Effective and accurate assessment of grassland above-ground biomass (AGB) especially via remote sensing (RS), is crucial for forage-livestock balance and ecological environment protection of alpine grasslands. Because of complexity and extensive spatial distribution of natural grassland resources, the RS estimation models based on moderate resolution imaging spectroradiometer (MODIS) data exhibited low accuracy and poor stability. In this study, various methods for estimating the AGB of alpine grassland vegetation using MODIS vegetation indices were evaluated by combining with meteorology, soil, topography geography and in situ measured AGB data (during grassland growing season from 2011 to 2016) in Gannan region. Results show that (1) five out of ten factors (elevation, slope, aspect, topographic position, temperature, precipitation and the concentration of clay and sand in the soil) exert significant effects on grassland AGB, with R2 0.04~0.39, and RMSE 859.68 kg/ha ~ 1075.09 kg/ha, respectively; (2) the accuracy and stability of AGB estimation model can be improved by constructing multivariate models, especially using multivariate non-parameter models; (3) the optimum estimation model is constructed on the basis of random forest algorithm (RF). Compared with univariate/multivariate parameter models, RMSE of RF model decreased 26.45 % - 44.27 %. Meanwhile, RF models can explain 89.41 % variation in AGB . This study presented a more suitable RS inversion model integrated MODIS vegetation indices and other effect factors. Besides, the accuracy of alpine grassland AGB estimation based on MODIS data were greatly improved. Thus, our study provides a scientific basis for effective and accurate estimating alpine grassland AGB.

Although unmanned aerial vehicles (UAVs) have been utilized in many aspects of steppe management, they have not been commonly used to monitor the soil moisture of steppes. To explore the technology of detecting soil moisture by UAV in a typical steppe, we conducted a watered test in the Loess Plateau of China, quantitatively revealing the relationship between the surface soil moisture and the visible images captured using an UAV. The results showed that the surface soil moisture was significantly correlated with the brightness of UAV visible images, and the surface soil moisture could be estimated based on the brightness of the visible images of the UAV combined with vegetation coverage. This study addresses the problem of soil moisture measurement in flat regions of arid and semi-arid steppes at the mesoscale, and contributes to the popularization of the use of UAVs in steppe ecological research.

Net primary productivity (NPP) and precipitation-use efficiency (PUE) are crucial indicators in understanding the responses of vegetation to global change. However, the relative contributions of climate change and human interference to the dynamics of NPP and PUE remain unclear. During the past few decades, the impacts of climate change and human activities on alpine grasslands on the Qinghai-Tibet Plateau (QTP) have been intensifying. The aims of the study were to investigate the spatiotemporal patterns of grassland NPP and PUE on the QTP during 2000–2017 and quantify how much of the variance in NPP and PUE can be attributed to the climatic factors (precipitation and temperature) and grazing intensity. The results showed that: (1) grassland NPP significantly increased with a rate of 0.6 g C m⁻² year⁻¹ over the past 18 years, mainly induced by the increased temperature and the enhanced precipitation. The temperature was the dominant factor for NPP interannual variation in mid-eastern QTP, and precipitation restrained vegetation growth most in the southwest and northeast. (2) The PUE was higher on the eastern and western parts of the plateau, but lower at the center. Regarding grassland types, the PUE of alpine steppe (0.19 g C m⁻² mm⁻¹) was significantly lower than those of alpine meadow (0.31 g C m⁻² mm⁻¹) and desert steppe (0.32 g C m⁻² mm⁻¹). (3) Precipitation was significantly and negatively correlated with PUE and contributed the most to the temporal variation of grassland PUE on the QTP (52.7%). (4) Furthermore, we found that the grazing activities had the lowest contributions to both NPP and PUE interannual variation, compared to temperature and precipitation. Thus, it is suggested that climate variability rather than grazing activities dominated vegetation changes on the QTP.

Surface soil moisture (SSM) is a key limiting factor for vegetation growth in alpine meadow on the Qinghai-Tibetan Plateau (QTP). Patches with various sizes and types may cause the redistribution of SSM by changing soil hydrological processes, and then trigger or accelerate alpine grassland degradation. Therefore, it is vital to understand the effects of patchiness on SSM at multi-scales to provide a reference for alpine grassland restoration. However, there is a lack of direct observational evidence concerning the role of the size and type of patches on SSM, and little is known about the effects of patches pattern on SSM at plot scale. Here, we first measured SSM of typical patches with different sizes and types at patch scale and investigated their patterns and SSM spatial distribution through unmanned aerial vehicle (UAV)-mounted multi-type cameras at plot scale. We then analyzed the role of the size and type of patchiness on SSM at both patch and plot scales. Results showed that: (1) in situ measured SSM of typical patches was significantly different (P < 0.01), original vegetation patch (OV) had the highest SSM, followed by isolate vegetation patch (IV), small bare patch (SP), medium bare patch (MP) and large bare patch (LP); (2) the proposed method based on UAV images was able to estimate SSM (0–40 cm) with a satisfactory accuracy (R² = 0.89, P < 0.001); (3) all landscape indices of OV, with the exception of patch density, were positively correlated with SSM at plot scale, while most of the landscape indices of LP and IV showed negative correlations (P < 0.05). Our results indicated that patchiness intensified the spatial heterogeneity of SSM and potentially accelerated the alpine meadow degradation. Preventing the development of OV into IV and the expansion of LP is a critical task for alpine meadow management and restoration.

Plateau pika (Ochotona curzoniae, hereafter pika) is considered to exert a profound impact on vegetation species diversity of alpine grasslands. Great efforts have been made at mound or quadrat scales; nevertheless, there is still controversy about the effect of pika. It is vital to monitor vegetation species composition in natural heterogeneous ecosystems at a large scale to accurately evaluate the real role of pika. In this study, we performed field survey at 55 alpine grassland sites across the Shule River Basin using combined methods of aerial photographing using an unmanned aerial vehicle (UAV) and traditional ground measurement. Based on our UAV operation system, Fragmentation Monitoring and Analysis with aerial Photography (FragMAP), aerial images were acquired. Plot-scale vegetation species were visually identified, and total pika burrow exits were automatically retrieved using the self-developed image processing software. We found that there were significant linear relationships between the vegetation species diversity indexes obtained by these two methods. Additionally, the total number of identified species by the UAV method was 71, which was higher than the Quadrat method recognition, with the quantity of 63. Our results indicate that the UAV was suitable for long-term repeated monitoring vegetation species composition of multiple alpine grasslands at plot scale. With the merits of UAV, it confirmed that pika’s disturbance belonged to the medium level, with the density ranging from 30.17 to 65.53 ha⁻¹. Under this density level, pika had a positive effect on vegetation species diversity, particularly for the species richness of sedge and forb. These findings conclude that the UAV was an efficient and economic tool for species monitoring to reveal the role of pika in the alpine grasslands.

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.