Land-cover change is a major cause of global ecosystem degradation, a severe threat to sustainable development and human welfare. In mountainous regions that cross national political boundaries, sensitive and fragile ecosystems are under complex disturbance pressures. Land-cover change may further exacerbate ecological risks in these regions. However, few studies have assessed the ecological risks in transboundary areas. This study focused on the Gandaki Basin (GRB), a typical transboundary region in the Himalayas. Based on the dynamic change in land cover, the landscape ecological risk index (ERI) model was constructed to assess the ecological risk in the GRB, revealing the evolution characteristics and spatial correlation of such a risk during the period 1990–2020. The results showed that all land cover types in the GRB have changed over the last 30 years. The interconversion of cropland and forestland was a distinctive feature in all periods. Overall, the medium and medium to low ecological risk level areas account for approximately 65% of the study area. The areas of high ecological risk were mainly distributed in the high elevation mountains of the northern Himalayas, while the low risk areas were located in the other mountains and hills of Nepal. In addition, the ecological risk in the Gandaki basin has shown a fluctuating trend of increasing over the past 30 years. However, there were different phases, with the order of ecological risk being 2020 > 2000 > 2010 > 1990. Ecological risks displayed positive spatial correlation and aggregation characteristics across periods. The high–high risk clusters were primarily located in the high and medium high ecological risk areas, while the low–low risk clusters were similar to low risk levels region. The findings provided the reference for ecosystem conservation and landscape management in transboundary areas.

Soil erosion is a serious ecological problem in the fragile ecological environment of the Tibetan Plateau (TP). Rainfall erosivity is one of the most important factors controlling soil erosion and is associated with the El Niño southern oscillation (ENSO). However, there is a lack of studies related to the spatial distribution and temporal trends of rainfall erosivity on the TP as a whole. Additionally, the understanding of the general influence of ENSO on rainfall erosivity across the TP remains to be developed. In this study, long-term (1971–2020) daily precipitation data from 91 meteorological stations were selected to calculate rainfall erosivity. The analysis combines co-kriging interpolation, Sen’s slope estimator, and the Mann–Kendall trend test to investigate the spatiotemporal patten of rainfall erosivity across the TP. The Oceanic Niño Index (ONI) and multivariate ENSO Index (MEI) were chosen as ENSO phenomenon characterization indices, and the relationship between ENSO and rainfall erosivity was explored by employing a continuous wavelet transform. The results showed that an increasing trend in annual rainfall erosivity was detected on the TP from 1971 to 2020. The seasonal and monthly rainfall erosivity was highly uneven, with the summer erosivity accounting for 60.36%. The heterogeneous spatial distribution of rainfall erosivity was observed with an increasing trend from southeast to northwest. At the regional level, rainfall erosivity in the southeastern TP was mainly featured by a slow increase, while in the northwest was more destabilizing and mostly showed no significant trend. The rainfall erosivity on the whole TP was relatively high during non-ENSO periods and relatively low during El Niño/La Niña periods. It is worth noting that rainfall erosivity in the northwest TP appears to be more serious during the La Niña event. Furthermore, there were obvious resonance cycles between the rainfall erosivity and ENSO in different regions of the plateau, but the cycles had pronounced discrepancies in the occurrence time, direction of action and intensity. These findings contribute to providing references for soil erosion control on the TP and the formulation of future soil conservation strategies.

Land cover products provide the key inputs for terrestrial change monitoring and modeling. Numerous land cover products have been generated in the past few decades, but their performance on the southeastern Tibetan Plateau remains unclear. This study analyzed 15 land cover products for consistency through compositional similarity and overlay analyses. Additionally, 1305 validation samples from four datasets were employed to construct confusion matrices to evaluate their accuracy. The results indicate the following: (1) Land cover products exhibit relatively high consistency in 62.92% of the region. (2) Land cover products are strongly influenced by terrain fluctuations, showing lower consistency at elevation below 200 m and instability in land cover classification with increasing elevation, particularly between 2800–4400 m and 4800–5400 m. (3) The accuracy for forest, water, and snow/ice is relatively high. However, there is a relatively lower accuracy for wetland and shrubland, necessitating more field samples for reference to improve classification. (4) The average values of the four validation datasets show that the overall accuracy of the 15 products ranges from 50.97% to 73.50%. For broad-scale studies with lower resolution requirements, the CGLS-LC100 product can be considered. For studies requiring a finer scale, a combination of multiple land cover products should be utilized. ESRI is recommended as a reference for built-up land, while FROM-GLC30 can be used for cropland, although misclassification issues should be noted. This study provides valuable insights for analyzing land cover types on plateaus to refine classification. It also offers guidance for selecting suitable land cover products for future research in this region.

Assessing cropland soil quality and identifying limiting indicators are crucial in guiding sustainable agricultural practices, particularly in fragile agricultural ecosystems. However, a unified understanding of key limiting indicators and the controlling factors of soil quality remains lacking due to the dual demands of environmental protection and agricultural production. To address this gap, 621 soil samples were collected across the “One River and Its Two Tributaries” agricultural region on the Tibetan Plateau. Twenty-four soil physicochemical indicators and eight environmental parameters were analyzed to evaluate soil quality and identify the key limiting factors. Meanwhile, the random forest model, correlation analysis, and the structural equation model were used to determine the factors influencing limiting indicator. The results showed that (1) croplands had a mean soil quality index (SQI) of 0.66 with obvious spatial heterogeneity, where eastern, northern, and southern regions exhibited higher SQI values than western and central valley areas. (2) Soil organic matter (SOM) was identified as the primary limiting indicator, with its limiting degree increasing with soil depth. (3) Total nitrogen is the strongest statistical predictor of SOM content in this region, while soil pH negatively impacted SOM. Environmental factors, including fertilizer amount, altitude, and normalized difference vegetation index, positively influenced SOM, whereas temperature and precipitation negatively affected it. (4) The structural equation model revealed effective intervention pathways for addressing SOM deficiency, highlighting targeted measures such as optimizing nitrogen management and increasing organic fertilizer application, particularly in relative lower-altitude areas. This study provides a unified perspective for managing soil quality and offers a potentially replicable framework for similar alpine ecosystems worldwide.

Climate change has caused substantial shifts in species’ ranges and vegetation distributions in local areas of the Himalayas. However, the spatial patterns and dynamic changes of the vegetation lines in the Himalayas remain poorly understood due to the lack of comprehensive vegetation line dataset. This study developed a method to identify vegetation lines by combining the Canny edge detection algorithm with elevation parameters and produced comprehensive vegetation line datasets with 30 m resolution in the Himalayas. First, the Modified Soil-Adjusted Vegetation Index (MSAVI) was applied to indicate vegetation presence. The image was then smoothed by filling (or removing) small non-vegetated (or vegetated) patches scattered within vegetated (or unvegetated) areas. Subsequently, the Canny edge detection algorithm was applied to identify vegetation edge pixels, and elevation differences were utilized to determine the upper edges of the vegetation. Finally, Gaussian function-based thresholds were used across 24 sub-basins to determine the vegetation lines. Field surveys and visual interpretations demonstrated that this method can effectively and accurately identify vegetation lines in the Himalayas. The R² was 0.99, 0.93, and 0.98, respectively, compared with the vegetation line verification points obtained through three different ways. The mean absolute errors were 11.07 m, 29.35 m, and 13.99 m, respectively. Across the Himalayas, vegetation line elevations ranged from 4125 m to 5423 m (5th to 95th percentile), showing a trend of increasing and then decreasing from southeast to northwest. This pattern closely parallels the physics-driven snowline. The method proposed in this study enhances the toolkit for identifying vegetation lines across mountainous regions. Additionally, it provides a foundation for evaluating the responses of mountain vegetation to climate change in the Himalayas.

Functional zoning diversifies the management of grazing intensity within protected areas (PAs). However, the complexity makes it difficult to assess grazing intensity and thus understand the effectiveness of PAs in reducing grazing intensity. In this study, grazing intensity in Madoi County, the Yellow River source region, was evaluated based on mapping gridded livestock in areas where grazing was permitted under management measures in functional zones. The effectiveness of PAs in reducing grazing intensity was then assessed by comparing the changes in grazing intensity in PAs and non-PAs. Furthermore, the contributions of climate change and grazing activity to vegetation changes were quantified using temperature (°C), precipitation (mm), grazing intensity (sheep units/ha), and the normalized difference vegetation index (NDVI) (a proxy of vegetation cover) data. Subsequently, the effects of reducing grazing intensity on vegetation changes were analyzed by comparing the contribution of grazing activity to vegetation changes inside and outside of PAs. The results showed that the average grazing intensity in PAs decreased by 0.23 sheep units/ha, which was higher than the decrease in non-PAs (0.07 sheep units/ha) as expected. Specifically, the average grazing intensity in the core, buffer, and experimental zones decreased by 0.36, 0.22, and 0.14 sheep units/ha, respectively, any of which was a greater reduction than that in non-PAs. The contribution of grazing activity to the increase in vegetation cover in PAs was 12% higher than that outside of PAs, indicating that the positive effect of grazing activity on vegetation changes in PAs was greater than that outside of PAs. The findings suggest that the establishment of PAs in the Yellow River source region are effective in reducing grazing intensity and enhance the positive role of grazing activity in vegetation changes. Our research provides a reference for analyzing the effectiveness of functional zoning in areas with large-scale grazing livestock.