Vegetation Mapping in the Permafrost Region: A Case Study on the Central Qinghai-Tibet Plateau

An accurate and detailed vegetation map is of crucial significance for understanding the spatial heterogeneity of subsurfaces, which can help to characterize the thermal state of permafrost. The absence of an alpine swamp meadow (ASM) type, or an insufficient resolution (usually km-level) to capture the spatial distribution of the ASM, greatly limits the availability of existing vegetation maps in permafrost modeling of the Qinghai-Tibet Plateau (QTP). This study generated a map of the vegetation type at a spatial resolution of 30 m on the central QTP. The random forest (RF) classification approach was employed to map the vegetation based on 319 ground-truth samples, combined with a set of input variables derived from the visible, infrared, and thermal Landsat-8 images. Validation using a train-test split (i.e., 70% of the samples were randomly selected to train the RF model, while the remaining 30% were used for validation and a total of 1000 runs) showed that the average overall accuracy and Kappa coefficient of the RF approach were 0.78 (0.68–0.85) and 0.69 (0.64–0.74), respectively. The confusion matrix showed that the overall accuracy and Kappa coefficient of the predicted vegetation map reached 0.848 (0.844–0.852) and 0.790 (0.785–0.796), respectively. The user accuracies for the ASM, alpine meadow, alpine steppe, and alpine desert were 95.0%, 83.3%, 82.4%, and 86.7%, respectively. The most important variables for vegetation type prediction were two vegetation indices, i.e., NDVI and EVI. The surface reflectance of visible and shortwave infrared bands showed a secondary contribution, and the brightness temperature and the surface temperature of the thermal infrared bands showed little contribution. The dominant vegetation in the study area is alpine steppe and alpine desert. The results of this study can provide an accurate and detailed vegetation map, especially for the distribution of the ASM, which can help to improve further permafrost studies.

Distinguishing Stoichiometric Homeostasis of Soil Microbial Biomass in Alpine Grassland Ecosystems: Evidence From 5,000 km Belt Transect Across Qinghai–Tibet Plateau

The biogeographic characteristics of soil microbial biomass stoichiometry homeostasis and also its mechanisms are commonly thought to be key factors for the survival strategies and resource utilization of soil microbes under extreme habitat. In this work, we conducted a 5,000-km transect filed survey in alpine grassland across Qinghai–Tibet Plateau in 2015 to measure soil microbial biomass carbon (MBC) and nitrogen (MBN) across alpine steppe and meadow. Based on the differences of climate and soil conditions between alpine steppe and meadow, the variation coefficient was calculated to investigate the homeostatic degree of MBC to MBN. Furthermore, the “trade-off” model was utilized to deeply distinguish the homeostasis degree of MBC/MBN between alpine steppe and meadow, and the regression analysis was used to explore the variability of trade-off in response to environmental factors in the alpine grassland. The results showed that the coefficient of variation (CV) of MBC/MBN in alpine meadow (CV = 0.4) was lower than alpine steppe (CV = 0.7). According to the trade-off model, microbial turnover activity of soil N relative to soil C increased rapidly and then decreased slightly with soil organic carbon (SOC), soil total nitrogen (STN), and soil water content across alpine meadow. Nevertheless, in alpine steppe, SOC/STN had a positive effect on microbial turnover of soil N. These results suggested that water, heat, and soil nutrients availability were the key factors affecting the C:N stoichiometry homeostasis of soil microbial biomass in Qinghai–Tibet Plateau (QTP)’s alpine grassland. Since the difference of survival strategy of the trade-off demands between soil C and N resulting in different patterns and mechanism, the stoichiometry homeostasis of soil microbial biomass was more stable in alpine meadow than in alpine steppe.

Tradeoff in the supply and demand for CO 2 dominates the divergence of net photosynthesis rates of functional plants in alpine ecosystems

As regional heterogeneity on the Qinghai Tibetan Plateau (QTP), the “greening rate” between alpine steppe in the west and alpine meadow ecosystems in the east is difference during the past several decades. To investigate the difference, the net photosynthetic rate (An) and the supply (mesophyll conductance ( g), stomatal conductance ( g)) and demand (the maximum rates of Rubisco carboxylase activity ( V) and photosynthetic electron transport ( J)) for CO of three plants functional types (PFTs) were measured. Other functional traits and influencing factors were compared among ecosystems along the altitudinal gradients of QTP. The An of the PFTs was simulated under potential future conditions. At high altitudes, grass was found to maintain a relatively stable An by decreasing V, J, and g, while slightly increasing g, compared with that at a low altitude. The An of sedge and shrubs increased with rising V, J and g and g values, resulting in a large increment in the An at low altitudes. Grass seemed to be less sensitive to the environment by reducing the supply of and holding onto CO , while sedge and shrub increased both. Grass and sedge should be divided into two PFTs rather than remaining as one based on their opposite physiological and morphological functions in response to climate change. The ecosystem at 3600 m was transitional. C was likely to be a more dominant factor than T in affecting the An of grass. The order of rising An in PFTs was shrub > sedge > grass and the An of alpine meadow was found to increase more under the two future climate scenarios.

Effects of Rainfall Manipulation on Ecosystem Respiration and Soil Respiration in an Alpine Steppe in Northern Tibet Plateau

The response mechanism of ecosystem respiration (Re) and soil respiration (Rs) to different water conditions is of great significance for understanding the carbon cycle under future changes in the precipitation patterns. We used seven precipitation treatments to investigate the effects of precipitation on Re and Rs on a typical alpine steppe in Northern Tibet. Precipitation was captured and relocated to simulate the precipitation rates of −25, −50, −75, 0 (CK), +25, +50, and +75%. The soil moisture was influenced by all the precipitation treatments. There was a positive linear relationship between the soil moisture and Re, Rs in the study area during the experiment (July–October). Soil volumetric water content (VWC), absolute water content (AWC), soil temperature (ST), aboveground biomass (AGB), bulk density, soil total nitrogen (TN), and alkaline hydrolysis nitrogen (AHN) were the predictors of Re and Rs. The multiple linear regression analysis showed that ST and AWC could explain 90.6% of Rs, and ST, AWC, and AHN could explain 89.4% of Re. Ecosystem respiration was more sensitive to the increased precipitation (+29.5%) whereas Rs was more sensitive to the decreased precipitation (−23.8%). An appropriate increase in water (+25 and +50%) could improve the Re and Rs, but a greater increase (+75%) would not have a significant effect; it could have an effect even lower than those of the first two. Our study highlights the importance of increased precipitation and the disadvantage of decreased precipitation on Re and Rs in an arid region. The precipitation changes will lead to significant changes in the soil properties and AGB, and affect Re and Rs, to change the climate of the alpine steppe in Northern Tibet in the future. These findings contribute to our understanding of the regional patterns of environmental C exchange and soil C flux under the climate change scenarios and highlight the importance of water availability to the regulating ecosystem processes in semi-arid steppe ecosystems. In view of these findings, we urge future researchers to focus on manipulating the precipitation over longer time scales, seasonality, and incorporating more environmental factors to improve our ability to predict and model Re and Rs and feedback from climate change.