Search

Understanding the spatiotemporal dynamics of carbon storage in regional ecosystem and its underlying mechanisms is essential for environmental science. This study presents an integrated geographical analysis framework to identify and analyze the primary factors influencing carbon storage across various spatial scales. Initially, a global-scale Geographic Detector identified key factors affecting carbon storage, refined later using an Ordinary Least Squares (OLS) model to mitigate multicollinearity and redundancy. Subsequently, a multi-scale geographic weighted regression model (MGWR) explored spatial heterogeneity and nuanced impacts of these factors across various spatial scales. Our findings reveal a consistent downward trend in carbon storage in Fujian Province, China, from 1995 to 2020, accompanied by significant spatial autocorrelation. Elevation, slope, annual precipitation, and distance to the expressway emerged as primary determinants, synergistically enhancing global-scale spatial variance in carbon storage. Slope and distance to the expressway have a broad influence, while the added value of the primary industry and annual precipitation show localized effects. Collectively, these factors positively impact carbon storage, although with considerable variability in spatial orientation and magnitude of influence. This framework highlights the scale-specific and heterogeneous nature of carbon storage drivers, offering fresh insights into the mechanisms governing its spatiotemporal patterns in regional ecosystems.

Land cover products provide the key inputs for terrestrial change monitoring and modeling. Numerous land cover products have been generated in the past few decades, but their performance on the southeastern Tibetan Plateau remains unclear. This study analyzed 15 land cover products for consistency through compositional similarity and overlay analyses. Additionally, 1305 validation samples from four datasets were employed to construct confusion matrices to evaluate their accuracy. The results indicate the following: (1) Land cover products exhibit relatively high consistency in 62.92% of the region. (2) Land cover products are strongly influenced by terrain fluctuations, showing lower consistency at elevation below 200 m and instability in land cover classification with increasing elevation, particularly between 2800–4400 m and 4800–5400 m. (3) The accuracy for forest, water, and snow/ice is relatively high. However, there is a relatively lower accuracy for wetland and shrubland, necessitating more field samples for reference to improve classification. (4) The average values of the four validation datasets show that the overall accuracy of the 15 products ranges from 50.97% to 73.50%. For broad-scale studies with lower resolution requirements, the CGLS-LC100 product can be considered. For studies requiring a finer scale, a combination of multiple land cover products should be utilized. ESRI is recommended as a reference for built-up land, while FROM-GLC30 can be used for cropland, although misclassification issues should be noted. This study provides valuable insights for analyzing land cover types on plateaus to refine classification. It also offers guidance for selecting suitable land cover products for future research in this region.

Exploring the changes and drivers of open-surface water bodies is essential for the well-being of humanity in the face of global climate change. As one of the core areas in the Qinghai-Tibet Plateau (QTP), the headwater region of the Yangtze, Yellow, and Lancang Rivers is a water source for billions of people in Asia. However, previous studies failed to provide complete open-surface water data and watershed-level analysis for this region. Utilizing the Google Earth Engine platform and vegetation-water algorithm, we interpreted open-surface water bodies from all available Landsat surface reflectance data and generated annual 30-m water body frequency maps for 1987–2021, which is a complete open-surface water body dataset in the study area. Compared to the Joint Research Centre’s Global Surface Water dataset, the dataset in our study maintains consistency in 90.1 % of pixels and achieved correct classification in 74.6 % of pixels within the inconsistent areas. The perennial water body area exhibited an increase from 232,335 to 293,534 ha. Instead, the seasonal water body area notably declined from 223,809 to 182,003 ha. The Yangtze River headwater region shows an increase in perennial water bodies, with no significant variation in seasonal water bodies. The Yellow River headwater region experiences an increase in perennial water bodies but a decrease in seasonal water bodies. Remarkably, in the Lancang River headwater region, with lower water body density, both the perennial and seasonal water body areas decreased by 36.8 % and 48.8 %, respectively. The divergent changes in the headwater region of the Three Rivers are primarily attributed to spatial heterogeneity in the temperature effects on water bodies. Anticipated increases in the temperature are expected to amplify these differences. Our findings suggest that climate-driven open-surface water body changes are ongoing in this core area in the QTP, threatening the water security of densely populated East and Southeast Asia in the future.

Satellite-based remote sensing enables the quantification of tropospheric NO2 concentrations, offering insights into their environmental and health impacts. However, remote sensing measurements are often impeded by extensive cloud cover and precipitation. The scarcity of valid NO2 observations in such meteorological conditions increases data gaps and thus hinders accurate characterization and variability of concentration across geographical regions. This study utilizes the Empirical Orthogonal Function interpolation in conjunction with the Extreme Gradient Boosting (XGBoost) algorithm and dense urban atmospheric observed station data to reconstruct continuous daily tropospheric NO2 column concentration data in cloudy and rainy areas and thereby improve the accuracy of NO2 concentration mapping in meteorologically obscured regions. Using Chengdu City as a case study, multiple datasets from satellite observations (TROPOspheric Monitoring Instrument, TROPOMI), near-surface NO2 measurements, meteorology, and ancillary data are leveraged to train models. The results showed that the integration of reconstructed satellite observations with provincial and municipal control surface measurements enables the XGBoost model to achieve heightened predictive accuracy (R2 = 0.87) and precision (RMSE = 5.36 μg/m3). Spatially, this approach effectively mitigates the problem of missing values in estimation results due to absent satellite data while simultaneously ensuring increased consistency with ground monitoring station data, yielding images with more continuous and refined details. These results underscore the potential for reconstructing satellite remote sensing information and combining it with dense ground observations to greatly improve NO2 mapping in cloudy and rainy areas.

Accurately tracking the changes in rice cropping intensity is a critical requirement for policymakers to formulate reasonable land-use policies. Southern China is a traditional region for rice multi-cropping, yet less is known about its spatial–temporal changes under the background of rapid urbanization in recent decades. Based on images from Landsat and MODIS and multiple land cover products, the gap-filling and Savitzky–Golay filter method (GF-SG), the enhanced pixel-based phenological features composite approach (Eppf-CM), random forest (RF), and the difference in NDVI approach (DNDVI) were combined to map the rice cropping pattern with a spatial resolution of 30 × 30 m over Southern China in 2000 and 2020 through Google Earth Engine (GEE). Subsequently, the spatial–temporal changes in rice cropping intensity and their driving factors were examined by Getis-Ord Gi* and geographical detector. The results showed that the produced rice cropping pattern maps exhibited high accuracy, with kappa coefficients and overall accuracies exceeding 0.81 and 90%, respectively. Over the past two decades, the planting areas of double-season rice in Southern China decreased by 54.49%, and a reduction was observed across eight provinces, while only half of the provinces exhibited an increase in the planting areas of single-season rice. Compared to the year 2000, the planting area of the conversion from double- to single-season rice cropping systems in 2020 was 2.71 times larger than that of the conversion from single- to double-season rice cropping systems. The hotspots of the change in rice cropping intensity were mainly located in the central part of Southern China (excluding the Poyang Lake Plain). The decline in the rural labor force, coupled with ≥10 °C accumulated temperature and topographical factors, plays a crucial role in the decreased intensity of rice cropping. Our findings can be beneficial for realizing regional agricultural sustainability and food security.

Eggshell membranes (ESM) contain 90% protein, 3% lipids, 2% sugars, and small amounts of minerals such as calcium and magnesium. Of the 90% of proteins present, 472 proteins species have been identified. ESM provide the initial mineralization platform for eggshell formation, and can be used for to produce adsorbents, cosmetics, and medical products because of their special physical structure and chemical composition. The special physical structure of the eggshell membrane, with disulfide bonds between and within the protein molecules and the cross-linking of lysine-derived and heterochain chains between the eggshell membrane, makes the membrane very difficult to dissolve, with a maximum solubility rate of only 62%. Also, the insolubility of ESM limits its development and use also any related research. Based on the physical structure and chemical composition of the eggshell membrane, this paper reviews the latest research on eggshell membrane separation and membrane protein solubilization to provide a reference for promoting the separation, dissolution, and rational development and use of the avian eggshell membrane.

Abstract Background Humoral immunity plays an important role in the prevention of canine distemper. Anti-CD virus (CDV) antibody has strong antiviral activity and is widely used in the treatment of CD. However, with the increase of CD cases, the availability of therapeutic CD antibody fell short of the clinical needs. Results The high-titer antiserum with the high-titer neutralizing activity against CDV was obtained from the donkeys (Dezhou Donkey) immunized with the inactivated CDV vaccine. The donkey anti-CDV IgG was purified from the donkey serum, which was identified to significantly inhibit the CDV replication in the cultured Vero cells and effectively reduce the clinical symptoms and increase the survival rates (75%) of CDV-infected dogs (Shih-tzu Dog), similar to that treated with the dog-derived anti-CDV IgG. These results indicate that donkey-derived IgG is a potential substitute for dog-derived IgG to treat the CD in clinic. Conclusions Administration of donkey-derived anti-CDV IgG can ameliorate clinical symptoms and inhibit virus replication, thereby increasing the survival of CDV-infected dogs. This study opens up a new source of therapeutic antibody for CD treatment.

Crosstalk between glomerular endothelial cells and glomerular epithelial cells (podocytes) is increasingly becoming apparent as a crucial mechanism to maintain the integrity of the glomerular filtration barrier. However, in vitro studies directly investigating the effect of this crosstalk on the glomerular filtration barrier are scarce because of the lack of suitable experimental models. Therefore, we developed a custom-made glomerulus-on-a-chip model recapitulating the glomerular filtration barrier, in which we investigated the effects of co-culture of glomerular endothelial cells and podocytes on filtration barrier function and the phenotype of these respective cell types. The custom-made glomerulus-on-a-chip model was designed using soft lithography. The chip consisted of two parallel microfluidic channels separated by a semi-permeable polycarbonate membrane. The glycocalyx was visualized by wheat germ agglutinin staining and the barrier integrity of the glomerulus-on-a-chip model was determined by measuring the transport rate of fluorescently labelled dextran from the top to the bottom channel. The effect of crosstalk on the transcriptome of glomerular endothelial cells and podocytes was investigated via RNA-sequencing. Glomerular endothelial cells and podocytes were successfully cultured on opposite sides of the membrane in our glomerulus-on-a-chip model using a polydopamine and collagen A double coating. Barrier integrity of the chip model was significantly improved when glomerular endothelial cells were co-cultured with podocytes compared to monocultures of either glomerular endothelial cells or podocytes. Co-culture enlarged the surface area of podocyte foot processes and increased the thickness of the glycocalyx. RNA-sequencing analysis revealed the regulation of cellular pathways involved in cellular differentiation and cellular adhesion as a result of the interaction between glomerular endothelial cells and podocytes. We present a novel custom-made glomerulus-on-a-chip co-culture model and demonstrated for the first time using a glomerulus-on-a-chip model that co-culture affects the morphology and transcriptional phenotype of glomerular endothelial cells and podocytes. Moreover, we showed that co-culture improves barrier function as a relevant functional readout for clinical translation. This model can be used in future studies to investigate specific glomerular paracrine pathways and unravel the role of glomerular crosstalk in glomerular (patho) physiology.

The Qinghai-Tibetan Plateau (QTP) is a global center of cold and dry, where the most extensive fragile alpine vegetation exists. Quantitative analysis of drought event characteristics and vegetation response to drought on the QTP is indispensable for understanding the increasing drought events in a warming climate which exacerbate adverse influence on extremely alpine ecosystems. Here, using the standardized precipitation evapotranspiration index (SPEI) and the normalized difference vegetation index (NDVI) from 2000 to 2018, this study analyzed the characteristics of drought events, their temporal impacts, and the stability of vegetation response to drought on the QTP. Results showed that: the characteristics of drought events on the QTP have clear spatial heterogeneity. When compared to the east monsoon region, most of the western regions have higher frequency and lower intensity of drought events. Drought has significant temporal effects on vegetation in grassland areas of the QTP during the growing season, which reach their peak in July and August. The 0–1-month and 3-month time scales were the optimal lagged and accumulated time during the growing season, respectively. The stability of vegetation response to drought showed significant spatial heterogeneity and varied with eco-geographical regions and vegetation types. Generally, forest areas showed high resistance (74.09) and resilience (2.26), followed by crop and grassland areas.

Identifying the spatial changes in farming–pastoral ecotone (FPE) is of utmost importance for the development of strategies for ecological protection in ecologically fragile areas. This study employed spatial autocorrelation and spatial clustering techniques to map FPE at the pixel scale using CLCD data with a spatial resolution of 30 × 30 m in the years 1985, 2000, and 2021, and then analyzed the changes of the FPE in northern China. The results showed that the FPE is mainly located at the border between the Inner Mongolia Autonomous Region and the adjacent provinces, which is along the Hu-line and the 400-mm isohyetal line. The area of the FPE was 63.94 × 104 km2, 62.90 × 104 km2, and 53.81 × 104 km2 in 1985, 2000, and 2021, respectively, accounting for 6.7%, 6.6%, 5.6% of the total land area in China. The FPE boundary moved northwestward during 1985–2021, demonstrating retreating, fragmenting, and shrinking tendencies. The decreased areas and the moving distances of the gravity center are six times and four times greater during 2000–2021 than that during 1985–2000, respectively. Moreover, the discontinuous change in FPE was mainly due to the increase in forest land, especially for the conversion of grassland to forest land. Our findings provide guidance for the construction of ecological civilization and the optimization of ecosystem structure in the farming–pastoral ecotone.

Identifying the spatiotemporal coupling characteristics of cropland-grain production-population is essential for the rational utilization of cropland and the evaluation of national and regional food security. Based on the grain production statistical data, GlobeLand30, and WorldPop data in the years 2000, 2010, and 2020, the spatiotemporal changes in China’s cropland area, grain production, and population and their coupling characteristics over the past two decades were detected at the grid level using the models of barycenter fitting and coupled dynamic analysis. The results showed that spatial change of cropland area in China was roughly characterized by the increase in the northwest and the decrease in the southeast; while grain production was characterized by an increase in the north and a decrease in the south, and population was roughly characterized by an increase in urban areas of the southeast coastal regions and a decrease in traditional agricultural areas. The barycenter of cropland area and that of grain production moved toward the northwest and the northeast, respectively, which mismatch the spatial pattern of hydro-thermal conditions of cropland resources in China and thus result in the increased risk of the national grain production system. Meanwhile, the barycenter of grain production and that of population continued to move in opposite directions overall, and the distances between their barycenters increased from 119.65 km in 2000 to 455.16 km in 2020, indicating that the phenomenon of ‘north-to-south grain diversion’ is intensifying. Our results highlight that the spatial mismatch of cropland-grain production-population in China has increased over the past two decades.

Detection of long-term vegetation dynamics is important for identifying vegetation improvement and degradation, especially for rapidly urbanizing regions with intensive land cover conversions. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) urban agglomeration has experienced rapid urbanization during the past decades with profound impacts on vegetation, so there is an urgent need to evaluate vegetation dynamics across land use/cover change (LUCC). Based on the normalized difference vegetation index (NDVI) during 2001–2020, we used coefficient of variation, Theil–Sen median trend analysis, and Hurst exponent to analyze the spatiotemporal change and future consistency of vegetation growth among the main LUCC in the GBA. Results demonstrated that low NDVI values with high fluctuations were mainly distributed in the central urban areas, whereas high NDVI values with low fluctuations were primarily located in the peripheral hilly mountains. The area-averaged NDVI showed an overall increasing trend at a rate of 0.0030 year−1, and areas with vegetation improvement (82.99%) were more than four times those with vegetation degradation (17.01%). The persistent forest and grassland and the regions converted from built-up to vegetation displayed the most obvious greening; NDVI in over 90% of these areas showed an increasing trend. In contrast, vegetation browning occurred in more than 60% of the regions converted from vegetation to built-up. Future vegetation change in most areas (91.37%) will continue the existing trends, and 80.06% of the GBA was predicted to develop in a benign direction, compared to 19.94% in a malignant direction. Our results contribute to in-depth understanding of vegetation dynamics during rapid urbanization in the GBA, which is crucial for vegetation conservation and land-use optimization.

Identifying the driving forces and mechanisms underlying vegetation change is critical to globally adapting to future climate change. However, this topic remains unclear for ecosystems within the Headwater Source Region of Brahmaputra and Indus (HSRBI), southwestern Tibetan Plateau. In this study, vegetation changes, in terms of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), were detected for breakpoints for the period of 2000–2017 by segment regression analysis. The effects of 14 potential environmental variables on vegetation dynamics were estimated and quantified via random forest modelling. Our results showed that monotonic increases (MI) and abrupt increases (AI) in NDVI accounted for 39.87% and 22.55% of vegetation change, respectively. In contrast, monotonic decreases (MD) and abrupt decreases (AD) only accounted for 19.38% and 7.65% of vegetation change, respectively. Our findings also confirmed that climate change was the most crucial driver of vegetation change in the HSRBI, and human disturbance was responsible for abrupt changes in vegetation. The primary MI pattern was affected by distance to the replenishment water source, which contributed to 21.36% of vegetation change. The distance to lakes was an essential factor associated with vegetation greening. AI was mainly affected by the interaction between human disturbance and climate change, which contributed to 27.12% and 26.89% of vegetation changes, respectively. Distance to residential areas was the most significant factor that linked the human disturbance to abrupt vegetation changes. AD and MD were inclined to be affected by the variation in climate variables. Finally, this study will help to identify multidimensional mechanisms of vegetation change and aid ecosystem stability in the headwater zones.

The Mt. Qomolangma (Everest) National Nature Preserve (QNNP) is among the highest natural reserves in the world. Monitoring the spatiotemporal changes in the vegetation in this complex vertical ecosystem can provide references for decision makers to formulate and adapt strategies. Vegetation growth in the reserve and the factors driving it remains unclear, especially in the last decade. This study uses the normalized difference vegetation index (NDVI) in a linear regression model and the Breaks for Additive Seasonal and Trend (BFAST) algorithm to detect the spatiotemporal patterns of the variations in vegetation in the reserve since 2000. To identify the factors driving the variations in the NDVI, the partial correlation coefficient and multiple linear regression were used to quantify the impact of climatic factors, and the effects of time lag and time accumulation were also considered. We then calculated the NDVI variations in different zones of the reserve to examine the impact of conservation on the vegetation. The results show that in the past 19 years, the NDVI in the QNNP has exhibited a greening trend (slope = 0.0008/yr, p < 0.05), where the points reflecting the transition from browning to greening (17.61%) had a much higher ratio than those reflecting the transition from greening to browning (1.72%). Shift points were detected in 2010, following which the NDVI tendencies of all the vegetation types and the entire preserve increased. Considering the effects of time lag and time accumulation, climatic factors can explain 44.04% of the variation in vegetation. No climatic variable recorded a change around 2010. Considering the human impact, we found that vegetation in the core zone and the buffer zone had generally grown better than the vegetation in the test zone in terms of the tendency of growth, the rate of change, and the proportions of different types of variations and shifts. A policy-induced reduction in livestock after 2010 might explain the changes in vegetation in the QNNP.

In the original article [...]

Land use and land cover (LULC) changes are regarded as one of the key drivers of ecosystem services degradation, especially in mountain regions where they may provide various ecosystem services to local livelihoods and surrounding areas. Additionally, ecosystems and habitats extend across political boundaries, causing more difficulties for ecosystem conservation. LULC in the Kailash Sacred Landscape (KSL) has undergone obvious changes over the past four decades; however, the spatiotemporal changes of the LULC across the whole of the KSL are still unclear, as well as the effects of LULC changes on ecosystem service values (ESVs). Thus, in this study we analyzed LULC changes across the whole of the KSL between 2000 and 2015 using Google Earth Engine (GEE) and quantified their impacts on ESVs. The greatest loss in LULC was found in forest cover, which decreased from 5443.20 km2 in 2000 to 5003.37 km2 in 2015 and which mainly occurred in KSL-Nepal. Meanwhile, the largest growth was observed in grassland (increased by 548.46 km2), followed by cropland (increased by 346.90 km2), both of which mainly occurred in KSL-Nepal. Further analysis showed that the expansions of cropland were the major drivers of the forest cover change in the KSL. Furthermore, the conversion of cropland to shrub land indicated that farmland abandonment existed in the KSL during the study period. The observed forest degradation directly influenced the ESV changes in the KSL. The total ESVs in the KSL decreased from 36.53 × 108 USD y−1 in 2000 to 35.35 × 108 USD y−1 in 2015. Meanwhile, the ESVs of the forestry areas decreased by 1.34 × 108 USD y−1. This shows that the decrease of ESVs in forestry was the primary cause to the loss of total ESVs and also of the high elasticity. Our findings show that even small changes to the LULC, especially in forestry areas, are noteworthy as they could induce a strong ESV response.

Advanced developments have been achieved in urban human population estimation, however, there is still a considerable research gap for the mapping of remote rural populations. In this study, based on demographic data at the town-level, multi-temporal high-resolution remote sensing data, and local population-sensitive point-of-interest (POI) data, we tailored a random forest-based dasymetric approach to map population distribution on the Qinghai–Tibet Plateau (QTP) for 2000, 2010, and 2016 with a spatial resolution of 1000 m. We then analyzed the temporal and spatial change of this distribution. The results showed that the QTP has a sparse population distribution overall; in large areas of the northern QTP, the population density is zero, accounting for about 14% of the total area of the QTP. About half of the QTP showed a rapid increase in population density between 2000 and 2016, mainly located in the eastern and southern parts of Qinghai Province and the central-eastern parts of the Tibet Autonomous Region. Regarding the relative importance of variables in explaining population density, the variables “Distance to Temples” is the most important, followed by “Density of Villages” and “Elevation”. Furthermore, our new products exhibited higher accuracy compared with five recently released gridded population density datasets, namely WorldPop, Gridded Population of the World version 4, and three national gridded population datasets for China. Both the root-mean-square error (RMSE) and mean absolute error (MAE) for our products were about half of those of the compared products except for WorldPop. This study provides a reference for using fine-scale demographic count and local population-sensitive POIs to model changing population distribution in remote rural areas.

Accurate information about the spatiotemporal patterns of rice paddies is essential for the assessment of food security, management of agricultural resources, and sustainability of ecosystems. However, accurate spatial datasets of rice paddy fields and multi-cropping at fine resolution are still lacking. Landsat observation is the primary source of remote sensing data that has continuously mapped regional rice paddy fields at a 30-m spatial resolution since the 1980s. However, Landsat data used for rice paddy studies reveals some challenges, especially data quality issues (e.g., cloud cover). Here, we present an algorithm that integrates time-series Landsat and MODIS (Moderate-resolution Imaging Spectroradiometer) images with a phenology-based approach (ILMP) to map rice paddy planting fields and multi-cropping patterns. First, a fusion of MODIS and Landsat data was used to reduce the cloud contamination, which added more information to the Landsat time series data. Second, the unique biophysical features of rice paddies during the flooding and open-canopy periods (which can be captured by the dynamics of the vegetation indices) were used to identify rice paddy regions as well as those of multi-cropping. This algorithm was tested for 2015 in Nanchang County, which is located on the Poyang Lake plain in southern China. We evaluated the resultant map of the rice paddy and multi-cropping systems using ground-truth data and Google Earth images. The overall accuracy and kappa coefficient of the rice paddy planting areas were 93.66% and 0.85, respectively. The overall accuracy and kappa coefficient of the multi-cropping regions were 92.95% and 0.89, respectively. In addition, our algorithm was more capable of capturing detailed information about areas with fragmented cropland than that of the National Land Cover Dataset (NLCD) from 2015. These results demonstrated the great potential of our algorithm for mapping rice paddy fields and using the multi-cropping index in complex landscapes in southern China.

Keywords: microfluidics; droplet; microcapsule; pressure-sensitive adhesive; bonding

Climate warming on the Tibetan Plateau has been regarded as an important driving force of regional environmental change. Although several studies have analyzed the shift of warming trends on this plateau within the context of a recent global warming “hiatus” since 1998, their disparate findings have hindered a comprehensive and regional understanding. Based on the daily mean temperature (Tmean), maximum temperature (Tmax), and minimum temperature (Tmin) collected from meteorological stations on the period of 1961−2017, we re-examined the timing and magnitude of temperature phase change using piecewise linear regression on the mid-south of Tibetan Plateau. The results show that among the trends in regional annual Tmean, Tmax and Tmin, the statistically significant change-point was observed only in annual Tmax (p < 0.01). The warming trend of annual Tmax has accelerated significantly since 1992 and has exceeded that of annual Tmin after 2000, causing a remarkable reversal from decline to increase in diurnal temperature range (DTR) (p < 0.01). Spatially, the occurrence time of change-points in Tmean, Tmax, and Tmin varied among stations, but most of them occurred before the mid-1990s. Besides, the trend shifts in Tmax/DTR during the cold season played a primary role in the significant trend shifts in annual Tmax/DTR. This study underscores that there is no significant shift of warming trends over the last two decades on the mid-south of Tibetan Plateau.

Alpine vegetation on the Tibetan Plateau (TP) is known to be sensitive to both climate change and anthropogenic disturbance. However, the magnitude and patterns of alpine vegetation dynamics and the driving mechanisms behind their variation on the TP remains under debate. In this study, we used updated MODIS Collection 6 Normalized Difference Vegetation Index (NDVI) from the Terra satellite combined with linear regression and the Break for Additive Season and Trend model to reanalyze the spatiotemporal patterns of vegetation change on the TP during 2000–2015. We then quantified the responses of vegetation variation to climatic and anthropogenic factors by coupling climatic and human footprint datasets. Results show that growing season NDVI (GNDVI) values increased significantly overall (0.0011 year−1, p < 0.01) during 2000–2015 and that 70.37% of vegetated area on the TP (23.47% significantly with p < 0.05) exhibited greening trends with the exception of the southwest TP. However, vegetation greenness experienced trend shifts from greening to browning in half of the ecosystem zones occurred around 2010, likely induced by spatially heterogeneous temporal trends of climate variables. The vegetation changes in the northeastern and southwestern TP were water limited, the mid-eastern TP exhibited strong temperature responses, and the south of TP was driven by a combination of temperature and solar radiation. Furthermore, we found that, to some extent, anthropogenic disturbances offset climate-driven vegetation greening and aggravated vegetation browning induced by water deficit. These findings suggest that the impact of anthropogenic activities on vegetation change might not overwhelm that of climate change at the region scale.