Search

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

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.

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.

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.

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.

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.

Pinus massoniana, an important timber, producing, and silvicultural species in southern China, exhibits high adaptability and wide distribution. This study utilizes the Maximum Entropy Model (MaxEnt), a species distribution model based on the theory of maximum entropy, to forecast the potential suitable distribution areas of P. massoniana in China under four climate change scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) for both present and future (2080) conditions. The research integrates and analyzes the effects of various environmental factors, including topography, soil, and population, on the distribution of P. massoniana. Additionally, a geographical detector is employed to assess the interaction between different environmental factors and their contribution to the variation in suitability zones.The findings indicate that the MaxEnt model accurately predicts the potential distribution areas of P. massoniana, with AUC values exceeding 0.94. Precipitation in the driest month (BIO14), population density (POP), and annual precipitation (BIO12) emerge as the main factors influencing the current distribution of P. massoniana. Notably, BIO14 has the greatest impact on the species' distribution (43%), followed by POP (32.7%). Furthermore, lower BIO14 values correspond to higher probabilities of pine distribution, while higher POP values correlate with increased pine distribution probabilities. The potential distribution of P. massoniana is primarily concentrated in southern China under current climatic conditions, encompassing a total suitable survival zone of 25.24 × 105 km2, accounting for 26.29% of China's total area. Among the regions, Guangxi exhibits the largest suitable area for survival, reaching 28.9 × 104 km2, implying that the environmental characteristics of Guangxi are conducive to P. massoniana's survival. Under future climate scenarios, the overall distribution pattern of the potential range of P. massoniana remains similar to the present one, with an increasing trend in area. Notably, the SSP3-7.0 emissions scenario shows the most significant increase in area, totaling 4.71 × 104 km2, suggesting that this particular scenario is more favorable for the distribution of P. massoniana. This study provides valuable scientific insights for the management, conservation, and rational site selection of P. massoniana.

The Giant Panda National Park (GPNP) is one of the first national parks established in China, and the consideration of its ecological sensitivity is an important part of the construction of ecological civilization. Taking GPNP as a case, the ecological sensitivity evaluation model of the national park was constructed on the basis of the sensitivity evaluation results of regional terrain, climate, vegetation, animal trace, landscape resource, water resource and human activity. The CRiteria Importance Through Intercriteria Correlation (CRITIC) method was introduced to obtain the comprehensive spatial distribution characteristics of ecological sensitivity, and the spatial autocorrelation analysis and explanatory factor analysis were carried out in order to provide specific suggestions for the ecological development and protection of the national park. The results showed that the ecological sensitivity gradually increases from the regional edge to the center, and the area is 12.36%, 28.24%, 31.94%, 21.56%, and 4.76%, respectively. The Moran's I of ecological sensitivity is 0.914, showing a high spatial autocorrelation in the region. The sensitive cold spots are distributed in the north, and the hot spots are distributed in the middle and south. In the case of ecological governance, priority can be given to these high value cluster areas. Human activities are the main explanatory factors for ecological sensitivity, and the sensitivity of a certain region can be influenced by positive human behavior. The interaction of most factors in pairs will enhance the explanatory power of a single factor on ecological sensitivity, which means that multiple methods of ecological protection will achieve better results. The research results of this paper provide a scientific basis for guiding the management and protection of ecologically sensitive zones in the study area.

Identifying the multiscale relationships of ecosystem services (ESs) and their influencing factors is helpful for scientific decision-making and regional sustainable development. Different from previous studies on trade-off and synergy relationships, this paper introduced a new perspective of the constraint effect and reports a third type of service relationship. This paper first assessed four typical ESs in the Pearl River Delta region, including water conservation (WC), soil conservation (SC), habitat quality (HQ), and natural recreation (NR). Applying segmented quantile regression, constraint lines for scatter plots of NR and three other ESs were extracted on 8 scale series. The key features of constraint lines were analyzed, and the multiscale constraint effects of four types of environmental factors on ESs were identified. The results showed that (1) NR-SC and NR-WC had a hump-shaped constraint relationship, while NR-HQ had a linear constraint relationship. The NR service not only has an amplifier effect on the trade-off and synergy strength relationship of other services, but its threshold range is also an important reference for realizing a win–win situation; (2) The threshold and key characteristics of the constraint line are closely related to the maximum and range values of vegetation coverage, slope, and landscape diversity; (3) Environmental factors had five constraining effects on ESs: hump-shaped, positive convex, negative convex, U-shaped and exponential, which not only limited the upper boundary of the ES supply but also had lower boundary constraint effects on specific services; and (4) When the vegetation coverage reached 70 %, the average annual precipitation reached 2000 mm, and the landscape diversity reached approximately 0.4, the total amount of ESs approached the maximum value. When the slope is close to 7.5°, monitoring of vegetation and soil erosion needs to be strengthened to reduce ecological risks. The application of the constraint line method in analyzing complex influence relationships has been proven to visualize nonlinear relationships and quantify the optimal values of influencing factors. It will help maintain the long-term stability of the entire ecosystem and become an effective tool for multiscale regional ecological planning.

Vegetation is an important indicator reflecting ecological environment stability, and monitoring vegetation change and understanding its potential driving force have important guiding significance for the adjustment and implementation of ecological restoration measures. As an ecological security barrier in northwest China, the Qilian Mountains (QLMs) play an important role in promoting the green development of social and economic development. Currently, the comprehensive driving effects of natural and anthropogenic factors on vegetation change in the QLMs are not clear. Based on the normalized difference vegetation index (NDVI), the spatiotemporal characteristics and trend changes in vegetation in the QLMs were systematically analyzed from 2000 to 2020 in this paper. The effects of natural and anthropogenic driving factors on vegetation change were explored using a geographic detector (GeoDetector). The results showed that the vegetation has improved continuously in the past 21 years, but the overall vegetation coverage was still low. The vegetation distribution was highly clustered, with a decreasing trend from east to west. Annual sunshine duration (q statistic = 0.3347) and distance to the rivers (q statistic = 0.2649) had the greatest explanatory power for vegetation change, while slope, aspect, and landform type had the least explanatory power. The interaction between elevation and sunshine duration, temperature and precipitation, temperature and sunshine duration, elevation and precipitation had the most explanatory power for vegetation change. Finally, we determined the ranges or categories of driving factors that were most suitable for vegetation growth by using a risk detector. The results of this study can help us further understand the potential driving mechanism of vegetation coverage variation in the QLMs and provide theoretical guidance for relevant managers to formulate ecological restoration measures and land management policies in the next step. It is of great significance to maintain the stability of the fragile ecological environment and prevent land degradation in arid and semiarid areas of Northwest China.

Substantial mineral resources are enriched in the arid endorheic basins; however, due to environmental constraints, these areas face water shortages as well as its extremely uneven spatiotemporal distribution, which restricts the development of local industry and agriculture. Identifying these areas of the high groundwater potential are useful for groundwater supply and its sustainable planning. In this study, the Qaidam Basin in Northwest China was taken as an example. We collected 17 conditioning factors (i.e., precipitation, evaporation, geology, soil, Topographic Wetness Index, Fractional Vegetation Cover, distance to rivers, river density, distance to roads, road density, distance to faults, fault density, slope, curvature, residential density, landcover, and geomorphology) affecting groundwater resources in arid areas. We also collected 139 groundwater samples and used random forest (RF), deep neural network (DNN) and convolutional neural network (CNN) (associated with one-hot encoding) to predict the groundwater potential in this area. The Qaidam Basin was discretized into 420,000 sample points calculated in turn by the above three models. Receiver operating characteristic (ROC) and area under the curve (AUC) were used to test the accuracy of the three methods. Results indicated that the prediction scores for the three methods were 0.742, 0.790, and 0.817, and the AUC was 0.783, 0.811, and 0.846, respectively. The result provided by CNN was more precise than the results provided by RF and DNN. Additionally, this study aims to investigate the effects of the aforementioned factors on groundwater potential. A total of 17 factors were combined with the Geodetector model to quantify their impacts and interactions on the groundwater potential of the Qaidam Basin. Results revealed that the critical factors affecting groundwater potential in the Qaidam Basin were geomorphology (0.183) and evaporation (0.144), and their combined contribution was 0.457. The influence of arbitrary two-factors on groundwater potential is larger than that of themselves, demonstrating linear or nonlinear enhancement between them and confirming that the factor selections were sensible. The method based on CNN-Geodetector provides a novel approach for calculating groundwater potential, selecting appropriate evaluation indicators and quantifying the driving factors in the arid endorheic basins.

Rapid and accurate surveys of land are essential for protecting ecosystems. This study aimed to survey the spatial–temporal land degradation patterns and explore its driving mechanism based on a geographical detector in Hainan Island from 2000 to 2020. First, the areas of forest, cultivated land, and construction land were analysed, and their mutation year was determined through the Mann-Kendall abrupt test. Then, change vector analysis was used to create a double-evaluation system by combining the normalized difference vegetation index and net primary productivity. Finally, the geographical detector was used to identify the driving mechanism of different land-use types. The results show that 2013 was the mutation year of the three land-use types. From 2000 to 2013 (Stage 1), the land degradation areas of forest, cultivated land, construction land, grassland, and unutilised land reached 1977.13, 613.25, 165.81, 261.06, and 4.25 km2, respectively. From 2013 to 2020 (Stage 2), the degraded land area accounted for only 0.91 % of the island's total area. From the single factor perspective, the elevation (DEM), least distance to residential area (LDP), and variety of mean annual temperature (VTem) were found to be the dominant factors of land degradation of cultivated land and grassland, forest, construction land in Stage 1. On the other hand, in Stage 2, the main factor of land degradation was the variety of the density of GDP (VGDP) in cultivated land, grassland, and construction land, while it was the variety of mean annual precipitation (VPre) in the forest. Moreover, all pair-factors provided a higher determinant power for degraded land areas than a single factor. From the interaction effect perspective in Stage 1, VPOP ∩ LDR (i.e. the variety of the density of population density and least distance to roads or railways), LDP ∩ LDR, and VTem ∩ LDR were the most significant pair-factors of the interaction effect on land degradation of cultivated land, forest and grassland, and construction land. In Stage 2, the most significant pair-factors of the interaction effect in different land-use types were DEM ∩ VPOP and VPre ∩ POP in degraded cultivated land, VPre ∩ DEM and VPre ∩ LDR in degraded forest land, DEM ∩ VTem in degraded grassland, and VGDP ∩ LDR in degraded construction land. Given the time-lag effects of factors and the attribution analysis of land degradation, we suggest limiting the construction of roads and residential areas, increasing biodiversity, and sheltering forests to protect terrestrial ecosystems in the future.

Coordinating ecological and socioeconomic development is the only way to achieve regional sustainability. In this paper, the total output value of ecosystem services was selected to evaluate the ecological environment, and socioeconomic indicators were selected to evaluate socioeconomic development. The coupling coordination degree (CCD) between the ecological environment and economy of counties in northern China was evaluated by combining an entropy method and a coupling coordination model. Spatial autocorrelation and a geographical detector model were used to reveal the spatial agglomeration characteristics and factors that influence the coordination degree of the ecological–economic system in northern China. Results showed that, in 2019, most counties were in the ecological–economic transition development stage. Among them, 321 counties had a CCD index between 0.4 and 0.5 (basic coordination stage); 209 counties had a CCD index between 0.5 and 0.6 (primary coordination stage); and 77 counties had a CCD index between 0.6 and 0.8 (moderate coordination stage). The global Moran’s I was 0.349, indicating that there was spatial agglomeration of ecological–economic coupling coordination at a county level. Low-low clusters were mainly found in the central and eastern central part of the study area, and high–high clusters were mainly found in northern Hebei province, Shandong peninsula, and northern Henan province. The factors that influenced the CCD index, ordered from the largest to the smallest, were landscape, terrain, traffic, and climate factors. The interactions between driving factors showed nonlinear and bilinear enhancement. The findings show that the coordination of socioeconomic and ecological development in northern China can be further improved. Relevant policies should emphasize the local ecological advantages, promote the transformation to ecological industrialization, and encourage ecologically and economically balanced development.

In the past few decades, dominating human development patterns have negatively affected ecosystem services (ESs). To sustainably supply multiple ESs and enhance human well-being, researchers should analyze ESs responses to antagonistic effects. This study uses the InVEST model to evaluate the key ESs of the Shanxi Province in 2000 and 2020. The geographical detector model was used to analyze the dominant factors of the spatial differentiation characteristics of ESs changes. We used the Multi-Scale Geographically Weighted Regression (MGWR) to identify the main drivers of ESs changes and capture the differences in spatial variation. The results were as follows: (1) From 2000 to 2020, soil conservation (SC), carbon storage (CS), grain productivity (GP), and total ecosystem services (TES) increased by 44.48%, 1.03%, 57.84, and 1.67% respectively. Water yield (WY) and habitat quality (HQ) decreased by 1.36% and 0.64%, respectively. (2) The interaction of anthropogenic, climate, vegetation, and geomorphological factors has a significantly greater impact for the spatial differentiation of ESs changes than any single factor, though anthropogenic factors dominate the spatial distribution of regional ESs changes. (3) There is obvious spatial heterogeneity in the properties and intensity of the correlations between driving factors and changes in ESs. Anthropogenic factors have significant negative effects on CS, WY, and HQ changes. Vegetation factors were the main driving force for the improvement of GP and TES, while the climatic factor was the main driving factor for SC changes. (4) The MGWR model achieved the optimal performance, and the four selected driving factors explain 61.9%, 81.3%, 97.1%, 56.7%, 81.6%, and 79.2% of the changes in CS, WY, SC, HQ, GP, and TES, respectively. Based on the results, we suggest that future ecosystem management, planning and decision making, should focus on maintaining the balance between anthropogenic activities and vegetation restoration. This study provides a convenient method to capture the relationship between ESs and drivers in geographic space, and provides a reference for the sustainable supply of ESs in the region and the world.

Fine particulate matter (PM2.5) threatens public health severely, which, luckily, can be governed by referring to its spatiotemporal distribution and key driving factors. However, very few explored it in the long-term, broadly, and systematically. In this study, nine Chinese key urban agglomerations were targeted to explore the spatial distribution of PM2.5 and the evolution of its major driving factors from 2000 to 2017. Spatiotemporal distribution and change tendency were evaluated by spatial autocorrelation analysis and dynamic time warping (DTW), and the entire research period was divided into four stages according to the evaluation. Subsequently, the geographical detector method (GDM) was adopted to quantify the relationship between anthropogenic and meteorological factors with PM2.5 concentration in the entire period and sub-stages. As the findings indicate: 1) In 2000–2017, PM2.5 concentration increased firstly in all agglomerations and then declined by fluctuation; it was mainly gathered in the plain areas where the core cities of urban agglomerations were located, with the highest concentration in North China. 2) Variation of PM2.5 concentration appeared similar tendency and regional aggregation, e.g., five urban agglomerations around the central plains urban agglomeration (CPUA) had similar patterns. 3) The Driving factors of PM2.5 showed noticeable spatiotemporal differences. It is indicated that the critical meteorological factors refer to temperature and air pressure, while the key anthropogenic factors are population density (PD) and road density (RD). Except for the population density showing a relatively persistent high influence on urban agglomeration, especially significant in northern China, the rest of the anthropogenic factors represented different characteristics. Specifically, the proportion of secondary industry (PSP) and gross domestic product per capita (GDPP) showed relatively strong effects in the early stage but weakening dramatically in the later stage. Foreign direct investment (FI) increased in developed urban agglomerations in the entire stage while showed a downward trend in underdeveloped cities. Road density was enhanced dramatically in the early stage but weakened slowly in the later stage. The findings reveal the change tendency of PM2.5 concentration in urban agglomerations and the evolution of its driving factors, which help the Chinese government adopt effective strategies to cope with pollution.