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.

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.

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.

The Alpine Periglacial Weathering Zone (APWZ) is a critical transitional belt between alpine vegetation and glaciers, and a highly sensitive region to climate change. Its dynamic variations profoundly reflect the surface environment’s response to climatic shifts. Taking Gongga Mountain as the study area, this study utilizes summer Landsat imagery from 1986 to 2024 and constructs a remote sensing method based on NDVI and NDSI indices using the Otsu thresholding algorithm on the Google Earth Engine platform to automatically extract the positions of the upper limit of vegetation and the snowline. Results show that over the past four decades, the APWZ in Gongga Mountain has exhibited a continuous upward shift, with the mean elevation rising from 4101 m to 4575 m. The upper limit of vegetation advanced at an average rate of 17.43 m/a, significantly faster than the snowline shift (3.9 m/a). The APWZ also experienced substantial areal shrinkage, with an average annual reduction of approximately 13.84 km², highlighting the differential responses of various surface cover types to warming. Spatially, the most pronounced changes occurred in high-elevation zones (4200–4700 m), moderate slopes (25–33°), and sun-facing aspects (east, southeast, and south slopes), reflecting a typical climate–topography coupled driving mechanism. In the upper APWZ, glacier retreat has intensified weathering and increased debris accumulation, while the newly formed vegetation zone in the lower APWZ remains structurally fragile and unstable. Under extreme climatic disturbances, this setting is prone to triggering chain-type hazards such as landslides and debris flows. These findings enhance our capacity to monitor alpine ecological boundary changes and identify associated disaster risks, providing scientific support for managing climate-sensitive mountainous regions.

Agricultural greenhouses (AGs) are an effective solution to address the growing demand for vegetables despite limited cropland, yet significant soil quality problems often accompany them, particularly in high-altitude regions. However, the effects of natural factors and production management on soil quality are not well understood in such fragile environments. This study analyzed soil quality differences between AGs and adjacent open cropland (OCs) in the Lhasa River Valley, Tibetan Plateau, based on 592 soil samples and 12 key soil physicochemical indicators. GeoDetector was used to identify the dominant factors and their interactions with these differences. The results showed that AG soils had significantly lower pH, with an average decrease of 20%, indicating acidification, while nutrient levels and total salinity were significantly higher compared to OC soils. Specifically, available phosphorus, available potassium, the soil fertility quality index, and total soluble salt increased by 281%, 102%, 38%, and 184%, respectively. Planting, topographic, and fertilizer factors were identified as the dominant factors contributing to these differences. Interaction analysis showed that the interaction of these factors increased the explanatory power by 20.2% to 41.32% compared to individual factors. The interaction between planting year and fertilizer type had the highest explanatory power for nutrient increases and pH decline, while fertilizer amount and slope aspect contributed to salinity accumulation. These findings provide valuable insights and practical guidance for optimizing AG management and ensuring sustainable agricultural development in high-altitude regions.