index (LAI) in the Yan Mountains over the last four decades using Global Land Surface Satellite (GLASS) data. The results showed a persistent increase of LAI (greening) over 20 % to 80 % of the study area in growing season, spring, summer and autumn. Anthropogenic activities caused the greening trend by crop management before 2000 and afforestation after 2000. The increasing rate of LAI varied with elevation, and the most significant increase occurred in areas between 300 and 900 m, and the lowest increase occurred in areas below 300 m. Moreover, we found that LAI was negatively correlated with land surface temperature and soil moisture, but positively correlated with precipitation and air temperature. The time-lag effect was found between hydrothermal factors and LAI in the past four decades. There was a time lag of 2–3 months between LAI changes and temperature/precipitation during the early and late stages of the growing season, and a time lag of 0–1 month during the middle stage. Specifically, there was no time lag in vegetation response to surface soil moisture, and a time lag of 2–3 months in vegetation response to land surface temperature from July to October. Our findings provide insights into how vegetation adapts to land surface and climatic hydrothermal conditions in mountain regions and can be used by governments to develop policies for ecological protection. [ABSTRACT FROM AUTHOR]

• Grassland degradation is predictable on a large scale by Random Forest. • Specific microbial groups, mainly Solirubrobacter, were screened as credible biomarkers. • Grassland degradation exhibited a longitudinal pattern and was correlated with current Net primary productivity. Extensive grassland degradation under climate change and intensified human activities has threatened ecological security and caused a variety of environmental problems. However, it is still challenging to predict the grassland degradation status on a large scale because it is a multi-factorial phenomenon with complex changes in ecosystem structure and function, which is hard to be fully characterized through mechanism models. The emergence of machine learning algorithms provides a potential to model complex systems and mine information from multi-source data without elucidating underlying mechanisms. Here, we utilized random forest and neural network algorithms to predict the grassland degradation represented by the net primary productivity (NPP) changing rate based on multi-source data including geographic, meteorological, plant traits, land use type and microbial variables in the Chinese Northern grassland. Particularly, the microbial roles in determining the degradation status were concerned. Results show that a high prediction precision was achieved by random forest model, rather than by neural network model, with a mean relative error of 16.9% and a mean square error of 9.273e-05. Besides identified longitude, arid index and current NPP state, specific soil microbial groups, mainly Solirubrobacter, were screened as credible biomarkers. Regarding model fitting, geographic, meteorological and plant variables explained 61.8% of the total variance, which was enhanced up to 72.8% by the rest microbial markers. These findings provide a theoretical basis to establish a pre-warning system for grassland management and policy-making. [ABSTRACT FROM AUTHOR]

[Display omitted] • The relationships between network topological characteristics and the different ESVs were analyzed. • The important sources and corridors are mainly located along the sides of the Wei River and the Fen River. • Higher ESVs related to water resources have better network connectivity. • The "one central source and three forested belts" radially ecological arrangement is planned. There are numerous ecological challenges that are of considerable concern caused by urbanization and natural calamities. The spatial pattern of the landscape is of great significance in maintaining species diversity and regulating material cycles and individual ecophysiological processes as a medium for ecosystem service functions. Resolving the conflicting relationships between people and land necessitates the precise identification of essential ecological sources. Therefore, there is an urgent need to investigate the relationship between ecological sources and ecosystem service values (ESVs) and to optimize the matter and energy.connectivity network. The study optimizes ecological network simulation using a penetration threshold, population density inverse distance formula, and geodetector weight analysis, constructs ecological spatial networks and evaluates their structure using the minimum cumulative resistance (MCR) model and complex network analysis, and discusses the relationship between network topological characteristics (NTCs) and the spatial pattern of the various ESVs. The results show that the major sources and corridors are primarily along the banks of the Wei River and the Fen River. The clustering coefficient has a significant negative correlation with both water supply and water regulation service values. It is found that the sources with higher ecological service values related to water resources have better network connectivity. This result plays a major reference role for the identification of several crucial ecological components, such as sources, nodes and stepping stones, in the middle Yellow River basin (MYRB). Ultimately, we propose strengthening the ecological protection of the eastern part of the Qinling Mountains and increasing the ecological communication capacity of the forestland on both sides of the river valley to create an optimized ecological pattern. [ABSTRACT FROM AUTHOR]

The clumping effect is the main issue causing the heterogeneity in vegetation canopies and the underestimation of leaf area index (LAI) obtained using indirect measurement methods. Significant efforts have been exerted to correct for the clumping effect and derive the true LAI. Recent research has shown that the fractal dimension (FD) is directly related to the clumping effect of foliage, yet practical methods are needed to calculate field estimates. Considering that widely used LAI applications such as digital hemispherical photography (DHP), tracing radiation and architecture of canopies (TRAC), and digital cover photography (DCP) estimate LAI with one-dimensional (1D) gap probability and gap size data, we propose a method to correct for the clumping effect using 1D FD. Resulting formulae describing the relationship between LAI, CI, and 1D FD were based on the box-counting method (BCM) and a binomial distribution model. Sixty-four simulated scenes including four RAdiation transfer Model Intercomparison (RAMI) actual canopies and field measurements from nine plots (four orchard plots and five coniferous forest plots) were used to validate the novel method. Results showed good agreement with reference LAI values for simulated scenes (R2 = 0.96 and RMSE = 0.35). The 1DFD method generated higher LAI estimates compared with the LAI measured using TRAC at the four orchard plots especially at high canopy closure, while its results were more consistent with LAI obtained by litter collection than those of comparable methods at coniferous forest plots (bias from −13.5% to 9.9% for DCP images, from −3.0% to 19.7% for DHP images, and from −3.8% to 17.0% for TRAC transects). Our validation efforts indicate that the method proposed herein corrects for the clumping effect of vegetated canopies more effectively with DCP images, DHP images, and TRAC measurement when compared with traditional indirect optical methods. The 1DFD method is expected to improve indirect measurement accuracy of LAI. • One-dimensional fractal dimension (1D FD) is explored for LAI field measurement. • A formula between 1D FD, LAI, and CI (clumping index) is rigorously deduced. • LAI and CI can be estimated using the formula, 1D FD, and gap fraction. • The new method corrects for clumping effect more properly than classical methods. • The novel method is suitable for many widely used LAI-measurement instruments. [ABSTRACT FROM AUTHOR]