The N deposition rate is notably increased in China, especially in the Qinghai-Tibetan Plateau (QTP). How plants respond to the projected N deposition on the alpine steppe is still in debate. In this study, to investigate the effects of N deposition on the plant community of the alpine steppe, we simulated N deposition at six different N addition rate levels (0, 8, 24, 40, 56, 72 kg N ha⁻¹ y⁻¹) from 2015 to 2019. Species composition and diversity were investigated as the assessment indices. The results showed that the importance value of grasses significantly increased with the increase of the N addition rate, while that of forbs significantly decreased. A high N addition rate (72 kg N ha⁻¹ y⁻¹) induced species composition change, making Leymus secalinus become the most dominant species within the entire plant community. Compared with the control (without N addition), species richness, Shannon–Weiner diversity, Simpson dominance and Pielou Evenness were significantly reduced under a high N addition rate. The changes of plant diversity in the alpine steppe were closely correlated with dynamics of soil nutrients, especially total carbon (TC), total phosphorus (TP) and ammonia nitrogen (NH₄-N). Our findings suggested that a high N deposition rate (72 kg N ha⁻¹ y⁻¹) could significantly change plant composition and reduce the diversity of the alpine steppe, though they were less affected by low N deposition rates at present. With the increase of the N deposition rate, plant composition and diversity of the alpine steppe may be negatively affected in the future. In addition, Leymus secalinus is more competitive than other species with an N deposition rate increase. Soil C, soil P and soil NH⁴-N variation induced by N deposition might play a key role in regulating changes in plant composition and diversity in the alpine steppe. In addition, longer term field investigation needs to be carried out to testify to this phenomenon with the increase of N deposition in the future.

The ability of fragile ecosystems of alpine regions to adapt and thrive under warming and nitrogen deposition is a pressing conservation concern. The lack of information on how these ecosystems respond to the combined impacts of elevated levels of nitrogen and a warming climate limits the sustainable management approaches of alpine grasslands. In this study, we experimented using a completely random blocked design to examine the effects of warming and nitrogen deposition on the aboveground biomass and diversity of alpine grassland plant communities. The experiment was carried out from 2015 to 2018 in four vegetation types, e.g., alpine desert, alpine desert steppe, alpine marsh, and alpine salinised meadow, in the Aerjin Mountain Nature Reserve (AMNR) on the Qinghai–Tibetan Plateau (QTP). We found that W (warming) and WN (warming plus N deposition) treatment significantly increased the aboveground biomass of all the vegetation types (p < 0.05) in 2018. However, W and WN treatment only significantly increased the Shannon diversity of salinised meadows in 2018 and had no significant effect on the Shannon diversity of other vegetation types. Such results suggested that long-term nitrogen deposition and warming can consistently stimulate biomass accumulation of the alpine plant communities. Compared with other vegetation types, the diversity of alpine salinised meadows are generally more susceptible to long-term warming and warming combined with N deposition. Warming accounts many of such variabilities, while short-term N deposition alone may not significantly have an evident effect on the productivity and diversity of alpine grasslands. Our findings suggested that the effects of short-term (≤4 years) N deposition on alpine vegetation productivity and diversity were minimal, while long-term warming (>4 years) will be much more favourable for alpine vegetation.

Nature reserves are among the most bio-diverse regions worldwide, and rapid and accurate identification is a requisite for their management. Based on the multi-temporal Sentinel-2 dataset, this study presents three multi-temporal modified vegetation indices (the multi-temporal modified normalized difference Quercus wutaishanica index (MTM-NDQI), the multi-temporal modified difference scrub grass index (MTM-DSI), and the multi-temporal modified ratio shaw index (MTM-RSI)) to improve the classification accuracy of the remote sensing of vegetation in the Lingkong Mountain Nature Reserve of China (LMNR). These three indices integrate the advantages of both the typical vegetation indices and the multi-temporal remote sensing data. By using the proposed indices with a uni-temporal modified vegetation index (the uni-temporal modified difference pine-oak mixed forest index (UTM-DMI)) and typical vegetation indices (e.g., the ratio vegetation index (RVI), the difference vegetation index (DVI), and the normalized difference vegetation index (NDVI)), an optimal feature set is obtained that includes the NDVI of December, the NDVI of April, and the UTM-DMI, MTM-NDQI, MTM-DSI, and MTM-RSI. The overall accuracy (OA) of the random forest classification (98.41%) and Kappa coefficient of the optimal feature set (0.98) were higher than those of the time series NDVI (OA = 96.03%, Kappa = 0.95), the time series RVI (OA = 95.56%, Kappa = 0.95), and the time series DVI (OA = 91.27%, Kappa = 0.90). The OAs of the rapid classification and the Kappa coefficient of the knowledge decision tree based on the optimal feature set were 95.56% and 0.95, respectively. Meanwhile, only three of the seven vegetation types were omitted or misclassified slightly. Overall, the proposed vegetation indices have advantages in identifying the vegetation types in protected areas.