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Aboveground biomass (AGB) of shrubs and low-statured trees constitutes a substantial portion of the total carbon pool in temperate forest ecosystems, contributing much to local biodiversity, altering tree-regeneration growth rates, and determining above- and belowground food webs. Accurate quantification of AGB at the shrub layer is crucial for ecological modeling and still remains a challenge. Several methods for estimating understory biomass, including inventory and remote sensing-based methods, need to be evaluated against measured datasets. In this study, we acquired 158 individual terrestrial laser scans (TLS) across 45 sites in the Yanshan Mountains and generated metrics including leaf area and stem volume from TLS data using voxel- and non-voxel-based approaches in both leaf-on and leaf-off scenarios. Allometric equations were applied using field-measured parameters as an inventory approach. The results indicated that allometric equations using crown area and height yielded results with higher accuracy than other inventory approach parameters (R2 and RMSE ranging from 0.47 to 0.91 and 12.38 to 38.11 g, respectively). The voxel-based approach using TLS data provided results with R2 and RMSE ranging from 0.86 to 0.96 and 6.43 to 21.03 g. Additionally, the non-voxel-based approach provided similar or slightly better results compared to the voxel-based approach (R2 and RMSE ranging from 0.93 to 0.96 and 4.23 to 11.27 g, respectively) while avoiding the complexity of selecting the optimal voxel size that arises during voxelization. [ABSTRACT FROM AUTHOR]

• Inter-frame landmark correspondences can provide substantial motion information. • Landmark constellation has a certain impact upon the navigation performance. • A fast landmark selection method is designed based on the error analysis. • The absolute and relative navigation results are integrated. Visual navigation is one of the enabling technologies for asteroid landing missions. Quality and efficiency are of paramount importance for determining its robustness and autonomy. This paper proposes a new navigation method, which integrates absolute and relative visual information for quality improvement, and a fast landmark selection method for efficiency purposes. The mapped 3D landmarks with their 2D projections measured in the descent image are primary inputs, and two navigation pipelines, using the extracted 3D-2D and 2D-2D landmark correspondences, are established separately. Then, error analysis, under measurement and system noises, is performed in detail, and based on it, a fast landmark selection algorithm is designed to improve the navigation efficiency. Moreover, the observation model for the attitude motion in adjacent image frames is constructed, followed by the fusion of absolute and relative navigation results using an extended Kalman filter. Finally, Monte Carlo simulations are conducted, and the numerical results demonstrate the advancement of the proposed methods in quality and efficiency. [ABSTRACT FROM AUTHOR]

• Predict lake level under climate scenarios by assimilating model and observation. • Incorporate the advantages of last ICESat-2 observations and hydrological model. • Integrated by Prophet forecasting procedure and Variational Bayesian Monte Carlo. • Enhance both short-term and long-term prediction accuracy. • Successfully predict lake level turning points driven by climate changes. Predicting future lake levels under climate change is critical for advancing our understanding of hydrological processes in a changing environment. However, continuous and long-term prediction of lake levels is challenging due to discrepancies in multi-source data and the lack of integration of hydrological models and climate scenarios. Physical and statistical models have been used for lake levels prediction, however, physical models are difficult to calibrate and statistical models often fail to account for the effects of climate changes. In this study, a lake level prediction model was proposed by assimilating the hydrophysical model based on water balance and the ICESat-2 observations using Variational Bayesian Monte Carlo. The model can predict monthly lake levels combining CMIP6-SWAT climate-driven projections and ICESat-2 observations. Short-term validation over 24 months showed the R2 was 0.91 and the RMSE was 5 cm between the proposed model and the ICESat-2 observation. The accuracy is superior to both the hydrophysical model based on water balance and the Prophet time series model. Long-term validation from 1978 to 2021 showed the proposed model has the potential to enhance prediction accuracy within 2 to 3 years compared with the hydrophysical model based on water balance and it is suitable for predicting long-term lake level trends. Notably, it successfully predicted the significant turning point around 2005 where lake levels shift from decline to increase based on past data. The water levels of Lake Qinghai were predicted to rise at a rate of 3.7 cm per year by 2050 under the SSP2-4.5 scenario. The proposed assimilation model combines the strengths of hydrological modeling based on water balance (incorporating the effects of climate change) and the latest ICESat-2 lake level observations (incorporating the effects of recent historical lake levels), improving the accuracy of short-term lake levels and long-term lake level trends prediction. Moreover, as the model is based on satellite remote sensing observations, it has the potential to be applied to any lake globally. [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] Structural information of grassland changes on the Tibetan Plateau is essential for understanding alterations in critical ecosystem functioning and their underlying drivers that may reflect environmental changes. However, such information at the regional scale is still lacking due to methodological limitations. Beyond remote sensing indicators only recognizing vegetation productivity, we utilized multivariate data fusion and deep learning to characterize formation-based plant community structure in alpine grasslands at the regional scale of the Tibetan Plateau for the first time and compared it with the earlier version of Vegetation Map of China for historical changes. Over the past 40 years, we revealed that (1) the proportion of alpine meadows in alpine grasslands increased from 50% to 69%, well-reflecting the warming and wetting trend; (2) dominances of Kobresia pygmaea and Stipa purpurea formations in alpine meadows and steppes were strengthened to 76% and 92%, respectively; (3) the climate factor mainly drove the distribution of Stipa purpurea formation, but not the recent distribution of Kobresia pygmaea formation that was likely shaped by human activities. Therefore, the underlying mechanisms of grassland changes over the past 40 years were considered to be formation dependent. Overall, the first exploration for structural information of plant community changes in this study not only provides a new perspective to understand drivers of grassland changes and their spatial heterogeneity at the regional scale of the Tibetan Plateau, but also innovates large-scale vegetation study paradigm. [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]

Vegetation cover fraction (fCover) and related quantities are basic yet critical vegetation structure variables in various disciplines and applications. Ground- and aerial-based proximal and remote sensing techniques have been widely adapted across multiple spatial extents. However, the definitions of fCover-related nomenclatures have not yet been fully standardized, leading to confusing terms and making comparing historic measures difficult. With the issues potentially arising from an increasing diversity of fCover and related quantities estimation methods and corresponding uncertainties, there is also a growing need to spread knowledge on the current advances, challenges, and perspectives, especially in the context of no such existing review for ground- and aerial- based estimation. This paper provides the current knowledge mainly concerning passive image-based methods and active light detection and ranging (LiDAR) -based methods. We first harmonized the definitions of fCover and its related quantities (e.g., effective canopy cover, crown cover, stratified vegetation cover, and canopy fraction). Secondly, the typical applications of fCover and related quantities over a range of scales, fields, and ecosystems were summarized. Thirdly yet importantly, we offered a comprehensive review of traditional non-imaging methods, image-based methods (e.g., segmentation, unmixing, and spectral retrieval), point cloud-based methods (e.g., rasterization), and LiDAR return-based methods (e.g., return number index and return intensity retrieval) across different platforms (i.e., ground, unmanned aerial vehicle (UAV) and airplane). Our investigation of fCover and related quantities estimation touches upon various vegetation ecosystems, including agriculture cropland, grassland, wetland, and forest. Finally, the current challenges and future directions were discussed, such as image signal processing under complex heterogeneous surfaces and stratified cover and non-photosynthesis cover retrieval. We, therefore, expect that this review may offer an insight into fCover and related quantities estimation and serve as a reference for remote sensing scientists, agronomists, silviculturists, and ecologists. [ABSTRACT FROM AUTHOR]

Resources can affect plant productivity and biodiversity simultaneously and thus are key drivers of their relationships in addition to plant–plant interactions. However, most previous studies only focused on a single resource while neglecting the nature of resource multidimensionality. Here we integrated four essential resources for plant growth into a single metric of resource diversity (RD) to investigate its effects on the productivity–biodiversity relationship (PBR) across Chinese grasslands. Results showed that habitats differing in RD have different PBRs—positive in low-resource habitats, but neutral in medium- and high-resource ones—while collectively, a weak positive PBR was observed. However, when excluding direct effects of RD on productivity and biodiversity, the PBR in high-resource habitats became negative, which leads to a unimodal instead of a positive PBR along the RD gradient. By integrating resource effects and changing plant–plant interactions into a unified framework with the RD gradient, our work contributes to uncovering underlying mechanisms for inconsistent PBRs at large scales. [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]

• A drought index was obtained for drought monitoring based on soil moisture series. • Drought characteristics (e.g., return period) were presented on a regional scale. • Wavelet analysis was adopted to study the area extent characteristics of drought. As the critical parameter of the agricultural drought process, soil moisture (SM) has the potential to monitor agricultural drought, which can provide practical support for water management. In this study, a drought index, namely Soil Dryness Index (SDI), was proposed based on long-term gridded ERA-5 land SM, and its performance was evaluated. Furthermore, we employed the run theory and copula to study the spatiotemporal drought characteristics based on this index in Inner Mongolia. Although the return period of drought events has been assessed in many studies, the drought tends to occur with a nonstationary frequency. The wavelet transform was then applied to drought area proportion time series in sub-regions to study the temporal characteristics of droughts. The results show that SDI performs well in monitoring agricultural drought. There is strong heterogeneity in the spatial distribution of drought events. Frequent and low-severity droughts dominated arid and semi-arid regions, while some high-severity droughts mainly occurred in humid and semi-humid regions (mainly in Hulunbuir and Xing'an). The droughts, especially those with high severity, have become much more frequent in the last two decades. The period from 2000 to 2010 had seen more frequent droughts in recent 40 years, and most 20-year return period droughts occurred during this period. These findings could contribute to a better understanding of spatiotemporal patterns of agricultural droughts and provide a reference for future drought trends in Inner Mongolia. [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]

To improve our capacity to map long-term vegetation dynamics in heterogeneous landscapes, this study proposed a new prior knowledge-based spatiotemporal enhancement method, namely, PK-STEM, to fuse MODIS and Landsat FPAR products following the remote sensing trend surface framework. PK-STEM uses historical Landsat FPAR images as prior knowledge and fuses them with new satellite-derived FPAR data. PK-STEM can work in three modes: 1) using only MODIS data; 2) using only Landsat data; and 3) using both MODIS and Landsat data. This study retrieved FPAR from Landsat images using a scaling-based method and tested the performance of PK-STEM in a regional application. For the entire year of 2012, we compared the performance of PK-STEM in different modes and with that of two typical spatiotemporal fusion methods, the enhanced spatial and temporal adaptive reflectance model (ESTARFM) and unmixing-based linear mixing growth model (LMGM). Then, a long time series FPAR data set at 30-m resolution and eight-day intervals was generated for 13 years (2000–2012). Our results show that PK-STEM in mode III is the most robust and accurate (root mean squared error (RMSE) = 0.062; mean $R = 0.851$) among the three modes and more accurate than ESTARFM (mean RMSE = 0.065; mean $R = 0.776$) and LMGM (mean RMSE = 0.074; mean $R = 0.734$). For the 12 years (2000–2011), PK-STEM also achieves high accuracies with mean RMSE = 0.066 and $R = 0.938$. PK-STEM is very flexible with a continual update mechanism and is efficient for long time series applications. [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]

• Non-linear patterns of microbial N genes occurred along precipitation gradient. • Breaking points were clearly related to certain soil types. • Attribution of different environmental factors varied by ecosystem. Despite the importance of microorganisms in soil nitrogen (N) cycling, studies on spatial patterns of microbial N-cycling gene abundances in temperate grasslands are still lacking, whose productivity is limited by N. Here, we investigated N functional genes in soil microorganisms from 60 sites in temperate grasslands across 1661 km in Inner Mongolia, China. Abundances of all N functional genes and 16S rDNA tended to decrease from northeast to southwest, consistent with the changing tend of precipitation but contrary to that of temperature. Non-linear saturation curves dominated patterns for abundances of most N functional genes and 16S rDNA along precipitation as they increased with the rising precipitation when <288–343 mm, but remained stable after these breaking points. Interestingly, these breaking points were clearly related to certain soil types, e.g. nifH did not change with precipitation in Chernozems and Gleysols, but increased with it in other soil types. Non-linear saturation patterns were also observed for most N functional gene and 16S rDNA abundances with temperature and soil total organic carbon. In contrast, no consistent spatial pattern for ratios of N functional genes: 16S rDNA was observed, which varied greatly by different N functional genes. Moreover, decay relationships were discovered for the abundance-based matrix of N functional genes over geographic distance. From the temperate meadows to typical steppes and to temperate desert steppes, the influence of relatively long-term environmental variables increased, but that of short-term ones decreased. Overall, we revealed non-linear patterns of N functional gene abundances along precipitation with a clear relationship with soil type, and discovered that attributions of geographic distance, plant community, historical-contingency and contemporary-disturbance to N functional gene community similarity decay were ecosystem–dependent. [ABSTRACT FROM AUTHOR]