Quantitative assessment of the impacts of climate change and human activities on marsh wetland is essential for the sustainable development of marsh wetland ecosystem. This study takes the marsh wetland in the Yellow River source region (YRSR) as the research object, using the method of residual analysis, the potential net primary productivity (NPPp) of marsh wetland vegetation in the YRSR between 2000 and 2020 was stimulated using the Zhou Guangsheng model, and the actual primary productivity (NPPa) of marsh wetland vegetation was download from MOD17A3HGF product, and the difference between them was employed to calculate the NPP affected by human activities, the relative contribution of climate change and human activities to the change of NPPa of marsh wetland vegetation was quantitatively evaluated. The results revealed that between 2000 and 2020, NPPa of marsh wetland vegetation increased in the YRSR by 95.76%, among which climate-dominated and human-dominated NPP change occupied by 66.29% and 29.47% of study areas, respectively. The Zoige Plateau in the southeast accounted for the majority of the 4.24% decline in the NPPa of the marsh wetland vegetation, almost all of which were affected by human activities. It is found that the warming and humidifying of climate, as well as human protective construction activities, are the important reasons for the increase of NPPa of marsh wetland vegetation in the YRSR. Although climate change remains an important cause of the increase in NPPa of marsh wetland vegetation, the contribution of human activities to the increase in NPPa of marsh wetland vegetation is increasing.

An accurate assessment of the stocking rate is crucial for maintaining the stable function and the sustainable use of the alpine grassland ecosystem. A new scenario design method to evaluate the reasonable stocking rate is presented in the current work. First, climate change is quantified by potential net primary productivity (${\mathrm{NPP}}_{\mathrm{p}}$) and measured by adopting the Zhou Guangsheng model, and the NPP generated by anthropogenic activities (${\mathrm{NPP}}_{\mathrm{h}}$) is estimated by the distinction between ${\mathrm{NPP}}_{\mathrm{p}}$ and actual NPP (${\mathrm{NPP}}_{\mathrm{a}}$) calculated with the application of the Carnegie–Ames–Stanford Approach (CASA) model. Second, using the ${\mathrm{NPP}}_{\mathrm{h}}$ and actual grassland productivity consumed by livestock (${\mathrm{NPP}}_{\mathrm{ac}}$), the reasonable stocking rate is obtained. Finally, the driving factors of NPP change in alpine grassland and the reasonable stocking rate are clarified in Qinghai Province during 2005–2018. The results reveal that the temperature of alpine grassland in Qinghai Province has a slight upward trend from 2005 to 2018, and precipitation displays a downward trend. The overall ${\mathrm{NPP}}_{\mathrm{p}}$ of alpine grassland demonstrated a downward trend, and precipitation is regarded as the major influencing factor. In addition, the overall ${\mathrm{NPP}}_{\mathrm{h}}$ of alpine grassland exhibited a downward trend. The ${\mathrm{NPP}}_{\mathrm{a}}$ demonstrated an overall upward trend, where 58.32% of the regional ${\mathrm{NPP}}_{\mathrm{a}}$ is in a state of growth, and 41.68% of the regional ${\mathrm{NPP}}_{\mathrm{a}}$ is in a state of degradation. According to contribution analysis, anthropogenic activities provided the primary driving factor to promote the restoration of alpine grassland in Qinghai Province. Moreover, the stocking rate must be reduced in 60.77% of the alpine grasslands in Qinghai Province, mostly situated in the eastern and southwestern parts of Qinghai Province, and the other areas must not increase future stocking rates. The current study can theoretically and technically support the construction of Qinghai as the green organic agricultural and livestock product demonstration province and the creation of an ecological civilization highland.

Unveiling the scale effects on the interactions and driving factors of ecosystem services (ESs) contributes to the differentiated conservation of ESs across multiple scales. Utilizing models such as Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), Self-organizing map (SOM), and Geographic Detector, we analyzed the spatiotemporal changes, interactions, and dominant driving factors of water yield (WY), soil conservation (SC), carbon storage (CS), and habitat quality (HQ) in the Three River Source Basin (TRSB) from 2000 to 2020 at both grid and sub-watershed scales; explored the impact of scale effects; and proposed ecological conservation strategies. The results indicated the following: (1) Over the 20 years, the average values of WY, SC, CS, and HQ in the TRSB were 146.12 mm, 10.95 t, 224.77 gC m⁻², and 0.6942, respectively, which exhibited a distribution with lower values in the northwest and higher values in the southeast. Over the study period, WY and SC experienced declines at rates of −7.26 mm per 5 years and −0.08 t per 5 years, while CS and HQ showed increases at rates of 6.97 gC m⁻² per 5 years and 0.007 per 5 years. (2) The relationships among WY, SC, and CS were primarily synergistic, whereas HQ exhibited more trade-offs with other ESs, and as the scale increased, the trade-off effects diminished, and synergistic effects were enhanced. (3) ESs were influenced by a combination of factors, and the interactions among these factors strengthened the explanatory power regarding ESs. Increasing vegetation cover and reducing irrational human activities were effective approaches to enhance the ESs in the TRSB. This study helps to expand the understanding of the relationships among ESs and the effects of scale, providing support for the protection and sustainable development of the TRSB ecosystem.

Clarifying the response of wetland changes to climate change can improve the scientific conservation and utilization capabilities of wetland ecosystems, which is vital for their sustainable development. In this study, the spatial distribution and area changes of the different types of wetlands in the Yellow-River-Source National Park (YRSNP) were obtained using the object-based classification method for the years 2000, 2010, and 2020. The relationship between wetland change and climate factors was investigated by combining grey relation analysis and correlation analysis. The response of wetland change to different climatic factors was consequently clarified. The results showed that the river wetlands and lake wetlands increased significantly from the year 2000 to 2010 (4.04% and 4.21%, respectively). However, the total wetland area demonstrated a decreasing trend (7.08%), primarily due to the significant decrease in the marsh wetlands (6.81%). The total wetland area demonstrated a slightly increasing trend from the year 2010 to 2020 (0.14%), in which river wetlands and lake wetlands increased by 3.25% and 2.09%, respectively, while the marsh wetlands demonstrated a tendency to be stable. From the year 2000 to 2010, 75.53% of precipitation and 27.68% of temperature demonstrated a significant increase and an obvious warm–humid climate trend. However, from the year 2010 to 2020, the trend of increasing precipitation weakened, the temperature decreased slightly, and the warm–humid climate trend was not significant. From the year 2000 to 2020, the YRSNP river wetlands and lake wetlands were significantly and positively correlated with temperature and precipitation, while the marsh wetlands were most affected by climate warming, especially the warm-season temperatures. The spatial–temporal difference was not obvious in the correlation coefficient between marsh wetlands area change and the precipitation and temperature. The results of the study can provide a theoretical basis and technical support for the conservation of wetland ecosystems in the Three-River-Source National Park.

The Yellow River Source National Park (YRSNP), one of largest alpine wetlands in China which serves as the origin of the Yellow River, is situated in the heart of the Qinghai–Tibetan Plateau. The alpine wetland ecosystem, which is its primary ecological system, is crucial for maintaining ecological balance, preserving biodiversity, and facilitating the sustainable development of the Yellow River Basin. In this study, based on the Google Earth Engine (GEE) platform combined with Landsat 5 7 8 remote sensing images, we used a random forest classification model to identify and classify the alpine wetland from 2000 to 2020 and analyze its pattern of dynamic changes. The main driving forces that drive the change of the alpine wetland area in the YRSNP from 2000 to 2020 are identified using a random forest regression analysis in combination with data on precipitation, temperature, potential evapotranspiration, soil moisture, and population density. The results show that: (1) From 2000 to 2020, the average overall accuracy of remote sensing classification and extraction of the YRSNP alpine wetlands is 0.8492 and the Kappa coefficient is 0.8051. (2) From 2000 to 2020, the shrinking trend of the YRSNP alpine wetland area is restrained. However, the lake wetland, marsh wetland, and marsh meadow all increase by 0.58%, 0.06%, and 3.34%, respectively, whereas the river wetland shows a declining trend. (3) The results of the identification of driving forces indicate that soil moisture is the main factor influencing the dynamic changes of the alpine wetland, although the decline in population density has a favorable impact on the alpine wetland. The results can provide scientific basis for maintaining the stability, diversity, and sustainability of the alpine wetland ecosystem in the Yellow River Source National Park.

Based on the status quo of cultivated land, this paper analyzes the existing problems and builds a dynamic monitoring system for cultivated land protection. It works by using the Internet of Things technology and geographic and national conditions to realize the systematization, informatization, and socialization of cultivated land protection, thereby promoting the sustainable development of cultivated land and social stability and harmony. This paper puts forward relevant countermeasures and suggestions for the protection of cultivated land. This paper constructs the farmland protection dynamic monitoring system, proposes the goals of the system construction, points out the ideas of the system construction, and designs and analyzes the key points of the system composition. The system includes seven subsystems, and the functions and specific contents of the subsystems are explained, respectively, to build the entire system. Then, we put forward the financial, technical, and implementation problems of the system, and find out countermeasures, expand the system research, propose the development of the dynamic monitoring system of cultivated land protection in the direction of intelligence and convenience, and expand the application to food safety and disasters. In terms of forecasting, comprehensive evaluation of the existence and operation of the system is carried out by designing evaluation indicators and weights for the operation of the system.