Fujia Yang

Vegetation change is regarded as a key indicator of environmental change and ecosystem functional evolution. In mountainous regions, vegetation dynamics arise from complex and non-linear interactions among climate, topography, land use, and human activities, yet the mechanisms governing these interactions remain poorly understood. Clarifying long-term vegetation trajectories and their interacting controls is essential for understanding ecosystem structure and function. In this study, we integrated machine learning and causal modeling by combining random forest (RF) and structural equation modeling (SEM) to quantify both the relative importance and the direct and indirect pathways of natural and anthropogenic drivers of vegetation change in the Longnan region from 2000 to 2020. The RF results showed that the selected driving factors explained 82.62% and 72.39% of the spatial variation in vegetation cover in 2000 and 2020, respectively, with climatic and anthropogenic factors ranking as the most important drivers. Although ecological restoration activities contributed to an overall improvement in vegetation conditions, land-use heterogeneity and ecological constraints imposed by high elevation jointly produced contrasting local responses, resulting in vegetation degradation in surrounding areas. SEM revealed that the net influence of anthropogenic activities shifted from positive to negative over time, mainly due to land-use change, indicating a reorganization of human–vegetation interactions. Climate effects remained positive, with precipitation having a stronger influence than temperature. Topography moderated vegetation responses, as slopes below 40° favored vegetation growth. Soil effects shifted from positive to negative, likely associated with changes in soil organic matter. By jointly applying RF and SEM, this study captures both non-linear responses and causal pathways, providing a system-level perspective on the complex mechanisms underlying vegetation change.

Wetlands, frequently termed the “kidneys of the Earth,” represent one of the most vital global ecosystems. Despite their limited spatial extent, plateau wetlands function as unique ecological units that play a pivotal role in the global carbon cycle, water resource regulation, and biodiversity conservation, while exhibiting acute sensitivity to climate change. Advances in remote sensing technology—characterized by macro-scale cover-age, temporal efficiency, and non-invasive operations—have established it as a corner-stone for the dynamic monitoring and analysis of these environments. This study presents a bibliometric synthesis of 2138 publications (1982–2024) retrieved from the Web of Science Core Collection. We systematically evaluated publication trajectories, international collaborative networks, disciplinary shifts, core journals, and the spatiotemporal evolution of research hotspots. Our findings reveal an exponential growth in scholarly output alongside a marked diversification of research fields. Geographically, research is predominantly clustered around the Tibetan Plateau, flanked by the Alps and the Himalayas, with sparse representation in other regions. Future endeavors should prioritize underrepresented low-latitude and remote regions through strengthened international synergy and the integration of emerging technologies, such as UAVs and hyperspectral sensors.

The Model for Urban Stormwater Improvement Conceptualization (MUSIC) serves as a key hydrological tool for simulating urban stormwater runoff pollution and evaluating the treatment performance in Water-Sensitive Urban Designs like constructed wetlands (CWs). However, a significant limitation exists in MUSIC’s current inability to model heavy metal contaminants, even though they are commonly found in urban stormwater and pose significant environmental risks. This eventually affects the model’s utility during critical planning phases for urban developments. Thus, there is a need to address this limitation. Field investigations were conducted across established CWs in residential and industrial catchments throughout Greater Melbourne, Australia. Through systematic monitoring and calibration, an approach was developed to extend MUSIC’s predictive capabilities to include several prevalent heavy metals. The results indicate that the enhanced model can generate plausible estimates for targeted metals while differentiating catchment-specific pollutant generation and treatment patterns. This advancement enhances MUSIC’s functionality as a planning support tool, enabling the preliminary assessment of heavy metal dynamics alongside conventional pollutants during both design and operational stages. The findings underscore the value of incorporating metal-specific parameters into stormwater models, offering improved support for urban water management decisions and long-term water quality protection.

Assessing the effectiveness of stormwater treating wetlands is limited due to the lack of resources, guidance, motivation and appropriate maintenance.