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.

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.

The asymmetric response of productivity to precipitation was recently proposed as an early warning signal for the shifts in temperate grassland function in China. It was hypothesized that the asymmetry was influenced by the increased growth of the newly emerged seedlings from the soil seed bank. Therefore, the seed density in the soil seed bank and the similarity between species composition of the vegetation and the soil seed bank should be maximized where asymmetry was maximized. However, this knowledge was still limited and unconfirmed. In this study, the desert steppe, typical steppe and the transition zone between them (with the highest asymmetry) were selected for studying the similarity index in both 2018 (dry year) and 2019 (wet year). Plant species composition was monitored in situ using an unmanned aerial vehicle. Soil seed bank samples were collected, and the seed bank density and species composition were then examined and identified in the laboratory. Results showed that: (1) The variation in vegetation species richness between the two years was the highest (41%) in the transition zone (p < 0.05), while it was only 7% and 13% for the desert steppe and typical steppe, respectively. The presence of herbaceous species mainly caused the differences in variation among three grassland types. (2) Seed density was the highest in the transition zone (114 seeds/m² and 68 seeds/m² in the transient and persistent soil seed bank, respectively) (p < 0.05). Additionally, herbaceous species were the main components of the soil seed bank. (3) The similarity index was the highest in the transition zone (p < 0.05), with 38%/44% and 33%/44% for the transient/persistent soil seed bank in 2018 and 2019, respectively. Our study demonstrated that variation in vegetation species composition was very similar to the composition of the seeds accumulated in the soil seed bank. These results warrant further investigation for the mechanism of asymmetric response of productivity to precipitation.