Given global climate change and rapid land cover changes due to human activities, accurately identifying, extracting, and monitoring the long-term evolution of wetland resources is profoundly significant, particularly in areas with fragile ecological conditions. Gansu Province, located in northwest China, contains all wetland types except coastal wetlands. The complexity of its wetland types has resulted in a lack of accurate and comprehensive information on wetland changes. Using Gansu Province as a case study, we employed the GEE platform and Landsat time-series satellite data, combining high-quality sample datasets with feature-optimized multi-source feature sets. The random forest algorithm was utilized to create wetland classification maps for Gansu Province across eight periods from 1987 to 2020 at a 30 m resolution and to quantify changes in wetland area and type. The results showed that the wetland mapping method achieved robust classification results, with an average overall accuracy (OA) of 96.0% and a kappa coefficient of 0.954 across all years. The marsh type exhibited the highest average user accuracy (UA) and producer accuracy (PA), at 96.4% and 95.2%, respectively. Multi-source feature aggregation and feature optimization effectively improve classification accuracy. Topographic and seasonal features were identified as the most important for wetland extraction, while textural features were the least important. By 2020, the total wetland area in Gansu Province was 10,575.49 km², a decrease of 4536.86 km² compared to 1987. The area of marshes decreased the most, primarily converting into grasslands and forests. River, lake, and constructed wetland types generally exhibited an increasing trend with fluctuations. This study provides technical support for wetland ecological protection in Gansu Province and offers a reference for wetland mapping, monitoring, and sustainable development in arid and semi-arid regions.

Economic development and trade activities are some of the main driving forces leading to land use and land cover changes (LUCC) with impacts on ecosystem services (ESs) functions. As the origin of the Belt and Road Initiative (BRI) initiated by China, Central Asia nations (CANs) provide a prism to examine the impact of LUCC and ESs changes brought by the BRI. The impacts of LUCC and ecological influences were evaluated. The land use transfer matrix and dynamic index, the Vector Autoregressive (VAR) model, the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST), the Carnegie Ames–Stanford Approach (CASA) model, and the Revised Wind Erosion Equation (RWEQ) model were used to evaluate the impact of export trade from the CANs to China (ETCC) on LUCC and ESs in the CANs before and after the BRI. Results showed that before and after BRI (2001–2020), agricultural land and construction land increased by 59,120 km² and 7617 km², respectively, while ecological land decreased by 66,737 km². The annual growth rate of agricultural land and the annual reduction rate of ecological land after the BRI were higher than that before the BRI, while the annual growth rate of construction slowed down. Among the ecological land, the forestland increased by 5828 km² continuously, while the grassland increased by 12,719 km² and then decreased of 13,132 km². The trends for LUCC spatial variation were similar. The development of ETCC positively affected the changes in agricultural and construction land in the CANs and negatively affected the changes in ecological land. The average contribution rates of the ETCC to changes in agriculture, construction, and ecological lands after the BRI were higher than those before the BRI. They increased by 5.01%, 3.33% and 5.01%, respectively. The ESs after the BRI improved compared with those before the BRI, indicating that, during short-term implementation of the BRI, ETCC growth also ensures the ecological protection of CANs. This study provides a reference for dealing with trade, land management and environmental protection relations between member countries of international economic alliances worldwide.

Leaf stoichiometry can enhance our understanding of leaf elements’ (C, N and P) concentrations and their corresponding ratios in an ecosystem with seasonal environment changes. This study quantified the seasonal dynamics of leaf stoichiometry of P. australis from Yangguan wetland, Dunhuang, China as a case study example. The leaf C concentration (LC) of P. australis changed between seasons and was 392.26 (g·kg⁻¹), 417.35 (g·kg⁻¹) and 392.58 (g·kg⁻¹) in spring, summer and autumn, respectively. Leaf N and P concentrations (LN and LP) were 23.49 (g·kg⁻¹), and 17.54 (g·kg⁻¹) and 5.86 (g·kg⁻¹), and 1.00 (g·kg⁻¹), 0.75 (g·kg⁻¹) and 0.16 (g·kg⁻¹), respectively, in the three seasons. The maximum (77.68) and the minimum values (17.00) of LC:LN were observed in the autumn and spring, respectively. Seasonal variations in LC:LP also showed a similar trend, with the greatest value of 3015.91 in autumn and the lowest value of 429.39 in spring. However, the highest (45.67) and the lowest values (24.18) of LN:LP were observed in autumn and summer, respectively, indicating that the growth of P. australis was mainly affected by P. Based on these results, it can be concluded that P. australis adopted a competition strategy during the early growth stage but took on a defense life strategy at the late growth stage to cope with various environments.

New urbanization often leads to land-use and land-cover change (LUCC), which inevitably affects ecosystem services (ESs). Although it is traditionally believed that urbanization reduces ecosystem services, some studies have shown that reasonable urban development facilitates ecosystem conservation. Previous studies have focused on the impacts of urbanization on either LUCC or ESs, with fewer dynamic assessments of the coordination of the three. Taking China’s contiguous poor areas (CPAs) as an example, this study applied coupling coordination, path analysis, and a multiscale geographically weighted regression (MGWR) model to identify the dynamic relationship among urbanization, land use, and the environment and then predicted their coupling coordination under shared socioeconomic pathways (SSP-RCP) in 2035 using the Patch Generation Land Use Simulation (PLUS) and a random forest model. The results of the study show that (1) urbanization, land-use change, and environmental loads in China’s CPAs showed an inconsistent upward trend. There was a slight overall decrease in ESs before 2013, which was consistent with the early stage of the Environmental Kuznets Curve (EKC); after that time, they showed different characteristics. (2) From 2000 to 2018, the coupling coordination degree of CPAs decreased slightly due to urbanization, geographic factors, and grassland and unused land. LUCC was essential to maintaining the system balance. The SN (southern contiguous poverty area) was at a basic level of coordination, while the other regions showed a moderate imbalance. (3) According to scenario projections, the degree of coupling coordination in all regions will increase by 2035. Environmental prioritization and sustainable routes are the best options for CPAs’ development. The SN is more stable, while the WN (western contiguous poverty area) has the lowest coupling coordination. (4) Environmentally friendly urbanization should be carried out with land management tailored to local conditions. Measures that could be recommended include establishing ecological pilot zones in SN areas, prioritizing the protection of grassland ecosystems in WN areas, and promoting intensive land use in the NN (northern contiguous poverty area). The present study offers a novel perspective on the interplay between the economy and the environment at the county level and achieves predictive coupling coordination through the integration of PLUS and random forest models. This investigation into coordinated urbanization–LUCC–ES development in CPAs yields valuable insights for enhancing environmental and economic well-being in similar regions within China, as well as globally.