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【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.

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.

Abstract 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.

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 in-depth 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.

Shrub encroaching on grasslands threatens grassland ecosystems and negatively affects human land use and livelihood. However, existing research on this subject is limited, having focused on small areas, thus hindering a comprehensive understanding of the diverse patterns of large-scale shrub invasions into grasslands. In recent decades, a significant encroachment of shrubs into Xilin Gol grassland has been observed in China, resulting in considerable damage to the grasslands. Therefore, this study aimed to investigate the rapid expansion of shrub-encroached grassland (SEG) from 1990 to 2020 in the Xilin Gol League, Inner Mongolia Autonomous Region, China, using long-term Landsat images. The spatial distribution characteristics of the new SEG areas were analyzed. The main factors driving the shrub encroachment in the study area were identified using a geographic detector. Our results revealed a continuous expansion of SEG in the study area over the past three decades, marked by an increase of 48.76 × 103 km2 and an average annual gain rate of 9.41 %. The increased SEG was primarily owing to grassland transition. The sprawl of the SEG was the most prominent in the western and northern regions of the study area, extending to gentle slopes of approximately 5° and low-middle elevations between 600–1300 m above mean sea level. Shrub encroachment was primarily influenced by surface temperature, wind speed, and relative humidity, with elevation, slope, and precipitation exerting relatively weaker influences. Rather than being driven by a singular factor, the expansion was a result of the combined influence of various factors. This study provides a valuable case study for understanding shrub invasion dynamics in arid and semi-arid regions globally.

With the escalation of global climate change and human activity, geohazards become increasingly frequent which cause severe casualties and property losses to local communities. To alleviate this situation and provide scientific guidance for risk reduction, it is imperative to address some of the basic questions related to geohazards, including: i) how to detect active geohazards (AGs) rapidly and automatically over a wide area; ii) how to determine the region with a high level of hazard activity; iii) what are the primary conditioning factors (CFs) of AGs; and iv) do factors operate independently or are they interconnected. To tackle these issues, we propose a universally applicable framework for wide area automated detection of AGs. The framework is based on multi-source Earth observations which capture surface deformation ranging from millimeters to meters. Our study has focused on the Hexi Corridor (HXC) in Gansu Province, China, covering an area of 210,000 km2 with a length of 1100 km. First, we construct an AGs database for the HXC with high automatic and rapid update capabilities, including a total of 4492 AGs (3652 active landslides and 840 land subsidence areas). Second, using the Geographic Detectors method, we determine the primary CFs including elevation, land surface temperature, and precipitation. We find that faults exert greater control over very slow-moving landslides, but are less effective over slow-moving landslides. Third, we analyzed the interactive effects of dual CFs on geohazard actives. Any interaction effect of dual CFs contributes to the bivariate enhancement of geohazard activity. This study significantly enhances the capabilities of the wide area automated detection of AGs, and provides a crucial dataset for hazard prediction and mitigation along the HXC.

Study region: The study area is located in Guizhou province, the centre of the karst region in southwest China. Study focus: As a typical karst region, Guizhou has long faced challenges with water scarcity. Quantifying water use efficiency (WUE) is crucial for the efficient use of water resources and sustainable management. This study examined the spatio-temporal evolution patterns and its driving factors on WUE in Guizhou using Theil-Sen's slope, partial correlation methods, and the geographic detector model. New hydrological insights for the region: (1) The multi-annual mean WUE of non-karst region (NKR, 2.75 gC kg−1 H2O yr−1) is higher than that of karst region (KR, 2.10 gC kg−1 H2O yr−1) during 2001–2020. (2) The rate of increase of WUE was three times higher in KR than in NKR. (3) Precipitation and temperature were identified as the key factors influencing WUE. Interactions between each environmental factors were observed to have both bi-variate and non-linear enhancement effects. Notably, in the NKR, soil moisture, vegetation type and leaf area index were the dominant factors influencing WUE, with interactions between environmental factors had both enhanced and weakened effect on WUE. (4) The synergistic effects of these interactions exceeded the individual or cumulative effects of the two environmental factors. These findings contribute to our understanding of the environmental factors driving changes in WUE in the KR.

Study region: Loess Plateau (LP), China Study focus: Evapotranspiration (ET) is crucial in the terrestrial hydrological cycle and serves as a vital indicator of water resource availability. However, the interactions between ET and vegetation, climate, and soil on the LP have been left unexplored, especially during the growing season and regarding ET thresholds for different land-use types. Therefore, we used ridge regression and geographic detector model to analyze ET response to fractional vegetation coverage (FVC), temperature, precipitation, land surface temperature (LST), and temperature vegetation dryness index (TVDI). Additionally, the elastic coefficient was used to determine the ET thresholds for dominant factors in different land-use types. New hydrological insight for the region: The study reveals FVC as the dominant factor driving ET changes, while also highlighting the synergistic effects of temperature, precipitation, LST, and TVDI with FVC. The synchronization of ET and FVC change trends in vegetation unchanged and restored areas underscores the significance of vegetation cover in regulating regional water cycles. Furthermore, the study provides valuable insights into the regional water cycle dynamics and offers a certain level of guidance for water management strategies tailored to different land-use types.

[Objective] Soil erosion is a prominent ecological problem in southwestern Hubei Province. A reasonable assessment of the vulnerability of soil erosion in this area and exploration of its driving mechanisms are prerequisites for effective soil and water conservation measures. [Methods] The effects of natural and socio-economic factors on soil erosion in southwest Hubei were analyzed from the perspective of the human-land coupling system. The evaluation index system of soil erosion vulnerability in southwest Hubei was constructed from three aspects: exposure, sensitivity, and adaptability. The comprehensive weighting method was used to determine the index weight. [Results] (1) From 2010 to 2020, the vulnerability of soil erosion in southwest Hubei generally showed a trend of decreasing first and then increasing. The vulnerability of soil erosion with strong and above intensity showed a fragmented distribution pattern, which was mainly concentrated in the central and southern parts of southwest Hubei and the western part of Yichang municipal district in the east. (2) The area with the highest sensitivity in the study area was concentrated at an altitude of 800 ~ 1 500 m, and the sensitivity was the largest at a slope of 25° ~ 35°, and showed a decreasing trend to both sides. When the slope was greater than 15°, the area with higher and higher sensitivity increased sharply; (3) Soil erosion vulnerability was higher than soil erosion intensity at higher and above soil erosion levels, and lower than soil erosion intensity at lower and below soil erosion levels. There was a synergistic trend between soil erosion vulnerability and soil erosion intensity. (4) The distribution pattern of soil erosion vulnerability was caused by a multi-factor synergy. The analysis of the geographical detector showed that slope, vegetation coverage, education quality, and urbanization rate have strong explanatory power for soil erosion vulnerability. [Conclusion] In the future, it is necessary to attach great importance to the protection of vegetation coverage in areas with a slope>15°, and to enhance the awareness of local residents about soil and water conservation by improving the quality of local education and strengthening the publicity of soil and water conservation.

[Objective] Snowmelt erosion is one of the main forms of soil erosion in the black soil area of Northeast China, which plays an important role in land degradation in the area. The erosivity of snowmelt runoff is the key factor for calculating the erosion amount of snowmelt runoff, so it is of great significance to study the erosivity of snowmelt runoff. The erosivity of snowmelt runoff is mainly affected by snow depth, radiation intensity and warming rate, and its main driving forces include snowmelt rate and surface runoff. [Methods] The erosivity of daily average snowmelt runoff in the past 31 years was calculated, the temporal and spatial distribution characteristics of snowmelt runoff erosivity in the black soil area of Northeast China were analyzed, and the main influence factors of snowmelt runoff erosivity in each region were analyzed by using geographic detectors. [Results] The daily average snowmelt runoff erosivity in the three black soil subregions from 1990 to 2020 showed a trend of first increasing and then decreasing, and gradually stabilized in recent years, and the erosivity of daily average snowmelt runoff for many years was 0.01 (MJ·mm)/(hm2·h·a). Spatially, the erosivity of multi-year daily average snowmelt runoff was in the range of 0~0.21 (MJ·mm)/(hm2·h·a), showing a pattern of small in the middle and large in the surrounding areas. The influence factors of snowmelt runoff erosivity in spatial were as follows, the snow depth was large in the north and small in the south, the solar radiation intensity decreased from west to east, and the warming rate decreased from north to south. The influence factor of snowmelt runoff erosivity were analyzed by ground detector, and it was found that the warming rate had the strongest effect in the Mengdong black soil subregion, while the snow depth had the strongest effect in the Songnen black soil subregion and the Sanjiang black soil subregion. [Conclusion] Analyzing the spatiotemporal characteristics and influence factors of snowmelt runoff erosivity in the black soil region of Northeast China from 1990 to 2020 had certain theoretical and practical significance for deepening the study of snowmelt runoff erosivity characteristics and snowmelt erosion prevention in the study area.

To explore the human-land relationship between traditional Tujia settlements and the natural environment, by applying the geographic information systems (GIS), this study constructs a suitability evaluation system for human settlements of the Tujia ethnic minority based on 12 indexes, including elevation, slope, aspect, landform, relief degree of land surface (RDLS), river, vegetation, soil, road, the ancient Sichuan salt route, temperature, and precipitation. It applies the geographical detector (Geodetector) to analyze the driving force of the settlement distribution, and the spatial pattern of human settlements suitability and the spatial relationship between the suitability and the settlement distribution are determined by fuzzy comprehensive evaluation method. The results reveal that among the 12 indexes, RDLS exercises the most significant impact on the spatial distribution of settlements, followed by the elevation, while slope has the most minor importance, and therefore the suitability for farming remained Tujia ancestors' primary concern when selecting the settlement locations. In addition, there appears to be a strong interaction between the indicators and the spatial distribution of the settlements, with the interaction effect of any two indicators being greater than that of one, suggesting that the ancestors selected the sites of their settlements based on comprehensive consideration. Moreover, the settlements are primarily located in more suitable areas with low hills and flat relief; however, some cluster in less suitable areas considering the source of income.

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.

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.

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.

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.

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.

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.

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.

Background: With the ever-increasing occurrence of extreme weather events as a result of global climate change, the impact of extreme temperatures on human health has become a critical area of concern. Specifically, it is imperative to investigate the impact of extreme weather conditions on the health of residents. Methods: In this study, we analyze the daily death data from 13 prefecture-level cities in Jiangsu Province from January 2014 to September 2022, using the distributed lag nonlinear model (DLNM) to comprehensively account for factors such as relative humidity, atmospheric pressure, air pollutants, and other factors to evaluate the lag and cumulative effects of extreme low temperature and high temperature on the death of residents across different age groups. Additionally, we utilize the Geographical Detector to analyze the effects of various meteorological and environmental factors on the distribution of resident death in Jiangsu Province. This provides valuable insights that can guide health authorities in decision-making and in the protection of residents. Results: The experimental results indicate that both extreme low and high temperatures increase the mortality of residents. We observe that the impact of extreme low temperatures has a delayed effect, peaking after 3–5 days and lasting up to 11–21 days. In contrast, the impact of extreme high temperature is greatest on the first day, and lasts only 2–4 days. Conclusion: Both extreme high and low temperatures increase the mortality of residents, with the former being more transient and stronger and the latter being more persistent and slower. Furthermore, residents over 75 years of age are more vulnerable to the effects of extreme temperatures. Finally, we note that the spatial distribution of resident deaths is most closely associated consistent with the spatial distribution of daily mean temperature, and there is significant spatial heterogeneity in deaths among residents in Jiangsu Province.

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.

Scrub typhus is a significant public health issue with a wide distribution and is influenced by various determinants. However, in order to effectively eradicate scrub typhus, it is crucial to identify the specific factors that contribute to its incidence at a detailed level. Therefore, the objective of our study is to identify these influencing factors, examine the spatial variations in incidence, and analyze the interplay of two factors on scrub typhus incidence, so as to provide valuable experience for the prevention and treatment of scrub typhus in Gannan and to alleviate the economic burden of the local population.This study employed spatial autocorrelation analyses to examine the dependent variable and ordinary least squares model residuals. Additionally, spatial regression modelling and geographical detector were used to analyze the factors influencing the annual mean 14-year incidence of scrub typhus in the streets/townships of Gannan region from 2008 to 2021. The results of spatial1 autocorrelation analyses indicated the presence of spatial correlation. Among the global spatial regression models, the spatial lag model was found to be the best fitting model (log likelihood ratio = −319.3029, AIC = 666.6059). The results from the SLM analysis indicated that DEM, mean temperature, and mean wind speed were the primary factors influencing the occurrence of scrub typhus. For the local spatial regression models, the multiscale geographically weighted regression was determined to be the best fitting model (adjusted R2 = 0.443, AICc = 726.489). Further analysis using the MGWR model revealed that DEM had a greater impact in Xinfeng and Longnan, while the southern region was found to be more susceptible to scrub typhus due to mean wind speed. The geographical detector results revealed that the incidence of scrub typhus was primarily influenced by annual average normalized difference vegetation index. Additionally, the interaction between GDP and the percentage of grassland area had a significant impact on the incidence of scrub typhus (q = 0.357). This study illustrated the individual and interactive effects of natural environmental factors and socio-economic factors on the incidence of scrub typhus; and elucidated the specific factors affecting the incidence of scrub typhus in various streets/townships. The findings of this study can be used to develop effective interventions for the prevention and control of scrub typhus.

Abstract Introduction Under globalization, human settlement has become a major risk factor affecting life. The relationship between humans and the environment is crucial for improving community resilience and coping with globalization. This study focuses on the key contradictions of community development under globalization, exploring community resilience by analyzing the mismatch between residents' health activities and the environment. Methods Using data from Shanghai downtown, including land use, Sports app, geospatial and urban statistics, this paper constructs a comprehensive community resilience index (CRI) model based on the DPSIR model. This model enables quantitative analysis of the spatial and temporal distribution of Community Human Settlement Resilience (CR). Additionally, the paper uses geodetector and Origin software to analyze the coupling relationship between drivers and human settlement resilience. Results i) The scores of CR showed a "slide-shaped" fluctuation difference situation; ii) The spatial pattern of CR showed a "pole-core agglomeration and radiation" type and a "ring-like agglomeration and radiation" type. iii) Distance to bus stops, average annual temperature, CO2 emissions, building density and number of jogging trajectories are the dominant factors affecting the resilience level of community human settlement. Conclusion This paper contributes to the compilation of human settlement evaluation systems globally, offering insights into healthy community and city assessments worldwide. The findings can guide the creation of similar evaluation systems and provide valuable references for building healthy communities worldwide.

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

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.

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.

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

A fundamental aspect of ensuring urban sustainability is a comprehensive understanding of the driving mechanisms behind the urban heat island (UHI) phenomenon. The primary objective of this study is to investigate the spatiotemporal variations and underlying mechanisms of the surface urban heat island (SUHI) in Hefei. The study employed the local climate zone (LCZ) method to analyze land surface morphology and spatial structure for 2014 and 2021. Subsequently, calculations were conducted to derive surface urban heat island intensity (SUHII), normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), gravity water index (GWI), building surface fraction (BSF), road density (RD), poi density (PD), and population density (PPD). The exploration of the mechanisms by which factors influence SUHI was conducted by utilizing both Pearson correlation analysis and geographic detector models. The results revealed that sparsely built (LCZ 9) and low plants (LCZ D) predominantly characterized built-up and natural coverage areas, respectively. The summer season was distinguished by the most extensive SUHI distribution and the highest SUHII levels. Significantly, SUHII consistently exceeded those of built-up LCZs when contrasted to natural LCZs. Large lowrise (LCZ 8) consistently displayed the highest SUHII levels, whereas water (LCZ G) consistently exhibited the lowest SUHII values. NDBI took precedence and showed a positive correlation with SUHI. Among the socio-economic factors, building height (BH) demonstrated a superior explanatory capability for SUHI compared to other variables. The interaction between NDBI and NDVI maximized the explanation of SUHI under different seasons. The findings of this study will serve as critical insights for urban planners and policymakers, enabling the development of scientifically-based and efficacious strategies to mitigate SUHI.

The Yellow River Source Area (YRSA) functions as an ecological barrier within the Yellow River Basin, playing a significant role in providing indispensable ecosystem services. Analyzing the ecosystem service value (ESV) of YRSA holds great significance in establishing ecological protection awareness and promoting ecological actions. In this study, we reveal the spatial and temporal characteristics of ESV in YRSA from 2000 to 2020 based on the land use change and equivalent factor method, and explore the driving mechanisms behind ESV heterogeneity using geographical detector. The results showed that from 2000 to 2020, ESV in the YRSA increased significantly, with an average increase rate of 9.12 × 1021seJ/5a, showing a spatial distribution pattern of low in the northwest and high in the southeast, and this imbalance is gradually weakening. The average annual contribution of grassland ESV reached 45 %, followed by water bodies (23 %). Ecosystem services in the YRSA are mainly dominated by regulating services, among which hydrological regulating services are dominated, with an average annual contribution rate of more than 40 %. Supply and regulation, support and cultural services both form a strong correlation synergy. Climate factors are the main drivers of spatial heterogeneity in ESV, further illustrating the sensitivity of the YRSA to climate change. Moreover, our results accentuate the integral role of the YRSA in furnishing ESV to the broader Yellow River Basin, which provides a theoretical basis and reference for decision makers to assess the ecological security of the ecological barrier zones.

With the frequent occurrence of worldwide extreme climate events, human-induced ecosystem degradation has seriously threatened the realization of the Sustainable Development Goals (SDGs), especially in arid ecologically fragile areas. Macro-scale ecological quality (EQ) monitoring and exploration of its driving mechanisms have become research hotspots. However, this research field still lacks a method framework with strong comparability, adaptability and transferability, which significantly restricts the applicability of research results. In this study, a method framework for exploring spatial and temporal changes in EQ and their driving mechanisms based on text summarization and information extraction is constructed. Taking Central Asia as a typical case, this study outlines its EQ driving paths, explores the influencing mechanisms of representative drivers, and verifies the effectiveness of this method framework based on the comparison of the spatial and temporal evolution of EQ at multiple scales. The results indicate that the overall EQ in Central Asia exhibited a slight inverted U-shaped trend, with its driving paths falling into five categories: urban expansion, agricultural development, resource extraction, climate change, and ecological protection. The fragmentation of areas with high EQ is the main landscape characteristic in Central Asia. Furthermore, land use intensity and agricultural water use efficiency are significant factors in Central Asia's EQ evolution. Over time, the interaction between anthropogenic and natural factors has played an essential role in EQ evolution in Central Asia, with interactions between altitude, climate aridity, agricultural water use efficiency, and land use intensity gradually intensifying. This study has an implication for the construction of method framework for EQ-related study in ecologically fragile areas at the macro scale.

IntroductionThis study aims to investigate the factors influencing ecosystem service trade-offs/synergies (TOSs) in major agricultural production areas in the southern hilly region and to propose optimization strategies to promote ecosystem sustainability in agricultural areas.MethodsThe study used a geographical detector to analyze the determinants influencing the ecosystem service trade-offs as well as correlation analysis, geographically weighted regression (GWR), and a geographical detector to analyze the spatial and temporal evolution of ecosystem service and TOS relationships from 2000 to 2020 in Hunan Province.ResultsThe results showed that the comprehensive value of ecosystem services in Hunan Province showed an increasing trend from 2000 to 2020. With spatial heterogeneity, the areas with high values were mainly distributed in the hilly areas in the west, south, and east of Hunan Province, and the areas with low values were mainly distributed in the Dongting Lake Plain and the Xiangzhong Hilly Basin. There was a trade-off relationship between food production (FP) and all other ecosystem services, of which FP has the strongest trade-off effect with habitat quality (HQ). The synergy effect between HQ, water yield (WY), carbon storage (CS), and soil conservation (SC) shows an increasing trend. Gross domestic product (GDP) and SLOPE are the dominant factors for the strength of trade-offs between food supply and other ecosystem services, and Digital Elevation Model (DEM) and Normalized Difference Vegetation Index (NDVI) are the dominant factors for the strength of synergy effects among ecosystem services. The strength of TOS effects of ecosystem services is determined by interactions or co-influences between the two services rather than by a single component.DiscussionThe results of this study can provide a reference basis for the enhancement of ecosystem services and the sustainable planning of agricultural landscapes in the southern hilly areas.

Evaluating the ecological vulnerability of the northwestern region of Liaoning Province, a typical ecologically vulnerable area in China, and exploring its inherent patterns help formulate effective measures for ecological protection and restoration. Existing studies in this region have focused on the county, city, or provincial level, failing to capture the overall ecological vulnerability. Based on the “sensitivity-degradation” model framework, this study employed the Analytic Hierarchy Process, Principal Component Analysis, and geographical detector methods to evaluate the ecological vulnerability in the northwestern region of Liaoning Province from 2000 to 2022 and analyzed the possible driving factors. The results showed that, from 2000 to 2022, areas with moderate or higher vulnerability accounted for an average of 64.7% of the total area, while severely and extremely vulnerable areas accounted for 32.5%. These severely and extremely vulnerable areas are mainly distributed along both banks of the Liuhe River and in the northern part of Liaoning Province, where it adjoins the Horqin Sandy Land. From 2000 to 2022, the overall ecological vulnerability in the northwestern region of Liaoning Province showed a decreasing trend, with the proportion of severe vulnerability significantly decreasing from 35.6% to 8.6%. Vegetation and land use were the primary factors leading to long-term changes in ecological vulnerability. This research can also provide an example for the similar issues in other study areas.

In recent years, urban blue-green space has received increasing attention as an important carrier of urban spatial vitality. It has become increasingly clear that urban blue-green spaces both cities and citizens, though these concepts of spatial vitality and the underlying mechanisms influencing spatial vitality have yet to be explored in depth. Using blue-green spaces in the Nanjing Main Districts as an example, this study employs precise Location Based Services (LBS) data as a proxy for spatial vitality to explore the influence of external factors and their interaction(s) on spatial vitality. The results demonstrate that (1) functional mixing and walking accessibility are the dominant external factors affecting blue-green spatial vitality; (2) the interaction of external factors has a significant impact on spatial vitality, and finally (3), that public transportation accessibility and regional economic conditions are the catalytic factors affecting spatial vitality, explaining the contradictory research results discovered by other scholars. This study provides a foundation for the planning and construction of urban blue-green spaces.

As the common wealth of all mankind, intangible cultural heritage carries the memory of history. The implementation of dragon and lion dance technical grade evaluation can be called pioneering work for the legacy inheritance. The purpose of this study is to analyze the distribution characteristics, trends, types, and driving factors of dragon and lion dance athletes. Athletes who were awarded a grade certificate by Chinese Dragon and Lion Dance Sports Association from 2018 to 2021 were taken as the analysis objects. In ArcGIS10.8, trend surface analysis and standard deviational ellipse are used to study the spatial distribution characteristics. Using the global and local Moran's I in GeoDa, this paper explores the concentration degree and types of dragon and lion dance athletes. The factors driving the distribution difference are analyzed with the help of geographical detectors. Continuous data such as total GDP, sports field areas, permanent populations, and altitude are taken as the software input after preprocessing. The results show that there are significant differences in the distribution of provinces and four geographical regions. For dragon dance athletes, there is a linear growth trend in the west-east direction and an inverted U-shape trend in the south-north direction. For lion dance athletes, there is a trend of log function in the west-east direction and an inverted U shape in the south-north direction. The global Moran's I is 0.2386, and there are obvious characteristics of H–H, L-L, and L-H aggregation types. Nomination of sports intangible cultural heritage, total GDP, permanent resident population, urbanization development level, the number of colleges and universities, and the proportion of tertiary industry are the leading factors, and the explanatory power of interactive factors is significantly enhanced. Therefore, it is suggested to strengthen the cultural heritage protection of dragon and lion dance, increase capital investment, enhance public participation, and raise government attention.

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.

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.

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.

It is necessary to further investigate the spatial considerations, temporal characteristics, and drivers of change affecting the beginning and end of soil freezing and thawing, including the maximum depth of the seasonal freezing (MDSF) and the active layer thickness (ALT) in Northeast China. Hourly soil temperature, among other data, from 1983–2022 were investigated, showing a delay of about 6 days in freezing. In contrast, thawing and complete thawing advanced by about 26 and 20 d, respectively. The freezing period and total freeze-thaw days decreased by about 29 and 23 days, respectively. The number of complete thawing period days increased by about 22 days, while the MDSF decreased by about 25 cm. The ALT increased by about 22 cm. Land Surface Temperature (LST) is the main factor influencing the beginning and end of soil freezing and thawing, MDSF and ALT changes in Northeast China; air temperature, surface net solar radiation, and volumetric soil water content followed. The influence of the interacting factors was greater than the single factors, and the interactive explanatory power of the LST and surface net solar radiation was highest when the soil started to freeze (0.858). The effect of the LST and the air temperature was highest when the soil was completely thawed (0.795). LST and the volumetric soil water content interacted to have the first explanatory power for MDSF (0.866) and ALT (0.85). The results of this study can provide scientific reference for fields such as permafrost degradation, cold zone ecological environments, and agricultural production in Northeast China.

In the circumstance of global change, studying the dynamic changes of vegetation and the factors that influence it holds immense practical significance to monitor the quality of the regional ecological environment and improve the structure and function of ecosystems. Therefore, based on NDVI data, combined with natural and anthropogenic activities, this study focused on the spatiotemporal evolution characteristics and stability of NDVI in the Chengdu-Chongqing region from 2001 to 2020 at multiple spatiotemporal scales, and quantitatively investigates the main influencing factors driving vegetation NDVI spatial differentiation using Theil-Sen Medium slope estimation, Mann-Kendall significance, stability analysis and geographical detector. The findings indicated that: (1) The overall NDVI exhibited favorable levels over the 2001–2020 period; (2) The upward trends observed in the NDVI values indicated an enhancement in the ecological environment of the investigation area; (3) The spatial coefficient of variation for NDVI in the investigation region was computed as 0.048, suggesting a high degree of stability in NDVI values across the region; (4) NDVI is primarily affected by a range of variances in natural phenomena and anthropogenic activities. The collective influence of environmental factors and anthropogenic activities holds a more substantial sway over NDVI. The research results aim to offer guidance for the execution of vegetation restoration initiatives and the establishment of policies for the preservation of the ecological environment in the upstream region of the Yangtze River.