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Light use efficiency (LUE) is a crucial indicator used to reflect the ability of terrestrial ecosystems to transform light energy. Understanding the long-term trends in LUE and its influencing factors are essential for determining the future carbon sink and carbon sequestration potential of terrestrial ecosystems. However, the long-term interannual variability of LUE in grasslands in northern China at the ecosystem scale is poorly understood due to the limitations of the year length and the coverage of the site data. In this study, we assessed the long-term LUE trends in the grasslands of northern China from 1982 to 2018 and then revealed the relationships between interannual variability in LUE and climate factors. Our study showed a substantial rising trend for LUE from 1982 to 2018 in the grasslands of northern China (3.42 × 10−3 g C/MJ/yr). Regarding the different grassland types, alpine meadow had the highest growth rate (4.85 × 10−3 g C/MJ/yr), while temperate steppe had the lowest growth rate (1.58 × 10−3 g C/MJ/yr). The climate factors driving LUE dynamics were spatially heterogeneous in grasslands. Increasing precipitation accelerated the interannual growth rate of LUE in temperate steppe, and increasing temperature accelerated the interannual growth rate of LUE in other types. In addition, the temporal dynamic of LUE showed different trends in relation to time scales, and the growth trend slowed down after 1998. Our results should be considered in developing future grassland management measures and predicting carbon cycle–climate interactions. [ABSTRACT FROM AUTHOR]

The Three Rivers Source Region (TRSR) is a natural habitat for rare animals and a genetic treasure trove of plateau organisms. It is an important eco-safety barrier in China and even Asia, and a priority of China's to promote ecological advancement. Precisely assessing the dynamics and mechanisms of alpine grassland ecosystem carbon budgets is beneficial for quantifying the response to climate change on a regional scale. The spatial distribution and dynamic changes in carbon fluxes in the TRSR from 1985 to 2018 were analyzed by the Theil–Sen + Mann–Kendall and ensemble empirical mode decomposition (EEMD) methods, and multiple linear regression was used to quantify the contribution of meteorological elements to the carbon flux trends. The results indicated that (1) the alpine grassland ecosystem was a weak carbon sink. The multiyear mean gross primary production (GPP) and net ecosystem production (NEP) in the TRSR were 147.86 and 11.27 g C/m2/yr, respectively. The distribution of carbon fluxes progressively decreased from east to west. (2) The carbon fluxes of the alpine grassland ecosystem were dominated by a monotonically increasing trend, with increasing rates of GPP and NEP of 1.31 and 0.40 g C/m2/yr, respectively. A total of 48.60% of the alpine grassland showed a significant increase in NEP, whereas only 0.21% showed a significant decrease during the research term. (3) The alpine meadow sequestered carbon better than the alpine steppe did and accounted for more than 60% of the regional carbon sink. (4) In a correlation analysis between NEP and temperature, precipitation and solar radiation, the positive correlation accounted for 89.67%, 90.51%, and 21.16% of the TRSR, respectively. Rising temperatures and increased precipitation were the main drivers contributing to the increase in NEP. Research on carbon budget variability and mechanisms can help guide preservation zoning initiatives in national parks. [ABSTRACT FROM AUTHOR]

The Qinghai-Tibet Plateau (QTP) is the largest mid-to low latitude and high-altitude permafrost. Open-pit coal mining and other activities have caused serious damage to the alpine ecological environment and have accelerated the degradation of permafrost on the QTP. In this study, the influence of open-pit coal mining on the time series ground surface deformation of the permafrost in the Muli region of the QTP was analyzed from 19 January 2018 to 22 December 2020 based on Landsat, Gaofen, and Sentinel remote sensing data. The primary methods include human-computer interactive visual interpretation and the small baseline subsets interferometric synthetic aperture radar (SBAS-InSAR) method. The results showed that the spatial distribution of displacement velocity exhibits a considerably different pattern in the Muli region. Alpine meadow is the main land use/land cover (LULC) in the Muli region, and the surface displacement was mainly subsidence. The surface subsidence trend in alpine marsh meadows was obvious, with a subsidence displacement velocity of 10–30 mm/a. Under the influence of changes in temperature, the permafrost surface displacement was characteristics of regular thaw subsidence and freeze uplift. Surface deformation of the mining area is relatively severe, with maximum uplift displacement velocity of 74.31 mm/a and maximum subsidence displacement velocity of 167.51 mm/a. Open-pit coal mining had resulted in the destruction of 48.73 km2 of natural landscape in the Muli region. Mining development in the Muli region had increased the soil moisture of the alpine marsh meadow around the mining area, resulting in considerable cumulative displacement near the mining area and the acceleration of permafrost degradation. [ABSTRACT FROM AUTHOR]

Temperature and precipitation are considered to be the most important indicators affecting the green-up date. Sensitivity of the green-up date to temperature and precipitation is considered to be one of the key indicators to characterize the response of terrestrial ecosystems to climate change. We selected the main grassland types for analysis, including temperate steppe, temperate meadow steppe, upland meadow, and lowland meadow. This study investigates the variation in key meteorological indicators (daily maximum temperature (Tmax), daily minimum temperature (Tmin), and precipitation) between 2001 and 2018. We then examined the partial correlation and sensitivity of green-up date (GUD) to Tmax, Tmin, and precipitation. Our analysis indicated that the average GUD across the whole area was DOY 113. The mean GUD trend was −3.1 days/decade and the 25% region advanced significantly. Tmax and Tmin mainly showed a decreasing trend in winter (p > 0.05). In spring, Tmax mainly showed an increasing trend (p > 0.05) and Tmin a decreasing trend (p > 0.05). Precipitation showed no significant (p > 0.05) change trend and the trend range was ±10 mm/decade. For temperate steppe, the increase in Tmin in March promotes green-up (27.3%, the proportion of significant pixels), with a sensitivity of −0.17 days/°C. In addition, precipitation in April also promotes green-up (21.7%), with a sensitivity of −0.32 days/mm. The GUDs of temperate meadow steppe (73.9%), lowland meadow (65.9%), and upland meadow (22.1%) were mainly affected by Tmin in March, with sensitivities of −0.15 days/°C, −0.13 days/°C, and −0.14 days/°C, respectively. The results of this study reveal the response of vegetation to climate warming and contribute to improving the prediction of ecological changes as temperatures increase in the future. [ABSTRACT FROM AUTHOR]

The source region of the Yellow River (SRYR) is an important water conservation and animal husbandry resource in China. It is of great significance to understand the patch pattern and ecological risk of alpine grassland in the SRYR for ecological environment management. This study first used 12 unmanned aerial vehicle (UAV) aerial images and eight moderate resolution imaging spectroradiometer (MODIS) vegetation index product MOD13Q1 images from July to August in 2019 to extract alpine grassland patch patterns in the SRYR, then constructed an ecological risk model based on the landscape vulnerability index and landscape disturbance index, and finally combined spatial self-reliance correlation and semi-variance analysis methods to explore the spatial distribution of ecological risks. The results showed that the patch fragmentation degree (Pi), area weighted shape index (AWMSI), and separation degree (Si) of the four grassland types in the SRYR are ordered as follows: alpine steppe > degraded meadow > alpine meadow > swamp meadow. Moreover, the greater the fractional vegetation cover (FVC), the greater the landscape dominance index (DOi), and the better the ecosystem stability. The spatial difference of ecological risk in the SRYR shows a situation of low risk in the east (ERImin = 1.5355) and high risk in the west (ERImax = 70.6429). High FVC was found in low and mild low risk areas where the vegetation types are mainly swamp meadow and shrub, while low FVC was found in high and mild high-risk areas where the vegetation types are mainly alpine steppe and degraded meadow. The spatial distribution of ecological risk of the SRYR has obvious positive spatial correlation (Moran's I = 0.863), the spatial aggregation distribution is distinct, and the local space has significant high-high aggregation and low–low aggregation phenomena. The results of this study reveal that patch characteristics have good indicative significance for alpine grassland ecological protection and should be considered in future studies. In addition, the ecological risk in the SRYR is relatively high, especially in the western region, which should be taken seriously in future ecological management and governance. [ABSTRACT FROM AUTHOR]

The alpine tree line ecotone, reflecting interactions between climate and ecology, is very sensitive to climate change. To identify tree line responses to climate change, including intensity and local variations in tree line advancement, the use of Landsat images with long-term data series and fine spatial resolution is an option. However, it is a challenge to extract tree line data from Landsat images due to classification issues with outliers and temporal inconsistency. More importantly, direct classification results in sharp boundaries between forest and non-forest pixels/segments instead of representing the tree line ecotone (three ecological regions—tree species line, tree line, and timber line—are closely related to the tree line ecotone and are all significant for ecological processes). Therefore, it is important to develop a method that is able to accurately extract the tree line from Landsat images with a high temporal consistency and to identify the appropriate ecological boundary. In this study, a new methodology was developed based on the concept of a local indicator of spatial autocorrelation (LISA) to extract the tree line automatically from Landsat images. Tree line responses to climate change from 1987 to 2018 in Wuyishan National Park, China, were evaluated, and topographic effects on local variations in tree line advancement were explored. The findings supported the methodology based on the LISA concept as a valuable classifier for assessing the local spatial clusters of alpine meadows from images acquired in nongrowing seasons. The results showed that the automatically extracted line from Landsat images was the timber line due to the restriction in spatial autocorrelation. The results also indicate that parts of the tree line in the study area shifted upward vertically by 50 m under a 1 °C temperature increase during the period from 1987 to 2018, with local variations influenced by slope, elevation, and interactions with aspect. Our study contributes a novel result regarding the response of the alpine tree line to global warming in a subtropical region. Our method for automatic tree line extraction can provide fundamental information for ecosystem managers. [ABSTRACT FROM AUTHOR]