Alpine Meadow Grassland Research Articles

Modern pollen assemblages in lake surface sediments and their relationships with vegetation, climate, and human activities in Yunnan, SW China

Modern pollen assemblages from moss polsters, topsoils, and lake surface sediments are crucial for interpreting palaeovegetational and palaeoclimatic conditions from fossil pollen records. While a large number of modern pollen assemblages exist from Yunnan Province, SW China, few are derived from lake surface sediments with depositional environments similar to those of fossil pollen records. In this study, we present modern pollen assemblages from lake surface sediments across 36 lakes in Yunnan, spanning spatially from the southeast to the northwest of the region. These lakes encompass a range of vegetation types, varying from alpine meadow grasslands to tropical seasonal and montane rainforests. Our findings demonstrate that modern pollen assemblages from lake surface sediments can effectively identify various vegetation zones. Redundancy analysis (RDA) reveals a strong correlation of pollen assemblages with climate factors (e.g., temperature and precipitation) but a weak correlation with human activities. This study suggests that modern pollen assemblages from lake surface sediments in Yunnan can be used not only to reflect changes in vegetation and climate, but also as reliable indicators for reconstructing the history of human activities to some extent.

Alpine Meadow Degradation Changed Soil Nitrogen Characteristics in the Eastern Qilian Mountains Area, Northwest China

ABSTRACT Nitrogen is the crucially sustainable element for most ecosystem including grassland. Fenced alpine meadow grassland (FG), lightly, moderately and heavily degraded (LD, MD and HD) alpine meadow in spring, summer and winter were studied to clear the effects of alpine meadow degradation on soil total nitrogen (TN), available nitrogen (AN), ammonium nitrogen (NH4 +-N), nitrate nitrogen (NO3 –-N), inorganic nitrogen (IN) content and the ratio of inorganic nitrogen to available nitrogen (IN/AN), and to find practical management strategies on the background of alpine meadow degradation and ecological environment restoration in the Eastern Qilian Mountains area, northwest China. The results showed that soil total nitrogen contents of the alpine meadow types were at a low level, ranged from 1.60 g·kg−1 to 5.15 g·kg−1, but soil available nitrogen contents was at a high level, ranged from 373.2 mg·kg−1 to 650.8 mg·kg−1in the study area. Alpine meadow degradation can seasonally change soil nitrogen status. Degraded alpine meadow had more inorganic nitrogen than fenced meadow in surface soil. Degradation only changed soil NO3 −-N profile distribution in winter and had no significant effect on profile distribution of TN, AN, NH4 +-N and IN. There were significant interactions of alpine meadow types, seasons and soil depth on soil available nitrogen, NH4 +-N, NO3 –-N and inorganic nitrogen content. Although soil available nitrogen increased under degradation, nitrogen input is needed in the restoration of heavily degraded alpine meadow during the summer growing season to improve vegetation coverage because of the low soil inorganic nitrogen and IN/AN ratio in summer growing season in 10–20 cm and 20–30 cm depth.

A transformer-based image detection method for grassland situation of alpine meadows

As a vital role in climate regulation, water conservation, and maintenance of ecological balance, the alpine meadow grassland is facing the threat of degradation. Detecting grassland topography, phytomass, and grassland damage are important for improving the alpine meadow situation. This study reports a Transformer-CNN method for detecting alpine meadows situations using UnmannedAerial Vehicle (UAV) - based RGB (Red, Green, and Blue) data. This method combines Oriented FAST and Rotated BRIEF (ORB) and brute force feature matching to complete image stitching and then uses the proposed model Am-mask to complete the image segmentation task. The result shows that ORB feature matching is more stable and fast than SIFT and SURF for alpine meadow image stitching. In addition, Transformer has great application potential in grassland image detection and introducing task prefix and sparse in pre-training enhances the model’s robustness. The AP value of the Am-mask model with Transformer was as high as 95.4%, about 10% higher than that of the original CNN models. In the experiment with unstitched images, the average precision of the eight trials was 95.16%, the average recall was 95.13%, and the average F1 value was 95.14%. For stitched images, the average precision, recall, and F1 value of the eight trials were 91.83%, 91.81%, and 91.82%, respectively. It was proved that the proposed method could save the inference cost of the model under the condition of ensuring the detection effect. This study may contribute to grassland environmental protection in alpine meadows.

Human activities dominant the distribution of Kobresia pygmaea community in alpine meadow grassland in the east source region of Yellow River, China

Kobresia pygmaea is the endemic and one of the most important species in the alpine meadow in the Qinghai-Tibet Plateau. It is the key stage in the management of degraded grassland, and irreversible degradation will take place after the degradation succession phases of the Kobresia pygmaea community. However, knowledge about the spatial distribution and driving factors were still unknown. In this study, the potential distribution of the Kobresia pygmaea community was determined using the BIOMOD niche model. Combine with the reality distribution based on remote sensing classification, the driving factors of climate and human activities were identified. The findings revealed that: (1) among all environmental factors, the maximum radiation, monthly temperature difference, driest period precipitation were the main climate influencing factors for the Kobresia pygmaea community distribution, and random forest model achieved the highest prediction accuracy and best stability of any niche model. (2) The potential distribution area of Kobresia pygmaea community was 653.25 km2 (account for 3.28% of the study area), and mostly located in northern and central of Zeku County, northeast of Henan County, and northeast, central, and eastern parts of Maqu County. (3) Climate factors driven 21.12% of Kobresia pygmaea community reality distribution, while human activities driven for 79.98%. Our results revealed that human activities dominant the reality distribution of Kobresia pygmaea community in alpine meadow grassland in the east source region of Yellow River, China.

Spatial variability of soil organic carbon fractions and aggregate stability along an elevation gradient in the alpine meadow grasslands of the Qilian Mountains, China

Spatial variability of soil organic carbon fractions and aggregate stability along an elevation gradient in the alpine meadow grasslands of the Qilian Mountains, China

Nematode communities indicate anthropogenic alterations to soil dynamics across diverse grasslands

Nematode communities are meaningful biological indicators of soil health and soil processes across different grassland types and management practices and analyses of nematode communities provide insight into structure, function, and sensitivity or resilience across multiple ecosystems. In three model grasslands: meadow steppe (MS), typical steppe (TS), and alpine meadow (AM), this current research examined responses of soil nematode communities and related edaphic characteristics to grazing, mowing, and crop cultivation at two soil depths. The research fills a critical knowledge gap by resolving multidirectional influences between local conditions, grassland management practices, and nematode communities. Across grassland types, nematode abundance in AM was greater than MS and TS grasslands, and nematodes were more abundant near the soil surface. Cultivation resulted in greater nematode abundance compared to all other management practices, and generally, bacterivores were the most dominant nematode trophic group. The TS and MS grasslands had relatively more bacterivores, exhibiting substantial influences on soil mineralization and organic matter decomposition pathways. The AM grassland showed relatively more plant feeding nematodes, driving soil mineralization pathways. Among the three management practices, crop cultivation had the greatest impact on nematode community structure and the soil environment, especially in relatively sensitive AM grasslands. In fact, AM soil environments responded most dramatically to cultivation, with nematode abundance, soil quality, and food web complexity increasing. However, soil ecosystem stability, food web reliance, and food web response to resources decreased in cultivated AM soils. Results indicate that unique environmental characteristics in the Qinghai-Tibet plateau drive substantially different AM grassland nematode community structure and soil conditions compared to TS or MS grasslands. As anthropogenic pressures on these ecosystems mount, it is critical to understand how different management practices influence grassland nematode communities, with cascading effects through soil environments.

Plant community change mediated heterotrophic respiration increase explains soil organic carbon loss before moderate degradation of alpine meadow

AbstractAlpine meadows on the Qinghai‐Tibet Plateau (QTP) store a huge amount of plant and soil carbon (C). The degradation of the alpine meadow has led to a significant loss of soil organic C (SOC), which endangers its weak C sink. The change in heterotrophic respiration was hypothesized as one of the primary biological processes for the SOC loss alongside the degradation of alpine meadow. However, little is known about how land degradation impacts Rh due to changes in plant communities and consequent labile organic C inputs. Rh, vegetation productivity, plant community composition, plant lignin concentration, leaf C/N, SOC, and soil labile C (SLC) were measured for alpine meadow grasslands with five degradation levels in 2 years. Our results showed that the SOC of the surface layer (0–10 cm) gradually decreased with land degradation. The plant lignin concentration and C/N ratio gradually increased with land degradation due to the proportion of sedges decreased while that of forbs increased, which consequently led to the soil C quality (SLC) increased before the moderate degradation level (MD) while significantly reduced in severe and very severe degradation levels (SD and VSD). The structural equation modeling result (SEM) revealed that the degradation‐induced change in soil C quality (SLC) due to an alteration in plant community composition relates to the Rh change with grassland degradation, and the Rh (C output) and aboveground biomass (AGB: C input) further mediated the loss of SOC alongside alpine meadow degradation. On average, Rh increased by 49% and 59% from the Intact to slight degradation level (SLD) and MD and reduced by 56% and 69% from MD to SD and VSD. The temperature sensitivity of Rh (Q10) was higher in the SLD (Q10 = 2.84) and MD (Q10 = 2.62) comparing with the Intact (Q10 = 2.58) respectively, but it was lower in SD (Q10 = 2.19) and VSD (Q10 = 1.65). AGB and belowground net primary productivity (BNPP) had no significant change until the MD, while reduced by 60%–90% in SD and VSD. Our results suggest that Rh and its Q10 change moderated by soil C quality alteration due to the plant community shifting could be one mechanism for the soil C loss before MD, but could not be the main pathway for the SOC loss at SD and VSD.

Mapping of Kobresia pygmaea Community Based on Umanned Aerial Vehicle Technology and Gaofen Remote Sensing Data in Alpine Meadow Grassland: A Case Study in Eastern of Qinghai–Tibetan Plateau

The Kobresia pygmaea (KP) community is a key succession stage of alpine meadow degradation on the Qinghai–Tibet Plateau (QTP). However, most of the grassland classification and mapping studies have been performed at the grassland type level. The spatial distribution and impact factors of KP on the QTP are still unclear. In this study, field measurements of the grassland vegetation community in the eastern part of the QTP (Counties of Zeku, Henan and Maqu) from 2015 to 2019 were acquired using unmanned aerial vehicle (UAV) technology. The machine learning algorithms for grassland vegetation community classification were constructed by combining Gaofen satellite images and topographic indices. Then, the spatial distribution of KP community was mapped. The results showed that: (1) For all field observed sites, the alpine meadow vegetation communities demonstrated a considerable spatial heterogeneity. The traditional classification methods can hardly distinguish those communities due to the high similarity of their spectral characteristics. (2) The random forest method based on the combination of satellite vegetation indices, texture feature and topographic indices exhibited the best performance in three counties, with overall accuracy and Kappa coefficient ranged from 74.06% to 83.92% and 0.65 to 0.80, respectively. (3) As a whole, the area of KP community reached 1434.07 km2, and accounted for 7.20% of the study area. We concluded that the combination of satellite remote sensing, UAV surveying and machine learning can be used for KP classification and mapping at community level.

Effect of plateau pika disturbance and patchiness on ecosystem carbon emissions in alpine meadow in the northeastern part of Qinghai–Tibetan Plateau

Abstract. Plateau pika (Ochotona curzoniae) disturbance and patchiness intensify the spatial heterogeneous distribution of vegetation productivity and soil physicochemical properties, which may alter the ecosystem carbon emission process. Nevertheless, previous research has mostly focused on the homogeneous vegetation patches rather than heterogeneous land surface. Thus, this study aims to improve our understanding of the difference in ecosystem respiration (Re) over heterogeneous land surface in an alpine meadow grassland. Six different land surface types, namely large bald patches, medium bald patches, small bald patches, intact grassland, above pika tunnel and pika pile, were selected to analyze the response of Re to pika disturbance and patchiness and the key controlling factors. The results showed that (1) Re in intact grassland was 0.22–1.07 times higher than pika pile and bald patches, (2) soil moisture (SM) of intact grassland was 2 %–11 % higher than that of pika pile and bald patches, despite the fact that pika disturbance increased the water infiltration rate while soil temperature (ST) in intact grassland was 1–3∘ less than pika pile and bald patches, (3) soil organic carbon (SOC) and total nitrogen (TN) in intact grassland were approximately 50 % and 60 % less than above pika tunnel, whereas they were 10 %–30 % and 22 %–110 % higher than pika pile and bald patches, and (4) Re was significantly correlated with SM, TN and vegetation biomass (P<0.05). Our results suggested that pika disturbance and patchiness altered the ecosystem carbon emission pattern, which was mainly attributed to the reduction in soil water and supply of substrates. Given the wide distribution of pikas and the large area of bald patches, the varied Re in heterogeneous land surfaces should not be neglected in the estimation of ecosystem carbon emissions at the plot or regional scale.

Modeling of Alpine Grassland Cover Based on Unmanned Aerial Vehicle Technology and Multi-Factor Methods: A Case Study in the East of Tibetan Plateau, China

Grassland cover and its temporal changes are key parameters in the estimation and monitoring of ecosystems and their functions, especially via remote sensing. However, the most suitable model for estimating grassland cover and the differences between models has rarely been studied in alpine meadow grasslands. In this study, field measurements of grassland cover in Gannan Prefecture, from 2014 to 2016, were acquired using unmanned aerial vehicle (UAV) technology. Single-factor parametric and multi-factor parametric/non-parametric cover inversion models were then constructed based on 14 factors related to grassland cover, and the dynamic variation of the annual maximum cover was analyzed. The results show that (1) nine out of 14 factors (longitude, latitude, elevation, the concentrations of clay and sand in the surface and bottom soils, temperature, precipitation, enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI)) exert a significant effect on grassland cover in the study area. The logarithmic model based on EVI presents the best performance, with an R2 and RMSE of 0.52 and 16.96%, respectively. Single-factor grassland cover inversion models account for only 1–49% of the variation in cover during the growth season. (2) The optimum grassland cover inversion model is the artificial neural network (BP-ANN), with an R2 and RMSE of 0.72 and 13.38%, and SDs of 0.062% and 1.615%, respectively. Both the accuracy and the stability of the BP-ANN model are higher than those of the single-factor parametric models and multi-factor parametric/non-parametric models. (3) The annual maximum cover in Gannan Prefecture presents an increasing trend over 60.60% of the entire study area, while 36.54% is presently stable and 2.86% exhibits a decreasing trend.

Annual N2O emissions from conventionally grazed typical alpine grass meadows in the eastern Qinghai–Tibetan Plateau

Annual nitrous oxide (N2O) emissions from high-altitude alpine meadow grasslands have not been effectively characterized because of the scarcity of whole-year measurements. The authors performed a year-round measurement of N2O fluxes from three conventionally grazed alpine meadows that represent the typical meadow landscape in the eastern Qinghai–Tibetan Plateau (QTP). The results showed that annual N2O emissions averaged 0.123±0.053 (2SD, i.e., the double standard deviation indicating the 95% confidence interval) kgNha−1yr−1 across the three meadow sites. N2O flux pulses during the spring freezing-thawing period (FTP) were observed at only one site, indicating a large spatial variability in association with soil moisture differences. Approximately 34–57% (mean: 46%) of the annual N2O emissions occurred in the non-growing season, highlighting the substantial importance of accurate flux observations during this period. The simultaneous observations showed conservative, marginal nitric oxide (NO) fluxes of 0.058±0.032 (2SD) kgNha−1yr−1. The N2O fluxes across the three field sites correlated negatively with the soil nitrate concentrations during the entire year-round period (P<0.05). Furthermore, a significant joint regulatory effect of topsoil temperature and moisture on the N2O and NO fluxes was observed during the relatively warm periods. Based on the results of the present and previous studies, a simple extrapolation roughly estimated the annual total N2O emission from Chinese grasslands to be 73±15 (2SD) GgNyr−1 (1Gg=109g). A linear dependence of the annual N2O fluxes on the aboveground net primary productivity (ANPP) was also found. This result may provide a simple approach for estimating the N2O emission inventories of frigid alpine or temperate grasslands that are ungrazed either in the summer or year round. However, further confirmation of this relationship with a wider ANPP range is still needed in the future studies.

Experimental Warming Aggravates Degradation‐Induced Topsoil Drought in Alpine Meadows of the Qinghai–Tibetan Plateau

AbstractClimatic warming is presumed to cause topsoil drought by increasing evapotranspiration and water infiltration, and by progressively inducing land degradation in alpine meadows of the Qinghai–Tibetan Plateau. However, how soil moisture and temperature patterns of degraded alpine meadows respond to climate warming remains unclear. A 6‐year continuous warming experiment was carried out in both degraded and undegraded alpine meadows in the source region of the Yangtze River. The goal was to identify the effects of climatic warming and land degradation on soil moisture (θ), soil surface temperature (Tsfc), and soil temperature (Ts). In the present study, land degradation significantly reduced θ by 4·5–6·1% at a depth of 0–100 cm (p &lt; 0·001) and increased the annual mean Tsfc by 0·8 °C. Warming with an infrared heater (radiation output of 150 W m−2) significantly increased the annual mean Tsfc by 2·5 °C (p &lt; 0·001) and significantly increased θ by 4·7% at a depth of 40–60 cm. Experimental warming in degraded land reversed the positive effects of the infrared heater and caused the yearly average θ to decrease significantly by 3·7–8·1% at a depth of 0–100 cm. Our research reveals that land degradation caused a significant water deficit near the soil surface. Experimental warming aggravated topsoil drought caused by land degradation, intensified the magnitude of degradation, and caused a positive feedback in the degraded alpine meadow ecosystem. Therefore, an immediate need exists to restore degraded alpine meadow grasslands in the Qinghai–Tibetan Plateau in anticipation of a warmer future. Copyright © 2017 John Wiley &amp; Sons, Ltd.

Evaluation of Remote Sensing Inversion Error for the Above-Ground Biomass of Alpine Meadow Grassland Based on Multi-Source Satellite Data

It is not yet clear whether there is any difference in using remote sensing data of different spatial resolutions and filtering methods to improve the above-ground biomass (AGB) estimation accuracy of alpine meadow grassland. In this study, field measurements of AGB and spectral data at Sangke Town, Gansu Province, China, in three years (2013–2015) are combined to construct AGB estimation models of alpine meadow grassland based on these different remotely-sensed NDVI data: MODIS, HJ-1B CCD of China and Landsat 8 OLI (denoted as NDVIMOD, NDVICCD and NDVIOLI, respectively). This study aims to investigate the estimation errors of AGB from the three satellite sensors, to examine the influence of different filtering methods on MODIS NDVI for the estimation accuracy of AGB and to evaluate the feasibility of large-scale models applied to a small area. The results showed that: (1) filtering the MODIS NDVI using the Savitzky–Golay (SG), logistic and Gaussian approaches can reduce the AGB estimation error; in particular, the SG method performs the best, with the smallest errors at both the sample plot scale (250 m × 250 m) and the entire study area (33.9% and 34.9%, respectively); (2) the optimum estimation model of grassland AGB in the study area is the exponential model based on NDVIOLI, with estimation errors of 29.1% and 30.7% at the sample plot and the study area scales, respectively; and (3) the estimation errors of grassland AGB models previously constructed at different spatial scales (the Tibetan Plateau, Gannan Prefecture and Xiahe County) are higher than those directly constructed based on the small area of this study by 11.9%–36.4% and 5.3%–29.6% at the sample plot and study area scales, respectively. This study presents an improved monitoring algorithm of alpine natural grassland AGB estimation and provides a clear direction for future improvement of the grassland AGB estimation and grassland productivity from remote sensing technology.

Evaluation of Above Ground Biomass Estimation Accuracy for Alpine Meadow Based on MODIS Vegetation Indices

Animal husbandry is the main agricultural type over the Tibetan Plateau, above ground biomass (AGB) is very important to monitor the productivity for administration of grassland resources and grazing balance. The MODIS vegetation indices have been successfully used in numerous studies on grassland AGB estimation in the Tibetan Plateau area. However, there are considerable differences of AGB estimation models both in the form of the models and the accuracy of estimation. In this study, field measurements of AGB data at Sangke Town, Gansu Province, China in four years (2013-2016) and MODIS indices (NDVI and EVI) are combined to construct AGB estimation models of alpine meadow grassland. The field measured AGB are also used to evaluate feasibility of models developed for large scale in applying to small area. The results show that (1) the differences in biomass were relatively large among the 5 sample areas of alpine meadow grassland in the study area during 2013-2016, with the maximum and minimum biomass values of 3,963 kg DW/ha and 745.5 kg DW/ha, respectively, and mean value of 1,907.7 kg DW/ha; the mean of EVI value range (0.42-0.60) are slightly smaller than the NDVI’s (0.59-0.75); (2) the optimum estimation model of grassland AGB in the study area is the exponential model based on MODIS EVI, with root mean square error of 656.6 kg DW/ha and relative estimation errors (REE) of 36.3%; (3) the estimation errors of grassland AGB models previously constructed at different spatial scales (the Tibetan Plateau, the Gannan Prefecture, and Xiahe County) are higher than those directly constructed based on the small area of this study by 9.5%–31.7%, with the increase of the modeling study area scales, the REE increasing as well. This study presents an improved monitoring algorithm of alpine natural grassland AGB estimation and provides a clear direction for future improvement of the grassland AGB estimation and grassland productivity from remote sensing technology.

CO&lt;SUB&gt;2&lt;/SUB&gt; fluxes and their driving factors over alpine meadow grassland ecosystems in the northern shore of Qinghai Lake, China

CO&lt;SUB&gt;2&lt;/SUB&gt; fluxes and their driving factors over alpine meadow grassland ecosystems in the northern shore of Qinghai Lake, China

Application of the 137Cs tracer technique to study soil erosion of alpine meadows in the headwater region of the Yellow River

The Cs-137 tracer technique was used to study soil erosion of alpine meadow grassland in two small river basins in the headwater region of the Yellow River. The results show that the levels of Cs-137 in soil samples from this alpine meadow vegetation zone exhibit an exponential distribution, generally within a depth of approximately 20 cm. Due to strong winds, freeze-thaw cycles and water, soil erosion was found to be stronger on the upper slope than on the lower slope, and except for the slope crest, the intensity of soil erosion at other sites was as follows: upslope < midslope < downslope. There was a significant negative correlation between the intensity of soil erosion and the extent of alpine meadow vegetation cover (P < 0.01). The mean soil erosion modulus exhibited a linear reduction trend with an increase in vegetation cover, and the correlation coefficient R (2) was a parts per thousand yen 0.997. The higher the degradation degree of the alpine meadow grassland, the greater is the soil erosion. The mean erosion modulus in the severely degraded meadow zone was 2.23 times greater than the one in the slightly degraded zone, and the maximum erosion modulus reached 2.96 x 10(6) kg/km(2)/a.