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.

Patchiness is known to affect soil water and soil temperature, and may trigger or accelerate alpine meadow degradation. However, there is a lack of direct evidence concerning the role of the size and type of patches on soil conditions. Here, we estimated the effects of typical patch types on the critical factors of soil water and soil temperature through an in situ survey and then analyzed the potential impacts of patchiness on alpine meadow degradation. The results showed that (1) the soil water storage (SWS) of typical patch types was remarkably different, and vegetation patches had higher SWS than bare patches; (2) with abundant precipitation, the isolated vegetation patch (IV) had higher SWS than the original vegetation patch (OV), accompanied by a decrease in the SWS of the surrounding medium bare patch (MP); (3) patchiness significantly altered the surface soil temperature, with the IV having the highest soil temperature (ST), followed by the OV and bare patches; and (4) the maximum mean value of evapotranspiration was observed in IV (3.85 mm/day), about 12.78% and 46.66% higher than in the bare patches and OV. Our findings indicated that patchiness intensified the heterogeneity of soil water and soil temperature. The IV could absorb soil water from surrounding MP for excessive evapotranspiration through constant high ST, potentially inducing or aggravating degradation. Increasing the connectivity of IV and preventing the transition of OV to IV is important for the stability and restoration of alpine meadow.

The cryosphere in the Himalaya-Karakoram (H-K) is widespread, and its services significantly affect the SDGs implementation in the region, in particular related to the ‘No poverty’ (SDG 1), ‘zero hunger’ (SDG 2), ‘good health and well-being’ (SDG 3), ‘work and economic’ (SDG 8) and ‘partnership for the goals’ (SDG 17). We here established the networks to illustrate the complex relationship of cryosphere services with national SDG priorities in the countries of H-K, including Afghanistan, Pakistan, India, China, Nepal and Bhutan. The cryosphere services contributing to the national SDG priorities and the key targets were elucidated in line with the centralities of the network. It was found that ‘freshwater’, ‘clean energy’, ‘runoff regulation’, ‘climate regulation’, ‘research and education’ and ‘infrastructure and engineering’ are the services that play critical roles in H-K, and they were then applied to assess the impact of cryosphere services on the national SDG priorities. We subsequently identified a set of principal indicators in relation to the key targets of national SDG priorities, which has the explanation up to 85% of six entry points (SEPs) to advance SDGs of each country in H-K. In conjunction with the centrality of the key targets to be contributed by the overall cryosphere services in the network for each country, the dependency of SEPs on the cryosphere services can be established through principal indicators in association with the national SDG priorities in H-K countries.

On-year and off-year phenomena are common in Moso bamboo forests and significantly affect economic value and ecological functions. However, observational evidence regarding the impact of these cycles on surface soil moisture (SSM) remains scarce, and little is known about the implications of their landscape patterns for regional water conservation. Here, we first quantified the spatial distribution and temperature vegetation drought index (TVDI) of on-year and off-year Moso bamboo forests based on remote sensing images and landscape metrics. We then analyzed the role of on-year and off-year phenomena and their landscape patterns on SSM. Results showed that: (1) the proposed index derived from remote sensing imagery extracted on-year and off-year Moso bamboo forests with satisfactory accuracy, and the areas were 161.4 km² and 173.5 km², respectively; (2) a significant disparity was observed in the TVDI between on-year and off-year Moso bamboo forests, and mismatched growth stages and phenological characteristics were identified as primary influencing factors; and the (3) landscape metrics of the perimeter–area ratio (PAR), proximity index (PROX), perimeter–area fractal dimension index (PAFRAC), connectance index (CONNECT), and aggregation index (AI) exhibited negative correlations with the TDVI, indicating that the high spatial connectivity of Moso bamboo forests enhances soil water conservation. Our findings suggested that on-year and off-year phenomena and their spatial distribution intensified the heterogeneity in SSM. Therefore, considerations regarding the connectivity and edge complexity within Moso bamboo forests should be prioritized in future management strategies to achieve a balance between economic benefits and ecological functions in water-deficient mountainous areas.