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[Display omitted] • The northwest area of the BTSSCP suffered severe dust-affected zones based on the dust flow trajectory. • The ESs in severe dust-affected areas were relatively poor. • ESs and the dust flow trajectory were integrated to identify ecological governance zones. • Governance suggestions were proposed for the ecological management zone combined with the driving forces. Achieving ecological conservation and governance based on ecosystem services (ESs) can balance regional ecological protection and development, which is of great significance for sustainable development. Existing research has mainly focused on the extraction of priority ecological conservation areas, thereby neglecting to consider the classification and management of ecological engineering areas. To improve regional ecological governance, we combine ESs and dust movement trajectory, using the Beijing-Tianjin Sandstorm Source Control Project (BTSSCP) as the research object to generate watersheds' ecological governance zones. Six ESs were assessed to determine ecological zones. Trajectory simulation of dust movement, using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model and potential source contribution function, was used to determined zones affected by dust. By combining these two aspects, five main ecological governance clusters were identified. We found that: (1) the area with low ESs in the northwest of the BTSSCP suffered from severe dust impacts; (2) slight dust-affected zones were mainly distributed in the eastern of the BTSSCP, and in the area, ESs gradually decreased from south to north; (3) ESs in the region was primarily influenced by natural factors and the influencing factors of ESs vary among different zones. These findings will not only contribute to the overall planning and governance of the BTSSCP, but can offer general guidance for the formulation of targeted ecological protection and restoration measures in other areas. [ABSTRACT FROM AUTHOR]

Changed land use patterns are the most direct manifestation of ecological engineering implemented by humans, being among the driving factors for changes in ecosystem services (ESs). However, different ecological engineering governance measures can lead to different changes in land use, resulting in corresponding changes to ESs and ecosystem service value (ESV), yet these changes is the content that needs to be clarified. In this study, four kinds of ESs, namely water yield, soil conservation, carbon storage and water purification, were quantified in part region of the Beijing-Tianjin Sandstorm Source Control Project (BTSSCP). In addition, eight scenarios were simulated according to the land use change caused by engineering management measures, and the ESs and ESV ensuing under these differing scenarios quantified accordingly. The results show that the main land use types in the study area from 2000 to 2020 were cropland and forestland, while during this period land use change mainly consisted of the expansion of grassland (+466.79 km2) and loss of cropland (−2095.27 km2). For different engineering management measures, protective governance will play a greater role in improving ESs than restoration governance. Protecting the existing governance outcomes from degrading can maintain a water yield of 36.85 × 108 m3, soil conservation of 73.72 × 106 t, carbon storage of 89.01 × 106 t, the purified water nitrogen content of 1.21 × 106 t, with the total ESV maintained at 54.237 billion yuan. Moreover, the high-investment counties clearly engage in ecological restoration that provides high ESV. Yet, there are still some counties with high project investment where the gains in ESV are low, such as Luanping county and Pingquan county. Therefore, it should be reasonably distributed based on the existing governance results in the next round of project investment allocation. In the past 20 years, engineering governance measures have significantly improved the ESs in the region. Nevertheless, looking ahead, it remains necessary to strengthen the emphasis on protecting governance outcomes. [Display omitted] • Scenario simulations were run under governance measures for ecological engineering. • Ecosystem service value (ESV) under differing governance measures were simulated. • ESV is higher when ecological protection rather than restoration measures are used. • Project investment vis-à-vis ESV under the different scenarios were analyzed. [ABSTRACT FROM AUTHOR]

• The RSDI is suitable for monitoring aeolian desertification. • The areas with significant restoration (34.1%) exceeded those with significant deterioration (6%) in the NSPB. • In the areas with significant restoration, 57.4% of the land was mainly affected by human activities. The northern sand-prevention belt (NSPB) is the key area for sand control in China, and the various ecological projects conducted there are important to the Chinese strategy for ecological security. In this paper, a new remote sensing-based desertification index (RSDI) based on principal component analysis was constructed from four aspects of vegetation greenness, surface moisture, soil grain size, and surface radiation. The overall evaluation accuracy of the index was 89.2%, and the kappa coefficient was 0.80, indicating high sensitivity to different degrees of aeolian desertification and suitability for multiscale research. The coefficient of variation, Mann–Kendall test, Theil–Sen median trend analysis, and residual analysis were used to analyze the spatiotemporal changes and driving forces of the RSDI in the NSPB from 2000 to 2020. The RSDI was used to compare aeolian desertification in different subregions, land use types, and ecological project areas. The important results are as follows: (1) the trend of the average RSDI was downward, but it increased significantly in 2008–2009 and 2013–2014; (2) the RSDI was characterized by relatively high volatility in 28.9% and moderate volatility in 27.1% of the area; (3) the areas with significant restoration (34.1%) greatly exceeded those with significant deterioration (6%), whereas 59.9% of the total area was stable; and (4) within the area with significant restoration, 57.4% was primarily affected by human activities, and 42.4% was primarily affected by climate change; however, most of the area with significant deterioration (71.1%) was affected by human activities. In general, the degree of aeolian desertification in the NSPB has decreased in the past 20 years and its ecological quality has continued to recover. However, unreasonable human activities still need to be reduced, and the ecological management of areas under serious threat of desertification needs to be strengthened. [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]

• It was found that the agricultural land expanded significantly, desert reduced from 1993 to 2019 in the oasis region in Hotan River Basin, China. • The key indicators of the coupling system of vegetation–climate, soil-adjusted vegetation index, albedo, surface temperature, and evapotranspiration were calculated and mapped their spatial-temporal dynamics. • We investigated how the different landscape patterns altered the interaction between vegetation and climate in the desert oasis ecosystem and quantified the effects with structural equation model. In the arid region, the oasis is characterized by a desert substance and a mosaic of vegetation. The biophysical processes and interactions between vegetation and the local climate in this kind of region are determined by the macroscopic structure of the ecosystems, i.e. landscape patterns. To understand how these landscape patterns impact regional hydro-heat coupling across space and evolve over time, we utilized remote sensing observational data and methods to examine the relationships among these factors. In this case, we focused on the oasis along the upstream of the Hotan River Basin in the Taklamakan Desert in Xinjiang of western China and employed the satellite imagery datasets of Landsat from 1993 to 2019 to investigate the dynamics of vegetation–climate factors. Based on the land use and cover change datasets, the landscape pattern metrics, including patch density (PD), contagion index (CONTAG), fractal dimension (FRAC), were calculated to measure the landscape features on the different aspects, i.e. the fragmentation, the connectivity, and the complexity. With the algorithm of land surface energy balance, the land surface indicators, including the soil-adjusted vegetation index (SAV I), albedo, surface irradiance temperature (T s), and evapotranspiration (ET), were calculated to represent the key process in the interaction of vegetation–climate. The temporal-spatial dynamics of the landscape patterns and the vegetation–climate metrics were mapped and demonstrated in a quantitative manner. The statistical results revealed that during the past decades, the agricultural land in the study area had significantly increased by 17 %. Grassland and shrubs had also expanded, while the desert area decreased from 82.57 % to 78.82 % of the total area, with an overall reduction rate of 1.4 %/10a. It was found the study area was getting warmer and dryer based on the general trends of T s and ET observed during the period of 1993–2019. The agricultural land had the highest PD and CONTAG , and the lowest FRAC. The agricultural land had the lowest T s and the highest ET. The results of structural equation model identified the decoupling effects of PD and CONTAG on the regional hydro-heat environment, while confirmed that FRAC had positive impact on the coupling between T s and ET. Our study bridged the landscape pattern with the regional vegetation–climate interaction and provided the suggestions for the landscape planning and management for a more sustainable arid region. [Display omitted] [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]