Accurate wetland mapping is essential for their protection and management; however, it is difficult to accurately identify seasonal wetlands because of irregular rainfall and the potential lack of water inundation. In this study, we propose a novel method to generate reliable seasonal wetland maps with a spatial resolution of 20 m using a seasonal-rule-based method in the Zhalong and Momoge National Nature Reserves. This study used Sentinel-1 and Sentinel-2 data, along with a bi-weekly composition method to generate a 15-day image time series. The random forest algorithm was used to classify the images into vegetation, waterbodies, bare land, and wet bare land during each time period. Several rules were incorporated based on the intra-annual changes in the seasonal wetlands and annual wetland maps of the study regions were generated. Validation processes showed that the overall accuracy and kappa coefficient were above 89.8% and 0.87, respectively. The seasonal-rule-based method was able to identify seasonal marshes, flooded wetlands, and artificial wetlands (e.g., paddy fields). Zonal analysis indicated that seasonal wetland types, including flooded wetlands and seasonal marshes, accounted for over 50% of the total wetland area in both Zhalong and Momoge National Nature Reserves; and permanent wetlands, including permanent water and permanent marsh, only accounted for 11% and 12% in the two reserves, respectively. This study proposes a new method to generate reliable annual wetland maps that include seasonal wetlands, providing an accurate dataset for interannual change analyses and wetland protection decision-making.

Monitoring the dynamics of wetland resources has practical value for wetland protection, restoration and sustainable utilization. Dongting Lake wetland reserves are well known for both their intra-annual and inter-annual dynamic changes due to the effects of natural or human factors. However, most wetland monitoring research has failed to consider the seasonal wetlands, which is the most fragile wetland type, requiring more attention. In this study, we used multi-source time series remote sensing data to monitor three Dongting Lake wetland reserves between 2000 and 2020, and the seasonal wetlands were separated from permanent wetlands. Multispectral and indices time series were generated at 30 m resolution using a two-month composition strategy; the optimal features were then selected using the extension of the Jeffries–Matusita distance (J_Bh) and random forest (RF) importance score; yearly wetland maps were identified using the optimal features and the RF classifier. Results showed that (1) the yearly wetland maps had good accuracy, and the overall accuracy and kappa coefficients of all wetland maps from 2000 to 2020 were above 89.6% and 0.86, respectively. Optimal features selected by J_Bh can improve both computational efficiency and classification accuracy. (2) The acreage of seasonal wetlands varies greatly among multiple years due to inter-annual differences in precipitation and evaporation. (3) Although the total wetland area of the three Dongting Lake wetland reserves remained relatively stable between 2000 and 2020, the acreage of the natural wetland types still decreased by 197.0 km², and the change from natural wetland to human-made wetland (paddy field) contributed the most to this decrease. From the perspective of the ecological community, the human-made wetland has lower ecological function value than natural wetlands, so the balance between economic development and ecological protection in the three Dongting Lake wetland reserves requires further evaluation. The outcomes of this study could improve the understanding of the trends and driving mechanisms of wetland dynamics, which has important scientific significance and application value for the protection and restoration of Dongting Lake wetland reserves.

As an important ecological barrier and animal husbandry resource base in arid and semi-arid areas, grassland degradation directly affects regional ecological security and sustainable development. Ili Prefecture is located in the western part of Xinjiang, China, and is a typical grassland resource-rich area. However, in recent years, driven by climate change and human activities, grassland degradation has become increasingly serious. In view of the lack of comprehensive evaluation indicators and the inconsistency of grassland evaluation grade standards in remote sensing monitoring of grassland resource degradation, this study takes the current situation of grassland degradation in Ili Prefecture in the past 20 years as the research object and constructs a comprehensive evaluation index system covering three criteria layers of vegetation characteristics, environmental characteristics, and utilization characteristics. Net primary productivity (NPP), vegetation coverage, temperature, precipitation, soil erosion modulus, and grazing intensity were selected as multi-source indicators. Combined with data sources such as remote sensing inversion, sample survey, meteorological data, and farmer survey, the factor weight coefficient was determined by analytic hierarchy process. The Grassland Degeneration Comprehensive Index (GDCI) model was constructed to carry out remote sensing monitoring and evaluation of grassland degradation in Yili Prefecture. With reference to the classification threshold of the national standard for grassland degradation, the GDCI grassland degradation evaluation grade threshold (GDCI reduction rate) was determined by the method of weighted average of coefficients: non-degradation (0–10%), mild degradation (10–20%), moderate degradation (20–37.66%) and severe degradation (more than 37.66%). According to the results, between 2000 and 2022, non-degraded grasslands in Ili Prefecture covered an area of 27,200 km², representing 90.19% of the total grassland area. Slight, moderate, and severe degradation accounted for 4.34%, 3.33%, and 2.15%, respectively. Moderately and severely degraded areas are primarily distributed in agro-pastoral transition zones and economically developed urban regions, respectively. The results revealed the spatial and temporal distribution characteristics of grassland degradation in Yili Prefecture and provided data basis and technical support for regional grassland resource management, degradation prevention and control and ecological restoration.

With the rapid development of e-commerce in China, “live broadcast + e-commerce” has become an emerging mode of the e-commerce industry. The speedy growth of the digital economy has also provided a favorable external environment for fresh agricultural products to ride on the e-commerce express train. As a result, more and more suppliers of fresh agricultural products choose to sell their products on e-commerce platforms and conduct live streaming. This paper utilizes the e-commerce supply chain field survey data, takes tomato as the specific analyzed variety, and studies the revenue distribution pattern of each subject in the supply chain. The purpose of this paper is to explore the optimization strategy of the revenue distribution of an agricultural e-commerce supply chain, and the results show that the agricultural e-commerce supply chain effectively promotes the matching of supply and demand of medium- and high-end agricultural products, realizes the quality premium of medium- and high-end agricultural products, and significantly improves the overall revenue level of the supply chain. The field survey found that under the supply chain model of “farmers + cooperatives + e-commerce platform”, farmers, cooperatives, and e-commerce platforms gained $2865, $3098, and $1111, respectively, for operating one mu of tomatoes in a year. According to the traditional Shapley value method, from the point of view of the contribution to the cooperative income, part of the income of the farmers and cooperatives should be compensated to the e-commerce platform, and the specific results are as follows: the income of the farmers should be reduced from $2865 to $2729, the income of the cooperatives should be reduced from $3099 to $2955, and the income of the e-commerce platform should be increased from $1111 to $1390. According to the Shapley value method based on the risk coefficient correction, from the perspective of risk compensation, part of the earnings of the e-commerce platform should be compensated to farmers and cooperatives, so as to establish a more equitable and reasonable pattern of earnings distribution, with the specific results: the earnings of farmers should be increased to $3205, the earnings of cooperatives should be increased to $3148, and the earnings of the e-commerce platform should be reduced to $722.

This paper explores the impact of e-commerce on profit margin from the perspective of scale using nation-level survey data from China. The results show that e-commerce can increase the profit margin of cooperatives, and that a higher proportion of sales via e-commerce strengthens profitability. Secondly, the effects of agri-e-commerce on cooperative profit margin is highly dependent on young talents with rich experience and high education level; in particular, female leaders have a significantly stronger effect on improving profit margins by using e-commerce than male leaders. Thirdly, the operating scale of cooperatives does not have a direct impact on the profit margins, but a bigger operating scale can significantly enhance the effect of e-commerce on profit margins. Lastly, a higher degree of standardization of cooperatives and products more clearly evidences the role played by e-commerce, including the number of brands and certification. Overall, this research provides a micro-foundation for cooperatives to better incorporate the key role of e-commerce under the background of platform monopoly and sheds light on how the government can formulate relevant policies to better support China’s e-commerce development.

Xinjiang grasslands are major ecological barriers and pastoral bases exposed to climate variability, grazing, land-use change, and socioeconomic pressures. This study assessed the vulnerability of grassland social–ecological systems in 81 counties from 2001 to 2020. A driver–pressure–state–impact–response (DPSIR) framework was integrated with CRITIC weighting and the TOPSIS model. Sen’s slope estimator and the Mann–Kendall test were used to detect temporal trends, and the obstacle degree model identified key constraints. The mean vulnerability index declined from 55.82 to 50.27, accompanied by a marked increase in counties with low or relatively low vulnerability. Vulnerability was highest in southern Xinjiang, where highly vulnerable counties were concentrated in densely populated oasis agricultural areas. Significant declines occurred in 80 of the 81 counties. The response, state, and pressure dimensions showed the highest obstacle degrees. Fiscal self-sufficiency, agricultural machinery power, the grassland–cropland ratio, economic density, and the remote sensing ecological index were the main obstacles. Declining vulnerability was associated with lower grazing intensity, a smaller bare land proportion, and increases in economic density, rural disposable income, and total agricultural machinery power, whereas the comparatively high vulnerability in southern Xinjiang was associated with limited fiscal capacity, lower ecological quality, and arid conditions. These findings support region-specific grassland conservation and differentiated county-level governance.