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Intangible cultural heritage (ICH) reflects a region's history and culture, serving as a significant indicator of regional identity and cohesion. The Grand Canal Basin in China is rich in historical traditions, containing a rich array of ICH resources. Analyzing the spatiotemporal distribution characteristics and influencing factors of ICH within the Grand Canal Basin of China can provide a scientific basis for developing cultural industries and promoting sustainable regional economic growth. This study employed GIS-based spatial analysis methods, including kernel density estimation, the mean nearest neighbor index, and standard deviation ellipse, to investigate the spatiotemporal distribution of 504 national-level ICH items (including extensions) in the Grand Canal Basin of China. The results demonstrate the significant spatial clustering of ICH, with concentrations in high-density regions, particularly at the northern and southern ends of the canal. There is significant regional disparity in the distribution of ICH, with an uneven quantity and structure, predominantly featuring traditional skills and traditional drama categories. The average centroid shift of ICH exhibits a north-to-south oscillatory trajectory. However, overall, it demonstrates a southward-moving trend. This study also underscores the impacts of urbanization, population density, economic development, and transportation infrastructure on ICH distribution. Among these factors, urbanization exerts the strongest influence on the spatial distribution of ICH. The impact of the natural environment is relatively minor; however, it remains a significant element that cannot be overlooked during development. This research offers valuable data and insights for local governments and institutions to formulate evidence-based strategies for the protection and sustainable utilization of ICH resources, promoting sustainable cultural and economic development in the Grand Canal Basin. [ABSTRACT FROM AUTHOR]

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]

Understanding the driving mechanisms behind surface urban heat island (SUHI) effects is essential for mitigating the degradation of urban thermal environments and enhancing urban livability. However, previous studies have primarily concentrated on central urban areas, lacking a comprehensive analysis of the entire metropolitan area over distinct time periods. Additionally, most studies have relied on regression analysis models such as ordinary least squares (OLS) or logistic regression, without adequately analyzing the spatial heterogeneity of factors influencing the surface urban heat island (SUHI) effects. Therefore, this study aims to explore the spatial heterogeneity and driving mechanisms of surface urban heat island (SUHI) effects in the Guangzhou-Foshan metropolitan area across different time periods. The Local Climate Zones (LCZs) method was employed to analyze the landscape characteristics and spatial structure of the Guangzhou-Foshan metropolis for the years 2013, 2018, and 2023. Furthermore, Geographically Weighted Regression (GWR), Multi-scale Geographically Weighted Regression (MGWR), and Geographical Detector (GD) models were utilized to investigate the interactions between influencing factors (land cover factors, urban environmental factors, socio-economic factors) and Surface Urban Heat Island Intensity (SUHII), maximizing the explanation of SUHII across all time periods. Three main findings emerged: First, the Local Climate Zones (LCZs) in the Guangzhou-Foshan metropolitan area exhibited significant spatial heterogeneity, with a non-linear relationship to SUHII. Second, the SUHI effects displayed a distinct core-periphery pattern, with Large lowrise (LCZ 8) and compact lowrise (LCZ 3) areas showing the highest SUHII levels in urban core zones. Third, land cover factors emerged as the most influential factors on SUHI effects in the Guangzhou-Foshan metropolis. These results indicate that SUHI effects exhibit notable spatial heterogeneity, and varying negative influencing factors can be leveraged to mitigate SUHI effects in different metropolitan locations. Such findings offer crucial insights for future urban policy-making. [ABSTRACT FROM AUTHOR]

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The Songhua River Basin is an important grain production base and ecological region in China, and studying its water supply and water quantity changes is of great significance for achieving scientific water allocation and ecological environment protection in the basin. This study employed the CSR RL06 Mascon data products derived from the GRACE and GRACE-FO gravity satellites from 2003 to 2020. For the missing data between GRACE and GRACE-FO, published reconstructed data were used to fill the gaps. The accuracy of the data in the study area was validated through comparison with GLDAS simulated water storage data. Based on this, the spatial and temporal variation characteristics of terrestrial water storage in the basin were analyzed using a combination of Theil-Sen and Mann-Kendall trend tests. Additionally, the Pearson correlation coefficient and geographical detector methods were utilized to delve into the influence of various factors, including precipitation (Pre), evapotranspiration (ET), population density (POP), normalized difference vegetation index (NDVI), ecological zones (Ez), and digital elevation model (DEM), on terrestrial water storage. Furthermore, aiming to address the issue of errors introduced by resampling when performing correlation analysis on remote sensing data of different resolutions, the traditional raster data resampling method was optimized. The research findings indicate the following: 1 The GRACE data products exhibit good accuracy in the study area, and the linear trend range of their changes exhibits a high correlation with GLDAS data. 2 The optimized resampling method is more suitable for spatial studies similar to Pearson correlation analysis, and it significantly improves the accuracy of most data compared to directly using raw data. 3 The water storage in the Songhua River Basin has shown an increasing trend during the study period, with a noticeable increase in the extreme value ratio. By the end of the study period, the spatial distribution of water storage in the region tended to stabilize, exhibiting a basic pattern of more water in the northeast and less in the southwest. 4 The primary individual factors causing changes in water storage in the basin are precipitation and evapotranspiration, and the combined effects of most factors manifest as nonlinear enhancement. [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]

Evaluating environmental sustainability in the transnational basin of the Tumen River (TBTR) is of great significance for promoting sustainable development in Northeast Asia. However, past research has mostly concentrated on a particular environmental element, making it impossible to thoroughly and effectively show the environmental sustainability dynamics in this transnational area. In this study, we attempted to reveal environmental sustainability trends in the TBTR from 2000 to 2021 using the Environmental Degradation Index (EDI) and analyze the driving forces using a geographical detector. It was found that the TBTR's environmental sustainability decreased significantly, with a degraded region (13,174.75 km2) accounting for 31.01% of the whole area from 2000 to 2021. The dynamics of environmental sustainability on the three sides of China, the Democratic People's Republic of Korea (DPRK), and Russia have shown significant differences, with the most significantly improved in environmental sustainability being the subregion of China. On the Chinese side, the area that significantly improved in environmental sustainability accounted for 26.19% of the area on the Chinese side, which was 1.17 times higher than that of the DPRK's side and 1.24 times higher than that of the Russian side. Land use intensity (LUI), land use and land cover (LULC), and population density (PD) were the most dominant driving forces for environmental sustainability dynamics on the three sides of China, the DPRK, and Russia. China, the DPRK, and Russia can improve international environmental cooperation to promote sustainable development in the TBTR and Northeast Asia. [ABSTRACT FROM AUTHOR]

The middle and lower reaches of the Yangtze River are frequently affected by the Western Pacific Subtropical High (WPSH) in summer. This leads to phenomena including air subsidence, high temperatures, low rainfall, and weak winds, all of which affect the urban heat island (UHI) effect. Currently, there are few studies on the influence of WPSH on the UHI effect. In this study, we analysed the temporal and spatial distributions of the influence of WPSH on the UHI effect by establishing two scenarios: with and without WPSH. We calculated the UHI intensity and the urban heat island proportion index (UHPI) to analyse the temporal and spatial distributions of the UHI effect. The geographical detector method was then used to analyse the factors influencing UHI. The results indicate the strong heat island effect during the day in provincial capitals and some developed cities. The area of high UHI intensity was larger under the influence of WPSH than in the years without WPSH. WPSH affected UHPI at both day and night, although the effect was more pronounced at night. The factors affecting daytime UHI intensity are mainly POP and NTL, O3 plays a large role in the years with WPSH control. The main factors affecting the UHI intensity at night are AOD, POP and NTL were mainly factors in the years without WPSH control, POP and WPSH were mainly factors in the years with WPSH control. The interactions of the factors are mainly POP and multi-factors during the daytime, and DEM and multi-factors during the nighttime. It was found that the UHI intensity was enhanced under the control of the WPSH, and the influencing factors of the diurnal UHI differed with and without the WPSH control, which ultimately provides realistic suggestions for mitigating the intensity of the UHI in areas affected by the WPSH. [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]

Since China's reform and opening up in 1978, the reclamation and abandonment of cropland in Xinjiang have become significant features of the land use change in the arid land of Northwest China. However, the spatiotemporal changes and driving mechanisms of cropland reclamation and abandonment over long time periods are still unclear, but this is crucial in understanding cropland changes in inland arid land, providing important insights for land management and agricultural development. Based on 40 years of remote sensing data on resources and the environment, this study examines the spatiotemporal characteristics of cropland reclamation and abandonment in Xinjiang over four periods since 1980. Additionally, it uses an optimal parameter geographical detector model to quantify the driving factors for each period. The results indicate that cropland reclamation experiences a "slow decrease–rapid increase" trend, forming a "V-shaped" pattern, while abandonment shows a "rapid decrease–slow decrease–slow increase" trend, forming a "U-shaped" pattern. These trends can be divided into three periods: 1980–1990 (unstable growth), 1990–2010 (stable growth), and 2010–2020 (growth with constraints). The movement pattern of cropland reclamation's center of gravity is "slightly southeast–slightly northeast–southwest", whereas the abandonment's center of gravity shifts "northeast–southwest–northeast". Further analysis reveals that the impact of agricultural technological investment and infrastructure on cropland reclamation has increased, while the influence of natural environmental factors has decreased. Although climate and water resources remain key factors in cropland abandonment, the influence of economic and social factors has gradually diminished, and the impact of agricultural mechanization has steadily risen. [ABSTRACT FROM AUTHOR]

In recent years, China's urbanization has accelerated, significantly impacting ecosystems and the carbon balance due to changes in urban land use. The spatial patterns of CO2 emissions from urban land are essential for devising strategies to mitigate emissions, particularly in predicting future spatial distributions that guide urban development. Based on socioeconomic grid data, such as nighttime lights and the population, this study proposes a spatial prediction method for CO2 emissions from urban land using a Long Short-Term Memory (LSTM) model with added fully connected layers. Additionally, the geographical detector method was applied to identify the factors driving the increase in CO2 emissions due to urban land expansion. The results show that socioeconomic grid data can effectively predict the spatial distribution of CO2 emissions. In the Yangtze River Economic Belt (YREB), emissions from urban land are projected to rise by 116.23% from 2020 to 2030. The analysis of driving factors indicates that economic development and population density significantly influence the increase in CO2 emissions due to urban land expansion. In downstream cities, CO2 emissions are influenced by both population density and economic development, whereas in midstream and upstream city clusters, they are primarily driven by economic development. Furthermore, technology investment can mitigate CO2 emissions from upstream city clusters. In conclusion, this study provides a scientific basis for developing CO2 mitigation strategies for urban land within the YREB. [ABSTRACT FROM AUTHOR]

Forest defoliating pests are significant global forest disturbance agents, posing substantial threats to forest ecosystems. However, previous studies have lacked systematic analyses of the continuous spatiotemporal distribution characteristics over a complete 3–5 year disaster cycle based on remote sensing data. This study focuses on the Dendrolimus superans outbreak in the Changbai Mountain region of northeastern China. Utilizing leaf area index (LAI) data derived from Sentinel-2A satellite images, we analyze the extent and dynamic changes of forest defoliation. We comprehensively examine the spatiotemporal patterns of forest defoliating pest disasters and their development trends across different forest types. Using the geographical detector method, we quantify the main influencing factors and their interactions, revealing the differential impacts of various factors during different growth stages of the pests. The results show that in the early stage of the Dendrolimus superans outbreak, the affected area is extensive but with mild severity, with newly affected areas being 23 times larger than during non-outbreak periods. In the pre-hibernation stage, the affected areas are smaller but more severe, with a cumulative area reaching up to 8213 hectares. The spatial diffusion characteristics of the outbreak follow a sequential pattern across forest types: Larix olgensis, Pinus sylvestris var. mongolica, Picea koraiensis, and Pinus koraiensis. The most significant influencing factor during the pest development phase was the relative humidity of the year preceding the outbreak, with a q-value of 0.27. During the mitigation phase, summer precipitation was the most influential factor, with a q-value of 0.12. The combined effect of humidity and the low temperatures of 2020 had the most significant impact on both the development and mitigation stages of the outbreak. This study's methodology achieves a high-precision quantitative inversion of long-term disaster spatial characteristics, providing new perspectives and tools for real-time monitoring and differentiated control of forest pest infestations. [ABSTRACT FROM AUTHOR]

Previous studies have focused on the linear relationship between recreation behavior and environmental variables. However, to inform the planning and design of recreational spaces, it is essential to understand the factors that contribute to differences in the spatial distribution of recreation behavior. This study investigates the characteristics of visitor behavior in urban mountain parks in Fuzhou City, Fujian Province, China. It describes the distribution of tourist numbers and the diversity of behaviors in these parks and explores the landscape driving factors of visitor behavior, as well as the interaction effects between the factors from the perspective of spatial driving forces. The results indicate that (1) The observed behaviors in the three parks are primarily access behaviors. The number of visitors and the diversity of behaviors show a high level in the morning and evening and a low level in the midday. (2) There was minimal variation in behavioral composition and behavioral diversity among the study plots of different elevation gradients in the three parks. However, the contrasts between different landscape types were more pronounced, with impermeable plazas exhibiting the highest behavioral diversity and park roads demonstrating the most homogeneous behavioral diversity. (3) The impact of environmental factors was more pronounced than that of landscape pattern factors. The environmental factors that most strongly influenced passing, dynamic, and static behaviors were spatial connectivity value, hard space proportion, and number of recreational facilities, respectively. In contrast, the hard space proportion was the strongest driver of behavioral diversity. Moreover, the interaction between the hard space proportion and spatial connectivity value was more pronounced in driving behavioral diversity, as well as the three behaviors. [ABSTRACT FROM AUTHOR]

【Objective】 The Fuhe River Basin is an important food production region in Northwestern China. It is increasingly affected by droughts. This paper analyses the occurrence of droughts and their key determinants in this region. 【Method】 The analysis was based on modelling. We simulated the changes in monthly soil water content from 1962 to 2019 using the SWAT model. The resulting data were then used to calculate the standardized soil moisture index. The characteristics of agricultural droughts were analyzed using the theory of runs, and the key determinants of the droughts were analyzed using the geographical detector. 【Result】 ① The coefficient of determination (R2) and the Nash efficiency coefficient (ENS) between the modelled and measured results in the calibration are both higher than 0.80; the coefficient of determination between the predicted soil moisture content and the measured soil moisture content is 0.52. ② The duration of droughts in different regions in the basin varied from 3.60 to 4.31 months, and the drought intensity ranged from 4.91 to 5.61, both varying spatially, higher in the North and South and lower in the center. ③ The influence of temperature, solar radiation, and wind speed on spatial variation in the droughts, characterized by the q-value, exceeds 0.30. Other environmental factors are nonlinearly interacted in their influence on the droughts. 【Conclusion】 The standardized soil moisture index can quantitatively characterize droughts in the Fuhe River Basin. Droughts in the basin vary spatially, with temperature, solar radiation and wind speed being the main contributing factors.

Abstract 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 The middle and lower reaches of the Yangtze River are frequently affected by the Western Pacific Subtropical High (WPSH) in summer. This leads to phenomena including air subsidence, high temperatures, low rainfall, and weak winds, all of which affect the urban heat island (UHI) effect. Currently, there are few studies on the influence of WPSH on the UHI effect. In this study, we analysed the temporal and spatial distributions of the influence of WPSH on the UHI effect by establishing two scenarios: with and without WPSH. We calculated the UHI intensity and the urban heat island proportion index (UHPI) to analyse the temporal and spatial distributions of the UHI effect. The geographical detector method was then used to analyse the factors influencing UHI. The results indicate the strong heat island effect during the day in provincial capitals and some developed cities. The area of high UHI intensity was larger under the influence of WPSH than in the years without WPSH. WPSH affected UHPI at both day and night, although the effect was more pronounced at night. The factors affecting daytime UHI intensity are mainly POP and NTL, O3 plays a large role in the years with WPSH control. The main factors affecting the UHI intensity at night are AOD, POP and NTL were mainly factors in the years without WPSH control, POP and WPSH were mainly factors in the years with WPSH control. The interactions of the factors are mainly POP and multi-factors during the daytime, and DEM and multi-factors during the nighttime. It was found that the UHI intensity was enhanced under the control of the WPSH, and the influencing factors of the diurnal UHI differed with and without the WPSH control, which ultimately provides realistic suggestions for mitigating the intensity of the UHI in areas affected by the WPSH.

Urban vegetation in China has changed substantially in recent decades due to rapid urbanization and dramatic climate change. Nevertheless, the spatial differentiation of greenness among major cities of China and its evolution process and drivers are still poorly understood. This study examined the spatial patterns of vegetation greenness across 289 cities in China in 2000, 2005, 2010, 2015, and 2018 by using spatial autocorrelation analysis on the Normalized Difference Vegetation Index (NDVI); then, the influencing factors were analyzed by using the optimal parameters-based geographical detector (OPGD) model and 18 natural and anthropogenic indicators. The findings demonstrated a noticeable rise in the overall greenness of the selected cities during 2000–2018. The cities in northwest China and east China exhibited the rapidest and slowest greening, respectively, among the six sub-regions. A significant positive spatial correlation was detected between the greenness of the 289 cities in different periods, but the correlation strength weakened over time. The hot and very hot spots in southern and eastern China gradually shifted to the southwest. While the spatial pattern of urban greenness in China is primarily influenced by wind speed (WS) and precipitation (PRE), the interaction between PRE and gross domestic product (GDP) has the highest explanatory power. The explanatory power of most natural factors decreased and, conversely, the influence of anthropogenic factors generally increased. These findings emphasize the variations in the influence strength of multiple factors on urban greenness pattern, which should be taken into account to understand and adapt to the changing urban ecosystem.

Abstract Background The urban green infrastructure (GI) network is an important conduit for ecological flows and plays a crucial role in improving regional habitats, especially in karst areas that are highly ecologically fragile and sensitive. However, the existing research only focuses on the construction of GI network in karst mountain cities, and the evolution characteristics of its elements and driving mechanism are not clear, which is of great significance for guiding urban land use planning and comprehensively improving the quality of the ecological environment. In view of this, this study took Qianzhong urban agglomeration as the study area, based on multi-source data, and identified ecological sources through ecological resilience analysis. Considering the special geographic environment, the rock exposure rate factor was added to correct the resistance surface, and the minimum cumulative resistance (MCR) and gravity model were coupled to extract the GI network. The complex network topology characterization parameter was introduced to assess the spatial and temporal variations of ecological sources and corridors. Finally, the geographical detector was used to identify the dominant influencing factors and interactions of the spatial distribution of the GI network. Results The results showed that from 2000 to 2020, the condition of GI network elements in the study area presented a decreasing and then an increasing trend. The ecological sources or corridors in highly urbanized areas were critical for ecological flow transport and the overall structural stability of the GI network. The influence of natural factors on the spatial distribution of the GI network gradually weakened, and the influence of human factors continuously increased. The spatial distribution of the GI network was influenced by multiple factors, and the interaction between all the factors was enhanced, which gradually changed from the interaction of natural factors to the interaction of human factors during the study period. Conclusions The research results will provide scientific references for the construction of an ecologically safe environment and sustainable development of karst mountain cities.

Abstract 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.

Objectives: The game between socio-economic development and ecological development has always been the core issue in coal areas, but the internal mechanism of tradeoff and cooperative dynamic change of ecosystem services in mining areas under long-term mineral resources development is still lacking in indepth research. Methods: Therefore, taking Shendong mining area as an example, this study used InVEST model to evaluate the changes of four major ecosystem service functions in Shendong mining area from 1990 to 2020, namely, water yield (WY), net primary productivity (NPP), soil conservation (SC) and habitat quality (HQ). Meanwhile, correlation analysis was used to explore the trade-off and synergistic relationship among these services. On this basis, the coupling effect between the four ecosystem services is further discussed by using the constraint line method. Finally, the key drivers of ecosystem service trade-offs/synergies in the region are explored by using geodetectors and the explanations of each influence factor for RMS errors are obtained. Results: The results show that 1) from 1990 to 2020, the water yield and soil retention in the mining area decrease first and then increase, and the net primary productivity and habitat quality increase slowly, mainly in the southeast of the mining area. 2) In terms of constraint relationship, all the four ecosystem services showed hump-like constraint relationship, that is, there was obvious constraint threshold effect. 3) In the Shendong mining area, the synergistic relationship is the dominant relationship between ecosystem services, and the tradeoff effect mainly occurs between water yield and habitat quality. 4) In terms of the driving mechanism of tradeoff/synergy, land use type, temperature, and rainfall are the main factors that cause the spatial differentiation of tradeoff synergy intensity among ecosystem services in Shendong mining area. Conclusions: The results of this study provide a scientific basis for the improvement of ecological environment and sustainable utilization of mineral resources under long-term exploitation. [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]

Net ecosystem productivity (NEP) is an important indicator for the quantitative evaluation of carbon sources/sinks in terrestrial ecosystems. An improved CASA model and soil respiration model, combined with MODIS and meteorological data, are utilized to estimate vegetation NEP from 2010 to 2020. A Theil–Sen trend analysis, a Mann–Kendall test, the Hurst index, and geographical detector methods were employed to analyze the spatiotemporal variations in NEP in Heilongjiang Province and its driving factors. The results show the following: (1) The overall NEP in Heilongjiang Province exhibited a fluctuating upward trend from 2010 to 2020, with a growth rate of 4.74 g C·m−2·yr−1, and an average annual NEP of 404 g C·m−2·yr−1. Spatially, NEP exhibits a distribution pattern of "low from east to west to high from north to south in the central region", with 99.27% of the area being a carbon sink. (2) Significant regional differences were observed in the spatial trend of NEP changes, with 78.39% of regions showing increasing trends and 17.53% showing decreasing trends. Future NEP changes are expected to continue, with regions showing a persistent increase (58.44%), potential decrease (19.95%), potential increase (5.65%), and persistent decrease (11.88%). (3) The geographical detector results indicate that altitude is the dominant factor affecting NEP, followed by slope, temperature, population density, etc. The interaction-detector results show that the interaction between each factor shows an increasing trend, and the interaction between any two factors is higher than that of a single factor. The research results can provide scientific references for reducing emissions, increasing sinks, and protecting ecosystems in Heilongjiang Province. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The 2017 Jiuzhaigou earthquake, registering a magnitude of 7.0, triggered a series of devastating geohazards, including landslides, collapses, and mudslides within the Jiuzhaigou World Natural Heritage Site. These destructive events obliterated extensive tracts of vegetation, severely compromising carbon storage in the terrestrial ecosystems. Net Primary Productivity (NPP) reflects the capacity of vegetation to absorb carbon dioxide. Accurately assessing changes in NPP is crucial for unveiling the recovery of terrestrial ecosystem carbon storage after the earthquake. To this end, we designed this study using the Moderate Resolution Imaging Spectroradiometer (MODIS) Net Primary Productivity datasets. The findings are as follows. NPP in the co-seismic landslide areas remained stable between 525 and 575 g C/m2 before the earthquake and decreased to 533 g C/m2 after the earthquake. This decline continued, reaching 483 g C/m2 due to extreme rainfall events in 2018, 2019, and 2020. Recovery commenced in 2021, and by 2022, NPP had rebounded to 544 g C/m2. The study of NPP recovery rate revealed that, five years after the earthquake, only 18.88% of the co-seismic landslide areas exhibited an NPP exceeding the pre-earthquake state. However, 17.14% of these areas had an NPP recovery rate of less than 10%, indicating that recovery has barely begun in most areas. The factor detector revealed that temperature, precipitation, and elevation significantly influenced NPP recovery. Meanwhile, the interaction detector highlighted that lithology, slope, and aspect also played crucial roles when interacting with other factors. Therefore, the recovery of NPP is not determined by a single factor, but rather by the interactions among various factors. The ecosystem resilience study demonstrated that the current recovery of NPP primarily stems from the restoration of grassland ecosystems. Overall, while the potential for NPP recovery in co-seismic landslide areas is optimistic, it will require a considerable amount of time to return to the pre-earthquake state. [ABSTRACT FROM AUTHOR]

At present, most of the research on shared electric bikes mostly focuses on the scheduling, operation and maintenance of shared electric bikes, while insufficient attention has been paid to the behavioral characteristics and influencing factors of shared cycling in plateau cities. This paper takes Kunming as a research case. According to the user's cycling behavior, the spatiotemporal cube model and emerging hotspot analysis are used to explore the spatiotemporal characteristics of the citizens' cycling in the plateau city represented by Kunming, and the method of geographical detectors is used to study the specific factors affecting the shared travel of citizens in Kunming and conduct interactive detection. The findings are as follows: ① the use of shared electric bikes in Kunming varies greatly on weekdays, showing a bimodal feature. In space, the overall distribution of cycling presents a "multi-center" agglomeration feature with distance decay from the center of the main urban area. ② The geographic detector factor detection model quantitatively analyzes the interactive influence between factors, providing a good supplement to the independent influence results of each factor. Through the dual factor interactive detection model, we found that the overall spatiotemporal distribution of cycling during each time period is most significantly affected by the distribution of service facilities, followed by transportation accessibility, land use, and the natural environment. The research results can assist relevant departments in governance of urban shared transportation and provide a reference basis, and they also have certain reference value in urban pattern planning. [ABSTRACT FROM AUTHOR]

Background: The urban green infrastructure (GI) network is an important conduit for ecological flows and plays a crucial role in improving regional habitats, especially in karst areas that are highly ecologically fragile and sensitive. However, the existing research only focuses on the construction of GI network in karst mountain cities, and the evolution characteristics of its elements and driving mechanism are not clear, which is of great significance for guiding urban land use planning and comprehensively improving the quality of the ecological environment. In view of this, this study took Qianzhong urban agglomeration as the study area, based on multi-source data, and identified ecological sources through ecological resilience analysis. Considering the special geographic environment, the rock exposure rate factor was added to correct the resistance surface, and the minimum cumulative resistance (MCR) and gravity model were coupled to extract the GI network. The complex network topology characterization parameter was introduced to assess the spatial and temporal variations of ecological sources and corridors. Finally, the geographical detector was used to identify the dominant influencing factors and interactions of the spatial distribution of the GI network. Results: The results showed that from 2000 to 2020, the condition of GI network elements in the study area presented a decreasing and then an increasing trend. The ecological sources or corridors in highly urbanized areas were critical for ecological flow transport and the overall structural stability of the GI network. The influence of natural factors on the spatial distribution of the GI network gradually weakened, and the influence of human factors continuously increased. The spatial distribution of the GI network was influenced by multiple factors, and the interaction between all the factors was enhanced, which gradually changed from the interaction of natural factors to the interaction of human factors during the study period. Conclusions: The research results will provide scientific references for the construction of an ecologically safe environment and sustainable development of karst mountain cities. [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]

Over the past few decades, China has undergone the largest and fastest urbanization process in world history. By 2023, Chengdu's urbanization rate had reached 80.5%, significantly higher than the national average of 66.16%. Studying the urbanization experience of Chengdu is of great significance for optimizing urban planning policies in Chengdu and other cities in China. Although much literature has explored the urbanization process from macro and micro perspectives, studies using a top-down approach to examine urban fringe expansion are relatively scarce. This study first applies the entropy weight method to analyze the spatial-temporal evolution trends of urban development, identifying areas of imbalanced development and prominent issues. Secondly, the K-means machine learning algorithm and nightlight data are used to reconstruct and classify urban regions, and a comparative analysis is conducted with administrative divisions to further identify unreasonable areas in urban spatial distribution and structure. Finally, POI data and the geographical detector method are used to analyze the micro-driving forces in areas of imbalanced development, identifying major limiting factors and solutions. The study found that the gap between urban and rural development in Chengdu is narrowing during the urbanization process, but there is severe differentiation in the second circle of Chengdu, where economic development is accelerating but residents' happiness is declining. Moreover, analysis based on urban nightlight data and land-use data reveals that the expansion areas on the urban-rural fringe are mainly concentrated in the second circle of Chengdu. Micro-level driving factor analysis found that the western region of the second circle has many but small urban settlements, with a dense road network but scattered functional areas. The eastern region has inefficient and extensive use of construction land. Additionally, the mismatch between student status and household registration has resulted in relatively lagging educational resource development, and high entry barriers have hindered the progress of urbanization, leading to low per capita welfare expenditure. These reasons are the main factors causing the decline in residents' happiness, and this impact shows significant differences at different temporal and spatial scales. Encouraging innovation in research and development or education can serve as a long-term and effective driving force for promoting sustainable urbanization. This study provides valuable insights for scientifically planning sustainable urban development and promoting the urbanization process. [ABSTRACT FROM AUTHOR]

In China, despite the fact that the atmospheric environment quality has continued to improve in recent years, the PM2.5 pollution still had not been controlled fundamentally and its driving mechanism was complex which remained to be explored. Based on the 1-km ground-level PM2.5 datasets of China from 2000 to 2020, this study combined spatial autocorrelation, trend analysis, geographical detector, and multi-scale geographically weighted regression (MGWR) model to explore the spatial–temporal evolution of PM2.5 in Shanxi Province and revealed its complex driving mechanism behind this process. The results reflected that (1) there was a pronounced spatial clustering of PM2.5 concentration within Shanxi Province, with PM2.5 concentrations decreasing from southwest to northeast. From 2000 to 2020, the levels of PM2.5 pollution demonstrated a decline over time, with its concentrations decreasing by 9.15 µg/m3 overall. The Hurst exponent indicated a projected decrease in PM2.5 concentrations in the central and northern areas of Shanxi Province, contrasting with an anticipated increase in other regions. (2) The geographical detector indicated that all drivers had significant influences on PM2.5 concentrations, with meteorological factors exerting the greatest effects then followed by human activity and vegetation cover showing the least effects. (3) Both gross domestic product and population density exhibited positive correlations with PM2.5 concentration, while vegetation fractional cover, wind speed, precipitation, and elevation exerted negative influences on PM2.5 concentration all over the space. This study enriched the research content and ideas on the driving mechanism of PM2.5 and provided a reference for similar studies. [ABSTRACT FROM AUTHOR]

Under the background of rapid urbanization, strengthening the research on the response and dynamic mechanism of ecosystem services to land use is conducive to the optimization of land space and ecological restoration and governance in megacities. Using Hefei City as a case study, we examined specific ecosystem services and analyzed how water yield, habitat quality, carbon storage, and soil conservation changed over time from 2000 to 2020. We utilized spatial information technology and the InVEST model to assess these changes. Additionally, we developed a comprehensive ecological service index (CES) and used Geodetector and regression models to investigate how ecosystem services responded to land use. In addition, we utilized the Patch-generating Land Use Simulation Model (PLUS) to simulate the spatial distribution of land use in 2030. This was performed under four different scenarios: natural development (ND), urban development (UD), cultivated land protection (CP), and ecological protection (EP). Furthermore, we assessed the effects of these land-use changes on ecosystem service functions by integrating the PLUS results with InVEST. The findings indicate the following: (1) between 2000 and 2020, farmland consistently remained the dominant land-use type in Hefei City while construction land experienced significant growth. Land-use conversion was prevalent during this period, and each ecological indicator exhibited noticeable geographic variation; (2) during the past 20 years, the comprehensive ecosystem service index (CES) exhibited clear spatial clustering patterns. The different types of land use showed significant quantitative relationships with CES. Specifically, cultivated land, forest land, grassland, and water area had positive correlations, while construction land had a negative correlation. Geodetector analysis revealed that the proportion of ecological land use had the greatest impact on the spatial differentiation of CES, followed by population density; (3) according to the PLUS simulation, the UD scenario results in a significant conversion of cultivated land and grassland into construction land, leading to the greatest decrease in CES. In the ND scenario, the areas with decreasing CES are mostly areas that have been converted from other land types to construction land. In contrast, the EP scenario shows an increase in forest land and grassland, which promotes the enhancement of multiple ecosystem service functions simultaneously. This indicates that the EP scenario is the most favorable for sustainable land-use development. The study investigates the impact of land-use changes on ecosystem services and evaluates the sustainability of regional land use. The findings have both theoretical and practical significance for effectively managing land use and regulating ecological functions in large cities. [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]

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background and objectiveSocial determinants of health (SDOH) broadly influence health levels. Research on health and its influencing factors can help improve health status. There is limited research on the spatial stratified heterogeneity of health status and the interactions between the factors influencing it. This study aimed to analyze the spatial characteristics of health outcomes in Beijing and identify its influencing factors.MethodsBased on the Healthy Beijing Initiative (2020–2030), we constructed health outcomes and five dimensions of the SDOH evaluation system. Our study measured the health outcomes and SDOH based on the latest data from 16 districts in Beijing in 2020–2022. We explored the spatial characteristics of health outcomes through descriptive and spatial autocorrelation analyses. Moreover, the Geographical Detector (GeoDetector) technique has been used to reveal the effect of SDOH and its interactions on health outcomes.ResultsA significant spatial stratified heterogeneity of health outcomes was observed, with the health outcomes mainly exhibiting two clustering types (high–high and low–low) with positive autocorrelation. The results of the geodetector showed that social and economic factors (q = 0.85), healthy lifestyle (q = 0.68) and health service (q = 0.53) could mainly explain the heterogeneity of health outcomes. Social and economic factors, healthy lifestyle and healthy environment gradually became the main influential factor in health outcomes over time. Furthermore, the interaction of any two factors on health outcomes was found to be more pronounced than the impact of a single factor.ConclusionThere existed obvious spatial stratified heterogeneity of health outcomes in Beijing, which could be primarily explained by social and economic factors, and healthy lifestyle and health service.

In Northeast China, permafrost is controlled by a combination of biotic, climatic, physiographic, and anthropogenic factors. Due to the complexity of these governing or influencing factors, it is challenging to exactly describe the features of the Xing’an permafrost in Northeast China. By integrating remote sensing (RS) and geographic information system (GIS) technologies, we have quantified these influencing factors of permafrost changes as an important approach to understanding the nature of latitudinal and mountain permafrost in Northeast China at the mid-latitudes in the Northern Hemisphere. In this study, we combine Geographical Detector (Geodetector) model, trend analysis, and multi-source RS data to quantify the controlling or influencing factors of permafrost thermal state and of permafrost changes, and explain the interactions among permafrost, environment, and climate. The results indicate that, at the regional scale, changes in the thermal state of permafrost are primarily governed or influenced by mean annual land surface temperature (MALST), precipitation, and snow cover duration (SCD). Topographic factors also affect the spatial patterns of permafrost development. Additionally, in the context of climate warming, the insulation effect of snow cover on the permafrost is weakened, or has been weakening. Moreover, the interactive effects among various factors significantly enhance their explanatory power for changes in the thermal state of permafrost. The study emphasizes the complexity of the interactions among permafrost, climate, and the environment, and highlights the significance of understanding these interactions for regional socio-economic development, ecological management, carbon pool stabilization, and research on future climate change in Northeast China.

Snow is an important water resource. The spatiotemporal distribution of snow, its influencing factors, and their interactions play important roles in understanding the response of snow cover to climate change, climate prediction, water resources management, and disaster control. This study analyzed the factors influencing snow variation over the Yangtze and Yellow River Headwater Region (YYRHR) using sensitivity analysis and a geographical detector model. The results showed that snow cover days (SCDs) and the snow cover fraction (SCF) showed non-significant increasing trends from 2001 to 2020 in the YYRHR, and the spatial distribution of SCDs and SCF were strongly consistent. The areas and the increasing trend of SCDs and SCF in the Yellow River Headwater Region (Yellow-RHR), were larger than that in the Yangtze River Headwater Region (Yangtze-RHR). SCDs were generally negatively sensitive to temperature, and positively sensitive to precipitation, and were more sensitive to precipitation. However, single-factor detection showed that the maximum temperature (TMAX) was the most prominent factor influencing snow cover variation (p

Abstract 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.

[Objective] To reveal the spatial and temporal evolution characteristics of soil water erosion in the middle reaches of the Yellow River in the rich and coarse sediment area from 2000 to 2020, and analyze its dynamic driving force. [Methods] Based on the RUSLE model, the annual soil water erosion modulus in the rich and coarse sediment area was calculated, and the variation characteristics of soil water erosion intensity in 2000, 2005, 2010, 2015, and 2020 were analyzed. The spatial-temporal characteristics of soil water erosion modulus were explored by using the Sen+MK trend analysis method combined with the Hurst index, and the factor probing in the parameter-optimal geographical detector with the interactive probing were used to quantify the explanatory power of six factors, namely average annual precipitation, elevation, slope, vegetation cover, land use/cover type, and soil type, on the spatial distribution of soil water erosion. [Results] (1) The area of moderate, intense, extremely intense and severe erosion in the rich and coarse sediment area decreased by 48.09%, 77.93%, 83.01%, and 36.13%, respectively, and the area of slight and mild erosion increased by 46.22% and 0.33%, respectively, in the five periods from 2000 to 2020. At the present stage, the sandy and coarse sandy area was dominated by slight and mild erosion, and the proportion of the two was 62.49% and 42.07% respectively. (2) The overall inter-annual change of soil water erosion modulus in the rich and coarse sediment area showed a fluctuating and significant downward trend, from 2 214.89 t/(km2·a) in 2000 to 1 169.44 t/(km2·a) in 2020. The spatial variation trend of soil water erosion modulus in the rich and coarse sediment area from 2000 to 2020 was mainly in a decreasing state, accounting for 76.13% of the total area, and would continue to be in a decreasing state in the future, with an area share of 62.50%. (3) The explanatory power of the interactions among the six factors was greater than that of single factor, and it was mainly manifested as nonlinear enhancement and double-factor enhancement; soil water erosion in the rich and coarse sediment area was dominated by precipitation and land use/cover in 2000—2005, and by vegetation cover and land use/cover in 2010—2020. [Conclusion] Soil water erosion condition in the rich and coarse sediment area will be improved continuously from 2000 to 2020; in the future, the soil water erosion modulus of 62.50% of the regions will continue to decline or decline in the future, but there is still a potential risk of increase in 20.44% of the area; the land use/cover pattern has changed by the project of returning farmland to forests and grassland, which made the soil water erosion in the rich and coarse sediment area. The driving force of soil water erosion in the rich and coarse sediment area changes dynamically; the slope factor needs to be fully considered when optimizing the land use/cover pattern for the prevention and control of soil water erosion in the rich and coarse sediment area in the future.

Spatial heterogeneity (SH), known as the second law of geography, has been a topic of extensive research. One common approach to analyzing SH involves comparing variances between and within strata to assess the impact of independent variables on the dependent variable. This method, known as spatial stratified heterogeneity (SSH) analysis, is often performed using the geographical detector model. Over time, several optimized versions of geographical detectors have emerged, focusing on discretizing single or dual variables. However, methods for discretizing three or more variables are still limited to the interaction detector, with research on spatial scale effects mainly focused on single factors. To overcome these limitations, an optimal multivariate-stratification geographical detector (OMGD) model has been developed. This model includes two additional modules: factor discretization optimization and scale detector. Fine-tuning factor discretization involves using five univariate and five cluster-based stratification methods to automatically explore the optimal discretization scheme for single factors or multi-factor combinations based on the Geodetector [Formula: see text] statistics. The scale detector can then iterate through various spatial scales to identify the optimal spatial scale for SSH analysis. Furthermore, the developed OMGD model has been tested with multiple case datasets to validate its applicability and robustness. The findings demonstrate that the OMGD model can effectively extract the main attributes of single factors and multi-factor combinations, providing a better explanation for geographical phenomena. It can also automatically determine the best spatial scale for SSH analysis, thereby enhancing the overall capability of conducting SSH analysis with the geographical detector.

The ecological environment of the Yellow River Source Area (YRSA) is crucial to the Qinghai-Tibet Plateau, and its ecological risk management is important for the ecological protection and sustainable development of the Qinghai-Tibet Plateau. this study improves the traditional evaluation method of landscape ecological risk(LER) based on ecosystem services and detects its spatial heterogeneity by using Geodetector. The study results are as follows: (1) The temporal distribution of LER in the YRSA is similar to its landscape vulnerability. In the past 20 years, the areas of its high, medium-high, medium, and medium-low ecological risk areas have been converted to low ecological risk areas to varying degrees. (2) Overall, the spatial difference in LER in the YRSA is significant, mainly dominated by HH and HL clustering. Meanwhile, the Moran’I index of the LER Index decreases year by year, indicating that the spatial aggregation of the LER decreases and the spatial autocorrelation weakens year by year. (3) Climatic factors have the most significant influence on the ecological risk of the landscape in the YRSA, followed by topographic factors and anthropogenic factors.

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]

Habitat quality is an important basis for human well-being and the achievement of sustainable development. Based on land-use data for the Bosten Lake Basin in 2000, 2005, 2010, 2015, and 2022, the PLUS and InVEST models are applied in this study to predict and analyze land-use changes and explore the spatial and temporal evolution characteristics of the region's habitat quality. Additionally, we use a geographic detector model to reveal the drivers of spatial variation in habitat quality. The results show that: (1) Land use in Bosten Lake Basin is dominated by grassland and bare land, with an area share of 93.21%. Habitat quality shows a trend of degradation followed by improvement, with a spatial pattern of high in the northwest and low in the southeast. (2) Habitat quality in 2030 increased from 2022 in all cases, with a mean of 0.354 for the natural development scenario, a maximum of 0.355 for the ecological development scenario, and a minimum of 0.353 for the economic development scenario. (3) The main drivers affecting habitat quality in the Bosten Lake watershed are DEM, mean annual precipitation (MAP), and GDP per capita. X1∩X4 (0.50) and X4∩X10 (0.51) are the interaction factors with the largest dominant effect in 2000, 2010 and 2020, respectively. [ABSTRACT FROM AUTHOR]

For a long time, human activities have been prohibited in ecologically protected areas in the Ebinur Lake Wetland National Nature Reserve (ELWNNR). The implementation of total closure is one of the main methods for ecological protection. For arid zones, there is a lack of in-depth research on whether this measure contributes to ecological restoration in the reserve. The Normalized Difference Vegetation Index (NDVI) is considered to be the best indicator for ecological monitoring and has a key role to play in assessing the ecological impacts of total closure. In this study, we used Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data to select optimal data and utilized Sen slope estimation, Mann-Kendall statistical tests, and the geographical detector model to quantitatively analyze the normalized difference vegetation index (NDVI) dynamics and its driving factors. Results were as follows: (1) The vegetation distribution of the Ebinur Lake Wetland National Nature Reserve (ELWNNR) had obvious spatial heterogeneity, showing low distribution in the middle and high distribution in the surroundings. The correlation coefficients of Landsat-8 and MODIS, Sentinel-2 and MODIS, and Sentinel-2 and Landsat-8 were 0.952, 0.842, and 0.861, respectively. The NDVI calculated from MODIS remote sensing data was higher than the value calculated by Landsat-8 and Sentinel-2 remote sensing images, and Landsat-8 remote sensing data were the most suitable data. (2) NDVI indicated more degraded areas on the whole, but the ecological recovery was obvious in the localized areas where anthropogenic closure was implemented. The ecological environment change was the result of the joint action of man and nature. Man-made intervention will change the local ecological environment, but the overall ecological environment change was still dominated by natural environmental factors. (3) Factors affecting the distribution of NDVI in descending order were as follows: precipitation > evapotranspiration > land use type > elevation > vegetation type > soil type > soil erosion > slope > temperature > slope direction. Precipitation was the main driver of vegetation change in ELWNNR. The synergistic effect of the factors showed two-factor enhancement and nonlinear enhancement, and the combined effect of the driving factors would increase the influence on NDVI. [ABSTRACT FROM AUTHOR]

Nanling Mountain region is a typical southern hilly region, which plays an important ecological and environmental protection role in China's overall land protection pattern. Based on the remote sensing image data of Longnan City in Nanling Mountain region in 2013, 2018 and 2023, this paper interpreted the land use type and analyzed the land use transfer situation by using land use transfer flow, and a land use transfer matrix. At the same time, based on the remote sensing ecological index (RSEI) model, the ecological environmental quality of Longnan City from 2013 to 2023 was retrieved. The temporal and spatial response model of the ecological environmental quality to land use transfer in Longnan City from 2013 to 2023 was discussed based on spatial autocorrelation and a geographical detector. The results show that from 2013 to 2023, the decrease of forest land (16.23 km2) and the increase of construction land (13.25 km2) were the main land use transfers in Longnan City. The ecological environment indexes of Longnan City in 2013, 2018 and 2023 were 0.789, 0.917 and 0.872, respectively, showing a trend of "first rising and then decreasing". The ecological environmental quality in the north of Longnan City was significantly lower than that in the south, and the poor ecological quality area appeared in and around the northern main urban area, showing a trend of "inward contraction". Forest land, garden land, grassland, cultivated land and water area have a positive impact on ecological environmental quality, while traffic land, construction land and other land have a negative impact on ecological environmental quality. The response of ecological environmental quality to different land use transfer modes is related to the change of the overall ecological environmental quality. The interaction between land use and land cover change (LUCC) and other factors had a great impact on the evolution of ecological environmental quality in Longnan City. [ABSTRACT FROM AUTHOR]

Clarifying the spatiotemporal distribution and influencing factors of mosque architecture in China's Hehuang region has significant positive implications for the overall protection and development of the region's architectural cultural heritage. This study utilizes field surveys and acquires POI data of traditional mosques built before 1993 in the region to analyze the distribution characteristics of mosques, aiming to explore future development trends of these religious structures. It also investigates the influencing factors, with the goal of emphasizing the primary and secondary factors affecting mosque distribution. The study finds the following: (1) Mosques are generally centered around the Huangshui Valley, displaying a "central clustering, peripheral dispersal" distribution pattern, forming a spatial structure of "two cores, one belt, multiple points", with distinct differentiation and overall uneven distribution. (2) Mosques are primarily situated at elevations between 2147 and 2764 m; on slopes less than 15°, in sunny and gentle slopes; within 20 km from rivers; within 14 km from roads; in areas receiving 400–500 mm annual rainfall; and within temperature ranges of 5.54–10.22 °C. (3) The study also finds that the spatial distribution of mosques is profoundly influenced by both natural geographical factors and human environmental factors. The better the natural location, the larger and denser the population, the richer the cultural resources, the higher the level of economic development, and the greater the concentration of Hui people, the more numerous and concentrated the mosques. (4) Population factors are the dominant factors for the clustered distribution of traditional mosques in the Hehuang area. Since the construction of mosques in the region is closely related to the number of Hui people and the proportion of Muslim adherents, areas with a high concentration of mosques also have relatively larger populations of Hui people. Temperature, precipitation, altitude, rivers, and roads are foundational factors for traditional mosques in the Hehuang area, influencing mosque distribution as external factors. [ABSTRACT FROM AUTHOR]

Rapid urbanisation has generated numerous environmental consequences, particularly regarding open space fragmentation. Open space fragmentation is the transformation of open space from a state of homogeneity, integration, and continuity to a state of heterogeneity, division, and incoherence. Nevertheless, one main obstacle to understanding this issue is how to address the spatial heterogeneity of the impact of urbanisation on open space fragmentation. Thus, this paper provides a comprehensive framework for the mechanistic associations between open space fragmentation and urbanisation in Hong Kong's built-up area. The results illustrate that both open space fragmentation index and urbanisation index values are noticeably higher in dense urban areas. Land urbanisation, represented by the percentage of construction land in total land, has the highest explanatory power for spatial differentiation in open space fragmentation, followed by social and population urbanisation factors. Furthermore, the relational interrelations of open space fragmentation drivers are the bivariate and nonlinear enhancement interactions. Social urbanisation and land urbanisation have the strongest bivariate enhancement interaction for the use fragmentation form and the largest nonlinear enhancement interaction for the internal fragmentation form. Based on initial urban planning, open space fragmentation is an adaptation outcome of population, land, and social urbanisation factors, and this self-organisation phenomenon has been further emphasised in the historical process. These insights significantly enrich our understanding of how urbanisation affects open space fragmentation and provide valuable guidance for better open space strategies. [ABSTRACT FROM AUTHOR]