Xilai Li

Accurately assessing grazing impacts is essential for sustaining alpine grasslands. Conventional approaches often rely on total forage productivity, an indirect and uncertain proxy for forage availability. In this study, we propose a novel framework for estimating grazing pressure that integrates residual biomass with grazing intensity, thereby overcoming the limitations and uncertainties inherent in total forage-based assessments. Our results reveal pronounced spatiotemporal variation in grazing intensity: lowland areas experienced the highest intensity early in the growing season, whereas upland areas became more heavily grazed later in the season. However, grazing intensity alone proved insufficient to explain grazing pressure or predict pasture degradation risk. Overlay analyses demonstrated that only 38.8% of high intensity areas identified as under high grazing pressure, and more than 40% of high intensity area exhibiting substantial aboveground biomass. These findings highlight the limited explanatory power of grazing intensity when considered in isolation. By explicitly incorporating standing biomass rather than relying merely on total production, the proposed framework reduces estimation uncertainty, enhances ecological realism, and provides a scalable, more accurate and practical tool for monitoring grassland utilization and degradation.

In the original publication [1], there were mistakes in affiliations, Tables 2 and 3 as published

Small object detection presents significant challenges in computer vision, often affected by factors such as low resolution, dense object distribution, and complex backgrounds, which can lead to false positives or missed detections. In this paper, we introduce SRM-YOLO, a novel small object detection algorithm based on the YOLOv8 framework. The model incorporates the following key innovations: Reuse Fusion Structure (RFS), which enhances feature fusion; SPD-Conv, which enables effective downsampling while preserving critical information; and a specialized detection head designed for small objects. Additionally, the MPDIoU loss function is employed to improve detection accuracy. Experimental results on the VisDrone2019 dataset show that SRM-YOLO significantly enhances detection accuracy, achieving a 5.2% improvement in mAP50 over YOLOv8n. Additionally, its superior performance on the SSDD and NWPU VHR-10 datasets further validates its effectiveness in small object detection tasks.

The establishment of artificial grasslands is a highly effective strategy for the rapid restoration of degraded grasslands. To investigate the dynamics of biomass production and plant diversity—two critical objectives of grassland restoration—and their relationship during the early stages of artificial grassland establishment, we conducted an experiment in Menyuan County, located in the northeastern region of the Qinghai-Tibet Plateau. The experiment involved sowing different combinations of species (one, three, six, and nine species). Using data collected over three years (2021–2023), we found that biomass production generally increased over time. Specifically, in the second year, biomass production exhibited a unimodal relationship with the number of sown species, while in the third year, it increased linearly with the number of sown species. Plant diversity, which includes both sown and naturally occurring species, initially increased with the number of sown species in the first year but decreased in mixed sown plots in the third year. In the first year, biomass production was not correlated with plant diversity, whereas in the second and third years, biomass production decreased as plant diversity increased. This pattern was primarily driven by the accelerated growth of Gramineae. These results highlight the complex dynamics between biomass production and plant diversity during the early stages of artificial alpine grassland establishment. Our findings suggest that a trade-off between biomass and biodiversity should be carefully considered when designing restoration strategies, as achieving both high productivity and biodiversity may require a more nuanced approach.

This study investigated the effects of plateau pika (Ochotona curzoniae) disturbances and altitude on soil organic carbon (SOC) storage characteristics, including SOC content and SOC density (SOCD). In this study, plateau pika outbreak areas and non-outbreak areas at different altitudes were compared in terms of vegetation biomass, soil physicochemical properties, SOC content and SOCD to establish the relationship between vegetation and soil characteristics (including SOC content and SOCD). The results showed that SOC and SOCD decreased significantly (p < 0.01) in plateau pika outbreak areas, but SOCD increased first and then decreased with elevation in non-outbreak areas. Soil total nitrogen (TN) content decreased significantly (p < 0.01) with elevation in both plateau pika outbreak and non-outbreak areas. There were significant differences (p < 0.05) in total phosphorus (TP) at low elevations and nitrate nitrogen (NO₃⁻-N) at high elevations between outbreak and non-outbreak areas, but other nutrients did not differ hugely between outbreak and non-outbreak areas at the same elevation. Correlation analysis revealed that belowground biomass (BGB) in the plateau pika outbreak area was significantly and positively correlated with SOC (p < 0.01); structural equation modeling (SEM) analysis revealed that altitude had a direct effect on SOC (path coefficient = −0.882, p < 0.001) in the plateau pika outbreak area, but only a reduced influence on SOC and SOCD in the non-outbreak area; nitrate nitrogen in the plateau pika outbreak area and TN were the key influencing factors, which exerted a strong direct influence on SOC and SOCD (path coefficient = −0.666 and 0.639 (p < 0.001), respectively). Therefore, increasing vegetation biomass and nitrogen nutrient content through reseeding pasture and fertilization can facilitate the accumulation and recovery of SOC and SOCD in the ecological restoration of degraded alpine meadows, and it is especially important to quickly enrich soil nitrogen content in the outbreak area of plateau pika populations at high altitudes.

Deep learning-based super-resolution (SR) techniques play a crucial role in enhancing the spatial resolution of images. However, remote sensing images present substantial challenges due to their diverse features, complex structures, and significant size variations in ground objects. Moreover, recovering lost details from low-resolution remote sensing images with complex and unknown degradations, such as downsampling, noise, and compression, remains a critical issue. To address these challenges, we propose ConvMambaSR, a novel super-resolution framework that integrates state-space models (SSMs) and Convolutional Neural Networks (CNNs). This framework is specifically designed to handle heterogeneous and complex ground features, as well as unknown degradations in remote sensing imagery. ConvMambaSR leverages SSMs to model global dependencies, activating more pixels in the super-resolution task. Concurrently, it employs CNNs to extract local detail features, enhancing the model’s ability to capture image textures and edges. Furthermore, we have developed a global–detail reconstruction module (GDRM) to integrate diverse levels of global and local information efficiently. We rigorously validated the proposed method on two distinct datasets, RSSCN7 and RSSRD-KQ, and benchmarked its performance against state-of-the-art SR models. Experiments show that our method achieves SOTA PSNR values of 26.06 and 24.29 on these datasets, respectively, and is visually superior, effectively addressing a variety of scenarios and significantly outperforming existing methods.

The swampy meadows atop the vast Qinghai–Tibet Plateau in West China fall into alpine, pediment, valley, floodplain, terrace, lacustrine, and riverine types according to their hydro-geomorphic properties. They have suffered degradation to various levels of severity due to climate change and external disturbance. In this paper, we studied the propensity of these types of swampy meadows to degrade from the topographic perspective. Evaluated against four degradation indicators of vegetation, hydrology, soil erosion, and pika (Ochotona curzoniae) damage, degradation severity at 106 swampy meadows representing all types of wetlands was graded to one of four levels, from which the field-based propensity to degrade (PtD) index value was derived. Judged against this index, terrace and alpine swampy meadows are the most prone to degradation while valley, lacustrine, and riverine swampy meadows are the least. The index value of a given swampy meadow type bears a close relationship (R² = 0.916) with its rate of change during 1990–2013, which confirms the validity of the proposed index in predicting the propensity of swampy meadows to change. The observed differential PtD of different types of swampy meadows is attributed primarily to elevation (R² = 0.746; p = 0.027) and, secondarily, to surface morphology (R² = 0.696; p = 0.039). Thus, the elevation at which a swampy meadow is situated is a more important factor to its PtD than its surface morphology. In particular, swampy meadows located at a higher elevation with a convex surface are much more prone to degradation than those at a lower elevation of a concave slope. Such findings can guide the proper management of different types of swampy meadows to achieve sustainable animal husbandry.

In view of the problem of sustainable restoration of vegetation in alpine mining areas, vegetation communities and physical and chemical properties of soil under different artificial restoration measures (i.e., grass monoculture, sowing quantity and topsoil replacement) were investigated for five consecutive years (2016–2020) in the sloped eastern area of a northern slag mound in the Jiangcang mining area of the Muli coalfield in Qinghai, China. The results showed that the vegetation characteristics of different sowing treatments with different kinds of grass species, such as Elymus nutans, Poa pratensis cv. Qinghai and Poa crymophila cv. Qinghai, were significantly different (p < 0.05). The content of soil available nitrogen and phosphorus was significantly different among different grass species (p < 0.05). Vegetation coverage and ramet density of sowing treatment five were significantly higher than those of a lower sowing quantity (p < 0.05). There was no significant difference in the vegetation characteristics among different grass species treatments five years after the restoration (p > 0.05). There was no significant difference in vegetation height of different soil covering treatments (p > 0.05). In the early stage of restoration, vegetation coverage and ramet density in TR2 (resurfacing soil 10 cm) and TR3 (resurfacing soil 15 cm) were significantly higher than those without treatment. Soil N, P and organic matter under the treatments of TR1 (resurfacing soil 5 cm), TR2 and TR3 were significantly higher than those in CK (p < 0.05) in the early stage of restoration, but there was no significant difference in soil N, P and organic matter after five years of restoration. Overall, the vegetation and soil characteristics showed a trend of increase first and then decrease during the 5-year restoration period under different artificial measures. There were significant differences in the vegetation and soil characteristics among different treatments in the second year of restoration (p < 0.05), but there was no significant difference between the first year and the fifth year of restoration, which indicated that vegetation and soil began to degrade after five years of restoration, and substrate nutrients in the mining waste soil could only support the short-term restoration of vegetation. Therefore, it is necessary to target matrix nutrients in future vegetation restoration in alpine mining areas.

Identifying the relative contributions of climate change and human activities to alpine grassland dynamics is critical for understanding grassland degradation mechanisms. In this study, first, the actual NPP (NPP_a) was obtained by MOD17A3. Second, we used the Zhou Guangsheng model to simulate the potential met net primary productivity (NPP_p). Finally, the NPP generated by anthropogenic activities (NPP_h) was estimated by calculating the difference between NPP_p and NPP_a. Then, the relative contributions of climate change and human activities to NPP changes in grasslands were quantitatively assessed by analyzing trends in NPP_p and NPP_a. Thereby, the drivers of NPP change in the Yellow River source grassland were identified. The results showed that the temperature and precipitation in the study area showed a warm-humid climate trend from 2000 to 2020. The NPP_p and NPP_a increased at a rate of 1.07 g C/m² and 1.51 g C/m² per year, respectively, while the NPP_h decreased at a rate of 0.46 g C/m² per year. It can be seen that human activities had a positive effect on the change of NPP in the Yellow River source grassland from the change rate. The relative contribution analysis showed that 55.90% of grassland NPP increased due to climate change, 40.16% of grassland NPP increased due to human activities, and the grassland degradation was not significant. The research results can provide a theoretical basis and technical support for the next step of the Yellow River source grassland ecological protection project.