ERA5 is the latest generation of ECMWF reanalysis product. Its precipitation data can be used to analyze the spatial and temporal characteristics of precipitation, extracting rainstorm and flood events, driving crop models, hydrological models, and land surface models. Studies on the effects of using reanalysis data to drive various models to simulate climate change are becoming more common in the context of climate change. However, as atmospheric reanalysis data, the data quality of ERA5 precipitation data will be affected by the errors of forecast products, observation data, and assimilation methods. The precision of reanalysis data will influence the uncertainty of related study results. As a result, verifying and evaluating the accuracy of reanalysis data is both necessary and urgent. To investigate the applicability of ERA5 precipitation data in China, daily precipitation data from 728 Chinese sites were used as a reference to examine the accuracy of ERA5 reanalysis precipitation data at various time scales (month, quarter), different climate zones and different elevation gradients using the Pearson correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), probability of detection (POD), false alarm rate, and equitable threat score. Furthermore, the capability of ERA5 to depict heavy rain and drought events was investigated. The results demonstrate that there are spatial and temporal differences in the ability of ERA5 precipitation data to identify daily precipitation events. Among the eight climatic zones, the north temperate zone has the highest accuracy. The r, RMSE, and MAE of ERA5 daily precipitation and station observation are 0.587, 4.040 mm·d^-1, and 1.472 mm·d^-1, respectively, in the north temperate zone. Precipitation data from the ERA5 are less accurate in the summer and autumn than in the winter and spring. The accuracy of ERA5 precipitation data in areas with elevations greater than >500 m is lower than in areas with elevations of less than ≤500 m. When the ERA5 data is used to identify the heavy rain, there is a large deviation from observations of the stations, and the larger the threshold (i.e., the stronger the heave rain), the larger the deviation is. The accuracy of standardized drought index (SPI) of different time scales calculated using ERA5 is different, varies with SPI on a three-month time scale having the highest accuracy. When using ERA5 data to identify drought events, the lower the threshold (that is, the greater the severity of the drought), the greater the error. Generally speaking, the drought capture ability of the north temperate zone, south temperate zone, and north subtropical zone is better. This research broadens the scope of validation of climate data sets and serves as a model for future research. These findings serve as a reference for related studies considering whether to use ERA5 data, as well as a tool for analyzing the uncertainties of related studies.

In the context of rural tourism in Turpan City of Xinjiang, China, this paper explores the influences of tourism involvement, restorative environment perception, and tourist satisfaction on tourists’ intention to revisit, together with the related moderating effect of consumption expectation. The study adopts a structural equation model based on attention recovery theory, in which tourist satisfaction and restorative environment perception as the mediating variables and consumption expectation is the moderating variable. Through data collected through a questionnaire survey, 374 valid samples were obtained. The study found that (1) Tourism involvement had a significant positive impact on restorative environment perception, tourist satisfaction and revisit intention, among which the effect on restorative environment perception is the largest and the effect on tourist satisfaction is the least. (2) Restorative environment perception and tourist satisfaction had multiple chain mediating effects in the structure of “tourism involvement-revisit intention”. (3) The influence of tourism involvement and tourists’ satisfaction on revisiting intention was positively regulated by consumption expectation. The traditional mediation model understates the role of restorative environment perception and many previous studies on this factor have considered it an independent variable. Few studies, in contrast, have considered consumption expectation. To explore the impacts on tourists’ willingness to revisit, the present study innovatively applies restorative environment perception as a mediating variable in the chain mediation effect and applies consumption expectation as a moderating variable, thus providing broader guidance for the governance of scenic locations. The study’s results provide a theoretical basis and a practical reference for the management and development of rural tourism in Turpan City.

Recently, climate change has become a topic of interest in the scientific community, governmental departments, and civil society. This paper begins with a description of the breakthrough technologies that have transformed nature followed by a systematic introduction of the increasing impact of human activities on nature. Since the Industrial Revolution, industrialization-driven urbanization has emerged as the dominant factor in climate change, considerably changing nature and human society. Due to the accelerating agglomeration effect of urbanization, the earth’s complex socio-ecological system is already in a crisis. We contend that climate change and urbanization, on the one hand, yield a series of negative impacts on the earth system, earth’s resources, ecology, environment, urban development, public health, and social resilience, and on the other hand, place new demands on earth science as a discipline as we enter the new geological era of the Anthropocene, which requires the development and redefinition of earth science. Consequently, earth science research will shift from being stratum based to being sphere based. Its objects and fields will expand from those of the pure earth system to those of the human-earth complex system. Correspondingly, the research focus will shift from the extraction of earth’s resources to the general habitability of the planet and the Anthropocene era, anthroposphere geography, cryosphere science, hydrosphere-anthroposphere interface, earth system dynamics, urbanization and urban geography, geoethics and semiotics, and EarthMAP (earth monitoring, analysis, and prediction) integration. Geography, as the “mother” of all natural and social sciences, will face new opportunities of development and prosperity, while human geography in particular will play a more important disciplinary role.

Land cover variations can change regional carbon storage capacity, thereby affecting global climate change. Investigating the impact of land cover changes on carbon stocks and predicting the carbon stocks under different land cover scenarios are crucial in the future for achieving the regional strategic goal of “carbon neutrality”. However, ecologically fragile (such as land degradation and desertification) areas in western China remain to be explored. This study selected the Turpan-Hami Basin in Xinjiang as the study area based on the land cover product data, PLUS model, and InVEST model. Furthermore, this paper discusses the spatiotemporal relationship between land cover changes and regional carbon storage and predicts and evaluates their spatiotemporal dynamic characteristics under sustainable development scenarios, maintaining the status quo development scenario, and economic priority development scenarios in 2025 and 2030. The results showed that: (1) In the last 20 years, the Turpan-Hami Basin has the most significant increase in cultivated land and bare land areas among its various land use types, followed by construction land. Conversely, grasslands have exhibited the most notable decreasing trend. Additionally, the conversion of grassland to cultivated and construction lands was the main transfer type. (2) In 2000, 2010, and 2020, the average carbon storage in the Turpan-Hami Basin was 26.01 t·hm^-2, 25.68 t·hm^-2, and 25.73 t·hm^-2, respectively, indicating a trend of first decreasing and then increasing. The cumulative average carbon storage decreased by 0.28 t·hm^-2, of which the carbon storage of soil organic matter accounted for the highest proportion, approximately 94% of the total carbon storage. Bare land and grassland contributed the most to carbon storage. (3) Under the three scenarios in 2030, no noticeable change was observed in forests, shrubbery, and wetlands; moreover, bare land showed a decreasing trend, while construction land showed an increasing trend in land cover. (4) By 2030, the total carbon storage of Turpan-Hami Basin under the sustainable development scenario increased by 0.18×10⁶ t than that in 2020 and decreased by 0.30×10⁶ t and 1.01×10⁶ t under the status quo development and economic development scenarios, respectively. Among the three scenarios, the carbon storage loss was highest under the economic development scenario. These results provide a basis for land use optimization and sustainable ecosystem development measures in the Turpan-Hami Basin.

Habitat quality is an important foundation for human well-being and sustainable development. It is of great significance for regional ecological conservation and sustainable land resource use. Therefore, this paper explores the changes in the spatial patterns of habitat quality in the Weihe River Basin, northwest China from the perspective of land-use change by analyzing three periods of land use data from 2000 to 2020 and coupling the PLUS and InVEST models to predict and evaluate future land use and habitat quality. In our analysis, we found that from 2000 to 2020, the areas of construction land and grassland in the Weihe River Basin increased year by year, the area of forest land rose slightly, and the area of cultivated land continuously decreased. However, we predict that the drastic land-use changes in the Weihe River Basin will decrease significantly and land-use changes will stabilize from 2020 to 2050. In addition, we further explored the spatial distribution and changes in habitat quality in the basin on the basis of the analysis of land-use changes. We found that the overall level of habitat quality in the basin was moderately high from 2000 to 2020, with a clear trend of habitat quality polarization, gradually increasing areas of low and high habitat quality, and gradually decreasing areas of moderate habitat quality. However, the overall level of habitat quality in the basin gradually increased, consistent with the increasing ecological improvement in the Weihe River Basin in recent years. From 2020 to 2050, the overall habitat quality in the basin will continue to increase, but the growth rate will slow down and the intensity of habitat quality changes will decrease. After analyzing land use and habitat quality, we explored the main reasons for their change. We found that regional ecological compensation policies and socioeconomic development were the main reasons for land-use changes. Meanwhile, habitat quality was closely related to the spatial pattern of land use, with higher and more stable habitat quality levels in areas with high concentrations of forested land and low disturbance of human activities, whereas the habitat quality tended to be poorer in plain areas where human activities are more intensive and urban development is more concentrated. However, it is worth acknowledging that the management and restoration of the ecologically fragile areas of the Loess Plateau in the watershed have led to a gradual improvement in habitat management, which is also an important reason for the overall improvement in the habitat quality in the watershed as a whole.

The attitudes of residents of tourist destinations towards tourism itself do not only influence the perception and experience of tourists,but also play a major role in the sustainable development of the tourism industry. In this paper,based on the analysis of favorable or unfavorable attitudes toward tourism and pro-environmental behavior,we propose the concept of “pro-tourism behavior”：the residents’ intention to enhance the development of local tourism. Based on the Social Representations Theory research framework,we constructed a “community relationship-benefit perception-behaviors” model and empirically investigated the impact of the community relationship on the pro-tourism behavior;moreover,we explored the mediation role of the tourism benefit perception and the moderation role of the tour event attachment. The results indicated that the community relationship significantly influences the tourist destination residents’ perception of the economic,social,and environmental benefits of tourism in a positive way; this positive perception,in turn,significantly and favorably influences the pro-tourism behavior. Overall,the residents’ perception of the economic,social,and environmental benefits plays a multi-chained mediation role between the community relationship and the pro-tourism behavior. Meanwhile,the tour event attachment moderates the relationship among the community relationship and the perception of the social and environmental benefits of tourism. In conclusion,the results of this research are important for the development and management of community tourism at tourist destinations.

Aerodynamic roughness is defined as the height at which the wind speed becomes zero under neutral and stable conditions. It is an important parameter for measuring the momentum and energy exchange between the underlying surface and atmosphere, and it is critical for investigating various surface processes and climate change. However, it has always been difficult to estimate aerodynamic roughness accurately at the regional scale, and there is no unified estimation model presently. Therefore, the parameterization of aerodynamic roughness is a topic worthy of further study. As a long-range monitoring method, remote sensing technology has the advantages of macroscopic and rapid acquisition of ground feature information, and its ability to achieve dynamic monitoring at the regional scale or a larger scale in estimating the aerodynamic roughness of vegetation-covered surfaces. Therefore, using remote sensing technology to estimate aerodynamic roughness has become a hot issue at home and abroad in recent years. In this study, the progress of research on aerodynamic roughness at home and abroad in recent years is systematically described. The estimation methods are divided into two categories: one is based on measured data, and the other is the remote sensing method, which is rapidly advancing. This study primarily introduces the method of estimating the aerodynamic roughness of the underlying surface of vegetation by remote sensing technology. Methods based on measured data include the canopy height fixed ratio method, field experiment method, and wind tunnel method; remote sensing methods include vegetation index, LIDAR, and multisource remote sensing synergistic methods. In addition, the advantages and disadvantages of the different methods are summarized at the end of each section. Finally, this study analyses the influence of meteorological factors and morphological characteristics of surface roughness elements on aerodynamic roughness and discusses the development trends and problems of remote sensing techniques in estimating aerodynamic roughness, aiming to provide ideas for subsequent research on remote sensing monitoring of aerodynamic roughness.

Soil salinization can hinder agricultural development. In this study, the degree of regional soil salinization was obtained to provide a theoretical reference for improving agricultural land quality. Using Yinchuan Plain of China as the study area with a grid size of 5 km×5 km, the soil salinity data of 166 sampling points at different depths were obtained. Combined with the Landsat 8 OLI image corresponding to the sampling time, the salt influence factor and salt index were used as input parameters, respectively, and soil salinity at field sampling points was used as output layer parameters. Support vector machine, back propagation neural network, and Bayesian neural network (BNN) were established as soil salinity inversion models. The determination coefficient and root mean square error of the different models were compared to screen the best model. Finally, soil salinization inversion at different depths was performed in the study area. The following results were obtained: (1) In the 0-20 cm soil salinity inversion model, the BNN model based on the influence factor variable group of salinization was the best, with a coefficient of determination (R²) and root mean square error (RMSE) of 0.618 and 2.986, respectively; the best inversion result of 20-40 cm soil salinity was the BNN model based on the salt index variable group (R²=0.651; RMSE=1.947); the comparative analysis of the modeling and verification effects of different variables of the selected algorithms revealed that the BNN model was the best inversion model with a better fitting degree than the other two models, and the introduction of a neural network had certain advantages in the model construction. (2) Non-salinized and mildly salinized soils were the main soil types in Yinchuan Plain. Soil salinization showed a low trend in the south and a high trend in the north. The 20-40 cm soil salinization was found to be lighter than the 0-20 cm soil salinization.

Determining the spatial distribution characteristics and changes in terrestrial water storage and understanding the reasons behind these terrestrial water storage changes (TWSC) are necessary for the sustainable and comprehensive management of water resources. Based on the data of the TWSC obtained by the gravity recovery and climate experiment satellite retrieval, this study first analyzes the trend and spatiotemporal variation characteristics of the TWSC in China using the Mann-Kendall trend test and empirical orthogonal function (EOF) analysis. Subsequently, 10 influencing factors were selected to comprehensively analyze their relationship with the TWSC by employing the following three methods: geographic detector, Pearson correlation analysis, and random forest. The 10 influencing factors were temperature, precipitation, standardized precipitation evapotranspiration index (SPEI), area proportion of impervious layer, area proportion of water body, normalized difference vegetation index (NDVI), elevation, slope, gross domestic product (GDP), and population. The results showed that areas with a significant increase in terrestrial water storage were mainly distributed in the areas near the Songhua River, Nenjiang River, and Songnen Plain, and the belt of the Qaidam Basin-Yangtze River-southeast coastal region, while areas with a significant decrease in terrestrial water storage were mainly distributed in southwest China and the belt of the Xinjiang-Loess Plateau-North China Plain. From high to low latitudes, the terrestrial water storage showed an alternating change pattern of high-low-high-low. Overall, meteorological factors had the strongest explanatory power for the TWSC, followed by socioeconomic factors and geomorphologic and geologic factors. Lag-correlation analyses showed that the monthly TWSC had a time lag response to precipitation, temperature, SPEI, and NDVI. The time lag of the monthly TWSC for each factor was different in the different regions. The response of TWSC to precipitation and SPEI mainly showed one-month lag, and the response of TWSC to temperature and NDVI mainly showed no lag (i.e. 0-month lag).

This study investigated the floodplain that is approximately 10 m away from the Yellow River near the Jinniu Street Wharf in Anning District, Lanzhou City in Gansu Province, China. The hydrogen and oxygen stable isotopes of the floodplain soil water were analyzed. Moreover, together with the line-conditioned excess of different water bodies, the variation characteristics of the soil water hydrogen and oxygen stable isotopes and the soil water content at different depths of the floodplain soil profile were studied. The process of precipitation replenishing the soil water in the piston and preferential flow modes was assessed. The results are presented herein. First, the local meteoric water line (δD=7.00δ¹⁸O+3.81, R²=0.95, P<0.001) in Lanzhou was significantly different from the global meteoric water line, and the slope was affected by evaporation. Second, the δD of the shallow soil water showed a larger variation. The deeper it was, the smaller the variation, which gradually stabilized. The δD value first decreased with depth, then increased, and finally stabilized. Meanwhile, the shallow soil water content was smaller and gradually increased with the soil depth, indicating that the river water replenished the soil water in the deep layer (>80 cm). Lastly, in April, June, and October, precipitation was observed to replenish the soil water in a plug flow mode, while in May and September, precipitation was observed to replenish the soil water in a preferential flow mode (i.e., soil water recharging in the flood plain is performed under two modes). Clarifying the soil water characteristics is greatly significant for the accurate analysis of the amount of precipitation infiltration in the soil and leads to a deep understanding of its replenishment process and an accurate evaluation of both groundwater replenishment and pollution analysis.

The Aral Sea was once the fourth largest lake in the world. Since the 1960s, due to the demands of agricultural irrigation, the regions of the Aral Sea basin have diverted a large amount of water from the Amu Darya and Syr Darya rivers, resulting in the rapid shrinkage of the Aral Sea, the reduction of water levels, and also the deterioration of water quality. By the beginning of the 21^st century, the lake’s surface had shrunk to 1/8 of its original size, and the dried lake bottom had become a salt desert with high salinity. Many biological species decreased and the ecological environment deteriorated, causing an ecological crisis. After the independence of the Central Asian countries, the conflict between the upper and lower parts of the Amu Darya River and Syr Darya River resulted from the lack of water, which seriously affected the relations between these countries. Despite repeated negotiations, the Central Asian countries failed to come up with a common solution to the Aral Sea water crisis. In the absence of regional cooperation, Kazakhstan took independent measures and saved part of the waters of the northern part of Aral Sea. Moreover, Uzbekistan also took a proactive approach to managing the Aral Sea crisis in recent years. The prevailing academic opinion is that the Aral Sea crisis is human-made and that linking it to global climate change is inappropriate. Thus, the Aral Sea crisis, which has been improved because of governance and groundwater recharge, may not disappear as previously predicted and considerable uncertainty remains as to how much it will recover. This study offers a complete discussion of the history, status, and prospects of the Aral Sea crisis to provide a reference for the governance of the arid environment in northwestern China.

Based on the time section data of 2010, 2015 and 2019, the modified gravitational model and social network method were used to analyze the temporal and spatial evolution characteristics of tourism economic links among 89 cities in the Yellow River Basin, and the influencing factors of tourism economic connection changes in the Yellow River Basin were analyzed by combining geographical detectors. The results show that: (1) The east-west multi-core network structure of the tourism economic network in the Yellow River Basin gradually formed, and the characteristics of regional differentiation and inter-provincial barrier effect between cities are significant. (2) The tourism economic linkage network has gone through the initial stage of polarization and large barriers, the transitional stage of organic connection and enhanced interaction, and the consolidation stage of balanced and cluster development. (3) A small number of core cities represented by Xi’an City and Zhengzhou City play a strong intermediary role in the tourism economic connection network. (4) The level of industrial structure, tourism resource endowment and environmental quality are the main influencing factors of tourism economic linkages, and the interaction between various factors can enhance the explanatory power of tourism economic linkages. The research results can provide effective optimization suggestions for the high-quality development of tourism economy in the Yellow River Basin.

Ecological vulnerability is a current focus of research on global environmental changes and regional sustainable development, as well as of research on ecological civilization construction and green development. In this study, Yanchi County in Ningxia, which is an agro-pastoral area in northern China, was taken as an example. Three remote sensing datasets were collected from 2000 to 2017, and the landscape pattern index method, grid technology, spatial autocorrelation analysis, and geographic detector models were used to assess the ecological vulnerability of the study area. The temporal and spatial patterns and their driving factors were evaluated comprehensively. The results showed that, during the study period, the landscape of Yanchi County was dominated by grassland and cultivated land. Together, the two land types accounted for more than 70% of the total study area. The grassland increased in area each year at an average annual rate of 0.82%. The farm land and unused land decreased in area by 44673.7 and 73033.1 hm², respectively. The woodland, water, and construction land types increased rapidly in area at average annual rates of 2.86%, 2.15%, and 4.80%, respectively. The number of patches of landscape in the study area decreased over time; the degree of fragmentation decreased, and the different land types had good connectivity. Generally, the land types showed regular and continuous evolution, and the ecological vulnerability of the study area decreased each year. Ecologically fragile areas were mainly distributed in Gaoshawo Town and Huamachi Town in the north and Hui’anbu Town in the west. Less ecologically fragile areas were mainly distributed in Mahuangshan Town, Dashuikeng Town, and Qingshan Township in the southeast. The less ecologically fragile area was continuously distributed from southeast to northwest, and the spatial autocorrelation Moran’s I was greater than 0.52, which indicates a positive correlation and represents significant agglomeration and heterogeneity. The results indicated that the number of landscape patches, landscape fragmentation, landscape dominance, and Shannon diversity index are the main influencing factors that affect the spatial heterogeneity of ecological vulnerability in the study area.

This paper evaluates the total amount and intensity of agricultural carbon emissions across 13 prefectures in Xinjiang, China from 2007 to 2020. It employs the Gini coefficient decomposition method to examine regional disparities in the intensity of agricultural carbon emissions in Xinjiang and utilizes the spatial Durbin model to assess the spatial spillover effects and driving factors of these emissions. The findings indicate that the evolution of total agricultural carbon emissions in Xinjiang during the study period can be categorized into three phases: a rapid increase, a continuous decline, and a steady rise, with straw burning being the predominant source of emissions, followed by livestock farming. The intensity of agricultural carbon emissions exhibited a pronounced downward trend throughout the period. Spatial disparities in the intensity of emissions at the beginning and end of the study period were substantial, characterized by lower levels in the north and higher levels in the south. Based on emission composition, Xinjiang can be classified into five distinct regional types. The Gini coefficients for the intensity of agricultural carbon emissions in Xinjiang as a whole, and specifically in its northern and southern parts, demonstrated a fluctuating downward trend, with inter-regional differences accounting for the majority of the overall disparities. Furthermore, there was a significant spatial agglomeration of emission intensity during the study period, with increasing spatial linkages among cities and towns over time. The primary influences on agricultural carbon emissions in Xinjiang were market and governmental factors. At the market level, factors such as industrial agglomeration, agricultural industry structure, agricultural development level, and planting structure were significantly positively correlated with emission intensity, accompanied by notable inter-regional spillover effects. From a governmental perspective, the level of environmental governance and the extent of regional disasters exhibited a significant negative correlation with the intensity of agricultural carbon emissions.

Digital economy empowers the tourism industry to transform intelligently; as a result, the overall crisis resistance and innovation transformation capacity of the tourism industry are enhanced, providing new opportunities for ecological protection and high-quality development in the Yellow River Basin. After systematically elaborating the theoretical basis of the impact of digital economy on the resilience of the tourism economy, the impact of the digital economy on the resilience of tourism economy in the Yellow River Basin and its mechanism of action are examined using fixed-effects models and difference-in-difference models based on panel data obtained from 81 prefecture-level cities in the Yellow River Basin from 2008 to 2020. The results revealed that: (1) The development of digital economy can directly promote the resilience of the tourism economy, and the conclusions from the study still hold after a series of robustness and endogeneity tests, including the establishment of the “Big Data Comprehensive Experimental Zone” as a quasi-natural experiment. (2) Digital economy indirectly improves the resilience of the tourism economy in the Yellow River Basin by promoting factor mobility and improving innovation levels. (3) Heterogeneity analysis reveals that the effect of digital economy on the resilience of the tourism economy in the Yellow River Basin is dependent on the geographical location and degree of tourism development, decreasing in the order of “midstream region>upstream region>downstream region”; digital economy renders a better effect in areas with high degree of tourism development. The results of the study provide a policy basis for the Yellow River Basin to promote the development strategy of “digital + tourism” and strengthen the tourism industry to prevent risks and cope with external shocks.

Promoting the modernization of agriculture and rural areas is a major task in the overall development of a powerful socialist modernization country. This study constructs a comprehensive evaluation index system to measure the level of agricultural and rural modernization development in 30 provinces (cities, autonomous regions) of China from 2010 to 2020 and analyzes their spatiotemporal characteristics. The coupling coordination degree model is introduced to analyze the coordination between agricultural and rural modernization and to explore its spatial correlation. Identify the shortcomings of agricultural and rural modernization development by judging the lagging types. The results are as follows: (1) From 2010 to 2020, the level of agricultural and rural modernization, rural modernization, and agricultural modernization development in China exhibited a fluctuating upward trend. Higher levels of agricultural and rural modernization are concentrated in small areas along the coast, whereas the rest of the areas are at lower levels. Furthermore, there is a significant spatial difference between the development level of agricultural and rural modernization. (2) The coupling and coordination between agricultural and rural modernization are constantly improving; however, the trend of opposition between high and low-value agglomeration areas is increasing. (3) Shanghai, Tianjin, and Beijing have a relatively low level of agricultural modernization development; Shandong, Zhejiang, and Fujian have a relatively low level of rural modernization development, whereas Jiangsu and Guangdong have a balanced level of both. Other regions belong to the category of barely coordinated or below, with backward levels of rural modernization development or low and imbalanced levels of agricultural and rural modernization development.

Clarifying the spatial distribution characteristics of red tourism resources and mastering their spatial accessibility are the prerequisite basis for tourism planning to formulate the red tourism routes. The red tourism resources in Inner Mongolia of China are rich and varied, having a great ethnic regional characteristics and playing an irreplaceable role in promoting ethnic identity and forging the Chinese national community. However, the limited cognition of the spatial distribution and accessibility of red tourism resources in Inner Mongolia has seriously hindered the high-quality development of red tourism. In this study, the kernel density and geographical concentration index were used to reveal the spatial distribution characteristics of red tourism resources in Inner Mongolia. The spatial accessibility measurement model of red scenic spots was constructed based on real-time road traffic conditions obtained from the path planning function of the Amap. Geographical detectors reveal the differences in accessibility for the red tourism spots. The following results were obtained: (1) The spatial distribution of red tourism spots in Inner Mongolia has obvious regional differences, with characteristics of “large dispersion and small agglomeration”. The largest kernel density values are observed in Hohhot City and Xing’an League, especially in Hohhot City. Obvious differences are found in the types of red tourism spots in each city. Hohhot City and Baotou City have relatively complete types, but the spatial distribution equilibrium is poor. (2) The cost of travel time between red tourism spots in Inner Mongolia is high, with an average travel time of 256.229 min. The accessibility of red tourism spots in Inner Mongolia is poor, and the internal difference is significant. The range of accessibility coefficient is 0.752-1.816. The spatial distribution of accessibility of red tourism spots shows the “center-periphery” circle gradually decreasing structure. The accessibility of red scenic spots in the Hulun Buir City and Alagxa League is low. (3) The spot regional location and regional spot density had the strongest explanatory power for the difference in accessibility, and the interaction of these two factors had the greatest explanatory power. The interaction of any two factors on accessibility differentiation is a two-factor enhancement or a nonlinear enhancement relationship, and no independent or weakening relationship is observed.

Fractional vegetation coverage (FVC) is an evaluation index that reflects the quality of the ecological environment. Understanding changes in fractional vegetation coverage is a critical part of developing a more ecological civilization, as well as regional agriculture, animal husbandry planning, and environmental protection. Based on the normalized difference vegetation index from 2001 to 2020, and taking into consideration the types of vegetation on cultivated land, forested land, and grassland, the mixed pixel dichotomy model is used to calculate fractional vegetation coverage. The spatial and temporal characteristics of fractional vegetation coverage in Taxkorgan Tajik Autonomous County of Xinjiang, China are analyzed using methods of spatial stability analysis, Sen+Mann-Kendall spatial trend analysis and linear regression. At the same time, combined with temperature and precipitation data, the impact of climate factors on FVC is analyzed using Spearman correlation analysis and GIS spatial analysis. The results showed that (1) over the past 20 years, fractional vegetation coverage was concentrated in the middle fractional vegetation coverage area, and fractional vegetation coverage decreased by 4.79%. (2) The spatial stability of fractional vegetation coverage fluctuated slightly, with fluctuations (37.3%) and moderate fluctuations (32.7%); The fractional vegetation coverage decreased both significantly and not significantly, with decreases of 3.8% and 54.5% of the total area, respectively. (3) The vegetation status of cultivated land, forested land, and grassland showed slight, varying degrees of degradation. Fractional vegetation coverage decreased by 4.57%, 6.32%, and 4.24% respectively. In terms of spatial stability values, which were 51.28%, 54.48%, and 52.29%, respectively, there was no significant reduction. (4) From 2001 to 2020, degrees of spatial stability and degradation on cultivated land were slower and more stable than on forested land and grassland, and forested land saw more significant degradation than cultivated land and grassland. Changes in fractional vegetation coverage observed in the study area were small and showed a slight downward trend. Human activity was the main contributing factor affecting vegetation growth.

On the basis of the stage changes of the influencing factors of tourism development, the index system of influencing factors of tourism development in the high-quality development stage is constructed, and the spatial differentiation causes of tourism development level in the Yellow River Basin of China in 2019 are explored using geodetector and geographically weighted regression. The following conclusions are drawn: (1) In the new development stage, the development of regional tourism is affected by traditional and new factors, such as the novel development concept, both forming the system of factors affecting regional tourism development in the high-quality development stage. (2) The level of tourism development in the Yellow River Basin shows a spatial divergence pattern of decreasing from downstream to upstream, with relatively large differences in the middle and upper reaches and small differences in the downstream areas and a relatively significant mismatch of supply and demand between the upper, middle, and lower reaches. (3) In single and comprehensive dimensions, each factor has a significant influence on tourism development. However, the new factors are stronger than the traditional factors, and the influence ranking of each factor has consistency. Thus, tourism development is remarkably influenced by the synergy between factors. (4) Among the traditional factors, including consumption level, transportation conditions, and tourism resource endowment, and among the new factors, including innovation capacity and green production, have a significant spatial heterogeneous influence on tourism development. However, their heterogeneous spatial effects are different.

Achieving high-quality tourism development relies on the rational layout of regional tourist attractions, which serves as an inherent requirement and fundamental guarantee. This study delves into the spatial pattern evolution and influencing factors of A-level scenic spots in northwestern China from 2010 to 2020. Using GIS spatial technology, we analyze the spatial and temporal distribution characteristics of scenic spots and construct an index system that encompasses social, economic, and environmental aspects. In addition, geographic detectors are employed to elucidate the internal mechanism of spatial pattern evolution and influencing factors. The findings reveal the following: (1) A noticeable imbalance exists in the number of A-level tourist attractions in northwestern China, with an increasing spatial concentration trend along a “northwest-southeast” axis. The central axis is delineated by the “Turpan City-Jiuquan City-Haibei Tibetan Autonomous Prefecture-Lanzhou City-Baoji City-Xi’an City” line. The spatial structure of scenic spots undergoes an evolution process from “scattered point” to “group-type patch” and ultimately to an “inverted L-shaped strip”. (2) Five influential factors, namely resident population, per capita GDP, tourism income, traffic mileage, and policy intensity, significantly shape the spatial pattern evolution of A-level tourist attractions. (3) The impact of social factors demonstrates a two-stage pattern of initial decline followed by steady increase, indicating rising influence. Economic factors exhibit periodic ups and downs in their influence, whereas environmental factors, although displaying a tendency to rise, experience a relatively stable range of change.