Hyperspectral technology has received great attention for estimating aboveground plant biomass. Corrections using ground spectrometer data have been proposed for more accurate integrated assessments of aboveground plant biomass. In this study, we sampled the aboveground plant biomass and the corresponding hyperspectral data from three types of alpine grassland ecosystems (alpine desert, alpine steppe, and alpine meadow) from the Qinghai-Tibetan Plateau to improve the biomass estimation accuracy. We used partial least squares regression and the vegetation indices (VIs) (using all two-band combinations (601*601combinations in all) involving 601 narrow bands between 400nm and 1000nm), and the optimal band combinations were used to calculate four most commonly used VIs. We found that the VIs are better than the least square method for alpine meadow, and that the most effective combinations were shown in the combinations of reflectance at the red edge (rather than red) and near-infrared bands(728nm,764nm). For alpine steppe and alpine desert, however, partial least squares regression is better than VIs. While the most important band for alpine steppe is between 600nm and 700nm in the red spectrum, the most important band for alpine desert is around 400nm. Our results can provide a theoretical basis for accurately estimating the aboveground plant biomass of different grassland types in the Qinghai Tibet Plateau.

Identifying the ecological risk patterns and the dominant ecological risks for ecosystems is crucial for optimizing regional ecological environment quality and formulating sustainable socioeconomic development polices. We established an ecological risk assessment (ERA) model for the Wuling Mountain region to identify the dominant ecological risks in the region and analyzed the constraint effects of the influencing factors on the ecological risk. The results indicated that the relatively high-risk areas were mainly distributed in the northeastern and western regions, along with scattered areas of high risk. The area which had dominant ecological risks was 89,500 km 2, accounting for 52.14% of the total area. Meanwhile, the regions with higher ecological risks had multiple dominant risk types. Among all influencing factors, NPP, population density (POP) and temperature (TMP) were highlighted as key factors affecting ecological risk, with an explanatory power consistently exceeding 0.20. There were three types of constraint effects of influencing factors on ecological risk, including logarithmic, negative convex and hump-shaped. Thresholds occurred for the constraint lines of TMP, precipitation (PRE), evapotranspiration (EVA), NPP, elevation (DEM) and slope (SLO), respectively, indicating that the stressors influencing ecological risk vary on either side of these thresholds. This finding is significant for policy formulation, as it allows for prioritization of adjustments based on the explanatory power and the magnitude of these threshold values. Overall, the results of this study provide a comprehensive regulatory framework and regionally targeted scientific support for ecological protection and restoration in the Wuling Mountain region.

Understanding the effects of climate change on plant phenological dynamics and growth patterns is critical for predicting climatic changes on the Qinghai-Tibetan Plateau (QTP). We used data over 21 years (1997 to 2017) for four dominant species on the QTP, namely Astragalus laxmannii (legume), Artemisia scoparia (forb), Kobresia humilis (sedge), Stipa purpurea (grass), and examined the relationships among climatic changes, plant phenology, growth pattern, and biomass. Most phenological periods in Stipa purpurea and Artemisia scoparia were delayed, whereas in Astragalus laxmannii, they were advanced. Soil temperature and maximum air temperature were the most important drivers. There were trade-offs between reproductive phenology and vegetative phenology, as well as between the length of the rapid growth period and the intrinsic growth rate. The impacts of the phenological or growth processes were species-specific. Our findings provide evidence of long-term changes and are of great significance for improving the accuracy of models.

Vulnerability assessment has become a critical issue and an important approach for regional sustainable development. The Qinghai Province, located in the northeastern part of the Qinghai-Tibet Plateau, suffers a severe grassland degradation caused by climate change and human activities. The degradation constrains the development of local animal husbandry and further affects the vulnerability of social system. In this study, based on the vulnerability assessment framework of exposure-sensitivity-adaptability, two index systems were established including ecological and social aspects. The entropy weight method was used to determine the index weight. The dynamic changes of the ecological vulnerability and social vulnerability of Qinghai province were assessed from 1995 to 2015. Results indicated that ecological vulnerability in Qinghai province increased from eastern part to the west, and decreased from northern to southern part, while social vulnerability showed an opposite trend. Key ecologically fragile areas were mainly located in the Qaidam Basin and western Three-River Headwaters Region (TRHR), while key social vulnerability areas were mainly distributed in the Qilian Mountains and eastern Qinghai province. The overall ecological vulnerability showed a decreasing trend through time, but increased in several local areas. Social vulnerability dropped significantly, especially in the eastern part of Qinghai province. The results will help to identify key vulnerable areas of Qinghai province and provide references for the ecological protection and restoration and the formulation of ecosystem management policies.