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Grassland is not only an important landscape of the Beijing–Tianjin sandstorm source control project area, but also a significant object of the Beijing–Tianjin sandstorm source control project. By taking the situation in 2000 as the base of comparison and using the established grassland vegetation growth model, the monitoring and evaluation of grassland vegetation dynamic variation in the project area from 2000 to 2010 was conducted based on MODIS 16 days NDVI data. The conclusions are as follows: (1) The comparative result of average growth between each year from 2001 to 2010 and the base year was on the good side in general; the grassland growth was good both in the early and later periods of grassland growth peak season than in the first years of the project implementation, indicating that the implementation of the Beijing–Tianjin sandstorm source project has significantly improved the growth conditions of grassland vegetation; (2) With regard to the annual dynamic variation of grassland growth, the area proportions of the grasslands, of which the average grassland growth was on the good side, fluctuated and increased slightly with the time changes in general. The area proportions of the grasslands, of which the average grassland growth was on the bad side, fluctuated and decreased in general. The area proportions of the grasslands with normal growth showed an increasing overall trend; (3) From the regional perceptive on four zones, including the northern arid grassland desertification control zone, Hunshandake sandy land control zone, the farming-pastoral area of desertified land control zone, and the water conservation zone of Yanshan hills and mountains, except that the grassland growth in the farming-pastoral area of desertified land control zone was bad, the average growth of other three zones was good each year from 2001 to 2010 compared with the base year. (4) In respect of space, the regions with big grassland growth variation in the project area were concentrated in the western and eastern sections of the northern arid grassland desertification control zone and the western section of Hunshandake sandy land control zone. The grassland growth variation in the water conservation zone of Yanshan hills and mountains and the farming-pastoral area of desertified land control zone were relatively stable. On one hand, the conclusions of this paper can evaluate the effectiveness of the project control, on the other hand, it can also provide scientific basics to grassland management departments, facilitate the rational utilization of grassland, and preserve the regional ecological balance. [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]

Shrub encroachment, characterized by the proliferation of shrubs into grasslands, is a challenge faced by grasslands worldwide that significantly impacts livestock production and ecosystem functions. Rapid and accurate estimation of shrub dominance is important for understanding changes in plant community structures and formulating grassland management policies. However, the limited spatial resolution of commonly used satellite imagery poses a challenge when estimating shrub dominance at the landscape scale. The rapid development of Unoccupied Aerial Vehicles (UAVs) has opened up new opportunities for cross-scale observations of shrub encroachment in grasslands by effectively bridging the scale gap between ground sampling and satellite image pixels while reducing the required groundwork. This study utilized ground reference data, UAV data (RGB, hyperspectral, and LiDAR), and satellite data (Sentinel-1 and Sentinel-2) to estimate shrub and total above-ground biomass (AGB) in temperate grasslands to map the shrub dominance. First, UAV data were applied at the plot scale for the classification of shrub and herbaceous vegetation using the maximum entropy model (MaxEnt), estimation of shrub AGB by employing the vegetation index weighted canopy volume model (CVM VI), and estimation of herbaceous AGB based on the partial least squares regression (PLSR). Second, UAV AGB mapping results were upscaled as samples at the landscape scale and integrated with satellite imagery to establish the shrub and total AGB models using the extreme gradient boosting (XGBoost). Finally, shrub dominance, represented as shrub AGB/total AGB, was mapped across the study area. We found that at the plot scale, the MaxEnt model achieved an overall accuracy of 0.990 for object-based classification. The CVM VI combined with canopy height model and narrow-band vegetation index achieved the highest accuracy for estimating shrub AGB (R2 = 0.821, RMSE = 30.1 g). The PLSR combined with features derived from all UAV data achieved the highest accuracy for estimating herbaceous AGB (R2 = 0.856, RMSE = 9.1 g/m2). At the landscape scale, the XGBoost achieved high accuracy for estimating both the shrub AGB (R2 = 0.719, RMSE = 4.2 g/m2) and total AGB (R2 = 0.961, RMSE = 5.0 g/m2). The high-precision mapping results further facilitate the generation of shrub dominance maps at a landscape scale. This study presents a more accurate and efficient framework for mapping shrub AGB, total AGB, and shrub dominance using multi-scale remote sensing data, which offers new approaches for large-scale grassland AGB mapping and monitoring of shrub encroachment in grasslands. • Multisensory UAV data were evaluated for shrub and herbaceous AGB estimation at the plot scale. • SAR and multispectral satellite data were used to map AGB and shrub dominance at the landscape scale. • Our multi-scale framework outperformed the traditional AGB estimation method. • Assessing shrub dominance is important for understanding shrub encroachment. [ABSTRACT FROM AUTHOR]

[Display omitted] • A selection strategy for base-period images of spatio-temporal fusion was proposed. • The spatio-temporal inconsistency between field samples and images was reduced. • The estimation accuracy of grassland aboveground biomass was improved. The accurate estimation of the aboveground biomass (AGB) is crucial for the sustainable utilization and management of grassland resources. Spatio-temporal inconsistencies between field samples and satellite images are major source of error in the estimation of grassland AGB. To solve this problem, this study selected the Three Rivers Headwater Region as the study area and proposed a selection strategy for base-period images of spatio-temporal fusion that was suitable for use at a large spatial scale in which cloud-free images are difficult to obtain. The spatial and temporal adaptive reflectance fusion model (STARFM) based on the selection strategy was used to generate a synthetic Normalized Difference Vegetation Index (NDVI) dataset with high spatial-temporal resolution by using the maximum value composite of GF-1 NDVI and MODIS NDVI to enhance the spatial-temporal quality of the images for field-scale application. Three estimation models for grassland AGB were then constructed by the random forest algorithm using synthetic NDVI, MODIS NDVI, GF-1 NDVI respectively, together with ancillary data. Following this, the estimation model with the highest accuracy was used to generate a 16-m eight-day time-series AGB in the growing season. The results showed: (1) The synthetic NDVI was correlated closely with the observed GF-1 NDVI, with an average R of 0.825 and a RMSE of 0.087. The temporal trend of the synthetic NDVI for each grassland type was highly consistent with that of the MODIS NDVI in the growing season with a correlation higher than 0.9. (2) The synthetic NDVI reduced the spatial difference between field samples and images to 16-fold, and the temporal difference was controlled to within four days under ideal conditions. (3) The synthetic NDVI improved the estimation accuracy of grassland AGB by about 15.9% and 19.7% (R2), and 13.7% and 17.5% (RMSE) relative to MODIS NDVI and GF-1 NDVI, respectively. (4) The time-series AGB revealed accurately the spatial distribution of and seasonal temporal variations in the grassland biomass. The results of this study may serve as scientific guidance for timely monitoring of grassland conditions and precise management of grassland resources in China. [ABSTRACT FROM AUTHOR]

• N deposition effects on soil C:N:P stoichiometry of ungrazed and grazed grassland varied. • Livestock grazing may weaken N deposition effects on soil C:N:P stoichiometry. • Climate variability and other environmental factors can also mediate N deposition effects. Increased nitrogen (N) deposition is expected to impact the grassland ecosystem by changing soil C:N:P stoichiometry. However, N deposition effects on soil C:N:P stoichiometry is still in debate. In addition, we still know less about how N deposition affects soil C:N:P stoichiometry in grazed grassland as most studies focus on natural grassland. Hence, it is necessary to examine N deposition effects on both ungrazed and grazed grassland and clarify the disparity. We conducted a simulated N deposition experiment in ungrazed and grazed grassland of two grassland types including alpine meadow (AM) and alpine steppe (AS) to test such effects. Results showed that N deposition effects on ungrazed and grazed grassland varied. Soil C, soil N, soil P, soil C:N, soil N:P and soil C:P were significantly affected by N deposition in ungrazed grassland while only soil N, soil P, soil C:N, soil N:P were significantly affected by N deposition in grazed grassland. Soil C and soil N:P were not sensitive to N deposition in grazed grassland. N deposition negatively affects soil P in ungrazed grassland yet positively affected soil P in grazed grassland. N deposition effects accounted for 30% in ungrazed grassland, while significantly decreased to 12% when grazing effects were considered suggesting that livestock grazing might weaken N deposition effects on soil C:N:P stoichiometry. In addition, other climate and environmental factors such as MAT, MAP, and grassland type can also mediate N deposition effects on soil C:N:P stoichiometry in both ungrazed and grazed grasslands. On the whole, our findings suggested that there existed a disparity of N deposition effects on soil C:N:P stoichiometry between ungrazed and grazed grassland, and livestock grazing may weaken N deposition effects on soil C:N:P stoichiometry in alpine grassland of the Qinghai-Tibetan Plateau. [ABSTRACT FROM AUTHOR]