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Investigating the evolution and drivers of multifunctional land use is essential for sustainable land management and regional biological conservation. This research focuses on the Hehuang Valley, where we developed an "ecological-social-production" evaluation system for assessing land use multifunctionality from the perspective of multifunctional landscape. Leveraging Geographic Information System technologies, we conducted a quantitative analysis of spatiotemporal variations in multifunctional land use across the valley in recently twenty years. Correlation coefficients were employed to identify trade-offs and synergies among various land use functions. Additionally, geographical detector and grey relational analysis models were utilized to pinpoint the factors influencing spatiotemporal changes in land use functions during the specified period. The results showed that: (1) During the period, the overall multifunctionality of land use in the Hehuang Valley exhibited an increasing trend. The economic production function of the land showed the highest growth, while the ecological and social functions showed lower growth. (2) In most areas of the Hehuang Valley, there was a positive correlation between social and economic production functions and a negative correlation between social and ecological functions, as well as between economic production and ecological functions. (3) Natural conditions were the main factors of spatial variation of land use comprehensive functions, but human factors, including land use intensity and the rate of farmland conversion to non-agricultural uses, were the primary drivers of temporal changes in multifunctional land use. The findings provide valuable references and scientific support for policymakers in optimizing land use and multifunctional landscape conservation. [ABSTRACT FROM AUTHOR]

Over the past decades, the drylands of the Yellow River Basin (YRBD) have undergone profound changes in landscape patterns and ecological dynamics, significantly impacting regional sustainable development. To assess the spatio-temporal variations of ecological risk in the YRBD and provide guidance for sustainable regional development, we constructed a coupled Land Use-Landscape Ecological Risk Model-Geographical Detector-PLUS framework for the assessment, analysis, and simulation of dryland landscape ecological risk (LER). The main findings are as follows: (1) Between 2000 and 2020, the area of built-up land, forest, grassland, and water in the YRBD increased, while the area of unused land and cropland decreased. (2) LER exhibited significant spatial heterogeneity, dominated by Sub-low and Low risks. High risk areas were primarily located in the western Inner Mongolia Plateau, whereas Low risk areas were prevalent in the Loess Plateau, with an overall decline in risk levels over the 20 years. (3) Water resources, ecological status, and human activities are the main driving factors affecting LER, with the impact of human activities becoming increasingly significant over the past 20 years. (4) Under three development scenarios in 2030, the LER is projected to further decrease, although the impact of these scenarios varies across different research sub-regions. Notably, the Ecological Priority Scenario emerges as more effective in mitigating regional LER. (5) Developing precise land use policies tailored to regional characteristics, continuously implementing ecological restoration projects, strengthening water resource management, and enhancing monitoring capabilities are effective ways to reduce LER in the YRBD. This study systematically quantified the impact of different development scenarios on LER in the YRBD, revealing its spatio-temporal characteristics, and emphasized the importance of planning guidance, ecological restoration, and risk monitoring to align regional development with ecological protection. The findings provide scientific evidence for ecological protection and sustainable development in the YRBD and other drylands, offering valuable insights for global dryland ecological risk management. [ABSTRACT FROM AUTHOR]

In the 21st century, the rapid urbanization process has led to increasingly severe urban heat island effects and other urban thermal environment issues, posing significant challenges to urban planning and environmental management. This study focuses on Zhengzhou, China, utilizing Landsat remote sensing imagery data from five key years between 2000 and 2020. By applying atmospheric correction methods, we accurately retrieved the land surface temperature (LST). The study employed a gravity center migration model to track the spatial changes of heat island patches and used the geographical detector method to quantitatively analyze the combined impact of surface characteristics, meteorological conditions, and socio-economic factors on the urban heat island effect. Results show that the LST in Zhengzhou exhibits a fluctuating growth trend, closely related to the expansion of built-up areas and urban planning. High-temperature zones are mainly concentrated in built-up areas, while low-temperature zones are primarily found in areas covered by water bodies and vegetation. Notably, the Normalized Difference Built-up Index (NDBI) and the Normalized Difference Vegetation Index (NDVI) are the two most significant factors influencing the spatial distribution of land surface temperature, with explanatory power reaching 42.7% and 41.3%, respectively. As urban development enters a stable stage, government environmental management measures have played a positive role in mitigating the urban heat island effect. This study not only provides a scientific basis for understanding the spatiotemporal changes in land surface temperature in Zhengzhou but also offers new technical support for urban planning and management, helping to alleviate the urban heat island effect and improve the living environment quality for urban residents. [ABSTRACT FROM AUTHOR]

Surface albedo plays a pivotal role in the Earth's energy balance and climate. This study conducted an analysis of the spatial distribution patterns and temporal evolution of albedo, normalized difference vegetation index (NDVI), normalized difference snow index snow cover (NSC), and land surface temperature (LST) within the Qilian Mountains (QLMs) from 2001 to 2022. This study evaluated the spatiotemporal correlations of albedo with NSC, NDVI, and LST at various temporal scales. Additionally, the study quantified the driving forces and relative contributions of topographic and natural factors to the albedo variation of the QLMs using geographic detectors. The findings revealed the following insights: (1) Approximately 22.8% of the QLMs exhibited significant changes in albedo. The annual average albedo and NSC exhibited a minor decline with rates of −0.00037 and −0.05083 (Sen's slope), respectively. Conversely, LST displayed a marginal increase at a rate of 0.00564, while NDVI experienced a notable increase at a rate of 0.00178. (2) The seasonal fluctuations of NSC, LST, and vegetation collectively influenced the overall albedo changes in the Qilian Mountains. Notably, the highly similar trends and significant correlations between albedo and NSC, whether in intra-annual monthly variations, multi-year monthly anomalies, or regional multi-year mean trends, indicate that the changes in snow albedo reflected by NSC played a major role. Additionally, the area proportion and corresponding average elevation of PSI (permanent snow and ice regions) slightly increased, potentially suggesting a slow upward shift of the high mountain snowline in the QLMs. (3) NDVI, land cover type (LCT), and the Digital Elevation Model (DEM, which means elevation) played key roles in shaping the spatial pattern of albedo. Additionally, the spatial distribution of albedo was most significantly influenced by the interaction between slope and NDVI. [ABSTRACT FROM AUTHOR]

Urban agglomerations significantly alter the regional thermal environment. It is urgent to investigate the evolution and influence mechanisms of urban agglomeration heat island intensity from a regional perspective. This study is supported by Google Earth Engine long-term MODIS data series. On the basis of estimating surface urban heat island intensity (SUHI) in the Yangtze River Delta urban agglomeration from 2001 to 2020 based on the suburban temperature difference method, the causes of heat islands in the urban agglomeration were analyzed by using geographical detector analysis. Additionally, the heat island proportion (PHI) and SUHI indicators were used to compare and analyze the changing characteristics of the urban heat island effect of ten representative cities. The research reveals the following: (1) The average SUHI of the study area increased from 0.11 °C in 2001 to 0.29 °C in 2020, with an average annual increase rate of 0.009 °C. (2) According to the results of the geographical detector analysis, SUHI was influenced by several driving factors exhibiting obvious seasonal variations. (3) SUHI difference between cities is significant in the summer (1.52 °C), but smallest in the winter; the PHI difference between cities is larger in the autumn (46.7%), while it is smaller in the summer. The research findings aim to effectively serve the formulation of collaborative development plans for the Yangtze River Delta urban agglomeration. [ABSTRACT FROM AUTHOR]

The present study aimed to examine the spatial characteristics of myopia and identify the socioeconomic and environmental factors influencing its prevalence. Myopia prevalence among children of school age of Han ethnicity in China was 56.6% in 2019, with the highest and lowest prevalence's in Shandong (66.8%) and Guizhou (47.3%), respectively. There was a spatial aggregation of myopia prevalence in China. Environmental factors (atmospheric PM2.5 concentration and forest coverage) and socioeconomic factors (gross domestic product per capita, per capita disposable income, hospital beds per thousand people, and Engel coefficient) have significant influences on myopia prevalence. The interaction of each factor on myopia showed nonlinear enhancement. Myopia prevalence among children of school age was spatially clustered, and environmental and socioeconomic conditions are associated with myopia prevalence. Our findings provide novel perspectives for the comprehensive prevention and control of myopia. [ABSTRACT FROM AUTHOR]

We measure the regional gaps in green total factor productivity (GTFP) growth by using the Dagum's Gini coefficient based on panel data for 306 cities from 1996 to 2017, then adopt a geographical detector to test the contribution of resource misallocation to the unbalanced growth in GTFP. The results show that Chinese agricultural GTFP continues to grow, but the overall growth gap has expanded year by year, mainly due to the inter-provincial gap. Compared with land, labor and machinery, fertilizer misallocation is the main factor driving the unbalanced growth in GTFP. Moreover, the interaction contribution of fertilizer misallocation with any one resource misallocation is higher than that in a single factor. Finally, resource misallocation also leads to unbalanced growth in technological progress and technical efficiency, but more so for the latter. Our research helps to provide a new solution to the "dilemma" of food security and ecological security from the perspective of optimizing resource allocation. [ABSTRACT FROM AUTHOR]

The Hemiptera insects are the largest incomplete metamorphosis insect group in Insecta and play a vital role in ecosystems and biodiversity. Previous studies on the spatial distribution of Hemiptera insects mainly focused on a specific region and insect, this study explored the spatial distribution characteristics of Hemiptera insects in China (national scale), and further clarified the dominant factors affecting their spatial distribution. We used spatial autocorrelation analysis, hot spot analysis, and standard ellipse to investigate the spatial distribution characteristics of Hemiptera insects in China. Furthermore, we used geographic detectors to identify the main factors affecting their spatial distribution under China's six agricultural natural divisions and explore the influencing mechanism of dominant factors. The results show that: (i) The spatial differentiation characteristics of Hemiptera insects in China are significant, and their distribution has obvious spatial agglomeration. The Hu Huanyong Line is an important dividing line for the spatial distribution of Hemiptera insects in China. From the city scale, the HH type (high‐high cluster) is mainly distributed on both sides of the Hu Huanyong Line. (ii) The hot spots of Hemiptera insects are mainly distributed in southwest China, along the Qinling Mountains, the western side of the Wuyi Mountains, the Yinshan Mountains, the Liupanshan Mountains, the Xuefeng Mountains, the Nanling Mountains, and other mountainous areas. (iii) Under agricultural natural divisions, the influence of natural environmental factors on the spatial distribution of Hemiptera insects is obviously different. Temperature and precipitation are the dominant factors. Natural factors and socio‐economic factors have formed a positive reinforcement interaction mode on the spatial distribution of Hemiptera insects. These can provide the decision‐making basis for biodiversity conservation and efficient pest control. [ABSTRACT FROM AUTHOR]

Green innovation has become one of the most effective ways to deal with ecological problems such as environmental pollution, global warming and resource depletion. A comprehensive and accurate understanding of regional differences and influencing factors of green innovation level is of great significance further to promote green innovation and to realize the harmonious coexistence between man and nature. This paper analyzes the regional differences of green innovation level in China from 2011 to 2020 by using GDI, Moran's I, Getis-Ord Gi*, and other indices at provincial, urban agglomeration and prefecture-level city scales, and identifies with geographical detectors, the leading factors influencing the spatial differentiation of green innovation level as well as their interactions. The results show that from 2011 to 2020, the overall level of green innovation in China gradually improves and the regional differences gradually decrease, and that the smaller the scale, the greater the regional differences. Green innovation level increases with the increase of urban agglomeration level and the expansion of city scale. There are differences in the spatial structure features of green innovation level at different scales, the degree of spatial agglomeration decreases as the scale reduces. At provincial and prefecture-level city scale, areas of higher green innovation level are mainly located at the east side of Hu Line, while at the urban agglomeration scale, they are mainly national urban agglomerations such as Yangtze River Delta and Pearl River Delta, and regional urban agglomerations such as Ha Chang and Shandong Peninsula Urban Agglomeration. The leading factors influencing the spatial differentiation of green innovation level and their interactions vary with the different scales. At provincial and prefecture-level city scales, the core factors are venture capital level, quality of faculty and education level. On the scale of urban agglomeration, the core factors are venture capital level and economic level. JEL Classification: M10, O32, R12. Plain language summary: This paper analyzes the regional differences of green innovation level in China from 2011 to 2020 by using GDI, Moran's I, Getis-Ord Gi*, and other indices at provincial, urban agglomeration and prefecture-level city scales, and identifies with geographical detectors, the leading factors influencing the spatial differentiation of green innovation level as well as their interactions. The results show that from 2011 to 2020, the overall level of green innovation in China gradually improves and the regional differences gradually decrease, and that the smaller the scale, the greater the regional differences. Green innovation level increases with the increase of urban agglomeration level and the expansion of city scale. There are differences in the spatial structure features of green innovation level at different scales, the degree of spatial agglomeration decreases as the scale reduces. At provincial and prefecture-level city scale, areas of higher green innovation level are mainly located at the east side of Hu Line, while at the urban agglomeration scale, they are mainly national urban agglomerations such as Yangtze River Delta and Pearl River Delta, and regional urban agglomerations such as Ha Chang and Shandong Peninsula Urban Agglomeration. The leading factors influencing the spatial differentiation of green innovation level and their interactions vary with the different scales. At provincial and prefecture-level city scales, the core factors are venture capital level, quality of faculty and education level. On the scale of urban agglomeration, the core factors are venture capital level and economic level. [ABSTRACT FROM AUTHOR]

The ongoing shifts in climate, coupled with human activities, are leading to significant land desertification; thus, understanding the long-term variations in ecosystem services as well as the driving factors has a significant value for ensuring ecological security in ecologically fragile arid regions. In this study, we used the RUSLE, RWEQ, CASA, and InVEST models to evaluate five typical ecosystem services (ESs) from 1990 to 2020 in the Gonghe Basin, including soil conservation, sand fixation, carbon sequestration, water yield, and habitat quality. Then, we analyzed the trade-offs between ESs and proposed scientific indications. Finally, we identified the driving mechanisms of ES spatiotemporal variations. The results showed that (1) the ecosystem services in the Gonghe Basin have, overall, improved over the past 30 years. Soil conservation, sand fixation, carbon sequestration, and water yield showed upward trends, while habitat quality showed a downward trend. (2) The relationships between ESs in the Gonghe Basin were characterized by strong synergies and weak trade-offs, with significant spatial heterogeneity in terms of the trade-off intensity. In addition, the implementation of ecological engineering may strengthen the intensity of the trade-offs. (3) Among all the factors (temperature, precipitation, wind speed, NDVI, land use type, slope, DEM and soil type) that affected ESs, NDVI had the greatest impact, and the explanatory power was 49%, followed by soil type. The explanatory power of the interactions between each factor was higher than that of a single factor, and the interaction between NDVI and soil type had the greatest impact. ESs increased by 12% mainly due to the implementation of ecological engineering projects and natural factors. The most suitable area for ESs was the southeastern edge of the Gonghe Basin. Our study will enrich the understanding of the mechanisms of ecosystem services in drylands and provide a scientific basis for the future implementation of ecological engineering on the Qinghai Tibet Plateau. [ABSTRACT FROM AUTHOR]

The Daling River Basin is an important ecological functional area in the western region of Liaoning with outstanding environmental problems. The monitoring of ecological and environmental quality in the basin and the analysis of driving factors are of great importance for the protection of the ecological environment and the improvement of economic quality. In this paper, the three periods of Landsat remote sensing images in 1995, 2010 and 2020 are used as the basic data, and platforms and technical means such as RS and GIS are used to decipher and extract the three periods of land use information, and to construct the land use type transfer matrix. The remote sensing ecological index (RSEI) was improved, and the principal component analysis method was applied to construct the improved remote sensing ecological index (IRSEI) model based on the greenness (NDVI), moisture (WET), heat (LST) and new dryness (N-NDBSI), so as to realize the dynamic monitoring of ecological and environmental quality in the study area. Based on the land use change, combined with the trend of improved remote sensing ecological index (IRSEI) of Daling River Basin, thus achieving the purpose of rapid and efficient dynamic monitoring of ecological quality of Daling River Basin from 1995 to 2020. A geoprobe model was then used to systematically assess the drivers of ecological quality in the catchment. The results show that the improved remote sensing ecological index (IRSEI) can efficiently and accurately obtain the spatial distribution pattern and temporal variation trend of IRSEI in the study area, which is more in line with the characteristics of indicators in this study area. The IRSEI in the study area showed an increasing trend from 1995 to 2020, from 0.4794 to 0.5615, and the proportion of benign ecological classes increased year by year during the period. Among the evaluation indicators, NDVI and N-NDBSI are the main factors affecting the environmental and ecological quality of the Daling River Basin, and the increase of vegetation cover, climate regulation and human activities have obvious promoting effects on the improvement of the ecological environment of the Daling River Basin. This study provides a scientific theoretical basis for the implementation of further ecological environmental protection measures. [ABSTRACT FROM AUTHOR]

[Objective]The aims of this study are to explore the coupling and coordination relationship and influencing factors of regional production-living-ecological spaces(PLES)function, and to guide the optimization of regional land spatial pattern and realize high-quality sustainable development.[Methods]Kunming was taken as an example. Based on the construction of the evaluation index system of the spatial function of PLE, Entropy weight method, linear weighting method, ternary diagram and coupling coordination degree model were integrated to quantitatively measure the spatial and temporal characteristics of the coupling and coordination of the spatial function of PLE in Kunming from 2000 to 2020, and geographical detectors were used to explore the dominant factors affecting the development of the spatial function of PLE in Kunming.[Results](1)From 2000 to 2020, the PL space function of Kunming City showed an overall upward trend, and the ecological space function showed a downward trend. Driven by policy measures and project construction site selection, the urban function positioning changed. The PLES function mainly experienced the EL and PLE advantage equilibrium area, and it was found that 2010 was an important turning point for entering the balanced development of PLE.(2)In the past 20 years, the overall fluctuation of the functional coupling coordination degree of PLE in Kunming City had been small, and it had experienced the basic coordination and moderate coordination stages, showing a pattern of high level around and low level in the middle in space.(3)The main factors affecting the coupling and coordination of PLES functions in Kunming were the per capita sown area and forestland area, and the interaction of factors increased the explanatory power of the development of PLES functions.[Conclusion]Among them, the habitat quality is less stable and volatile in the development of PLES functions. [ABSTRACT FROM AUTHOR]

The identification and quantification of the ecological risks, sources and distribution of heavy metals in purple soils are essential for regional pollution control and management. In this study, geo-accumulation index (Igeo), enrichment factor (EF), pollution index (PI), potential ecological risk index (RI), principal component analysis (PCA) model and geographical detector (GD) were combined to evaluate the status, ecological risk, and sources of heavy metals (HMs) in soils from a typical purple soil areas of Sichuan province. The results showed that the average contents of As, Cd, Cr, Cu, Hg, Ni, Pb and Zn in purple soil were 7.77, 0.19, 69.5, 27.9, 0.077, 30.9, 26.5 mg/kg and 76.8 mg/kg, and the Igeo, EF and RI of topsoil Hg and Cd in designated area was the highest, and the average contents of Hg and Cd in topsoil were obviously greater than respective soil background value in Sichuan province and purple soil. The hot spots for the spatial distribution of 8 HMs were mainly focused in the southwest and northeast of the designated area, and there were also significant differences for 8 HMs distribution characteristics in the profile soil. Cu comes from both anthropogenic and natural sources, Zn, Ni and Cr mainly come from natural sources, but As, Pb, Hg and Cd mainly derived from human activities. GD results showed that soil texture (X18), altitude (X4), total nitrogen (TN), clay content (X3), sand content (X2) and silt content (X1) had the greatest explanatory power to 8 HMs spatial differentiation.This study provides a reference for understanding the status and influencing factors of HM pollution in typical purple soil, and lays a theoretical foundation for the environmental treatment of purple soil in China. [ABSTRACT FROM AUTHOR]

A series of ecological and environmental issues often occur in the coal resources and large-scale exploitation activities. The Qinling Mountains have been the most vulnerable ecological areas in China. This study aims to explore the temporal and spatial evolution of mine habitat quality in the northern foot of the Qinling Mountains for regional ecological protection and sustainable development. A systematic investigation was implemented to clarify the impact of human activities on the ecological environment of mining areas under small river basins. The land use pattern was extracted from the Xiangyu mining area in the Feng River basin, according to the remote sensing images in 1985, 2002, 2010, and 2022. The InVEST model and geographical detector were used to quantitatively analyze the habitat quality and driving factors. The results showed that the land use in the Xiangyu mining area was mainly from the dry land to the forest and industrial land from 1985 to 2022, where the transfer area of forest reached 18 245 505.71 m², indicating the remarkable project performance of returning farmland to forest and grassland. Large ranges of bare and cultivated land were occupied by the development of the urban economy after 2010, where the industrial land was expanded rapidly. The habitat quality index decreased from 0.50 to 0.49, whereas, the standard deviation increased from 0.06 to 0.09, indicating the decreasing habitat quality and the ever-expanding spatial difference. Medium and low habitat quality were concentrated mainly in the areas with bare and construction land. In addition, the overall average degree of habitat degradation decreased from 0.48 to 0.47, but the rapid expansion of industrial land continued to affect the surrounding forests. There was a high spatial consistency in the distribution of habitat quality, habitat degradation, and land use patterns. The geographical detector showed that the influence degree of driving factors on habitat quality was ranked in the descending order of the land use (0.82), DEM (0.40), temperature (0.31), precipitation (0.30), population (0.29), NDVI (0.26), GDP (0.22), slope (0.12), and aspect (0.11). Among them, land use was the main cause of the fragile ecological environment. The coupled influence of natural with human factors on the ecological environment was greater than that of single factors. The interaction between land use and natural factors showed a double-factor enhancement, illustrating habitat quality was the result of natural factors and human factors. Research on mining areas should focus mainly on the land use policy and the impact of natural factors on engineering activities. Timely measures should be taken after high-intensity engineering activities, such as industrial construction and mining areas. The vegetation can be reconstructed to restore the water quality in the watershed. This finding can provide the scientific reference for the evolution of the ecological environment in similar mining areas, thus promoting the optimization of landscape patterns and the construction of ecological civilization. [ABSTRACT FROM AUTHOR]

China's low-carbon development path will make significant contributions to achieving global sustainable development goals. Due to the diverse natural and economic conditions across different regions in China, there exists an imbalance in the distribution of carbon emissions. Therefore, regional cooperation serves as an effective means to attain low-carbon development. This study examined the pattern of carbon emissions and proposed a potential joint emission reduction strategy by utilizing the industrial carbon emission intensity (ICEI) as a crucial factor. We utilized social network analysis and Local Indicators of Spatial Association (LISA) space-time transition matrix to investigate the spatiotemporal connections and discrepancies of ICEI in the cities of the Pearl River Basin (PRB), China from 2010 to 2020. The primary drivers of the ICEI were determined through geographical detectors and multi-scale geographically weighted regression. The results were as follows: 1) the overall ICEI in the Pearl River Basin is showing a downward trend, and there is a significant spatial imbalance. 2) There are numerous network connections between cities regarding the ICEI, but the network structure is relatively fragile and unstable. 3) Economically developed cities such as Guangzhou, Foshan, and Dongguan are in the center of the network while playing an intermediary role. 4) Energy consumption, industrialization, per capita GDP, urbanization, science and technology, and productivity are found to be the most influential variables in the spatial differentiation of ICEI, and their combination increased the explanatory power of the geographic variation of ICEI. Finally, through the analysis of differences and connections in urban carbon emissions under different economic levels and ICEI, the study suggests joint carbon reduction strategies, which are centered on carbon transfer, financial support, and technological assistance among cities. [ABSTRACT FROM AUTHOR]

To clearly describe the structural characteristics of China's rare earth innovation network (REIN), this study used data on China's rare earth patents since 2001 in the Incopat global patent database to analyze the structural characteristics of China's REIN from 2001 to 2020 at the provincial and municipal levels using social network analysis. The study finds the following: (1) The overall characteristics of the network show that the connectivity and diversification of China's REIN is increasing. (2) The network spatial pattern analysis shows that the REIN exhibits a radial spatial pattern centered on Beijing at both the provincial and municipal levels. (3) Research on network nodes shows that the network is dense in eastern regions and sparse in western regions, showing distinct characteristics of coastal and resource agglomeration. Not all regions with high levels of economic development play leading roles in the REIN. Small and medium-sized cities with abundant rare earth resources also play important leading roles due to their high levels of rare earth innovation. At the provincial and municipal levels, the REIN consists of four subgroups, among which there is not significant factional diversity. (4) In terms of influencing factors, economic development level, technology absorptive capacity, innovation output level, infrastructure, and market leadership have significant impacts at the provincial and municipal levels. In addition, government leadership, industrial structure, and education level have significant impacts at the municipal level. The study clarified China's technological accumulation in the field of rare earths and provided practical guidance for the innovative development of China's rare earth industry. [ABSTRACT FROM AUTHOR]

Examining the spatiotemporal dynamics and determinants of land urbanization is critical for promoting healthy urban development and the rational use of land resources. Based on the dataset consisting of land use change data and selected factors in 2010 and 2020, this study used visual analysis to reveal the spatiotemporal dynamics of land urbanization across prefecture-level cities in China. Meanwhile, the driving forces underlying land urbanization were examined by using geographical detector technique. Following are the findings: 1) we find that there exist notable spatial variances in land urbanization across prefecture-level cities. Currently, the differentiation in land urbanization between the northern and southern cities is more pronounced than that between the coastal and inland cities, or between the eastern and western cities. Prefecture-level cities located in central and western China have experienced the most rapid growth in land urbanization. Conversely, the growth rate in northeastern China is the lowest, while the velocity in eastern China remains relatively stable. By using spatial autocorrelation analysis, this study reveals that the land urbanization level in prefecture-level cities has significant spatial agglomeration. 2) We further find that land urbanization in China is influenced by factors related to urban land supply and demand, and urban population growth, economic growth, land financial and political incentive have greater impact on land urbanization than other factors. 3) We also find that the impacts of determinants on China's land urbanization vary over time, the explanatory power of economic development increased, while the explanatory power of state forces declined. We argue that integrating the supply and demand factors of land urbanization can provide a more comprehensive understanding of the driving mechanisms underlying land urbanization in China and other transitional countries, and help decision-makers in these countries formulate more detailed and specific land urbanization policies. [ABSTRACT FROM AUTHOR]

In order to provide reference for solving the problems of agricultural water shortage and water environment pollution in Zhejiang, an agricultural water use efficiency index was constructed, and an ultra-efficiency SBM model was used to measure the agricultural water use efficiency in Zhejiang Province from 2010 to 2020. The kernel density estimation method was used to analyze the spatio-temporal evolution law of agricultural water use efficiency. Finally, the main influencing factors of agricultural water use efficiency were analyzed using a geographical detector. The agricultural water use efficiency in Zhejiang Province increased from 1.153 in 2010 to 1.661 in 2020, with an average annual growth of 3.71% and an average of 1.535 in recent years, which is always in a state of effective utilization. Over the years, the overall trend of fluctuation rose, stability was strong, and the influence of pure technical efficiency was great. Although the agricultural water use efficiency in northern Zhejiang is higher than that in southern Zhejiang, the regional differences are decreasing. Economic development level, the government′ s emphasis on agricultural water use, agricultural planting structure, and water resource abundance were the main factors influencing the spatial differentiation of agricultural water use efficiency in Zhejiang Province. At the same time, the spatial variation of agricultural water use efficiency is not the result of a single factor, but of the nonlinear enhancement under joint action of many factors. Therefore, Zhejiang Province needs to further promote the efficiency of agricultural water use by improving farmers′ income level, increasing agricultural financial investment, promoting water-saving irrigation technology, and improving farmers′ consciousness of water-saving. [ABSTRACT FROM AUTHOR]

This study was conducted to analyze the changing trends of vegetation resilience and climate change resistance in Fujian Province from 2000 to 2020, and to explore the driving factors and response mechanisms of vegetation resilience at multiple scales. An enhanced vegetation index (EVI) derived from MOD13Q1 product was utilized to establish a multivariate linear regression model with climate indices to analyze trends in vegetation resilience and resistance. The impact mechanisms of climate, topography, and road factors on vegetation resilience were analyzed at multiple scales using a geographic detector and multiple logistic regression models. It was found that the changing trend of vegetation resilience exhibited notable spatial heterogeneity, with enhanced trends mainly observed in the northern region. Overall, vegetation resistance to precipitation showed a weakening trend, while resistance to potential evapotranspiration weakened in the northern region but strengthened in other areas. The results of geographic detector factor analysis indicate that vegetation resilience is associated with multiple factors across different scales, including precipitation, temperature, topography (elevation and slope), as well as the kernel density of national, provincial, and township-level roads. Multiple logistic regression results indicate that between 2000 and 2020, the positive impacts of temperature, precipitation, and slope on vegetation recovery have strengthened, while the positive influences of elevation, population, and gross domestic product(GDP) on vegetation degradation have gradually weakened. From 2010 to 2020, regions with higher railway density exhibited a weaker promoting effect on vegetation recovery, while areas with higher provincial-level road density demonstrated a stronger inhibiting effect on vegetation recovery. Overall, vegetation in Fujian Province is undergoing positive succession, with its responsiveness to human activities and topographic factors gradually diminishing. Meteorological and road-related factors are becoming more prominent in influencing vegetation dynamics. [ABSTRACT FROM AUTHOR]

Vegetation greenness change is the result of the combination of natural and anthropogenic factors. Understanding how these factors individually and collectively affect vegetation dynamics and whether their spatial heterogeneity has any effect on vegetation greenness change is the crucial investigation area. Previous studies revealed the distinct characteristics of spatial and temporal heterogeneity in the impact factors influencing vegetation greenness change across various regions, often assuming a linear contribution mechanism between vegetation greenness change and these drivers. However, such a simplistic assumption fails to adequately capture the real-world dynamics of vegetation greenness change. Thus, this study firstly used geographical detector (Geodetector) to quantitatively measure the contribution of each factor to vegetation greenness change considering spatial heterogeneity in the Yangtze River Economic Belt (YREB) during the growing season from 2000 to 2020, then selecting significant factors from numerous drivers with the recursive feature elimination algorithm combined with a random forest model (RFE-RF), which is able to reduce redundant features in the data and prevent overfitting. Finally, four stable impact factors and the spatial heterogeneity of some factors contributing to vegetation greenness change were identified. The results show that approximately 83% of the regional vegetation has shown an overall increasing trend, while areas undergoing rapid development predominantly experienced a decline in greenness. Single factor screened by Geodetector with the explanatory power greater than 10% for vegetation greenness change included temperature (Tem), population density (PD), the land-use/land-cover (LULC), DEM, wind speed, and slope. The RFE-RF method identified precipitation (Pre) and CO2 emissions as additional influential factors for vegetation greenness change, in addition to the first four factors mentioned previously. These findings suggest that the four stable factors consistently influence vegetation greenness change. Combined with the principles of the algorithms and the above results, it was found that the spatial heterogeneity of wind speed and slope has an effect on vegetation greenness change, whereas the spatial heterogeneity of Pre and CO2 emissions has minimal effect. [ABSTRACT FROM AUTHOR]

During the last decades, the association between the quality of the urban living environment and individual subjective well-being (SWB) has received wide attention in many metropolitan areas across the world. However, few studies have focused on the effects of the quality of the urban living environment on SWB from a human perception perspective and concerned the underdeveloped areas such as resource-based cities. Drawing on a large-scale social survey data set collected from the City Health Examination Evaluation (CHEE) in China, this paper employed the Geographical Detector method to reveal the association between the CHEE and individual subjective well-being in four resource-based cities in China (Ganzhou, Huangshi, Jingdezhen, and Luoyang). The results showed that the SWB and CHEE of the respondents in the resource-based cities are generally good, with mean evaluation scores of 4.15 and 4.27, respectively. The factor detector results indicated that the eight subdimensions of the CHEE are all significantly associated with individual SWB in the resource-based cities, with the q-statistic values in the following decreasing order: life amenities > urban management > urban vitality > diversity and inclusiveness > security and resilience > convenient transportation > urban landscape and scenery > ecological livability. Furthermore, individual socioeconomic attributes, such as gender, hukou, house tenure, car ownership, age, household income, and occupation, were also significantly associated with individual SWB. The interaction factor results revealed that there are bilinear enhancing and nonlinear enhancing effects on individual SWB after the interaction between the subdimensions in the CHEE. Regarding city types, the average evaluation scores of the CHEE and SWB in the declining cities, mature cities, and regenerating cities decreases in turn, and the subdimensions of the CHEE had heterogeneous effects on individual SWB across city types. The findings in this study could provide insights into improving individual SWB in China's resource-based cities by enhancing the quality of the urban living environment. [ABSTRACT FROM AUTHOR]

Northwest Hubei Province is a region prone to soil erosion. Investigating the spatiotemporal variations in soil erosion and identifying its underlying causes can offer valuable insights for guiding soil and water conservation efforts in the area. Based on the RUSLE model, we quantitatively analyzed the spatiotemporal variations in soil erosion in Northwest Hubei from 2005 to 2020. A geographical detector model was used to study the dominant factors of the spatial and temporal differentiation patterns of soil erosion, the degree of interactive coupling between multiple factors, and quantitative attribution of soil erosion-prone areas. The results indicate that, overall, the intensity of soil erosion in Northwest Hubei continued to decrease from 2005 to 2020. The average soil erosion modulus decreased by 16.3 t/(km²·a), and the soil erosion volume decreased by 200,000 t in 2020 compared to in 2005. Northwest Hubei showed a spatially divergent pattern of strong soil erosion intensity in the west and south and weak soil erosion in the central and north, with slight and mild erosion as a whole (accounting for 93% of the total erosion area). The land-use types in the study area were mainly forest land, and the proportions of forest land in 2005, 2010, 2015, and 2020 were over 60%. The soil erosion intensity of cultivated land was the highest among all land-use types. The area proportion of mild and above grade erosion intensity in this type was 85%, followed by forest land and grassland, with the percentage of the area of mild erosion intensity being 33.96% and 26.63%, respectively. From 2005 to 2020, each land-use type was transformed into another, and the transfer matrix results showed that cultivated land and grassland were mostly transformed into forest land, with a transfer area of 11.98%. Topographic factors markedly affected the soil erosion pattern: the soil erosion intensity in northwestern Hubei first increased and then decreased with the increase in elevation; the most significant erosion occured in the elevation zones of >200-500 m and >500-800 m, with proportions of the total erosion area being 47.7% and 31.8%, respectively. The soil erosion pattern was different under different slopes, and the area of >8° -25° was dominated by moderate, strong, and very strong erosion (the proportion of erosion is 55.4%); 65.6% of the area of >25° is strong and above high-intensity erosion. Given the impact of topographic factors on soil erosion, effective arrangements for soil erosion prevention and control measures should be made, keeping in mind the elevation distribution law and slope as the primary influencing factors. The results of geographical detection showed that slope and land use were the dominant factors affecting soil erosion, and the explanatory power of the two factors on soil erosion (q=0.479) was better than that of a single factor. Areas with slopes>35°, at elevations ranging between 500 and 800 m, showing an annual rainfall erosivity of 4,950.55-6,378.09 MJ·mm/(hm²·h·a), and cultivated land as the main land use type were identified as high-risk erosion areas. The results provide a scientific basis for soil erosion prevention, control, and land-use optimization in northwestern Hubei. [ABSTRACT FROM AUTHOR]

Against the backdrop of a series of major decisions such as the "Rural Revitalization" strategy and promoting the comprehensive revitalization of Northeast China, combined with the actual situation of the three provinces in Northeast China, the focus is on agricultural output and the integration of rural construction measurement systems, and a new agricultural modernization indicator evaluation system is established, based on the agricultural basic data of three northeastern provinces from 2010 to 2019, entropy method, multiple linear weighting function method, spatial clustering analysis method（spatial autocorrelation）, and geographic detector were used to analyze the spatiotemporal pattern of agricultural modernization development level in the three northeastern provinces, as well as the obstacle factors affecting agricultural modernization development through reverse tracking.The results indicate that the overall development level of agricultural modernization in the three northeastern provinces from 2010 to 2019 was relatively low, but the growth rate was fast. The differentiation of agricultural modernization levels in inland cities in the region was significant, with agricultural technology investment, farmers’ income, agricultural sustainable development ability, and rural civilization construction level being the main obstacles.Based on the research results, it is proposed to further increase financial investment in agricultural technology, accelerate the pace of green and sustainable development of agriculture, construct a long-term policy mechanism, and effectively promote the sustained increase of farmers’ income in order to promote agricultural modernization and high-quality development in Northeast China. [ABSTRACT FROM AUTHOR]

Recent research has revealed that the dynamics of autumn phenology play a decisive role in the inter-annual changes in the carbon cycle. However, to date, the shifts in autumn phenology (EGS) and the elements that govern it have not garnered unanimous acknowledgment. This paper focuses on the Yellow River Basin (YRB) ecosystem and systematically analyzes the dynamic characteristics of EGS and its multiple controls across the entire region and biomes from 1982 to 2015 based on the long-term GIMMS NDVI3g dataset. The results demonstrated that a trend toward a significant delay in EGS (p < 0.05) was detected and this delay was consistently observed across all biomes. By using the geographical detector model, the association between EGS and several main driving factors was quantified. The spring phenology (SGS) had the largest explanatory power among the interannual variations of EGS across the YRB, followed by preseason temperature. For different vegetation types, SGS and preseason precipitation were the dominant driving factors for the EGS in woody plants and grasslands, respectively, whereas the explanatory power for each driving factor on cultivated land was very weak. Furthermore, the EGS was controlled by drought at different timescales and the dominant timescales were concentrated in 1–3 accumulated months. Grasslands were more significantly influenced by drought than woody plants at the biome level. These findings validate the significance of SGS on the EGS in the YRB as well as highlight that both drought and SGS should be considered in autumn fall phenology models for improving the prediction accuracy under future climate change scenarios. [ABSTRACT FROM AUTHOR]

Settlements and cultivated land are important production and living spaces in promoting rural revitalization. However, few studies have explored the relationship between rural settlements and cultivated land from spatiotemporal interaction perspective. This paper analyzed the spatiotemporal conversion and the interactive mechanism between rural settlements and cultivated land in a karst mountainous area (Qixingguan District of Guizhou Province) with fragile ecological environment in China during 2009–2018. The results showed that the expansion of rural settlements and the loss of cultivated land coexisted in Qixingguan District. Only 2.68% of the new cultivated land was reclaimed from rural settlements, whereas 85.45% of the new rural settlements occupied cultivated land. Six spatial expansion modes of rural settlements when occupying cultivated land were identified. Among these six modes, the area of the edge-expansion & along traffic roads (EA) mode accounted for 52.75%. The occupation by rural settlements made the cultivated land landscape more fragmented. The area ratio index of cultivated land to rural settlements (ARICR) of Qixingguan District averaged 18.75 in 2009 and 17.21 in 2018, respectively. The ARICR reduced in all township administrative regions. Cultivated land with suitable slope condition for farming or without rocky desertification was more likely to be occupied by rural settlements. The probability of cultivated land occupied by rural settlements increased with the decrease of the distance to traffic roads, towns, and old rural settlements. The better the economic and social development of the township administrative regions, the more the ARICR decreased, while the richer the agricultural resources and the better the rural development of the township administrative regions, the less the ARICR decreased. The optimal reconstruction path of rural settlements, the comprehensive conservation path of cultivated land and the urban-rural integration development path in karst mountainous area were proposed. The findings would contribute to our understanding of the spatiotemporal interaction between rural settlements and cultivated land, and would provide a theoretical basis for promoting the coordinated development of rural man-land relationship and rural revitalization in karst areas. [ABSTRACT FROM AUTHOR]

Taking 219 national traditional Dong villages in Hunan, Guangxi and Guizhou as the research object, the spatial distribution characteristics and their influencing factors are explored by ArcGIS spatial analysis technology, combined with the nearest neighbor index, kernel density estimation, geographical detector and other methods. The results showed that the distribution of traditional Dong villages in the study area is agglomerative and uneven, and it is obviously distributed in the border areas of each county. Dong people have a strong ecological consciousness. They construct villages in accordance with the situation of mountains and the topography and landform to avoid adverse geographical conditions, and the villages have the characteristics of being close to the river gathering and along the road network layout. The distribution of traditional Dong villages is affected by many factors such as natural environment and social economy. The driving forces in descending order are highway density, altitude, river network density, land average GDP, average annual temperature, population density, average annual rainfall, slope direction and gradient. Analysis of interaction between two factors both showed the enhancement effect, and the interaction results of highway density and river network density had the greatest driving force on the distribution of traditional Dong villages. [ABSTRACT FROM AUTHOR]

Architectural heritage refers to buildings, complexes, and sites with historical, cultural, artistic, technological, and geographical values, including ancient buildings, historical buildings, places of interest, dwellings, and industrial sites. China's 20th-Century Architectural Heritage List is a state-level list that includes architecture of historical, cultural, technological, and artistic value in China in the 20th century. It is the carrier of the past century and the monument to witnessing the change in human knowledge, culture, technology, and even art. This list is from China, a country with a vast land area, a densely populated population, and numerous architectural relics. This study used ArcGIS to analyze 597 cases in 6 batches in China's 20th-Century Architectural Heritage List. Its spatial structure was studied by calculating the nearest neighbor index, Gini coefficient, imbalance index, and kernel density. The results showed that the distribution of the Chinese modern architectural heritage resources is cohesive and uneven in China. Next, the geographical detector model was used to analyze its influencing factors from the perspective of 12 factors. This study found that the spatial distribution of this type of resource was condensed. The provincial level showed a distribution pattern of seven centers with one core and multiple scattered points. Its distribution in 34 administrative regions is extremely uneven, with 57.29% being located in North and East China. It also focused on analyzing five influencing factors, namely, topography, regional status, culture and education, social and economic development level, and external contact. Exploring its spatial structure and influencing factors will not only enable a comprehensive understanding of the development context and current situation of 20th-century architectural heritage, but also provide a reference for its protection and sustainable use. [ABSTRACT FROM AUTHOR]

According to the 7th National Population Census, China is experiencing rapid growth of its ageing population, with large spatial disparities in the distribution of older folks in different regions. And yet, scant comparative research has been conducted on the two regions of Zhejiang and Jilin in particular, which differ considerably in economic development but witness nearly the same ageing trend. In response, this article compares Zhejiang, an advanced economic province, with Jilin, with its relatively low level of economic development, to explore the ageing issue and analyse the spatial correlation between older populations and socioeconomic factors. Using the spatiotemporal data analysis and geographical detector approaches, we obtain three significant findings: 1. both provinces have maintained steady rates of increase in ageing; 2. the older populations in Zhejiang and Jilin are mostly concentrated in the provincial capitals and nearby cities with reasonably established economies; and 3. the factors, including local fiscal expenditures, beds in hospitals and nursing homes, and coverage of social security, show a highly similar spatial pattern between older populations in Zhejiang and Jilin. The q-values of all the selected socioeconomic factors in Jilin showed a growth trend, indicating that the spatial correlation between these factors and ageing is strengthening year on year, that is, the resources gained from the socioeconomic development of Jilin have shifted steadily toward old-age services. As a consequence, a vicious circle of the slowing down of the economic growth drives away working forces and quickens the pace of population ageing, is present. From a policy perspective, Jilin province is strongly dependent on state-owned enterprises characterised by institutional rigidity, an inflexible market economy and an under-developed private sector, all of which are profoundly influenced by ageing. The consequence is large population outflows of young people. In contrast, the economy of Zhejiang province is partially decoupled from the ageing trend, so the gap in level of development between its counties has been narrowing. The policy implication here is that Zhejiang represents an active private economy that has coped successfully with ageing by attracting young migrants and developing new forms of development, such as the digital economy. [ABSTRACT FROM AUTHOR]

A watershed ecosystem is a compound ecosystem composed of land and rivers, and its health is closely related to the sustainable development of the region it is located in. The Yihe River Basin (YRB) in central China's Henan province, which is located in the north–south transition zone and has a mountain–hill–plain landscape from the upstream to the downstream, is adopted as the research area in this study. A watershed ecosystem health assessment system is constructed based on an ecosystem vigor–organization–resilience–service supply and demand harmony (EVORSH) framework and utilized to assess the ecosystem health in the YRB by taking a 3 km × 3 km grid as the evaluation unit. Thirteen factors are selected from natural and human social factors, and from them, the factors that influence watershed ecosystem health through the generation of spatial heterogeneity are identified using the geographical detector model. The following findings are obtained. (1) The mean value of ecosystem health levels in the YRB is 0.65 and at the good level. The ecosystem health has considerable spatial heterogeneity. The areas with high–high concentration are distributed in the mountains in the upper reaches of the YRB, and the areas with low–low concentration are mainly distributed in the plain areas in the middle reaches of the YRB. (2) The geographical detector result shows that 9 of 13 factors have a considerable impact on the spatial distribution of the YRB's ecosystem health. The interaction between two factors is enhanced synergically. The decisive power of population density, rainfall, and potential evapotranspiration are more than 0.5, so these three are the main factors that influence the distribution of ecosystem health in the YRB. (3) The EVORSH framework is suitable for the measurement of ecosystem health in the YRB. The evaluation result is consistent with the actual situation in the YRB. A 3 km × 3 km grid is used as the basic research unit, and it can more accurately and scientifically express the spatial heterogeneity of ecosystem health in the YRB compared with the macro evaluation unit. This study can provide a scientific basis for ecological protection and high-quality development planning in the YRB. By integrating multi-dimensional data and methods, the EVORSH framework proposed in this study can quickly and scientifically assess the status of ecosystem health, identify the influencing factors of spatial heterogeneity, and could be applied in other similar watersheds. [ABSTRACT FROM AUTHOR]

[Objective] In order to provide reference for ecological protection, construction and sustainable development of Haihe River Basin, the temporal and spatial changes of vegetation coverage (FVC) in growing season and its driving forces were investigated. [Methods] Based on MODIS NDVI remote sensing data and 18 influencing factors of the same period, trend analysis and M-K significance test were used to analyze the spatiotemporal variation characteristics of vegetation coverage during the growing season of Haihe River Basin from 2001 to 2019. The spatial differentiation characteristics and driving forces are discussed by means of geographic detector. [Results] During the growing season of Haihe River Basin from 2001 to 2019, the vegetation coverage showed a significant upward trend, with a linear tendency rate of 0.063/decade, and the growth rate slowed down after 2011. The vegetation coverage was relatively high in the Bohai Bay area and some urban areas. The area of improvement area was much larger than that of degradation area, and the improvement part was dominated by extremely significant improvement, accounting for 60.42% of the total basin area. The spatial variation of vegetation coverage in Haihe River Basin during the growing season was mainly determined by the proportion of forest land and the proportion of mixed forest-grass land, and the explanatory power was more than 30%. The strongest explanation for the interaction of vegetation coverage in Haihe River Basin during the growing season was the ratio of mixed forest and grass land and the ratio of farmland. [Conclusion] Overall vegetation coverage increased significantly in Haihe River Basin, and the main driving force was the proportion of forest land and the proportion of mixed forest and grass land. [ABSTRACT FROM AUTHOR]

[Objective] The study aimed to expiore the carrying capacity of cultivated land resources and its internal driving mechanism, formulate cultivated land policies and efficiently manage and protect cultivated land, maintain the sustainable utilization of cultivated land resources and ensure food security. [ Method] Theil index, coefficient of variation, center of gravity model and other methods were used to analyze the spatial and temporal differentiation characteristics of cultivated land resources carrying capacity in Guangdong province at the secondary, municipal and county levels. On this basis, the geographical detector was used to explore the driving mechanism of its spatial differentiation. [Result] ( i) From 2000 to 2020, the Theil index and coefficient of variation of the carrying capacity of cultivated land resources in Guangdong province at the secondary district, municipal and county scales showed an increasing trend and increased with the decrease of spatial scale, and the imbalance of the carrying capacity of cultivated land resources at the county level was the most significant. ( ii) During the study period, the overall carrying capacity of cultivated land resources in Guangdong province continued to decline, with the largest change at the county level. The low-value areas formed an aggregated distribution in the Pearl River Delta region and gradually formed an inverted ' U' pattern. The gravity center of the carrying capacity of cultivated land resources moved to the southwest in a wave-like manner. (iii) The driving factor force at the municipal scale was generally greater than that of the county scale. The carrying capacity of cultivated land resources at the municipal scale was significantly affected by the per capita gross agricultural product, and at the county scale was significantly affected by the per capita cultivated land area. The carrying capacity of cultivated land resources in Guangdong province was generally driven by social and economic development and resource endowment conditions. ( iv) The factor interactions at different scales was characterized by two-factor enhancement and nonlinear enhancement, and the factor interaction showed a synergistically enhanced effect. Factors such as natural site conditions and production factor input levels can significantly enhance the interaction between two factors. [ Conclusion] Therefore, it is suggested to increase the income from agricultural production in Guangdong province, balance the contradiction between food supply and demand, help agricultural development, provide technical and financial capital for rural production system, increase financial support, and promote the transformation of food production, so as to improve the carrying capacity of cultivated land resources in Guangdong province and ensure the sustainable utilization of cultivated land resources. [ABSTRACT FROM AUTHOR]

[Objective] Exploring the erosion problem caused by grassland degradation in Gannan Prefecture can help understand the local soil and water losses, which is conducive to the construction of ecological security barrier on the Qinghai-Tibet Plateau. Methods] This study revealed the temporal and spatial changes of soil erosion in Gannan Prefecture in 2000, 2010 and 2019 based on the RIJSI'E model, and analyzed the erosion factors of Gannan Prefecture through geographical detector. Results] The overall degree of erosion in Gannan Prefecture is relatively light, mainly with slight erosion and mild erosion. The areas with strong, extremely strong and severe erosion in Gannan Prefecture are relatively small, but the areas and intensities of these types of erosion show the increasing trend with time. The spatial difference of soil erosion in Gannan Prefecture is obvious, and the soil erosion intensity in the western region is lower than that in the eastern region. The distributions of moderate, strong, very strong, and severe erosion areas are similar, showing a relatively obvious aggregation state. Zhuoni County, Diebu County and Zhouqu County show higher erosion risk than other counties. The erosion grade of 87.14% of Gan nan Prefecture did not change from 2000 to 2()19, and the transferred areas were mainly mild erosion and mild erosion areas. The areas with increased erosion levels have a higher proportion of area than areas with decreased erosion levels, which mainly distribute in Maqu County, Luqu County. Xiahe County. Zhuoni County and Diebu County. Land use and topographic undulation are the main factors affecting ecological vulnerability in Garman Prefecture. The project of returning farmland to forest (grass), agricultural and animal husbandry activities, the advancement of urbanization, and the complex topography of Gan nan Prefecture all have the greater impacts on erosion. There is enhancement when land use acting with population density, slope, elevation, ave rage annual rainfall and vegetation coverage. Conclusion] The overall erosion in Gan nan Prefecture is light, but the local erosion is strong and shows an increasing trend with time. Land use change and topographic factors have the great impacts on the erosion. Tn the future, more attention should be paid to land use change and grassland degradation, do a good job of protection and restoration should be strengthened, and the erosion deterioration risk in ecological risk areas with complex topography should be be concerned in Gan nan Prefecture. [ABSTRACT FROM AUTHOR]

Understanding how public service accessibility is related to housing prices is crucial to housing equity, yet the heterogeneous capitalisation effect remains unknown. This study aims to investigate the spatial effect of public service accessibility on housing prices in rapidly urbanising regions. Here, we propose a novel methodological framework that integrates the hedonic price model, geographical detector model and the spatial association detector model to understand housing equity issues. The rapidly rising housing prices, vastly transformed urban planning and heterogeneous land use patterns make the urban centre of Wuhan a typical case study. High-value units of public service accessibility are concentrated in built-up areas, while low-value units are located at the urban fringe. The results indicate that larger public services have more significant clustering effects than smaller ones. Recreational, medical, educational and financial facilities all have capitalisation effects on housing prices. Both the geographical detector model and the spatial association detector model could identify the drivers of housing prices, but the explanatory power of the latter is greater and could enhance the validity and reliability of the findings. We further find that the explanatory power of the driving factors on housing prices obtained from the spatial association detector model is greater than that of the geographical detector model. Based on the spatial association detector model, the main drivers of public service facilities are accessibility to restaurants and bars and accessibility to ATMs. In addition, there are bivariate or nonlinear enhancement effects between each pair of driving factors. This approach provides significant insights for urban environmental development planning and local real estate planning. [ABSTRACT FROM AUTHOR]

• A method to classify and quantify factors affecting site contamination is proposed. • Potential pollution source is key factor in determining the distribution of pollution. • Soil permeability is the dominant factor influencing the diffusion of heavy metals. Smelting activities are the main pathway for the anthropogenic release of heavy metals (HMs) into the soil-groundwater environment. It is vital to identify the factors affecting HMs pollution to better prevent and manage soil pollution. The present study conducted a comprehensive investigation of HMs in soil from a large abandoned Zn smelting site. An integrated approach was proposed to classify and quantify the factors affecting HMs pollution in the site. Besides, the quantitative relationship between hydrogeological characteristics, pollution transmission pathways, smelting activities and HMs pollution was established. Results showed that the soils were highly contaminated by HMs with a pollution index trend of As > Zn > Cd > Pb > Hg. In identifying the pollution hotspots, we conclude that the pollution hotspots of Pb, As, Cd, and Hg present a concentrated distribution pattern. Geo-detector method results showed that the dominant driving factors for HMs distribution and accumulation were the potential pollution source and soil permeability. Additionally, the main drivers are variable for different HMs, and the interaction among factors also enhanced soil HMs contamination. Our analysis illustrates how the confounding influences from complex environmental factors can be distilled to identify key factors in pollution formation to guide future remediation strategies. [Display omitted] [ABSTRACT FROM AUTHOR]

Over the past two decades, the location and morphology of the coastline, as well as the land use/land cover (LULC) in the Circum-Bohai region in China, have undergone significant changes due to rapid industrialization and urbanization. Analyzing the temporal and spatial variation in coastal lines and LULC can provide a meaningful basis for the rational allocation of land resources. Using Landsat TM/OLI series dates from the Google Earth Engine (GEE) platform, this study applied the Linear Superposition Water Index (LSWI) and the Otsu threshold method (OTSU) algorithm to extract and analyze the coastline of the Circum-Bohai region. Additionally, the Random Forests (RF) method was employed to extract LULC information in the coastal zone. Using the geographical detector, we further explored the influence of social and economic factors, as well as natural factors, on spatial differentiation mechanisms of LULC change in the Circum-Bohai. Our results show that between 2000 and 2020, the Circum-Bohai coastline generally expanded towards the ocean by a total of 1062.99 km. The highest rate of change occurred during 2010 to 2015, and human activities were the primary cause of most of the changes, with the exception of the Yellow River Delta, where natural factors were dominant. The main types of LULC in the study area from 2000 to 2020 were farmland and construction land. The area of farmland proportion decreased by 1.75%, while the area of construction land proportion increased from 16.73% to 29.54%. Our findings indicate that the degree of land use in the Circum-Bohai is deepening. Based on our factor detection analysis, the added value of the secondary industry was the most critical influencing factor on LULC. Furthermore, the combined effect of the added value of the secondary industry and gross domestic product (GDP) has a significant driving impact on LULC. These findings can provide reference and data support for the sustainable development and comprehensive management of land resources. The relevant departments can use these results to prompt corresponding policies for the rational allocation of land resources. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has led to a significant increase in e-commerce, which has prompted residents to shift their purchasing habits from offline to online. As a result, Smart Parcel Lockers (SPLs) have emerged as an accessible end-to-end delivery service that fits into the pandemic strategy of maintaining social distance and no-contact protocols. Although numerous studies have examined SPLs from various perspectives, few have analyzed their spatial distribution from an urban planning perspective, which could enhance the development of other disciplines in this field. To address this gap, we investigate the distribution of SPLs in Tianjin's central urban area before and after the pandemic (i.e., 2019 and 2022) using kernel density estimation, average nearest neighbor analysis, standard deviation elliptic, and geographical detector. Our results show that, in three years, the number of SPLs has increased from 51 to 479, and a majority were installed in residential communities (i.e., 92.2% in 2019, and 97.7% in 2022). We find that SPLs were distributed randomly before the pandemic, but after the pandemic, SPLs agglomerated and followed Tianjin's development pattern. We identify eight influential factors on the spatial distribution of SPLs and discuss their individual and compound effects. Our discussion highlights potential spatial distribution analysis, such as dynamic layout planning, to improve the allocation of SPLs in city planning and city logistics. [ABSTRACT FROM AUTHOR]

To achieve sustainable urban development, it is essential to gain insight into the spatial and temporal differentiation characteristics and the driving mechanisms of the urban thermal environment (UTE). As urbanization continues to accelerate, human activity and landscape configuration and composition interact to complicate the UTE. However, the differences in UTE-driven mechanisms at different stages of urbanization remain unclear. In this study, the UTE of Shenyang was measured quantitatively by using the land surface temperature (LST). The spatial and temporal differentiation characteristics were chronologically studied using the standard deviation ellipse (SDE) and hotspot analysis (Getis–Ord Gi*). Then, the relationship between human activities, landscape composition and landscape configuration and LST was explored in a hierarchical manner by applying the geographical detector. The results show that the UTE in Shenyang continues to deteriorate with rapid urbanization, with significant spatial and temporal differentiation characteristics. The class-level landscape configuration is more important than that at the landscape level when studying UTE-driven mechanisms. At the class level, the increased area and abundance of cropland can effectively reduce LST, while those of impervious surfaces can increase LST. At the landscape level, LST is mainly influenced by landscape composition and human activities. Due to rapid urbanization, the nonlinear relationship between most drivers and LST shifts to near-linear. In the later stage of urbanization, more attention needs to be paid to the effect of the interaction of drivers on LST. At the class level, the interaction between landscape configuration indices for impervious surfaces, cropland and water significantly influenced LST. At the landscape level, the interactions among the normalized difference building index (NDBI) and other selected factors are significant. The findings of this study can contribute to the development of urban planning strategies to optimize the UTE for different stages of urbanization. [ABSTRACT FROM AUTHOR]

Tarim River is the longest inland river in China, which irrigates nearly one-third of Xinjiang's land in the last century. The previous unreasonable exploitation and utilization have posed a great threat to the Tarim River, particularly with the economic development and global climate change in recent years. The ecological environment of the Tarim River basin has been improved significantly, after a series of comprehensive ecological control measures, such as ecological water transfer in 2000. However, a serious challenge still remains on the sustainability of ecological restoration in the mainstream of the Tarim River under water transport projects. This study aims to clarify the dynamic change characteristics of vegetation cover in the mainstream of the Tarim River, in order to accelerate the process of vegetation protection and restoration. MODIS data was also collected from 2001 to 2021. Trend analysis and coefficient of variation were used to determine the development trend and stability of vegetation. The influence of driving factors on vegetation coverage was investigated by correlation analysis and geographical detector. The results showed that a relatively stable rising trend was found in the vegetation coverage of the mainstream of Tarim River, particularly with an annual growth rate of 2.84%. The changing trend was outstanding in seasonal fluctuations. The vegetation coverage was as high as 28.56% in summer, especially in August, but the lowest in April. There were obvious regional differences in the vegetation coverage, with the descending order of the upstream, midstream, and downstream. In 2021, the vegetation coverage in the upstream was 1.91 times as much as that in the midstream, and 3.34 times as much as that in the downstream in August. Vegetation coverage increased in 66.5% of Tarim River, most of which (49.0%) increased significantly. The combined effect of natural factors and human activities was the main reason for the rapid increase and spatial difference of vegetation coverage in the mainstream, among which the human activities posed the more direct impact on vegetation coverage. The geographical detector showed that the influence of each driving factor on the vegetation cover was ranked in descending order as follows: Land use (0.69), GDP (0.41), population density (0.37), DEM (0.30), rainfall (0.28), soil type (0.22), temperature (0.16), slope (0.04), slope aspect (0.01). The impact of human factors on the vegetation cover was mainly reflected in the change in land use type. Climatic factors (such as rainfall and temperature) presented a low correlation with the vegetation cover. The spatial distribution characteristics of vegetation cover were attributed to the interaction of multiple influencing factors. Future human activities can be more closely related to the change of vegetation cover and ecological environment evolution in the mainstream, with the influence of population growth, social and economic development. This finding can also provide the scientific basis to further understand the characteristics and driving factors of vegetation cover in the mainstream of Tarim River, particularly for the local ecological environment protection. [ABSTRACT FROM AUTHOR]

The distribution characteristics of park green space in Wuhan City were studied by standard deviation ellipse (SDE) method, average nearest neighbor index, kernel density estimation (KDE) method and scale index method. At the same time, the influencing factors of its distribution were discussed from three aspects of nature, social economy and culture with a geographical detector. The results show that (1) the distribution of park green space in Wuhan was relatively concentrated, and the directionality was not strong. The distribution mode was clustered mode. There were four main aggregation centers, which were located in Jianghan District, Qingshan District, Dongxihu District and Wuchang District respectively. Among them, the scale index of Jianghan District was the highest, up to 0.590 2, while that of Xinzhou district was the lowest, only 0.004 7. (2) The order of interpretation intensity of park green space distribution in Wuhan was population density (0.382) > regional GDP (0.278) > location of historical relics (0.165) > urbanization rate (0.136) > watershed zoning (0.069) > altitude (0.012). (3) The interaction of population density and watershed zoning had the strongest explanation for the distribution of park green space in Wuhan. Except for the infl uence of population density, the interaction of urbanization rate and regional GDP had the strongest explanation for the distribution of park green space in Wuhan. [ABSTRACT FROM AUTHOR]

【Objective】 The present paper aimed to study the spatial distribution characteristics and influencing factors of high-standard farmland in Xinjiang, in order to provide decision-making basis and data support for the construction of high-standard farmland in Xinjiang in the future.【Method】 In the study, GIS spatial analysis technology, nuclear density analysis and geographic detector were used to study the spatial differentiation and influencing factors of high-standard farmland in Xinjiang. 【Results】(i) Through the spatial analysis of nuclear density, it was found that the high-standard farmland in Xinjiang was obviously clustered spatially, and there were two significant high-density agglomeration areas in northern and southern Xinjiang, with Tacheng area and Changji Prefecture as the ‘polar core’ in northern Xinjiang, and Aksu area and Kashgar area as ‘polar core’ in southern Xinjiang.(ii) From the perspective of the distribution of construction potential, counties and cities with high construction potential were mainly distributed in the western prefectures of Xinjiang division, and counties and cities with low construction potential were mainly distributed in the eastern prefectures of Xinjiang zoning. The counties and cities with lower construction potential in southern Xinjiang were the most distributed, and the research units in the higher construction potential area had no obvious concentration in space, and the research units in the high construction potential area were mainly distributed in the Tacheng area and Aksu area in northern Xinjiang and the Aksu region and Kashgar area in southern Xinjiang. The counties with high construction potential were mainly distributed in the left prefecture of Xinjiang division, and the counties with low construction potential were mainly distributed in the right prefecture of Xinjiang division.(iii) At the county scale, policy factors had a greater impact on the scale of high-standard farmland construction, and the core interaction factors of each district were related to policy factors; At the plaque scale, it was significantly affected by socioeconomic factors, and the core interaction factors were all related to socioeconomic factors and traffic location factors. The interaction detection results mainly included two interaction types: Nonlinear enhancement and two-factor enhancement, and each influence factor had obvious interaction enhancement effect on the construction scale of high-standard farmland under different partitions. 【Conclusion】 The spatial accumulation of high-standard farmland in Xinjiang is obvious, and natural environmental factors and policy regulatory factors are the main driving factors affecting the construction scale of high-standard farmland. [ABSTRACT FROM AUTHOR]

The vigorous development of information and communications technology has accelerated reshaping of the financial industry. The COVID-19 pandemic has further catalyzed the demand for digital financial services. Digital financial inclusion relies on information technology to overcome spatial limitations. In this case, the research question is whether it adheres to the spatial laws governing conventional financial activities. This study uses exploratory spatial data analysis and a geographical detector to elucidate the spatiotemporal characteristics and factors influencing digital financial inclusion at the county level in China (Data don't include that of Hong Kong, Macao and Taiwan of China) from 2014 to 2020. The research findings indicate: first, China's county-level digital financial inclusion is generally increasing and exhibits significant spatial autocorrelation. Second, population density, level of traditional financial development, government regulation, and education level are key determinants of China's county-level digital financial inclusion. Third, policies should be differentiated by region to narrow the spatial gap in digital financial inclusion. The results provide a reference for other developing countries on using digital technology to develop financial inclusion. [ABSTRACT FROM AUTHOR]

Knowledge of aerosol dynamics is essential to combating atmospheric aerosol pollution, and there is a growing interest in aerosol changes and their drivers. However, the effects of interactions between natural and anthropogenic drivers of aerosol are not well understood. Here, we analyse changes in aerosol optical depth (AOD) in Xinjiang, China using the multiangle implementation of atmospheric correction aerosol products over 2001–2019 and investigate the driving factors of aerosol dynamics using a random forest (RF) and a geographical detector. The results show the dominant AOD changes in the quasi‐period of 3.21 months, 7.86 months, 1.19 years, for seasonal, half‐year, and interannual variations of aerosols. The increasing and then decreasing nonlinear trends were observed in the interannual AOD variation during 19 years period. The importance ranking results of the two models indicated that meteorological factors dominated the spatial distribution of AOD in Xinjiang (72.73% for RF and 65.78% for the geographic detector), and that interactions between factors enhanced the explanatory power in AOD changes. In addition, the influence of anthropogenic factors on AOD was increasing in North Xinjiang, and the influence of precipitation and population on AOD was increasing in East Xinjiang. However, the influence of drivers on AOD in South Xinjiang was basically constant over time, showing the spatially heterogeneous relationship between AOD and drivers. This study emphasized the spatial heterogeneity and driving factors of small‐scale aerosols in arid regions and so can guide targeted air pollution prevention and control in local areas. [ABSTRACT FROM AUTHOR]

Taking 24 Miao villages in Leigong Mountain Area of Guizhou Province as the case study, based on the survey data of placeness perception, this paper uses the cluster analysis method to divide the 24 ethnic villages into five types with different degrees of tourism development, and measures the placeness perception. With the help of geographic detector method, this paper makes factor detection and interactive detection on the four dimensions of landscape, text, symbol and emotion that affect village residents' placeness perception in Leigong Mountain area. The results show that: (1)There are differences in village residents' placeness perception in different stages of tourism development, among which the village residents in the stable stage have the weakest placeness perception. (2) From the development stage to the stable stage, the residents' perception of landscape, text and symbol dimensions is enhanced, but the perception of emotional dimension is weakened. (3) From the development stage to the stable stage, the function of placeness elements has changed from daily life to entertainment and leisure to industrial development. (4) The placeness perception of all kinds of villages is enhanced by two factors after two-way interaction, and the interaction of landscape dimension and symbol dimension has the greatest impact on placeness perception. The above conclusions provide a theoretical basis for avoiding the "homogeneity" and "non placeness" of tourism development in ethnic villages and promoting the coordinated development of tourism and human land relationship in ethnic villages. [ABSTRACT FROM AUTHOR]

The atmospheric dust caused by the cement industry is one of the main components of air pollutants. China is the largest producer and consumer of cement. It is challenging to balance cement needs and environmental protection. Based on the emission source data, this study examined the spatial and temporal patterns of PM2.5 by the cement industry's contribution (PM2.5Cement). The annual value of PM2.5Cement decreased from 1.40 × 106 µg/m3 in 2010 to 0.98 × 106 µg/m3 in 2017, which was reduced by 30.31%. I used the standard deviation ellipse and gravity center transfer method and identified that the cement industry center shifted from the east to the midwest of China, where a high-density population exists and a large portion of the population is exposed to the air pollution. The geographical detector method was used to analyze the contribution of the natural environment, green development, and socioeconomic development to PM2.5Cement. The main driving factors were identified as the socioeconomic development and the traffic conditions in 2010, which was giving way to the regional independent innovation in 2017. The cement industry's contributions to atmospheric PM2.5 vary spatially, suggesting that green development and optimized location for the cement industry are crucial to reducing the size of the population exposed to the pollutants. [ABSTRACT FROM AUTHOR]

Understanding the long-term dynamics and driving factors behind small and micro wetlands is critical for their management and future sustainability. This study explored the impacts of natural and anthropogenic factors on the spatiotemporal evolution of these areas in Wuxi area using the support vector machine (SVM) classification method and the geographic detector model based on Landsat satellite image data from 1985 to 2020. The results revealed that: (1) Natural wetlands were prominent in Wuxi area, with an average proportion of 70%, and although they exhibited a downward trend over the last ten years, the scale of natural small and micro wetlands increased 1.5-fold—from 4349.59 hm2 in 1985 to 10,841.59 hm2 in 2020. (2) The small and micro wetlands in Wuxi area had obvious seasonal variations, with most being 0.1–1 hm2 and 1–3 hm2, respectively. From the perspective of spatial distribution, they were primarily distributed in Yixing district, which accounts for 34% of Wuxi area. (3) The distribution of small and micro wetlands was systematically affected by natural and human activities. The main factors that affected the distribution of small and micro wetlands were the average annual temperature and GDP, with the interactions between all factors being nonlinear and bi-linear. The influences of natural factors on small and micro wetlands were weakened, with human activities steadily emerging as the dominant factor that affected their distribution. The results of this study can provide supportive data and a scientific basis for the ecological restoration and protection of wetlands. [ABSTRACT FROM AUTHOR]

Due to climate change and human activities, the eco-environment quality (EEQ) of eco-fragile regions has undergone massive change, especially in the Tibet Plateau. The Qilian Mountains (QLM) region is an essential ecological function zone in the northeastern Tibet Plateau, which plays a vital role in northwestern China's eco-environmental balance. However, EEQ changes in the QLM during the 21st century remain poorly understood. In this study, the spatiotemporal variations of the EEQ in the QLM were analyzed from 2000 to 2020 using a remote sensing ecological index (RSEI). The EEQ driving factors are identified by the geographic detector, and the spatial influence of critical factors is represented by a geographically weighted regression model. The results show low EEQ in the QLM. From 2000 to 2020, the EEQ initially slightly improved, then deteriorated, and finally gradually recovered. Spatially, the EEQ shows an increasing trend from northwest to southeast. Moran's I of EEQ remains at around 0.95, representing high spatial aggregation. "High–High" and "Low–Low" clustering features dominate in the local spatial autocorrelation, indicating the EEQ of the QLM is polarized. Precipitation is the dominant positive factor in the EEQ, with a q statistics exceeding 0.644. Furthermore, the key factors (precipitation, distance to towns, distance to roads) affecting EEQ in different periods vary significantly in space. From results we can draw the conclusion that the natural factors mainly control the spatial patterns of EEQ, while the human factors mainly impact the temporal trend of EEQ, the EEQ in the QLM has been significantly improved since 2015. Our findings can provide theoretical support for future eco-environmental protection and restoration in the QLM. [ABSTRACT FROM AUTHOR]

Although understanding the carbon and water cycles of dryland ecosystems in terms of water use efficiency (WUE) is important, WUE and its driving mechanisms are less understood in Central Asia. This study calculated Central Asian WUE for 2001–2021 based on the Google Earth Engine (GEE) platform and analyzed its spatial and temporal variability using temporal information entropy. The importance of atmospheric factors, hydrological factors, and biological factors in driving WUE in Central Asia was also explored using a geographic detector. The results show the following: (1) the average WUE in Central Asia from 2001–2021 is 2.584–3.607 gCkg−1H2O, with weak inter-annual variability and significant intra-annual variability and spatial distribution changes; (2) atmospheric and hydrological factors are strong drivers, with land surface temperature (LST) being the strongest driver of WUE, explaining 54.8% of variation; (3) the interaction of the driving factors can enhance the driving effect by more than 60% for the interaction between most atmospheric factors and vegetation factors, of which the effect of the interaction of temperature (TEM) with vegetation cover (FVC) is the greatest, explaining 68.1% of the change in WUE. Furthermore, the interaction of driving factors with very low explanatory power (e.g., water pressure (VAP), aerosol optical depth over land (AOD), and groundwater (GWS)) has a significant enhancement effect. Vegetation is an important link in driving WUE, and it is important to understand the mechanisms of WUE change to guide ecological restoration projects. [ABSTRACT FROM AUTHOR]

Background: With the gradual increase of residents' income and the continuous improvement of medical security system, people's demand for pursuing higher quality and better medical and health services has been released. However, so far little research has been published on China's high quality medical resources (HQMR). This study aims to understand the spatiotemporal variation trend of HQMR from 2006 to 2020, analyze regional disparity of HQMR in 2020, and further explore the main factors influencing the distribution of HQMR in China. Methods: The study selected Class III level A hospitals (the highest level medical institutions in China) to represent HQMR. Descriptive statistical methods were used to address the changes in the distribution of HQMR from 2006 to 2020. Lorentz curve, Gini coefficient (G), Theil index (T) and High-quality health resource density index (HHRDI) were used to calculate the degree of inequity. The geographical detector method was used to reveal the key factors influencing the distribution of HQMR. Results: The total amount of HQMR in China had increased year by year, from 647 Class III level A hospitals in 2006 to 1580 in 2020. In 2020, G for HQMR by population was 0.166, while by geographic area was 0.614. T was consistent with the results for G, and intra-regional contribution rates were higher than inter-regional contribution rates. HHRDI showed that Beijing, Shanghai, and Tianjin had the highest allocated amounts of HQMR. The results of the geographical detector showed that total health costs, government health expenditure, size of resident populations, GDP, number of medical colleges had a significant impact on the spatial distribution of HQMR and the q values were 0.813, 0.781, 0.719, 0.661, 0.492 respectively. There was an interaction between the influencing factors. Conclusions: China's total HQMR is growing rapidly but is relatively inadequate. The distribution of HQMR by population is better than by geography, and the distribution by geography is less equitable. Population size and geographical area both need to be taken into account when formulating policies, rather than simply increasing the number of HQMR. [ABSTRACT FROM AUTHOR]

This study investigated the spatial heterogeneity of gravel with different particle sizes and the factors influencing these in gravelsand mulched fields. Based on 102 sampling points in a gravel-sand mulched field, descriptive statistics of gravel with different particle size were conducted to analyze the characteristics of the spatial variation and aggregation patterns of gravel with different particle sizes via two geostatistical methods, semi-covariance function and global spatial autocorrelation analysis. Then, the main factors influencing the spatial variation of gravel proportion were identified using a geographic probe model. The main influencing factors were then used to visualize the percentage of gravel with different particle sizes and obtain their distribution characteristics via cooperative geographically weighted regression kriging. The results showed that the spatial variation coefficients of gravel with different particle sizes were all greater than 10%, which was a moderate variation. The spatial heterogeneity ratios of the gravel size varied over a wide range (0.26%–83.48%), indicating that their spatial heterogeneity and variability were influenced by both stochastic and structural factors. Spatial auto correlation analysis showed that the global Moran′s I index of gravel of each particte size was higher than 0.674 and the Z-values were all greater than 1.96, indicating highly significant spatial dependence characteristics for gravel of different particle sizes with aggregated distribution. The explanatory power of the spatial variance of different particle sizes in the gravel-sand mulched fields was in the order of planting year> slope aspect> surface roughness> slope> profile curvature> plane curvature> relief degree of land surface. The single factor explanatory power of planting year was significantly higher than that of the other factors. The spatial distribution of gravel with different particle sizes in the soil of the gravel-mulched field varied significantly under different environmental conditions, with the distribution appearing aggregated. The planting year, slope aspect, and surface roughness were the main factors influencing the spatial distribution of gravels of different particle sizes in gravel-sand mulched fields. [ABSTRACT FROM AUTHOR]

Desertification is one of the most harmful ecological disasters on the Mongolian Plateau, placing the grassland ecological environment under great pressure. Remote-sensing monitoring of desertification and exploration of the drivers behind it are important for effectively combating this issue. In this study, four banners/counties on the border of China and Mongolia on the Mongolian Plateau were selected as the target areas. We explored desertification dynamics and their drivers by using remote sensing imagery and a product dataset for the East Ujimqin Banner and three counties in Mongolia during the period 2000–2015. First, remote sensing information on desertification in the fourth phase of the study area was extracted using the visual interpretation method. Second, the dynamic change characteristics of desertification were analyzed using the intensity analysis method. Finally, the drivers of desertification and their explanatory powers were identified using the geographical detector method. The results show that the desertification of the East Ujimqin Banner has undergone a process of reversion, development, and mild development, with the main transition occurring between slight (SL) and non-desertified land (N), very serious desertified land (VS), and water areas. The dynamics of desertification in this region are influenced by a combination of natural and anthropogenic factors. Desertification in the three counties of Mongolia has undergone processes of development, mild development and mild development with SL and vs. as the main types. Desertification in Mongolia is mainly concentrated in Matad County, which is greatly affected by natural conditions and has little impact from anthropogenic activities. In addition, the change intensity of desertification dynamics in the study area showed a decreasing trend, and the interaction between natural and anthropogenic drivers could enhance the explanatory power of desertification dynamics. The research results provide a scientific basis for desertification control, ecological protection, and ecological restoration on the Mongolian Plateau. [ABSTRACT FROM AUTHOR]