Mismatch in elevational shifts between satellite observed vegetation greenness and temperature isolines during 2000-2016 on the Tibetan Plateau

Interannual precipitation variability dominates the growth of alpine grassland above-ground biomass at high elevations on the Tibetan Plateau

The impact of global climate change on mountainous regions with significant elevational gaps is complex and often unpredictable. In particular, alpine grassland ecosystems, are experiencing changes in their spatial patterns along elevational gradients, which increases their vulnerability to degradation. Therefore, a more detailed understanding of spatiotemporal changes in alpine grassland productivity along elevational gradients and an elevation-dependent characterization of the effects of climatic variables on grassland productivity dynamics are essential. Thus, we conducted a study in the Tibetan Plateau, where we collected 2251 above-ground biomass (AGB) observations collected from 1986 to 2020. Mean annual temperature (TMP), annual precipitation (PRE), interannual precipitation variability (CVP), and snowmelt (SNMM) were chosen as influential variables. Using the Random Forest algorithm, we generated an AGB raster dataset covering the period 1989-2020 based on earth observation data at 30 m resolution to examine the dynamics of alpine grasslands and their response to climate change with respect to elevation. The results showed that the AGB of alpine grassland on the Tibetan Plateau was 49.17 g/m2. We observed an increasing trend in grassland AGB at high elevations, with a growth rate of about 0.28 g/m2 per year within the interval of 3100-4800 m. However, above the elevation of approximately 4400-4600 m, we observed a decoupling trend between grassland AGB and TMP. Moreover, at most elevations, the proportion of maximum partial correlation coefficients for CVP, PRE, and SNMM surpassed that of TMP. We found the dominant role of precipitation variability on grassland AGB dynamics, with 22.80 % and 18.86 % for CVP+ and CVP-, respectively. The proportion of CVP+ did not vary much at different elevations, whereas the proportion of CVP- increased with elevation, varying between 12.85 and 30.25 %. In the future, precipitation on the Tibetan plateau is expected to increase, potentially reversing its original positive impact.

Altitude explains insignificant autumn phenological changes across regions with large topography relief in the Tibetan Plateau

The start of the growing season (SGS) and the end of the growing season (EGS) are widely employed in global change studies to represent the spring and autumn phenology, respectively. Despite the Tibetan Plateau (TP) experiencing significant warming in recent decades, EGS has exhibited only slight changes. Previous studies have concentrated on exploring the environmental regulation of phenology, ignoring the distinctive influences of elevation. Therefore, a more in-depth investigation into the underlying mechanism is warranted. In this study, we investigate the variability of EGS among alpine vegetation regions at different elevations and conduct an analysis based on satellite data. Phenology data of alpine vegetation are extracted from SPOT NDVI dataset spanning from 1999 to 2018, using a piecewise-logistic-maximum-ratio method. We analyze the factors influencing EGS trends at different elevations. The results show that the overall insignificant variation in EGS is mainly attributed to altitude. With the altitude increasing, the annual mean EGS experiences a delay of 0.28 days/100 m below 3500 m, while it advances by 0.2 days/100 m above 3500 m. The opposing shift in elevation below and above 3500 m leads to this counteraction. Elevation emerges as the predominant factor influencing EGS trends, explaining the highest variations (38 %), followed by SGS (22 %) and precipitation (22 %). The elevation effect is most pronounced in areas with substantial topography fluctuations. Moreover, the elevation lapse rate of EGS (ELR_EGS) exhibits an opposite trend with growing season (GS) temperature and a similar trend with GS precipitation between the regions below and above 3500 m, ultimately linking to this counteraction. This study underscores elevation is a critical regulator of vegetation EGS responses to climatic changes over the TP, revealing significant spatial heterogeneities in these responses.

Spatial variations in the difference in elevational shifts between greenness and temperature isolines across the Tibetan Plateau grasslands under warming

Climate warming has induced widespread isotherm shifts toward higher elevations on the Tibetan Plateau, but elevational shifts of vegetation greenness (indicated by Normalized Difference Vegetation Index, NDVI) do not necessarily keep pace with the isotherm shifts. Thus, there should be spatial variations in the difference between the velocities of vertical movement of greenness isolines (VNDVI) and isotherms (VT) across the Tibetan Plateau grasslands. Using satellite-observed NDVI and gridded climate data during 2000-2017, we found uphill shifts of the isotherms in 81.8 % of the surveyed areas, mainly in the eastern, central, southwestern, and northeastern parts, whereas upward shifts of the greenness isolines were observed only in 49.7 % of these areas, mainly in the southeastern, west-central, and southwestern edge of Tibetan Plateau grasslands. In the areas where both the greenness isolines and isotherms shifted uphill, VDNVI was faster than VT in the west-central and northeastern parts, and VNDVI was smaller than VT in the western, south-central, central, and southeastern regions; the difference between VNDVI and VT was positively related with elevational gradient of NDVI (NDVIEG) in the areas where NDVIEG was negative and the temporal trend of NDVI was positive, and was negatively related with NDVIEG and temporal trends of NDVI and temperature in the areas where NDVIEG was positive and temporal trend of NDVI was negative. Our results revealed spatial heterogeneity in the difference in the elevational shifts between the isotherm and vegetation greenness isoline across the Tibetan Plateau grasslands, which is related with both diverse adaptation to local environment (NDVIEG) and complex responses of vegetation greenness to warming in terms of both direction and magnitude. These findings have important implications for the prediction of vegetation production and carbon cycle and the adaptive management of alpine grasslands under climate change.

A remote sensing method for mapping alpine grasslines based on graph-cut

Climate change has induced substantial shifts in vegetation boundaries such as alpine treelines and shrublines, with widespread ecological and climatic influences. However, spatial and temporal changes in the upper elevational limit of alpine grasslands ("alpine grasslines") are still poorly understood due to lack of field observations and remote sensing estimates. In this study, taking the Tibetan Plateau as an example, we propose a novel method for automatically identifying alpine grasslines from multi-source remote sensing data and determining their positions at 30-m spatial resolution. We first identified 2895 mountains potentially having alpine grasslines. On each mountain, we identified a narrow area around the upper elevational limit of alpine grasslands where the alpine grassline was potentially located. Then, we used linear discriminant analysis to adaptively generate from Landsat reflectance features a synthetic feature that maximized the difference between vegetated and unvegetated pixels in each of these areas. After that, we designed a graph-cut algorithm to integrate the advantages of the Otsu and Canny approaches, which was used to determine the precise position of the alpine grassline from the synthetic feature image. Validation against alpine grasslines visually interpreted from a large number of high-spatial-resolution images showed a high level of accuracy (R2 , .99 and .98; mean absolute error, 22.6 and 36.2 m, vs. drone and PlanetScope images, respectively). Across the Tibetan Plateau, the alpine grassline elevation ranged from 4038 to 5380 m (5th-95th percentile), lower in the northeast and southeast and higher in the southwest. This study provides a method for remotely sensing alpine grasslines for the first-time at large scale and lays a foundation for investigating their responses to climate change.

Reconstructing High-Spatiotemporal-Resolution (30 m and 8-Days) NDVI Time-Series Data for the Qinghai–Tibetan Plateau from 2000–2020

As the largest and highest alpine ecoregion in the world, the Qinghai–Tibetan Plateau (QTP) is extremely sensitive to climate change and has experienced extraordinary warming during the past several decades; this has greatly affected various ecosystem processes in this region such as vegetation production and phenological change. Therefore, numerous studies have investigated changes in vegetation dynamics on the QTP using the satellite-derived normalized-difference vegetation index (NDVI) time-series data provided by the Moderate-Resolution Imaging Spectroradiometer (MODIS). However, the highest spatial resolution of only 250 m for the MODIS NDVI product cannot meet the requirement of vegetation monitoring in heterogeneous topographic areas. In this study, therefore, we generated an 8-day and 30 m resolution NDVI dataset from 2000 to 2020 for the QTP through the fusion of 30 m Landsat and 250 m MODIS NDVI time-series data. This dataset, referred to as QTP-NDVI30, was reconstructed by employing all available Landsat 5/7/8 images (>100,000 scenes) and using our recently developed gap-filling and Savitzky–Golay filtering (GF-SG) method. We improved the original GF-SG approach by incorporating a module to process snow contamination when applied to the QTP. QTP-NDVI30 was carefully evaluated in both quantitative assessments and visual inspections. Compared with reference Landsat images during the growing season in 100 randomly selected subregions across the QTP, the reconstructed 30 m NDVI images have an average mean absolute error (MAE) of 0.022 and a spatial structure similarity (SSIM) above 0.094. We compared QTP-NDVI30 with upscaled cloud-free PlanetScope images in some topographic areas and observed consistent spatial variations in NDVI between them (averaged SSIM = 0.874). We further examined an application of QTP-NDVI30 to detect vegetation green-up dates (GUDs) and found that QTP-NDVI30-derived GUD data show general agreement in spatial patterns with the 250 m MODIS GUD data, but provide richer spatial details (e.g., GUD variations at the subpixel scale). QTP-NDVI30 provides an opportunity to monitor vegetation and investigate land-surface processes in the QTP region at fine spatiotemporal scales.

Analyzing the Spatiotemporal Vegetation Dynamics and Their Responses to Climate Change along the Ya’an–Linzhi Section of the Sichuan–Tibet Railway

Vegetation dynamics and their responses to climate change are of significant spatial and temporal heterogeneity. The Sichuan–Tibet Railway (STR) is a major construction project of the 14th Five-Year Plan for Economic and Social Development of the People’s Republic of China that is of great significance to promoting the social and economic development of Sichuan–Tibet areas. The planned railway line crosses areas with a complex geological condition and fragile ecological environment, where the regional vegetation dynamics are sensitive to climate change, topographic conditions and human activities. So, analyzing the vegetation variations in the complex vertical ecosystem and exploring their responses to hydrothermal factors are critical for providing technical support for the ecological program’s implementation along the route of the planned railway line. Based on MOD13Q1 Normalized Difference Vegetation Index (NDVI) data for the growing season (May to October) during 2001–2020, a Theil-Sen trend analysis, Mann–Kendall test, Hurst exponent analysis and partial correlation analysis were used to detect the vegetation dynamics, predict the vegetation sustainability, examine the relationship between vegetation change and hydrothermal factors, regionalize the driving forces for vegetation growth and explore the interannual variation pattern of driving factors. The growing season NDVI along the Ya’an–Linzhi section of the STR showed a marked rate of increase (0.0009/year) during the past 20 years, and the vegetation’s slight improvement areas accounted for the largest proportion (47.53%). Among the three hydrothermal parameters (temperature, precipitation and radiation), the correlation between vegetation growth and the temperature was the most significant, and the vegetation response to precipitation was the most immediate. The vegetation changes were affected by the combined impact of climatic and non-climatic factors, and the proportion of hydrothermal factors’ combined driving force slightly increased during the study period. Based on the Hurst exponent, the future vegetation sustainability of the area along the Ya’an–Linzhi section of the STR faces a risk of degradation, and more effective conservations should be implemented during the railway construction period to protect the regional ecological environment.

Characteristics of Greening along Altitudinal Gradients on the Qinghai–Tibet Plateau Based on Time-Series Landsat Images

The Qinghai–Tibet Plateau (QTP) is ecologically fragile and is especially sensitive to climate change. Previous studies have shown that the vegetation on the QTP is undergoing overall greening with variations along altitudinal gradients. However, the mechanisms that cause the differences in the spatiotemporal patterns of vegetation greening among different types of terrain and vegetation have not received sufficient attention. Therefore, in this study, we used a Landsat NDVI time-series for the period 1992–2020 and climate data to observe the effects of terrain and vegetation types on the spatiotemporal patterns in vegetation greening on the QTP and to analyze the factors driving this greening using the geographical detector and the velocity of the vertical movement of vegetation greenness isolines. The results showed the following: (1) The vertical movement of the vegetation greenness isolines was affected by the temperature and precipitation at all elevations. The precipitation had a more substantial effect than the temperature below 3000 m. In contrast, above 3000 m, the temperature had a greater effect than the precipitation. (2) The velocity of the vertical movement of the vegetation greenness isolines of woody plants was higher than that of herbaceous plants. (3) The influence of slope on the vertical movement of vegetation greenness isolines was more significant than that of the aspect. The results of this study provided details of the spatiotemporal differences in vegetation greening between different types of terrain and vegetation at a 30-m scale as well as of the underlying factors driving this greening. These results will help to support ecological protection policies on the QTP.

Estimation of Daily and Instantaneous Near-Surface Air Temperature from MODIS Data Using Machine Learning Methods in the Jingjinji Area of China

Meteorologically observed air temperature (Ta) is limited due to low density and uneven distribution that leads to uncertain accuracy. Therefore, remote sensing data have been widely used to estimate near-surface Ta on various temporal scales due to their spatially continuous characteristics. However, few studies have focused on instantaneous Ta when satellites overpass. This study aims to produce both daily and instantaneous Ta datasets at 1 km resolution for the Jingjinji area, China during 2018–2019, using machine learning methods based on remote sensing data, dense meteorological observation station data, and auxiliary data (such as elevation and normalized difference vegetation index). Newly released Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 surface Downward Shortwave Radiation (DSR) was introduced to improve the accuracy of Ta estimation. Five machine learning algorithms were implemented and compared so that the optimal one could be selected. The random forest (RF) algorithm outperformed the others (such as decision tree, feedforward neural network, generalized linear model) and RF obtained the highest accuracy in model validation with a daily root mean square error (RMSE) of 1.29 °C, mean absolute error (MAE) of 0.94 °C, daytime instantaneous RMSE of 1.88 °C, MAE of 1.35 °C, nighttime instantaneous RMSE of 2.47 °C, and MAE of 1.83 °C. The corresponding R2 was 0.99 for daily average, 0.98 for daytime instantaneous, and 0.95 for nighttime instantaneous. Analysis showed that land surface temperature (LST) was the most important factor contributing to model accuracy, followed by solar declination and DSR, which implied that DSR should be prioritized when estimating Ta. Particularly, these results outperformed most models presented in previous studies. These findings suggested that RF could be used to estimate daily instantaneous Ta at unprecedented accuracy and temporal scale with proper training and very dense station data. The estimated dataset could be very useful for local climate and ecology studies, as well as for nature resources exploration.

How Does Spring Phenology Respond to Climate Change in Ecologically Fragile Grassland? A Case Study from the Northeast Qinghai-Tibet Plateau

Vegetation phenology is an important indicator of global climate change, and the response of grassland phenology to climate change is particularly sensitive in ecologically fragile areas. To enhance the ecological security of the Tibetan Plateau, it is crucial to determine the relationship between fluctuations in the start of the growing season (SOS) and the response to environmental factors. We investigated the trends of the intra-annual (ten-day) and interannual spatiotemporal dynamics of the SOS on the Northeast Qinghai-Tibet Plateau (NQTP) from 2000–2020 with MOD09GA data. We identified the response relationships with environmental factors (climate, terrain) using the maximum value composite method and the Savitzky–Golay filtering and dynamic threshold method. The SOS was concentrated from the 110th to 150th days; the average annual SOS was on the 128th day, with a spatial pattern of “early in the east and late in the west”. The overall trend of the SOS was advanced (45.48%); the regions with the advanced trend were mainly distributed in the eastern part of the NQTP. The regions with a delayed SOS were mainly concentrated in the higher-altitude regions in the southwest (38.31%). The temperature, precipitation and SOS exhibited a reverse fluctuation trend around the midpoint of 2010. Precipitation affected the SOS earlier than temperature. When temperature became a limitation of the SOS, precipitation had a more significant regulatory effect on the SOS. The SOS and aspect, slope and altitude were distributed in axisymmetric, pyramidal and inverted pyramidal shapes, respectively. The SOS on shaded slopes was earlier and more intensive than that on sunny slopes. With increasing slope, the area of the SOS decreased, and it occurred later. The SOS area was largest at 4500–5000 m and decreased at lower and higher altitude intervals. The SOS occurred later as altitude increased.

Limited increase in asynchrony between the onset of spring green-up and the arrival of a long-distance migratory bird

For many migrant bird species around the world, climate change has been shown to induce changes in the timings of arrival and the onset of spring food availability at breeding sites. However, whether such changes enlarged asynchrony between the timings of spring arrival of long-distance migratory birds and onset of vegetation greenness increase remain controversial. We used a 29-year phenological dataset to investigate the temporal changes in spring first-sighting date (FSD) of a long-distance migratory bird (barn swallow, Hirundo rustica), from observations at 160 local breeding sites across northern China, and the vegetation green-up onset date (VGD), determined from satellite observations of vegetation greenness. We found that both FSD and VGD trended earlier at over two-thirds of the breeding sites. FSD significantly advanced at 26.9% of the sites, and VGD significantly advanced at 23.8% of the sites. The degree of asynchrony between FSD and VGD changed significantly at one-third of the breeding sites (22.5% with an increase versus 11.3% with a decrease), leading to a limited increase of phenological mismatch. We speculated that climate change did not disrupt the climatic connections between most breeding sites and corresponding non-breeding sites (wintering grounds and migration routes). Our findings suggest that climate change may not greatly increase phenological mismatch between first arrival date of barn swallows and VGD at breeding sites. Importantly, this study should serve as a cue to encourage ecologists and conservation biologists to expand the context under which to explore the ecological consequences of phenological shifts beyond asynchrony, such as individual survival, population demography and ecosystem-level consequences.

Vegetation Greenness Variations and Response to Climate Change in the Arid and Semi-Arid Transition Zone of the Mongo-Lian Plateau during 1982–2015

Vegetation greenness dynamics in arid and semi-arid regions are sensitive to climate change, which is an important phenomenon in global climate change research. However, the driving mechanism, particularly for the longitudinal and latitudinal changes in vegetation greenness related to climate change, has been less studied and remains poorly understood in arid and semi-arid areas. In this study, we investigated changes in vegetation greenness and the vegetation greenness line (the mean growing season normalized difference vegetation index (NDVI) = 0.1 contour line) and its response to climate change based on AVHRR-GIMMS NDVI3g and the fifth and latest global climate reanalysis dataset from 1982 to 2015 in the arid and semi-arid transition zone of the Mongolian Plateau (ASTZMP). The results showed that the mean growing season NDVI increased from the central west to east, northeast, and southeast in ASTZMP. The vegetation greenness line migrated to the desert during 1982–1994, to the grassland during 1994–2005, and then to the desert during 2005–2015. Vegetation greenness was positively correlated with precipitation and negatively correlated with temperature. The latitudinal variation of the vegetation greenness line was mainly affected by the combination of precipitation and temperature, while the longitudinal variation was mainly affected by precipitation. In summary, precipitation was a key climatic factor driving rapid changes in vegetation greenness during the growing season of the transition zone. These results can provide meaningful information for research on vegetation coverage changes in arid and semi-arid regions.

Ecological Safety Assessment and Analysis of Regional Spatiotemporal Differences Based on Earth Observation Satellite Data in Support of SDGs: The Case of the Huaihe River Basin

Terrestrial ecosystems provide a variety of benefits for human life and production, and are a key link for achieving sustainable development goals (SDGs). The basin ecosystem is one type of terrestrial ecosystem. Ecological security (ES) assessments are an important component of the overall strategy to achieve regional sustainable development. The Huaihe River Basin (HRB) has the common characteristics of most basins, such as high population density, a rapidly developing economy, and many environmental problems. This study constructed an ES evaluation system by applying a pressure-state-response framework as an assessment method for the sustainable development of basins. Taking the HRB as an example, this study determined the ES status of the region from 2001 to 2019 and analyzed crucial factors for any variation observed by combining remote sensing and climate data, relevant policies, and spatial information technology. The results highlight the importance of reserves and the negative impact of urban expansion on ES. Additionally, the enactment of policies had a positive impact on ES, whereas precipitation had a negative effect on ES in most areas of the HRB. Based on these results, the government should strengthen the protection of forests, grasslands, and wetlands and improve water conservation facilities. This study provides guidance for the subsequent economic development, environmental protection, and the achievements of SDG 15 in the HRB.

Monitoring Vegetation Greenness in Response to Climate Variation along the Elevation Gradient in the Three-River Source Region of China

The Three-River Source Region (TRSR) is vital to the ecological security of China. However, the impact of global warming on the dynamics of vegetation along the elevation gradient in the TRSR remains unclear. Accordingly, we used multi-source remote sensing vegetation indices (VIs) (GIMMS (Global Inventory Modeling and Mapping Studies) LAI (Leaf Area Index), GIMMS NDVI (Normalized Difference Vegetation Index), GLOBMAP (Global Mapping) LAI, MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index), MODIS NDVI, and MODIS NIRv (near-infrared reflectance of vegetation)) and digital elevation model data to study the changes of VGEG (Vegetation Greenness along the Elevation Gradient) in the TRSR from 2001 to 2016. Results showed that the areas with a positive correlation of vegetation greenness and elevation accounted for 36.34 ± 5.82% of the study areas. The interannual variations of VGEG showed that the significantly changed regions were mainly observed in the elevation gradient of 4–5 km. The VGEG was strongest in the elevation gradient of 4–5 km and weakest in the elevation gradient of >5 km. Correlation analysis showed that the mean annual temperature was positively correlated with VIs, and the effect of the mean annual precipitation on VIs was more obvious at low altitude than in high altitude. This study contributes to our understanding of the VGEG variation in the TRSR under global climate variation and also helps in the prediction of future carbon cycle patterns.

Elevational Movement of Vegetation Greenness on the Tibetan Plateau: Evidence from the Landsat Satellite Observations during the Last Three Decades

The Tibetan Plateau, the highest plateau in the world, has experienced strong climate warming during the last few decades. The greater increase of temperature at higher elevations may have strong impacts on the vertical movement of vegetation activities on the plateau. Although satellite-based observations have explored this issue, these observations were normally provided by the coarse satellite data with a spatial resolution of more than hundreds of meters (e.g., GIMMS and MODIS), which could lead to serious mixed-pixel effects in the analyses. In this study, we employed the medium-spatial-resolution Landsat NDVI data (30 m) during 1990–2019 and investigated the relationship between temperature and the elevation-dependent vegetation changes in six mountainous regions on the Tibetan Plateau. Particularly, we focused on the elevational movement of the vegetation greenness isoline to clarify whether the vegetation greenness isoline moves upward during the past three decades because of climate warming. Results show that vegetation greening occurred in all six mountainous regions during the last three decades. Increasing temperatures caused the upward movement of greenness isoline at the middle and high elevations (>4000 m) but led to the downward movement at lower elevations for the six mountainous regions except for Nyainqentanglha. Furthermore, the temperature sensitivity of greenness isoline movement changes from the positive value to negative value by decreasing elevations, suggesting that vegetation growth on the plateau is strongly regulated by other factors such as water availability. As a result, the greenness isoline showed upward movement with the increase of temperature for about 59% pixels. Moreover, the greenness isoline movement increased with the slope angles over the six mountainous regions, suggesting the influence of terrain effects on the vegetation activities. Our analyses improve understandings of the diverse response of elevation-dependent vegetation activities on the Tibetan Plateau.

Vegetation Expansion on the Tibetan Plateau and Its Relationship with Climate Change

The natural shift in land cover from non-vegetated to vegetated land is termed as vegetation expansion, which has substantial impacts on regional climate conditions and land surface energy balance. Barrens dominate the northwestern Tibetan Plateau, where vegetation is predicted to expand northwestward with the ongoing climate warming. However, rare studies have confirmed such a forecast with large-scale vegetation monitoring. In this study, we used a landcover dataset, classified according to the International Geosphere–Biosphere Program criteria, to examine previous model-based predictions and the role of climate on the expansion rate across the plateau. Our results showed that shrublands, open forests, grasslands, and water bodies expanded while evergreen and deciduous broadleaf forests, croplands and barrens shrank during the period 2001–2018. Vegetation expanded by 33,566 km2 accounting for about 1.3% of the total area of this plateau and the land cover shifting from barrens to grasslands was the primary way of vegetation expansion. Spatially, the vegetation expanded northwestward to lands with colder, drier, and more radiation in the climate. Increasing precipitation positively correlated with the vegetation expansion rate for the arid and semi-arid northwest Tibetan Plateau and warming contributed to the vegetation expanding in the semi-humid southeast Tibetan Plateau. Our results verified the predictions of models and highlighted the “greening” on barrens in recent years.

Contrasting Effects of Temperature and Precipitation on Vegetation Greenness along Elevation Gradients of the Tibetan Plateau

The Tibetan Plateau (TP) is one of the most sensitive regions to global climate warming, not only at the inter-annual time scale but also at the altitudinal scale. We aim to investigate the contrasting effects of temperature and precipitation on vegetation greenness at different altitudes across the TP. In this study, interannual and elevational characteristics of the Normalized Difference Vegetation Index (NDVI), temperature, and precipitation were examined during the growing season from 1982 to 2015. We compared the elevational movement rates of the isolines of NDVI, temperature, and precipitation, and the sensitivities of elevational NDVI changes to temperature and precipitation. The results show that from 1982 to 2015, the elevational variation rate of isolines for NDVI mismatched with that for temperature and precipitation. The elevational movements of NDVI isolines were mostly controlled by precipitation at elevations below 2400 m and by the temperature at elevations above 2400 m. Precipitation appears to plays a role similar to temperature, and even a more effective role than the temperature at low elevations, in controlling elevational vegetation greenness changes at both spatial and interannual scales in the TP. This study highlights the regulation of temperature and precipitation on vegetation ecosystems along elevation gradients over the whole TP under global warming conditions.

Vegetation Change and Its Relationship with Climate Factors and Elevation on the Tibetan Plateau

As the "roof of the world", the Tibetan Plateau (TP) is a unique geographical unit on Earth. In recent years, vegetation has gradually become a key factor reflecting the ecosystem since it is sensitive to ecological changes especially in arid and semi-arid areas. Based on the normalized difference vegetation index (NDVI) dataset of TP from 2000 to 2015, this study analyzed the characteristics of vegetation variation and the correlation between vegetation change and climatic factors at different time scales, based on a Mann-Kendall trend analyses, the Hurst exponent, and the Pettitt change-point test. The results showed that the vegetation fractional coverage (VFC) generally increased in the past 16 years, with 60.3% of the TP experiencing an increase, of which significant (p < 0.05) increases accounted for 28.79% and were mainly distributed in the north of the TP. Temperature had the largest response with the VFC on the seasonal scale. During the growing season, the correlation between precipitation and sunshine duration with VFC was high (p < 0.05). The change-points of the VFC were mainly distributed in the north of the TP during 2007-2009. Slope and elevation had an impact on the VFC; the areas with large vegetation change are mainly distributed in slopes <20° and elevation of 3000-5000 m. For elevation above 3000-4000 m, the response of the VFC to precipitation and temperature was the strongest. This study provided important information for ecological environment protection and ecosystem degradation on the Tibetan Plateau.

Increasing sensitivity of alpine grasslands to climate variability along an elevational gradient on the Qinghai-Tibet Plateau

Monitoring and mapping the sensitivity of grassland ecosystems to climate change is crucial for developing sustainable local grassland management strategies. The sensitivity of alpine grasslands to climate change is considered to be high on the Qinghai-Tibet Plateau (QTP), yet little is known about its spatial pattern, and particularly the variations between different elevations. Here, based on the Normalized Difference Vegetation Index (NDVI) and three climate variables (air temperature, precipitation, and solar radiation), we modified a vegetation sensitivity index-approach to capture the relative sensitivity of alpine grassland productivity to climate variability on the QTP during 2000-2016. The results show that alpine grasslands on the southern QTP are more sensitive to climate variability overall, and that the climate factors driving alpine grassland dynamics are spatially heterogeneous. Alpine grasslands on the southern QTP are more sensitive to temperature variability, those on the northeastern QTP display strong responses to precipitation variability, and those on the central QTP are primarily influenced by a combination of radiation and temperature variability. The sensitivity of alpine grasslands to climate variability increases significantly along an elevational gradient, especially to temperature variability. This study underscores that alpine grasslands at higher elevations on the QTP are more sensitive to climate variability than those at lower elevations at the regional scale.