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Coal fires are disasters that occur when underground coal seams are subjected to combustion conditions induced by natural or human factors. This study attempts to investigate the impact of coal fires on the surrounding environment by assessing the eco-environmental quality and its dynamic changes in the Surablak coal fire area. To achieve this, an improved remote sensing ecological index (termed RSEIds) is introduced to assess and track the quality and dynamics of eco-environmental conditions in the Surablak coal fire area from 1990 to 2022. Subsequently, this index is combined with a geographic detector (GeoDetector) model to identify potential factors influencing eco-environmental quality. The findings indicate that (1) compared with the established Remote Sensing Ecological Index (RSEI), the RSEIds provides a high degree of precision in reflecting the eco-environmental conditions within the regions affected by coal fires, (2) the eco-environmental quality within the Surablak coal fire area underwent a continuous deterioration from 1990 to 2022, with the area of ecological degradation constituting 53.41% of the study region, (3) regions with excellent and good RSEIds values are mainly found in the forested mountainous regions located in the northern section of the coal fire area, whereas regions with poor and fair RSEIds values largely coincide with the coal fire locations, and (4) since 2006, the distance to the coal fire has become the key factor influencing eco-environmental quality in the Surablak area, while temperature and precipitation remained important factors. The outcomes of this study will provide essential references for guiding ecological restoration and promoting sustainable development in coal fire areas.

As one of the major public health security issues, pulmonary tuberculosis had a global death rate of 1.6 million in 2021 alone, ranking 13th in the world, posing a great threat to society and families. Analyzing the temporal and spatial distribution and evolution trend of tuberculosis, discussing the exposure factors and studying the environmental background that affects the incidence can provide the basis for accurate prevention and control and promote the healthy and stable development of society. Based on the county scale, this study determined the high-incidence areas through hot spot analysis and selected nine districts and counties covering meteorological stations and air monitoring stations. The explanatory power of each factor to the incidence of pulmonary tuberculosis was analyzed by geographical detector, and the main influencing factors were explored. The results show that the following: (1) The number and incidence of pulmonary tuberculosis in Gansu Province declined from 2020 to 2022. (2) The influence of meteorological conditions such as temperature, precipitation and air pressure on pulmonary tuberculosis in different regions shows significant regional differences. Although the meteorological influence in adjacent regions shows certain convergence, the change in wind speed has no significant influence on the risk of pulmonary tuberculosis. (3) PM10, altitude, temperature, population density and GDP per capita have strong explanatory power to the incidence of tuberculosis, and the interaction between any two factors exceeds the effect of a single factor in explanatory power, showing the characteristics of two-factor enhancement and nonlinear enhancement.

Given the growing interest in sustainable tourism and the need to preserve ecological integrity, understanding how tourism resource development can coexist with biodiversity conservation is crucial, especially in ecologically sensitive regions. The Dabie Mountain region, with its rich biodiversity and cultural heritage, is a prime area for exploring the balance between tourism and environmental protection. Our study examined the spatial configuration of ecotourism resources in the Dabie Mountains, focusing on the role of ecosystem services, biodiversity hotspots, and traditional villages. Using the InVEST model, kernel density analysis, and a geographic detector, we found that the study area primarily consisted of lower-habitat-quality regions. Approximately 48,914.05 km2 (96.82% of the total area) was classified as low-quality habitat, presenting potential risks to biodiversity and ecosystem services in these regions. Higher habitat quality was observed in the eastern and southern regions, particularly in Yue Xi County, which exhibited superior biodiversity and habitat conditions compared to other administrative areas. Class V habitats, covering 39.89 km2 (31.69% of Class V area), were located in areas with greater terrain relief and proximity to water bodies, which contribute to higher biodiversity. This study identified a potential ecotourism development space of 649.79 km2, with a spatial distribution aligned with natural landscape features that support biodiversity conservation. Yue Xi and Jinzhai together accounted for 49.84% of the total ecotourism development space. Our study emphasizes the need to balance biodiversity conservation with tourism development, ensuring that ecotourism goals are met while preserving ecological integrity and cultural heritage. These findings provide a strong foundation for future research on biodiversity conservation in tourism development and offer insights for achieving sustainable ecological management in the region.

The lower reaches of the Jinsha River, serving as a vital ecological barrier in southwestern China and playing a crucial role in advancing targeted poverty alleviation efforts, remain underexplored in terms of the coupling between ecological and economic development, creating a gap in understanding the region’s sustainable development potential. This study combines the remote sensing ecological index (RSEI) derived from MODIS data and the biodiversity richness index (BRI) based on land use data to create the ecological environment index (EEI) using a weighted approach. It also develops the economic development index (EDI) from economic data using the entropy weight method. By integrating the EEI and EDI, the study calculates key metrics, including the ecological–economic coupling degree (EECD), coupling coordination degree (EECCD), and relative development degree (EERDD), and examines their spatiotemporal changes from 2000 to 2020. Additionally, the study applies a geographic detector model to identify the spatial drivers of the EEI, an obstacle factor diagnosis model to pinpoint the main barriers to EDI, and a neural network model to uncover the underlying forces shaping EECCD. The results indicate that: (I) From 2000 to 2020, the overall improvement rate of the ecological and economic subsystems was greater than that of the ecological–economic coupling system. The entire region is still in the Running-In Stage, and the coordination level has been upgraded from near imbalance to marginal coordination. About 85% of the counties’ EERDDs are still in the EDI Behind EEI Stage. (II) The structural composition of the EEI shows a pattern of low Dry Hot Valley Area and high in other areas, mainly driven by natural factors, although human activities had a notable impact on these interactions. (III) Originating from an impact model primarily driven by economic factors and supplemented by ecological factors, both EDI and EECCD exhibit a pattern of high in the south and low in the north, with improvements spreading northward from the urban area of Kunming. The development gradient differences between 24 poverty-stricken counties and 16 non-poverty-stricken counties have been reduced. (IV) For the six types of ecological–economic coupling development zones, it is essential to adopt localized approaches tailored to the differences in resource and environmental characteristics and development stages. Key efforts should focus on enhancing ecological protection and restoration, increasing financial support, implementing ecological compensation mechanisms, and promoting innovative models for sustainable development.

As a vital conservation area for water sources in the Yellow River Basin, understanding the spatial-temporal dynamics of vegetation coverage is crucial, along with the factors that affect it, to ensure ecological preservation and sustainable development of the Yellow River Source Region (YRSR). In this paper, we utilized Landsat surface reflectance data from 2000 to 2020 using de-clouding and masking methods implementing the Google Earth Engine (GEE) cloud platform. We investigated spatial-temporal changes in vegetation coverage by combining the maximum value composite (MVC), the dimidiate pixel model (DPM), the Theil–Sen median slope, and the Mann–Kendall test. The influencing factors on vegetation coverage were quantitatively analyzed using a geographic detector, and future tendencies in vegetation coverage were predicted utilizing the Future Land Use Simulation (FLUS) model. The outcomes suggested the following: (1) On the temporal scale, vegetation coverage exhibited a general upward trend between 2000 and 2020, with the YRSR showing a yearly growth rate of 0.23% (p < 0.001). In comparison to 2000, the area designated as having extremely high vegetation coverage increased by 19.3% in 2020. (2) Spatially, the central and southeast regions have higher values of vegetation coverage, whereas the northwest has lower values. In the study area, 75.5% of the region demonstrated a significant improvement trend, primarily in Xinghai County, Zeku County, and Dari County in the south and the northern portion of the YRSR; conversely, a notable tendency of degradation was identified in 11.8% of the area, mostly in the southeastern areas of Qumalai County, Chenduo County, Shiqu County, and scattered areas in the southeastern region. (3) With an explanatory power of exceeding 45%, the three influencing factors that had the biggest effects on vegetation coverage were mean annual temperature, elevation, and mean annual precipitation. Mean annual precipitation has been shown to have a major impact on vegetation covering; the interconnections involving these factors have increased the explanatory power of vegetation coverage’s regional distribution. (4) Predictions for 2030 show that the vegetation coverage is trending upward in the YRSR, with a notable recovery trend in the northwestern region. This study supplies a theoretical foundation to formulate strategies to promote sustainable development and ecological environmental preservation in the YRSR.

The landscape of the Poyang Lake wetland is significantly influenced by changes in water levels, impacting the distribution of habitats for migratory birds. While long-term effects of water level variations have been extensively studied, short-term impacts on Siberian crane habitats and their ecological vulnerability remain poorly understood. This study utilized 35 years (1987–2022) of Landsat remote sensing data and daily water level records from Poyang Lake to examine the effects of short-term water level fluctuations on the spatial distribution and ecological vulnerability of Siberian crane habitats. The geographic detector method was employed to quantify the explanatory power and interaction effects of factors, including short-term water level fluctuations, on ecological vulnerability. The findings reveal significant differences in the habitats of wintering Siberian cranes across various water level intervals and short-term fluctuation patterns. Short-term water level fluctuations can result in the largest suitable wintering habitat area for Siberian cranes, reaching 1856.41 km2 in this study. These habitats are highly sensitive to short-term water level changes, with rising and falling trends potentially leading to habitat loss. Oscillating water levels in the short term create broader and more concentrated habitats. Notably, fluctuations at low water levels support the sustainability and stability of crane habitats. Furthermore, short-term water level trends and nature reserves play a critical role in maintaining habitat ecological vulnerability; well-managed and protected nature reserves exhibit significant explanatory power, both in single-factor analysis and in their interaction with other environmental factors. Specifically, these protected areas show explanatory power exceeding the 20% threshold for both water level fluctuations and ranges, highlighting the crucial role of anthropogenic management in mitigating ecological vulnerability. This study emphasizes the necessity of scientifically informed regulation of short-term water level fluctuations to protect Siberian crane habitats and provides a strong scientific basis for decision-making support.

Investigating the spatiotemporal variations in Aerosol Optical Depth (AOD) in terrestrial ecosystems and their driving factors is significant for deepening our understanding of the relationship between ecosystem types and aerosols. This study utilized 1 km resolution AOD data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Mann–Kendall (M-K) trend test to analyze the spatiotemporal variations in AOD in seven ecosystems in Northern Xinjiang from 2001 to 2023. The geographic detector model was employed to investigate the effects of driving factors, including gross domestic product, population density, specific humidity, precipitation, temperature, wind speed, soil moisture, and elevation, on the distribution of AOD in the ecosystems. The results indicate that over the past 23 years, wetlands had the highest annual average AOD values, followed by settlements, farmlands, deserts, grasslands, others, and forests, respectively. Furthermore, the AOD values decrease with increasing ecosystem elevation. The annual mean of AOD in Northern Xinjiang generally shows a fluctuating upward trend. The M-K test shows that the proportion of area with an increasing trend in AOD in the settlement ecosystems is the highest (92.17%), while the proportion of area with a decreasing trend in the forest ecosystem is the highest (21.78%). On a seasonal scale, grassland, settlement, farmland, forest, and wetland ecosystems exhibit peak values in spring and winter, whereas desert and other ecosystems only show peaks in spring. Different types of ecosystems show different sensitivities to driving factors. Grassland and forest ecosystems are primarily influenced by temperature and altitude, while desert and settlement ecosystems are most affected by wind speed and humidity. Farmlands are mainly influenced by wind speed and altitude, wetlands are significantly impacted by population density and humidity, and other ecosystems are predominantly affected by humidity and altitude. This paper serves as a reference for targeted air pollution prevention and regional ecological environmental protection.

Land use and land cover change (LUCC) significantly influences the dynamics of carbon storage in thin terrestrial ecosystems. Investigating the interplay between land use alterations and carbon sequestration is crucial for refining regional land use configurations, sustaining the regional carbon balance, and augmenting regional carbon storage. Using land use data from the Pearl River Delta Urban Agglomeration (PRDUA) from 2010 to 2020, this study employed PLUS-InVEST models to analyze the spatiotemporal dynamics of land use and carbon storage. Projections for the years 2030, 2040, and 2050 were performed under three distinct developmental scenarios, namely, natural development (ND), city priority development (CPD), and ecological protection development (EPD), to forecast changes in land use and carbon storage. The geographic detector model was leveraged to dissect the determinants of the spatial and temporal variability of carbon storage, offering pertinent recommendations. The results showed that (1) during 2010–2020, the carbon storage in the PRDUA showed a decreasing trend, with a total decrease of 9.52 × 106 Mg, and the spatial distribution of carbon density in the urban agglomeration was imbalanced and showed an overall trend in increasing from the center to the periphery. (2) Clear differences in carbon storage were observed among the three development scenarios of the PRDUA between 2030 and 2050. Only the EPD scenario achieved an increase in carbon storage of 1.10 × 106 Mg, and it was the scenario with the greatest potential for carbon sequestration. (3) Among the drivers of the evolution of spatial land use patterns, population, the normalized difference vegetation index (NDVI), and distance to the railway had the greatest influence on LUCC. (4) The annual average temperature, annual average rainfall, and GDP exerted a significant influence on the spatiotemporal dynamics of carbon storage in the PRDUA, and the interactions between the 15 drivers and changes in carbon storage predominantly manifested as nonlinear and double-factor enhancements. The results provide a theoretical basis for future spatial planning and achieving carbon neutrality in the PRDUA.

Vegetation greenness has been one of the most widely utilized indicators to assess the vegetation growth status for the better ecological environment. However, in typical tourist regions, the impact of the geographical environment, socioeconomic development, and tourism development on vegetation greenness changes is still a challenge. To address this challenge, we used the Google Earth Engine (GEE) cloud platform combined with a series of Landsat remote sensing images to calculate the fractional vegetation cover (FVC) which can be used as an indicator to characterize the spatiotemporal evolution of vegetation greenness in Hainan Island from 2000 to 2020. Furthermore, we employed geographic detector and structural equation models to quantify the relative importance and explanatory power of the geographical environment, socioeconomic development, and tourism development on vegetation greenness changes and to clarify the interaction of mechanisms of various factors in Haikou and Sanya. The results show that the annual growth rate of the FVC in Hainan Island was 0.0025/a. In terms of spatial distribution, the trend of the FVC changes was mainly characterized by non-significant and extremely significant improvement, accounting for 35.34% and 29.38% of the study area. Future vegetation greenness was dominated by weak counter-persistent increase and weak persistent increase. The geographical environmental factors were the main factors affecting vegetation greenness in Haikou, followed by the socioeconomic and the tourism development factors, while the geographical environmental factors also dominate in Sanya, followed by the tourism development factors and finally the socioeconomic factors. Specifically, the spatial distribution of vegetation greenness was primarily influenced by land use types, elevation, slope, and travel services. Geographical environmental factors could indirectly affect changes in socioeconomic and tourism development, thereby indirectly affecting the spatial distribution of vegetation greenness. These findings can provide some significant implications to guide the ecological environmental protection for sustainable development in Hainan Island in China.

Studying spatial and temporal characteristics of land use changes and the driving factors in the Yellow River Basin as well as simulating and predicting future land use is crucial for resource management, ecological protection, and regional sustainable development in the Yellow River Basin. Based on the China Land Cover Dataset (CLCD) of the Yellow River Basin from 1990 to 2021, this study employs various methods such as the Mann–Kendall test and sliding t-test, land use dynamics, the land use transfer matrix, the standard deviation ellipse, the center of gravity migration model, and a geographic detector to explore the spatial and temporal characteristics of land use changes and driving forces in the Yellow River Basin over the past 30 years. Additionally, the study predicts land use types in the study area for the year of 2030 by using the Future Land Use Simulation (FLUS) model. The results show the following: (1) From 1990 to 2021, the area of forest, grassland, water, and impervious surfaces increased significantly, while the area of cropland, shrub, barren land, and wetlands decreased significantly. The most actively changing land use types are cropland, grassland, barren land, and impervious surfaces. (2) The center of gravity for shrub and impervious surfaces shifted westward, while wetlands showed a trend of obvious concentrated distribution, and the remaining land use types exhibited stable directional distributions. (3) Economic factors had a stronger driving effect on land use changes than topographic and climatic factors. The land use changes in the Yellow River Basin are influenced by the coordinated driving forces of multiple factors. (4) In 2030, the main land use types in the Yellow River Basin are still expected to be cropland, grassland, and forest. However, there will be a significant expansion of impervious surfaces and forest land, with substantial encroachment on cropland and grassland.

Accurate estimation of the distribution of POC in the sea surface is an important issue in understanding the carbon cycle at the basin scale in the ocean. This study explores the best machine learning approach to determine the distribution of POC in the ocean surface layer based on data obtained using satellite remote sensing. In order to estimate and verify the accuracy of this method, it is necessary to obtain a large amount of POC data from field observations, so this study was conducted in the Mediterranean Sea, where such data have been obtained and published. The research initially utilizes the Geographic Detector (GD) method to identify spatial correlations between POC and 47 environmental factors in the region. Four machine learning models of a Bayesian optimized random forest (BRF), a backpropagation neural network, adaptive boosting, and extreme gradient boosting were utilized to construct POC assessment models. Model validation yielded that the BRF exhibited superior performance in estimating sea-surface POC. To build a more accurate tuneRanger random forest (TRRF) model, we introduced the tuneRanger R package for further optimization, resulting in an R2 of 0.868, a mean squared error of 1.119 (mg/m3)2, and a mean absolute error of 1.041 mg/m3. It was employed to estimate the surface POC concentrations in the Mediterranean for May and June 2017. Spatial analysis revealed higher concentrations in the west and north and lower concentrations in the east and south, with higher levels near the coast and lower levels far from the coast. Additionally, we deliberated on the impact of human activities on the surface POC in the Mediterranean. This research contributes a high-precision method for satellite retrieval of surface POC concentrations in the Mediterranean, thereby enriching the understanding of POC dynamics in this area.

Vegetation plays an important role in absorbing carbon dioxide and accelerating the achievement of carbon neutrality. As the ecological barrier of North China, the Taihang Mountains are pivotal to the ecological construction project of China. Nevertheless, the dynamic development of the vegetation carbon sink in the region and the impact factors on the sink have not been systematically evaluated. This study employed a comprehensive approach, utilising remote sensing technology and meteorological and topographic data, in conjunction with the net ecosystem productivity (NEP) estimation model to reveal the characteristics of vegetation carbon sinks in the Taihang Mountain, and then revealed the dynamics evolution of the NEP and the inter-annual trend by using Theil–Sen Median slope estimation, the Mann–Kendall test, and the coefficient of dissociation and analysed the driving roles of the influencing factors by using the parameter optimal geographic detector. Our findings suggest that the NEP in the Taihang Mountain area has a clear growth trend in time, the average value of NEP in the Taihang Mountain area is 289 gC-m−2-a−1 from 2000 to 2022, and the spatial distribution shows the characteristics of high in the northeast and low in the middle and west, with a gradual increase from the northeast to the southwest; the areas with high fluctuation of NEP are mainly distributed in the areas around some cities that are susceptible to the interference of natural or anthropogenic factors. The vegetation carbon sinks in the Taihang Mountains are influenced by a variety of natural factors, among which the explanatory power of each natural factor is as follows: DEM (0.174) > temperature (0.148) > precipitation (0.026) > slope (0.017) > slope direction (0.003). The natural factor DEM had the strongest explanatory power for NEP changes, and the two-by-two effects of the natural factors on vegetation carbon sinks were all significantly stronger than the effects of a single factor, in which the interaction between DEM and precipitation had the strongest explanatory power; distinguishing from climate change factors, the contribution of anthropogenic activities to NEP changes in more than 90% of the area of the Taihang Mountainous Region was more than 60%, and the driving force of anthropogenic factors on NEP changes in the Taihang Mountainous Region was significantly stronger than that of natural climate change. The contribution of anthropogenic factors to NEP changes in the Taihang Mountains was significantly stronger than that of natural climate change. The results of this study can not only provide a reference for carbon reduction and sink increase and ecological restoration projects in the Taihang Mountains but also benefit the research paradigm of vegetation carbon sequestration in other regions.

Urban development of clusters situated along rivers significantly affects the health of the river ecosystems, the quality of urban environments, and the overall well-being of local communities. Ecosystem service supply value (ESSV) measures the delivery of ecosystem goods and services within a specific timeframe in a particular area. Using the lower Yellow River urban agglomeration (Shandong section) as a case, we comprehensively applied land use structure and intensity change analysis, quantitative calculation of ESS, and geographical probe methods to unveil ESS and its mechanism of response to the spatio-temporal evolution of the intensity of land use in urban agglomeration along the river. The key results were as follows: (1) Over the past two decades, farmland and construction land areas have continued to decrease and increase, respectively, with the intensity of land use change being highest from 2005 to 2010. (2) ESS has continued to rise over the past 20 years, with the income in 2020 being 11.142 billion yuan, an increase of 31.13%. The “low-value areas” are mainly concentrated in Liaocheng City, Dezhou City, and Tai’an City, which are characterized by predominantly flat terrains where farmland constitutes the principal land use type. Conversely, “high-value areas” are largely in the counties bordering the Yellow River, including the upper estuary in the north and the rugged, southeastern terrains. (3) Areas with concentrated ESSV were primarily localized in the northern estuary area and along the Yellow River in a scattered point-like pattern. The spatial distribution of hotspots has become increasingly concentrated, transitioning from points to planes. Conversely, cold spots initially increased in number before subsequently decreasing. Waterbody was the most sensitive ESSV-determining factor. (4) The spatial heterogeneity of ESSV emerges as a consequence of the interaction of multiple factors, and among these interactions, those involving NDVI and POP contain the greatest explanatory power. Our findings are expected to offer a scientific foundation for optimizing land spatial patterns and enhancing ecological management in the lower Yellow River region.

As urbanization in China progresses, urban spatial development is transitioning from rapid expansion to more intensive and compact growth. This study examined the role of physical geography and environmental factors in shaping the urban spatial development in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Based on the current natural conditions, we selected evaluation indices from topography, hydrogeology, climatic conditions, and natural disasters. These indices were used to create a carrying capacity and suitability evaluation system for development land under natural constraints. Finally, the spatial development potential of the city was finalized by taking into account the current state of the built-up area of the city. Meanwhile, we employed the Optimal Parameters-based Geographical Detector and assessed the impact of 14 natural factors on the spatial development of urban built-up areas. In 2020, the GBA had 52,168.77 km2 of land suitable for construction, of which 34,241.13 km2 was highly suitable (61.29%) and 17,927.64 km2 was moderately suitable (32.09%). At the Bay Area level, 90.15% of the development potential remains untapped; at the city level, Zhaoqing City has the highest potential at 99.56%, while Macao has the lowest at 26.83%. Key factors influencing urban development include silty sand content, annual average relative humidity, and cumulative temperature above 0 °C, with varying impacts across different urban scales. At the Bay Area level, the silty sand content, annual average relative humidity, and cumulative temperature above 0 °C are the main influencing factors on the spatial development of urban built-up areas; at the city level, the main factors are annual average relative humidity and cumulative active temperature above 0 °C. This study reveals the important influence of natural environmental factors on urban spatial development, which is conducive to promoting sustainable development of land resources in GBA.

The accelerated urbanization taking place across Xinjiang in recent years has vastly improved the quality of life for people living in the region. However, to achieve rational urban growth and sustainable regional development, a deeper understanding of the spatial and temporal patterns, spatial morphology, and driving factors of urban sprawl is crucial. Nighttime light (NTL) data provide a novel approach for studying the spatial and temporal changes in urban expansion. In this study, based on DMSP-OLS and NPP-VIIRS data, we analyze the spatiotemporal characteristics of urban changes using the standard deviation ellipse and employ the geographical detector to analyze the impact of natural environmental and socioeconomic factors on the dynamic rate of urban expansion. The results reveal the following. (1) The overall accuracy of urban area extraction is above 80%, and the urban area of Xinjiang has expanded about 9.1 times over the past 30 years. Further, the growth rate from 2007 to 2017 exceeds the growth rate from 1992 to 1997, with the center of gravity of urban development shifting to the southwest. (2) The 5a sliding average temperature and average annual precipitation in the study area in 1992–2022 are 6.08 °C and 169.72 mm, respectively, showing a decrease in the urbanization rate followed by an increase, due to a rise in temperature and precipitation levels. (3) By combining the results of geographical detector factor detection and interaction detection, precipitation is determined to be the main controlling factor, while air temperature and GDP are secondary factors. This study presents new findings on the correlation between urban spatial and temporal changes and climate in Xinjiang, thus providing a scientific reference for future research on urban expansion and natural environment evolution.

The relationship between land use changes and regional carbon storage is closely linked. Identifying evolving trends concerning and influencing factors on carbon storage under future scenarios is key in order to achieve the “dual carbon” goals. Using Chongqing as a case study, this study integrated the advantages of the PLUS model, InVEST model, and a geographic detector model. It conducted simulations of land use type data under scenarios of natural development (ND) and ecological protection (EP), and identified evolving trends and influencing factors regarding carbon storage. The results were as follows: (1) the PLUS model demonstrated excellent simulation performance, with a Kappa coefficient above 0.85 and an overall accuracy above 0.90. During the study period, significant changes occurred for cultivated land, forested land, water bodies, and construction, which were closely related to carbon storage; (2) carbon storage in Chongqing showed a decreasing trend, with a decrease of 10.07 × 106 t C from 2000 to 2020. Under the ND scenario, carbon storage was projected to decrease by 10.54 × 106 t C in 2030 compared to 2020, and it was expected to stabilize from 2030 to 2050. At the county level, Youyang, Fengjie, and Wuxi had the highest carbon storage, while Nanchuan, Jiangbei, and Dadukou had the lowest; (3) the spatial distribution of carbon storage presented an “eastern hotspot western cold spot aggregation” pattern. The proportions of regions with a decreased, unchanged, and increased aggregation of carbon storage in Chongqing during 2000–2010 and 2010–2020 were 2.99%, 95.95%, 1.06%; and 4.39%, 92.40%, 3.21%, respectively. The trend indicated a decrease in the aggregation of carbon storage, and future carbon storage was expected to stabilize; (4) elevation, terrain fluctuation, NDVI, annual average temperature, annual average precipitation, and nighttime light index had influence values of 0.88, 0.81, 0.61, 0.86, 0.77, and 0.81 on carbon storage, respectively, with different combinations of influencing factors having a greater impact. In the future, ecological priority and green development concepts should be followed, and comprehensive improvement of regional development conditions should be pursued to enhance carbon storage, thereby promoting the achievement of the “dual carbon” goals. This study provided an analytical path and data support for formulating optimized carbon storage policies at the regional level.

Biodiversity is profoundly influenced by habitat quality, and Harbin, a provincial capital situated in a cold climate zone, stands out as one of China’s regions most susceptible to the repercussions of climate change. To ensure the city’s continued sustainable growth, a thorough assessment of habitat quality must be conducted. This study employs a comprehensive approach integrating the InVEST model, the PLUS model, a landscape pattern analysis, geographic detector, and a geographically weighted regression model. The goal is to assess how land use and habitat quality have changed in Harbin City, investigate factors contributing to spatial heterogeneity in habitat quality, thoroughly examine evolutionary patterns under the inertial development scenario from 2030 to 2050, and propose spatial optimization strategies. There are four key findings. First, from 2000 to 2020, agricultural land and forest were Harbin City’s two most prevalent land use types. The most notable transition occurred from forest to grassland, and the expansion of construction land primarily resulted from its encroachment into agricultural areas. Second, within the area of study, the landscape heterogeneity increased while simultaneously experiencing a decrease in connectivity, and the landscape had a tendency toward a more fragmented spatial distribution. Third, overall habitat quality rose between 2000 and 2020 but declined between 2030 and 2050. There was a “weak in the west and high in the east” distribution pattern in the spatial heterogeneity of habitat quality. Fourth, population density has the most impact on habitat quality, with the NDVI and GDP close behind. Conversely, precipitation and slope had comparatively smaller influences on habitat quality. Natural factors combined had a primarily favorable influence on habitat quality across the research region in terms of spatial distribution. Conversely, population density had a discernibly detrimental impact. Given these findings, this study suggests targeted strategies to optimize habitat quality. These recommendations are relevant not only for biodiversity conservation but also for the development of an ecologically sustainable community, particularly in a cold climate region.

Regional integration, as an essential measurement for solving unbalanced and uncoordinated regional development, plays an important role in achieving regional sustainable development. In this study, we aimed to construct a systematic research framework to facilitate the development of regional integration. Using 31 prefecture-level cities in the urban agglomeration in the Middle Reaches of the Yangtze River (MYR-UA) as case sites, this study applied box plots, kernel density estimation, GIS visualization tools, Markov chains, and geographic detectors to reveal the dynamic spatiotemporal evolution and factors influencing regional integration from 2009 to 2018. The results indicate that the level of regional integration and its subsystem development is suboptimal in MYR-UA; the temporal characteristic shows an upward fluctuating trend, and the spatial distribution shows remarkable spatial correlation and clustering characteristics. Additionally, we found that the level of regional integration development in MYR-UA has both “path dependence” and “self-locking” effects, and the spatial lag type has a crucial impact on the degree of regional transfer stability. The dominant factors affecting regional integration development include the GDP per capita, economic openness, industrial structure, proportion of education expenditure in fiscal expenditure, urbanization rate, proportion of environmental investment in fiscal expenditure, population density, capital flow, information flow, and technology flow. Finally, based on the findings of this study, policy recommendations for promoting regional integration are proposed.

Different choices of living space for urban residents are concrete reflections of a social class divide. Emerging residential space differentiation is a critical issue in Chinese cities. This paper aims to explore the current situations and mechanisms of residential spatial differentiation, and reveal its social responses in Chinese cities. Taking Wuhan, the largest city in central China, as an example, this research divides the residential space based on social resource structures. It analyzes and compares spatial differentiation and influencing factors of different residential areas and houses with different prices in Wuhan by spatial differentiation indicators and geographic detector. The results show that residential areas are divergent due to differences in urban resource spatial structure, with few and concentrated high-quality residential areas. The spatial differentiation level of different residential areas also varies, with central location, landscape, educational resources, and other dominant scarce resources being the key to residential spatial differentiation in most of China’s big cities. In addition, residential spatial differentiation can lead to issues including housing wealth inequality, resource deprivation, and class identity solidification. In the end, this paper puts forward policy implications on alleviating residential spatial differentiation and optimizing allocation of residential resources.

Chlorophyll is a vital indicator of vegetation growth; exploring its relationship with external influencing factors is essential for studies such as chlorophyll remote sensing retrieval and vegetation growth monitoring. However, there has been limited in-depth exploration of the spatial distribution of leaf chlorophyll content (LCC) and its influencing factors across large-scale areas with varying climates and terrains. To investigate the primary influencing factors and degrees of various environmental factors on LCC, this study employed the Geodetector Model (GDM) and the LCC satellite products in Sichuan Province in 2020 to investigate the impact of relationships between nine environmental factors (meteorology, topography, and vegetation types) and the ecosystem LCC at a regional scale. The results indicated the following: (1) Elevation (q-value = 49.31%) is the primary factor determining photosynthesis in Sichuan Province, followed by temperature (46.10%) and vegetation types (40.73%). The impact of topographical factors on LCC distribution is higher than that of meteorological factors and vegetation types in terrain with complex topography. The elevation effectively distinguishes the variations in climate factors and vegetation types. (2) Combining the influencing factors pairwise increased the combined q-values. The combination of elevation with other factors yielded the highest combined q-value. (3) The q-values for all influencing factors are higher in winter and spring and lowest in summer. Different influencing factors exhibited more substantial constraints on vegetation photosynthesis during winter and spring, significantly reducing influence during summer. (4) The different primary factors drive or constrain vegetation photosynthesis in different climate zones due to their distinct temperature and humidity characteristics. The findings of this study provide a basis for future research on vegetation change analysis and dynamic monitoring of vegetation LCC in different terrains.

As the main expenditure item in water balance, evapotranspiration has an important impact on the surface ecosystem. Assessing the impact of changes in meteorological elements on evapotranspiration is essential to identify the spatiotemporal heterogeneity of hydrographic responses to climate changes. Based on the actual evapotranspiration (ETa) product (GPR-ET) generated by Gaussian process regression (GPR), as well as temperature and precipitation datasets, our study employed various statistical analysis methods, including geographic detector, the center of gravity migration model, spatial variation coefficients, and partial differential models, to investigate the spatiotemporal variation in ETa in China from 2000 to 2018. The analysis covered future trends in ETa changes and the contribution of meteorological factors. Our results showed that the ETa in northwest China had stronger spatial heterogeneity and the mean value was generally lower than that in the southeast. But the center of gravity of ETa was shifting towards the northwest. In most areas, the future trend was expected to be inconsistent with the current stage. ETa in the regions of north and west was mainly driven by precipitation, while its increase in southeast China was largely attributed to temperature. In addition to spatial variations, the joint enhancement effect of temperature and precipitation on ETa exists. According to the contribution analysis, precipitation contributed more to the change in ETa than temperature. These findings have enhanced our comprehension of the contribution of climate variability to ETa changes, providing scientific proof for the optimization apportion of future water resources.

Freeze–thaw erosion is one of the three major soil erosions in China, including wind erosion and hydraulic erosion, which leads to the destruction of the natural environment, the imbalance of economic development, a threat to personal safety, and irreversible disaster to the country and people. China’s permafrost area accounts for about one-fifth of the country’s land area, and the seasonal permafrost area accounts for half of China’s land area, mainly concentrated in the Qinghai–Tibet Plateau, Xinjiang Province, Heilongjiang Province, Gansu Province, and other regions. In order to establish an evaluation system for the ecological function reserve of the Greater Hinggan Mountains, nine evaluation indicators were selected from the perspectives of topography, climate, soil, and vegetation conditions. GIS technology, a multiple collinearity test, and principal component analysis were used to comprehensively evaluate the freeze–thaw erosion in the ecological function reserve of the Greater Hinggan Mountains. This study categorized the evaluation results into five intensity levels, from micro to severe. Finally, the degree of influence of different influencing factors on freeze–thaw erosion and the interactions between the factors were analyzed using a geographic detector. The results showed the following: (1) The intensity of freeze–thaw erosion in the study area gradually increased from west to east, and the comprehensive evaluation results were −0.2552 to 0.7581. Overall, moderate, severe, and mild erosion accounted for 29.83%, 25.9%, and 21.54% of the total area of the freeze–thaw zone, respectively. (2) Soil moisture content and the annual range in temperature were the main factors contributing to freeze–thaw erosion. The degree of influence of the two effects on freeze–thaw erosion (q = 0.5997) was better than that of the single-factor effect.

It is imperative to achieve the high-quality development of fisheries and green transformation of mariculture. Based on the data of marine aquaculture fisheries in China from 2006 to 2019, this research uses the Super-SBM (Slacks-Based Measure) model to measure the efficiency of marine green aquaculture in China and analyzes the regional gap, evolution trend, and driving factors of marine green aquaculture efficiency in nine coastal provinces in China. The results are as follows: (1) The Super-SBM measurement results showed that the efficiency of marine green aquaculture in China showed a fluctuating upward trend from 2006 to 2019. The results of nuclear density estimation and Dagum Gini coefficient show that there is an obvious regional gap in the efficiency of marine green aquaculture in China, and the regional gap is increasing with time. The Dagum Gini coefficient decomposition results show that inter-regional differences and supervariable density differences were the main sources of marine green aquaculture efficiency. The results of barycentric elliptic standard deviation show that the gravity center of marine green aquaculture efficiency in China shifted to the south from 2006 to 2019, indicating that the efficiency of marine green aquaculture in southern China has had a high degree of improvement. (2) The spatial Markov chain results show that spatial proximity plays a key role in the state transfer of marine green aquaculture efficiency. The higher the efficiency of marine green aquaculture in neighboring provinces, the greater the effect on improving the efficiency of marine green aquaculture in the province. The state transfer of marine green aquaculture efficiency usually occurs between similar horizontal states, and there is less “leapfrog” transfer. (3) The results of the geographic detector show that the number of professional mariculture practitioners is the core driving factor and plays a leading role in the regional gap in the efficiency of marine green aquaculture in China. The explanatory power of the interaction of the two factors on the regional gap of marine green aquaculture efficiency is more than 90%, which is much higher than the explanatory power of a single factor. The regional gap of marine green aquaculture efficiency is the result of multiple driving factors.

Exploring the complex dynamic relationship between urban–rural integrated development and land-use efficiency can contribute to most efficient urban–rural land-use and the rational promotion of urban–rural integrated development. This study established an evaluation model of urban–rural integrated development, adopted the super-efficiency SBM model to measure land-use efficiency, and studied the evolution of the spatial–temporal patterns of urban–rural integrated development and land-use efficiency coupling in the Yellow River Basin. We also examined the factors affecting them with the help of the coupling coordination degree model, non-parametric kernel density estimation, and geographic probes. The results indicate the following: (1) Within the study period, the coupled coordination of urban–rural integrated development and land-use efficiency was similar to the spatial distribution characteristics of land-use efficiency, both showing a “high at both ends and low in the middle” trend. (2) The coupled coordination increased over time; however, a lagging land-use efficiency was a crucial impediment to improving the coupling coordination degree. (3) Carbon emissions, urbanization rate, and per capita GDP were key drivers. The results of this study can provide a reference for local governments in the Yellow River Basin and other similar areas to propose paths to optimize the allocation of urban and rural land-use.

Oasification and desertification are two essential processes of land use and cover (LULC) change in arid regions. Compared to desertification, which is widely regarded as the most severe global ecological issue, the importance of oasification has not received universal recognition. However, neglecting oasification can lead to detrimental outcomes to the effectiveness of ecological governance by affecting the comprehensiveness of environmental policies proposed only based on desertification. Therefore, this study incorporates oasification into the examination of desertification by analyzing land use data for five representative periods spanning from 1980 to 2020, as well as socioeconomic and environmental data from 2000 to 2010. The aim is to evaluate the spatial and temporal dynamics of oasification and desertification in the Manas River Basin and identify the underlying factors driving these processes. The findings indicated that (1) the general trend of oasification and desertification exhibited the expansion of oases and the retreat of deserts. Specifically, the oasification area showed a “decrease-increase-decrease” pattern over time, while the desertification area consistently decreased. (2) In terms of spatial distribution, oasification and desertification displayed a transition from scattered and disordered patterns to an overall more organized pattern, with the hotspot area of desertification shifting from Shawan County to Manas County over time. (3) Population density, average land GDP, soil type and annual precipitation significantly influenced the degree of oasification, with driving force q-values above 0.4, which were the key factors driving oasification. Population density and average land GDP significantly affected the degree of desertification, with driving force q-values above 0.35, which were the key factors driving desertification. The driving force of all factors increased significantly after the interaction, and socioeconomic factors influenced oasification and desertification more than other factors. The study’s findings aim to provide a scientific basis for land resource use, ecological governance and sustainable development in the Manas River basin.

Understanding spatiotemporal shifts in vegetation and their climatic and anthropogenic regulatory factors can offer a crucial theoretical basis for environmental conservation and restoration. In this article, the normalized difference vegetation index (NDVI) of the Miaoling area from 2000 to 2020 is studied using a trend analysis and the Mann–Kendall mutation test (MK test) to review the vegetation’s dynamic changes. Our study uses the Hurst index, a partial correlation analysis, and a geographic detector to investigate the contributions of climate change and human activities to regional vegetation changes and their drivers. We found that Miaoling’s annual average NDVI was between 0.66 and 0.83 in 2000–2020, with a mean of 0.766. The overall trend was slow upward (0.0009/year), and 53.82% of the region continued to grow and gradually increased from west to east in the spatial domain, among which the karst regional NDVI distribution area and its growth rate were higher than those of non-karst sites. Based on correlations between climatic factors and NDVI, precipitation seasonality (coefficient of variation, CV) had the strongest correlation (positive correlation) with NDVI, while vapor pressure deficit (VPD) had a negative correlation with NDVI. In the interaction, human activities played a dominant role in the influence of NDVI on the vegetation of Miaoling. The night light index had the most explanatory power on the NDVI (q = 0.422), and the interaction between anthropogenic factors and other factors dominated its explanatory power. This study has academic and practical importance for the management, protection, and sustainable development of karst basins.

Resilience evaluation is an important foundation for sustainable rural development. Taking the 57 counties in Guangdong province as examples, this study used the CRITIC method to construct a comprehensive evaluation index system for rural resilience and identified the main influencing factors and their spatial heterogeneity on the basis of the geographical detector method and multiscale geographically weighted regression. The results showed that: (1) Most of the counties in Guangdong province had medium or higher values of comprehensive resilience, and the high-value areas were mainly located in the Pearl River Delta region. (2) The comprehensive resilience and each dimensional resilience measure exhibited significant positive spatial correlations. (3) The geographic detector results showed that the per capita gross regional product and the number of industries above the scale were the main influencing factors for rural resilience, and each influencing factor had an enhanced effect after interaction. (4) The effect of each factor on rural resilience demonstrated spatial heterogeneity. Specifically, the proportion of secondary and tertiary industries showed negative effects in some counties in eastern and northern Guangdong and positive effects in other counties.

Urbanization profoundly impacts the global carbon cycle and climate change. Many studies have shown that both urban vitality and urban carbon emissions are deeply affected by spatial planning and city structure. However, the specific relationship between urban vitality and urban carbon emissions is rarely studied. An index system of urban vitality was established from four aspects: social, economic, cultural, and environmental. After analyzing the spatial distribution characteristics of urban vitality combined with spatial syntax and the TOPSIS model, this paper further investigated the influence of urban vitality-building factors on the distribution of urban carbon emissions based on the Geodetector method. The research results show that: (1) Xuzhou shows obvious spatial differences in urban vitality, mainly decreasing from the center to the surrounding areas, with a small vitality center in the northeast. (2) The impact of different dimensions of vitality on urban carbon emissions is apparently different. (3) Facilities’ aggregation has the weakest explanatory power for urban carbon emissions, while the NDVI has the highest explanatory power. This study helps to clarify the spatial correlation and influence mechanism between urban vitality and urban carbon emissions. Finally, some suggestions are proposed to construct low-carbon and high-vitality cities.

Ground-level ozone pollution causes adverse health effects, and the detailed influences of meteorological factors and precursors on ozone at an hourly scale need to be further understood. We conducted an in-depth analysis of the phase relationships and periods of ground-level ozone in Shunyi station, Beijing, and contributing factors using wavelet analysis and geographic detectors in 2019. The combined effects of different factors on ozone were also calculated. We found that temperature had the strongest influence on ozone, and they were in phase over time. NO2 had the greatest explanatory power for the temporal variations in ozone among precursors. The wavelet power spectrum indicated that ozone had a periodic effect on multiple time scales, the most significant being the 22–26 h period. The wavelet coherence spectrum showed that in January–March and October–December, NO2 and ozone had an antiphase relationship, largely complementary to the in-phase relationship of temperature and ozone. Thus, the main influencing factors varied during the year. The interactions of temperature with NO2 significantly affected the temporal variations in ozone, and explanatory power surpassed 70%. The findings can deepen understanding of the effects of meteorological factors and precursors on ozone and provide suggestions for mitigating ozone pollution.

The impact of the built environment on the ridership of ride-hailing results depends on the spatial grid scale. The existing research on the demand model of ride-hailing ignores the modifiable areal unit problem (MAUP). Taking Chengdu as an example, and taking the density of pick-ups and drop-offs as dependent variables, 12 explanatory variables were selected as independent variables according to the “5D” built environment theory. The nugget–sill ratio (NSR) method and optimal parameter-based geographical detector (OPGD) model were used to determine the optimal grid scale for the aggregation of the built environment variables and the ridership of ride-hailing. Based on the optimal grid scale, the optimal data discretization method of the explanatory variables was determined by comparing the results of the geographic detector under different discretization methods (such as the natural break method, k-means clustering method, equidistant method, and quantile method); we utilized the geographic detector model to explore the relative importance and the interactive impacts of the explanatory variables on the ridership of ride-hailing under the optimal grid scale and optimal data discretization method. The results indicated that: (1) the suggested grid scale for the aggregation of the built environment and ride-hailing ridership in Chengdu is 1100 m; (2) the optimal data discretization method is the quantile method; (3) the floor area ratio (FAR), distance from the nearest subway station, and residential POI (point of interest) density resulted in a relatively high importance of the explanatory variable that affects the ridership of ride-hailing; and (4) the interactions of the diversity index of mixed land use ∩ FAR, distance to the nearest subway station ∩ FAR, transportation POI density ∩ FAR, and distance to the central business district (CBD) ∩ FAR made a higher contribution to ride-hailing ridership than the single-factor effect of FAR, which had the highest contribution compared with the other explanatory variables. The proposed grid scale can provide the basis for the partitioning management and scheduling optimization of ride-hailing. In the process of adjusting the ride-hailing demand, the ranking results of the importance and interaction of the built-environment explanatory variables offer valuable references for formulating the priority renewal order and proposing a scientific combination scheme of the built-environment factors.

The regions near the Tropic of Cancer are a latitudinal geographical zone with typical climatic, topographic, and human landscape features. It is necessary to explore the region’s net primary productivity (NPP) dynamics as it combines complex topography, various vegetation types, and intense human activities. The study sets the transect near the Tropic of Cancer (TCT) and uses the Carnegie–Ames–Stanford (CASA) model to estimate the NPP from 2000 to 2020. After using the RESTREND method, the paper calculates and compares the relative contributions of climate variability and anthropogenic activities to NPP changes. Finally, the geographical detector (Geodetector) model is applied to evaluate how anthropogenic and natural factors affect spatial distribution patterns and NPP changes. The results indicated that the average annual NPP is 820.39 gC·m−2·yr−1 during the 21 years. In addition, when the NPP varies, it increases over the entire study area, with a slope of 4.81 gC·m−2·yr−1, particularly in the western region. Across the entire research area, 63.39% and 77.44% of the total pixels positively contribute to climate variability and human activities in NPP, with a contribution of 0.90 and 3.91 gC·m−2·yr−1, respectively. Within the western, central, and eastern regions, anthropogenic activities have a stronger impact on NPP than climate variability, particularly pronounced in the eastern region. Furthermore, vegetation cover is the dominant factor in the spatial patterns and NPP trends across the TCT and the three regions. In contrast, climate factors are shown to be less influential in NPP distribution than in the western region. The results also demonstrated that the effect of population density and the GDP on NPP gradually rises. Two-factor interaction is much larger than any individual factor, with the dominant interaction factor being vegetation cover with climatic factors. Lastly, the findings revealed that anthropogenic activities positively promote NPP accumulation across the TCT, thus highlighting the importance of human activity-led ecological restoration and ecological protection measures that contribute to regional carbon sequestration and carbon balance.

Accurate quantification of the contributions of climatic and anthropogenic factors to the variation in NPP is critical for elucidating the relevant driving mechanisms. In this study, the spatiotemporal variation in net primary productivity (NPP) in China during 2000–2020, the interactive effects of climatic and anthropogenic factors on NPP and the optimal characteristics of driving forces were explored. Our results indicate that NPP had obvious spatial differentiation, an overall increasing trend was identified and this trend will continue in the future for more than half of the pixels. Land use and Land cover and precipitation were the main factors regulating NPP variation at both the national scale and the sub-region scale, except in southwest China, which was dominated by altitude and temperature. Moreover, an interactive effect between each pair of factors was observed and the effect of any pair of driving factors was greater than that of any single factor, manifested as either bivariate enhancement or nonlinear enhancement. Furthermore, the responses and optimal characteristics of NPP concerning driving forces were diverse. The findings provide a critical understanding of the impacts of driving forces on NPP and could help to create optimal conditions for vegetation growth to mitigate and adapt to climate changes.

Revealing the spatial dynamics of vegetation change in Chongqing and their driving mechanisms is of major value to regional ecological management and conservation. Using several data sets, including the SPOT Normalized Difference Vegetation Index (NDVI), meteorological, soil, digital elevation model (DEM), human population density and others, combined with trend analysis, stability analysis, and geographic detectors, we studied the pattern of temporal and spatial variation in the NDVI and its stability across Chongqing from 2000 to 2019, and quantitatively analyzed the relative contribution of 18 drivers (natural or human variables) that could influence vegetation dynamics. Over the 20-year period, we found that Chongqing region’s NDVI had an annual average value of 0.78, and is greater than 0.7 for 93.52% of its total area. Overall, the NDVI increased at a rate of 0.05/10 year, with 81.67% of the areas undergoing significant expansion, primarily in the metropolitan areas of Chongqing’s Three Gorges Reservoir Area (TGR) and Wuling Mountain Area (WMA). The main factors influencing vegetation change were human activities, climate, and topography, for which the most influential variables respectively were night light brightness (NLB, 51.9%), annual average air temperature (TEM, 47%), and elevation (ELE, 44.4%). Furthermore, we found that interactions between differing types of factors were stronger than those arising between similar ones; of all pairwise interaction types tested, 92.9% of them were characterized by two-factor enhancement. The three most powerful interactions detected were those for NLB ∩ TEM (62.7%), NLB ∩ annual average atmospheric pressure (PRS, 62.7%), and NLB ∩ ELE (61.9%). Further, we identified the most appropriate kind or range of key elements shaping vegetation development and dynamics. Altogether, our findings can serve as a timely scientific foundation for developing a vegetative resource management strategy for the Yangtze River basin that duly takes into account local climate, terrain, and human activity.

Basic education resources are basic urban and rural social public security resources, and their spatial distribution is an important issue related to people’s livelihoods and social justice. Taking Wuhan as a case study, this paper analyzed the spatial distribution characteristics of rural basic education resources based on the methods of the average nearest neighbor index, imbalance index, kernel density analysis and two-step floating catchment area and then used geographic detector analysis to detect its influencing factors. The following findings were obtained: (1) Rural kindergartens and elementary schools in Wuhan City’s new urban districts showed a clustered distribution pattern, while secondary schools showed a uniform distribution trend. The spatial distribution of rural basic education resources is poorly balanced, with a tendency to cluster in Huangpi District, Xinzhou District and Caidian District; the overall spatial distribution density of rural basic education resources showed the distribution characteristics of “block-like clustering and multicenter development”. (2) The spatial accessibility of kindergartens showed a spatial pattern of “large dispersion and small clustering”, with multiple high-value clustering areas; and the accessibility of elementary and secondary schools showed a spatial pattern of high in the south and low in the north. (3) The population, economy and education development level are the main factors affecting the spatial distribution of rural basic education resources, while the influence of infrastructure construction is weak. The core influencing factors of the spatial distribution of each type of basic education resource are both consistent and different. According to the interaction factor detection, the spatial distribution of rural basic education resources in Wuhan City’s new urban districts is the result of the combined effect of multiple factors.

Analyses of landscape patterns, analyses of land-use/land-cover evolution characteristics, and a driving force analysis during the expansion of urban agglomerations can assist urban agglomerations in solving ecological and environmental problems; moreover, these tools can provide a reference for urban land-use structure optimization and urban landscape planning. In this study, the rapid urbanization and urban agglomeration of small- and medium-sized cities were evaluated; specifically, the urban agglomeration of the northern slopes of the Tianshan Mountains (UANSTM) was assessed. Using multi-period (1995, 2000, 2005, 2010, 2015, 2018) land-use data and statistical yearbook data, we calculated the landscape index and comprehensive land-use degree index using the Moran index and geographic probe methods. We studied the expansion patterns and spatial distribution patterns of urban land and quantitatively explored the influence mechanisms of natural and socioeconomic factors on the degree of land use to clarify the characteristics and driving forces of land-use evolution. The results show the following: the area of urban land continued to increase between 1995 and 2018; the dominance of the landscape indexes within each time period changed with urban development; and intra-urban landscape heterogeneity is gradually increasing. In terms of the spatial and temporal distribution of the degree of land use, the expansion of urban agglomeration is centered on the city of Urumqi, spreading towards the cities of Changji and Shihezi; the rest of the counties and cities are fast urban-expansion zones. Under the influence of multiple source factors, the expansion of the UANSTM depends on the distribution of oases, which is mainly influenced by the distribution of vegetation and the density of the river network and can only rely on the ecological carrying capacity of oases for production and life. The results of the study can provide a basis for decision-making processes surrounding the future layout of the UANSTM ecological environment.

The Loess Plateau is a typical ecologically sensitive area that can easily be perturbed by the effects of human activities and global climate change. Therefore, it is necessary to develop tools to monitor the environmental quality in the LP quickly and accurately. To reveal the spatio-temporal changes in environmental quality in the LP from 2000 to 2020, we used the Moderate-Resolution Imaging Spectroradiometer (MODIS) products on the Google Earth Engine platform and constructed the remote sensing ecological index (RSEI) through principal component analysis (PCA). Then, Sen–Mann–Kendall methods were applied to determine the changing trend of the environmental quality of the LP. Finally, natural and anthropogenic factors affecting the environmental quality were probed using a geographical detector model. The results showed that: (1) the average RSEI values in 2000, 2010 and 2020 were 0.396, 0.468 and 0.511, respectively, displaying an upward trend from 2000 to 2020, with a growth rate of 0.005 year−1. The overall environment quality was moderate (0.4–0.6). (2) In terms of spatial distribution, the environmental quality was excellent in the southeast and poor in the northwest of the LP. The areas with improved environmental quality (84.51%) were located in all the counties, whereas the areas with degraded environmental quality (8.11%) occurred in the north and southeast of the study area. (3) Greenness, heat, wetness, dryness and land use types were prominent factors affecting RSEI throughout the study period; additionally, the total industrial gross domestic product showed a growing influence. The contribution of multi-factor interaction was stronger than that of single factors. The results will provide a reference and a new research perspective for local environmental protection and regional planning.

This paper constructs an evaluation indicator system for urban resilience in China on four dimensions—economy, environment, society, and infrastructure. The evaluation indicator is used by the entropy weight method to measure the resilience levels of 138 cities in 8 urban agglomerations from 2005 to 2018. Using the Theil index and variance decomposition method, we explore the size and sources of urban resilience differences among the eight urban agglomerations from the dual perspectives of space and structure and employ geographic detectors to identify the driving factors behind their differences. The results show that although the overall resilience level of the eight urban agglomerations is not high, it shows an upward trend. The differences within the eight urban agglomerations are the main spatial sources of urban resilience differences and economic resilience is the main structural source of urban resilience differences. Moreover, economic resilience and social resilience have the greatest contribution and driving effect on the resilience differences of BTH, YRD, PRD, MYR, CC, GP, and HC urban agglomerations, but the difference in resilience of CP is mainly caused by the difference in infrastructure resilience. Compared with the single factor, the impact of the interaction of each factor is even greater.

Scientific analysis of the spatial features and driving factors of homestead agglomeration in different landform types of mountainous and hilly areas in Southwest China is of great significance for the optimization of national spatial patterns and high-quality rural development. This paper selects 22 villages in 3 towns with different landform types in Chongqing and examines the spatial features and driving factors of homestead agglomeration in different landform types from the perspectives of terrain gradient, kernel density estimation, farmer household agglomeration status, and landscape index. We analyzed the agglomeration spatial features of different landform types and explored their driving factors and mechanisms. It was found that (1) the distribution of homesteads in mountainous and hilly areas has obvious terrain gradient characteristics, and the layout of platform area homesteads is not constrained by terrain conditions. (2) Chongqing homesteads show a spatially dispersed pattern, but the degree of homestead spatial agglomeration varies significantly among different landform types, with those in mountainous and hilly areas being mainly dispersed and those in platform areas being mainly clustered. (3) Homestead spatial agglomeration is characterized by a combination of factors inside and outside the rural system and farmers’ willingness. There are differences in the driving factors of homestead agglomeration in different landform types. Homestead agglomeration in mountainous and hilly areas is mainly driven externally, while homestead agglomeration in platform areas is mainly driven internally. In both situations, farmers’ willingness must be fully considered. The results of this study can provide a scientific basis for the spatial planning and optimal allocation of land resources in the southwestern hilly area.

Population migration, accompanied by urbanization, has led to an increase in the urban-settled population. However, quantitative studies on the distribution of urban-settled population, especially at fine scale, are limited. This study explored the relationship between characteristics of human perceived environment and the distribution of settled population, and proposed a quantitative method to predict the distribution trend of settled population. Through the semantic segmentation of street view images and accessibility calculation based on traffic isochrone and points-of-interest, we determined human perception factors. The influence of human perception factors was quantified using the geographic detector method, and the settlement intention index (SII) was constructed combining the analytic hierarchy process to predict the distribution trend of settled population. The results indicated the following. (1) Human perception was one of the important factors influencing the distribution of urban-settled population, and the cycling accessibility to traffic facilities was closely related to the distribution of settled population. (2) The accessibility and visibility of green space with low independent influence portrayed a strong enhancement on the interactive effect of other perception factors. (3) The SII mapping of Beijing showed that the SII was reliable. This study analyzes the role of human perception in shaping the environment, and provides reference for population-related urban planning problems.

The urban surface temperature is a complex integrated natural-human geographic phenomena; with the development of geostatistical methods and the application of multisource data, its research has gradually shifted from a single perspective to a study that integrates multiple factors such as nature and humanity. However, based on the context of the integration of natural and human factors and mutual constraints of each factor, the research on the mechanism of influence on urban habitat thermal environment needs to be further deepened. Therefore, this paper explores the spatial and temporal heterogeneity of urban surface temperature in Zhengzhou City during the summer of 2013–2020 from the perspective of multi-source data fusion, and uses the Geodetector model to quantitatively reveal the main influencing factors of urban surface temperature and the impact of superimposed factors on the compound effect of surface temperature. The results show that: (1) the urban thermal environment in the central of Zhengzhou city (region within the first ring) is obvious, and it is mainly concentrated in commercial and densely populated areas. (2) According to trend analysis, the northwest-southeast direction of the city continues to increase in temperature from 2013–2020, coupled with the direction of urban development. (3) Among the factors affecting urban surface temperature, normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), tasseled cap wetness (TCW), and human elements are particularly typical. NDVI and TCW are strongly negatively correlated with the urban thermal environment, while NDBI and human elements are strongly positively correlated. (4) Mitigation of the urban thermal environment can start with the interaction mechanism of positive and negative factors. This study provides new ideas for the mechanism analysis of spatial and temporal evolution patterns of the urban thermal environment under multifactorial constraints, and provides suggestions and decisions for promoting green and sustainable urban development.

Aerosols are an important component of the atmospheric system. Long time-series observations for aerosols are essential for examining global climate change and the ecological environment. Based on Google Earth Engine and MODIS MCD19A2 data, we monitored the spatio-temporal dynamic characteristics of the aerosol optical depth (AOD) in Central Asia from 2001 to 2020. The effects of six environmental factors on the AOD distribution were explored using a geographic detector model and analysed in combination with the land-use/land-cover change (LUCC) and desertification in different periods. The results showed that the average multi-year AOD in Central Asia was 0.1442, with insignificant interannual variations. The high-value areas were mainly distributed in the Aral Sea and surrounding areas of the Tarim Basin in Xinjiang, with notable seasonal variations. The evaluation results for the influencing factors showed that the relative humidity and precipitation had a large effect on the spatial distribution of the AOD. The LUCC directly affected contributions to the AOD. Desertification of land provides rich dust sources, which are the main aerosol sources in Central Asia, thus exacerbating dust aerosol pollution. This study investigated the temporal and spatial characteristics and influencing factors of the AOD in Central Asia, providing a theoretical basis for the prevention and control of air pollution.

Vegetation phenology and its spatiotemporal driving factors are essential to reflect global climate change, the surface carbon cycle and regional ecology, and further quantitative studies on spatiotemporal heterogeneity and its two-way driving are needed. Based on MODIS phenology, meteorology, land cover and other data from 2001 to 2019, this paper analyzes the phenology change characteristics of the Yangtze River Delta from three dimensions: time, plane space and elevation. Then, the spatiotemporal heterogeneity of phenology and its driving factors are explored with random forest and geographic detector methods. The results show that (1) the advance of start of season (SOS) is insignificant—with 0.17 days per year; the end of season (EOS) shows a significant delay—0.48 days per year. The preseason temperature has a greater contribution to SOS, while preseason precipitation is main factor in determining EOS. (2) Spatial differences of the phenological index do not strictly obey the change rules of latitude at a provincial scale. The SOS of Jiangsu and Anhui is earlier than that of Zhejiang and Shanghai, and EOS shows an obvious double-clustering phenomenon. In addition, a divergent response of EOS with elevation grades is found; the most significant changes are observed at grades below 100 m. (3) Land cover (LC) type is a major factor of the spatial heterogeneity of phenology, and its change may also be one of the insignificant factors driving the interannual change of phenology. Furthermore, nighttime land surface temperature (NLST) has a relatively larger contribution to the spatial heterogeneity in non-core urban areas, but population density (PD) contributes little. These findings could provide a new perspective on phenology and its complex interactions between natural or anthropogenic factors.

It is of great significance to explore the dynamic coupling relationship between industrial integration and urban land-use efficiency (ULUE) in order to promote high-quality urban development. This paper presents a system for evaluating industrial integration and ULUE which combines the internal theoretical relationships between the two. Based on this system, this paper analyzes the evolution process and the mechanism behind the coupling coordination relationship between industrial integration and ULUE with the help of the coupling coordination model, nonparametric kernel density estimation, and the geographical detector method. The results indicate the following: (1) The coupling coordination relationship between industrial integration and ULUE in the Yangtze River Economic Zone experienced an overall steady increase during the investigation period. However, there were great spatial differences in the coupling coordination relationship among cities. (2) The lag of the industrial integration level was the key area of resistance to the improvement of the coupling coordination level. (3) The interaction between industrial integration and ULUE in the Yangtze River Economic Zone was very significant. In particular, the impact of industrial agglomeration and factor flow on the ULUE and the impact of land on industrial integration in terms of ecological environment were prominent. In addition, the interaction of different detection factors was mainly manifested as a double factor enhancement effect and nonlinear enhancement effect.

Analyzing the distribution characteristics and influencing factors of cultivated land in complex geomorphic areas is significant in evaluating the agricultural farming environment and formulating cultivated land protection measures. This study extracted cultivated land information based on multi-source remote sensing data, and analyzed the geomorphic differentiation of cultivated land distribution and climate response from 2000 to 2020 using the Gini coefficient, spatial autocorrelation analysis and geographic detector. The results show that cultivated land is mainly distributed in low-altitude hills and low-altitude small undulating mountains, and secondarily in low-altitude alluvial and proluvial plains and platforms. Moreover, from 2000 to 2020, the cultivated land in the high-altitude and high-altitude undulating mountains and medium and high-altitude undulating mountains in the Northwest Plateau of Sichuan showed an upward trend, while the cultivated land in the Sichuan Basin mainly increased from the north and south to the middle of the basin. In addition, the highest temperature has the strongest ability to explain the spatial heterogeneity of cultivated land. From the calculation results of the influence coefficient of a single climatic factor and the combined effect of multiple climatic factors, the main factors that affect the distribution of cultivated land are different in different geomorphological regions. Finally, it is proposed to formulate a long-term strategy for agricultural production to adapt to climate change in complex geomorphic areas in order to reduce the negative impact of environmental change on agricultural production.

Sports tourism is an emerging tourism product. In the sports and tourism industry, resource mining is the foundation that provides positive significance for theoretical support. This study takes China’s sports tourism boutique projects as the study object, exploring its spatial distribution pattern through the average nearest neighbor index, kernel density, and spatial autocorrelation. On the strength of the wuli–shili–renli system approach, the entropy value method and geographic detector probe model are used to identify the driving factors affecting the spatial distribution pattern. Findings reveal the following: (1) From 2013 to 2014, the sports tourism resources in China present a distribution pattern with the Yangtze River Delta urban agglomeration as the high-density core area and the Guizhou–Guangxi border area and the western Hubei ecological circle as the sub-density core areas. (2) From 2014 to 2018, China’s sports tourism boutique projects increased by 381, and the regional differences among various provinces tended to converge. The high-density core area remained unchanged. The sub-density cores are now the Yunqian border area of the Karst Plateau, the Qinglong border area of the Qilian Mountains, and the Jinji border area of the Taihang Mountains, shaping the distribution trends of “depending on the city, near the scenery” and “large concentration, small dispersion”. (3) The proportion of provincial sports tourism development classified as being in the coordinated stage is 61.29%. (4) The explanatory power of the factors affecting the spatial layout in descending order is natural resource endowment, sports resource endowment, transportation capacity, industrial support and guidance, market cultivation and development, people’s living standards, software and hardware services, and economic benefit effects. The explanatory power of the interaction of two different factors is higher than that of the single factor.

Cities are growing higher and denser, and understanding and constructing the compact city form is of great importance to optimize sustainable urbanization. The two-dimensional (2D) urban compact form has been widely studied by previous researchers, while the driving mechanism of three-dimensional (3D) compact morphology, which reflects the reality of the urban environment has seldom been developed. In this study, land surface temperature (LST) was retrieved by using the mono-window algorithm method based on Landsat 8 images of Xiamen in South China, which were acquired respectively on 14 April, 15 August, 2 October, and 21 December in 2017, and 11 March in 2018. We then aimed to explore the driving mechanism of the 3D compact form on the urban heat environment (UHE) based on our developed 3D Compactness Index (VCI) and remote sensing, as well as Geo-Detector techniques. The results show that the 3D compact form can positively effect UHE better than individual urban form construction elements, as can the combination of the 2D compact form with building height. Individually, building density had a greater effect on UHE than that of building height. At the same time, an integration of building density and height showed an enhanced inter-effect on UHE. Moreover, we explore the temporal and spatial UHE heterogeneity with regards to 3D compact form across different seasons. We also investigate the UHE impacts discrepancy caused by different 3D compactness categories. This shows that increasing the 3D compactness of an urban community from 0.016 to 0.323 would increase the heat accumulation, which was, in terms of satellite derived LST, by 1.35 °C, suggesting that higher compact forms strengthen UHE. This study highlights the challenge of the urban 3D compact form in respect of its UHE impact. The related evaluation in this study would help shed light on urban form optimization.

Investigating the spatial distribution of urban forest biomass and its potential influencing factors would provide useful insights for configuring urban greenspace. Although China is experiencing an unprecedented scale of urbanization, the spatial pattern of the urban forest biomass distribution as a critical component in the urban landscape has not been fully examined. Using the geographic detector method, this research examines the impacts of four geographical factors (GFs)—dominant tree species, forest categories, land types, and age groups—on the aboveground biomass distribution of urban forests in 1480 plots in Xi’an, China. The results indicate that (1) the aboveground biomass and four GFs show obvious heterogeneity regarding their spatial distribution in Xi’an; (2) the dominant tree species and age group which impacts the patterns of aboveground biomass are the primary GFs, with the independent q value (a statistic metric used to quantify the impacts of GFs in this study) reaching 0.595 and 0.202, respectively, while the forest category and land type were weakly linked to the spatial variation of aboveground biomass, with a q value of 0.087 and 0.076, respectively; and (3) the interactions among these four GFs also tend to contribute to the distribution pattern of aboveground biomass. The interactions between GFs achieved a larger impact than the sum of impacts that were independently obtained from the factors. Our results showed that the method of using a geographical detector is a useful tool in the urban area, and can reveal the driver pattern of aboveground biomass and provide a reference for city planning and management.