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Enhancing soil organic carbon (SOC) content in farmland is crucial for soil quality maintenance and food security. However, the relationship between crop rotation and SOC sequestration remains unclear. We used sample data on SOC, collected in September of every year, from cultivated land for quality monitoring from 2017 to 2021, combined with spatially extracted planting system information, and focused on the effects of wheat–maize crop rotation on SOC in the Sushui River Basin. An analysis of variance (ANOVA) indicated that the SOC content was only significantly different between wheat monoculture and maize monoculture. Among the three cropping systems, wheat–maize rotation did not show absolute superiority. The Geodetector analysis showed that the planting system dominated the spatial distribution of soil organic carbon (p = 0.05), but its explanatory factor was only 5%, and the explanatory power was significantly improved after interaction with other factors. Geographically weighted regression showed that wheat–maize rotation had a trade-off effect with elevation and synergistic effects with rainfall and pH. It displayed a synergistic effect with temperature in the southwest and a trade-off effect in the northeast. The degrees of trade-offs and synergy varied spatially among all interacting factors. We focused on the spatial heterogeneity of soil organic carbon in a small watershed, and the results had scientific significance for the layout of planting systems according to local conditions and the improvement in soil organic carbon levels.

Land use change monitoring is a common theme in achieving sustainable development, while research on ecological barrier transition zones is relatively scarce. This study quantitatively analyzes the characteristics and patterns of land use change in Western Jilin, located in the transitional zone between the northeast forest belt and the northern sand prevention belt, from 1990 to 2020. Land dynamic change index and transition matrix are used to quantify the rates and intensities, and conversions between different land use types over time, respectively. Geodetector is adopted to analyze the impact of 12 factors on 12 types of land use change, such as using the factor detector to quantify the influence of temperature on the conversion from cropland to unused land. The results indicate that from 1990 to 2020, there have been noticeable changes in the area of various land use types in western Jilin. However, the conversion types are relatively limited, mainly involving interchanges between cropland, grassland, unused land, and water bodies. The cropland has increased by 20% overall, but 16% of that increase occurred from 1990–2000. The woodland area has steadily increased at a growth rate of 5–8% from 2000–2020, aligning with sustainable development strategies. Water bodies and grasslands are undergoing continuous recovery, and a positive growth trend is predicted to emerge by 2030. The built-up land is steadily expanding. The influencing factors vary for different types of land-use change. In a short time, policy factors play a significant role in land use, such as the implementation of the “River-lake Connection Project”, which has helped to reduce water-body fragmentation and enabled the stable recovery of water resources. However, in the long term, multiple topographic, climatic, and anthropogenic factors exhibit interactive effects in the land use change process in the area. Governments can take corresponding measures and management policies based on the influence of these factors to allocate and plan land use rationally.

Human activities have caused different degrees of land-use change on different topographic gradients, with impacts on the landscape and ecosystem. Effectively preventing and addressing ecological risk (ER) and achieving harmonious coexistence between humans and nature are important aspects of sustainable development. In this study, we used Gansu Province as an example, adopted five periods of land-use data in 1980, 1990, 2000, 2010 and 2020, and used the geoinformatic Tupu method and the terrain distribution index to study land-use changes under different topographic gradients, and then constructed the landscape ecological risk assessment (LERA) model based on the landscape pattern index to analyze landscape ecological risk (LER) spatiotemporal changes under different topographic gradients, and finally explored the LER driving factors using the geodetector model. The results showed that (1) the dominant land-use types were unused land and grassland, accounting for approximately 74% of the land. The situation of transferring and changing each type was more drastic. The distribution and changes in cropland and built-up land were easily found in low topographic gradient areas with low elevations and small slopes; the distribution and changes in woodland, grassland and water areas were easily found in high topographic gradient areas with high elevations and large slopes. (2) The landscape ecological risk index (LERI) was 0.018, 0.019, 0.019, 0.019 and 0.020, respectively, with spatial expressions of high in the northwest and low in the southeast. Low LER was concentrated in high topographic gradient ecological reserves; high LER was concentrated in low topographic gradient human interference areas and high topographic gradient natural environmental complex areas. (3) Natural factors mainly acted on the LER on moderate and high topographic position gradients; socioeconomic factors mainly acted on the LER on low topographic position gradients. Human interference interacted with natural factors more than human interference alone on LER. This study can provide a scientific basis for ensuring ecological security and sustainable development in areas with complex topography and geomorphology.

It is of great scientific value to study the spatial differences and influencing factors of carbon emission intensity (CEI) in urban agglomerations (UAs), and it also has reference significance for China in formulating energy-saving and emission-reduction policies to achieve the target of carbon neutrality. Taking 165 prefecture-level cities in 19 UAs in China from 2007 to 2019 as the research object, this study investigated the spatial differences of CEI in UAs using exploratory spatial data analysis and explored the influencing factors of CEI via Geodetector. The results showed the following: (1) The CEI of the UAs showed a downward trend. (2) The CEI of the UAs has typical spatial agglomeration characteristics, where the North comprises mainly high-high and low-high types, whereas the South is primarily high-low and low-low types. (3) The influencing factors of CEI have undergone a transformation from industrial structure to population urbanization.

Human activities and global environmental changes have transformed terrestrial ecosystems, notably increasing vegetation greenness in China. However, this greening is less effective across the Hu Huanyong Line (Hu Line). This study analyzes dynamic changes and driving factors of nine vegetation net primary productivities (NPPs) in regions divided by the Hu Line using remote sensing data, trend analysis, and the Geodetector model. Findings reveal that from 2001 to 2022, 38.22% of regional vegetation NPP in China increased, especially in the Loess Plateau, Sichuan Basin, and Northeast Plains, while 2.39% decreased, primarily in the southeastern region and southern Tibet. Grasslands contributed 39.71% to NPP north of the Hu Line, and cultivated vegetation contributed 50.58% south. The driving explanatory power of factors on vegetation NPP on the north side of the Hu Line is generally greater than that on the south side. Natural factors primarily drive NPP changes, with human activities having less impact. Combined factors, particularly climate and elevation, significantly enhance the driving explanatory power (q, 0–1). The joint effects of elevation and precipitation on grassland NPP dynamics (q = 0.602) are notable. GDP’s influence on broadleaf forests north of the Hu Line (q = 0.404) is significant. Grasslands respond strongly to land use changes and population density, with a combined effect of q = 0.535. Shrubs, alpine vegetation, and meadows show minimal response to individual factors (q < 0.2). These findings offer insights for devising ecological protection measures tailored to local conditions.

The industrial landscape constitutes a crucial aspect of a region’s historical and cultural identity, serving as a valuable asset in the development of industrial tourism. Exploring the industrial landscape supports initiatives in industrial tourism, acts as a catalyst for community revitalization, and contributes to sustainable urban progress. The primary objective of this research was to investigate the spatial distribution characteristics and underlying determinants of China’s industrial landscape (CIL) to inform urban planning, cultural heritage preservation, and sustainable development initiatives. This study utilized analytical tools, such as the nearest neighbor index, geographic concentration index, and hot spot analysis, to comprehensively examine the spatial distribution of CIL. Additionally, Geodetector was employed to explore the correlating factors behind this distribution. The findings reveal the following: (1) CIL exhibited a pronounced agglomerative spatial pattern characterized by a high degree of concentration, significant disparities, and substantial spatial autocorrelation. (2) Over time, the agglomeration of CIL varied, intensifying initially and then diminishing, with the center of gravity of its distribution shifting eastward before subsequently moving westward in a directional trend resembling “northeast–southwest”. (3) There was a diverse array of industrial landscape types within China, with notable disparities in the prevalence of different categories. The manufacturing and transportation sectors boasted the highest number of heritage sites. (4) The distribution pattern of CIL was shaped by factors such as the level of economic development, socio-demographic conditions, transportation infrastructure, and cultural milieu. The interplay between these factors had a substantial impact on this distribution pattern.

Current source apportionment models have successfully identified emission sources and quantified their contributions. However, when being utilized for heavy metal source apportion in soil, their accuracy needs to be improved, regarding migration patterns. Therefore, this work intended to improve the pre-existing principal component analysis and multiple linear regression with distance (PCA-MLRD) model to effectively locate pollution pathways (traffic emissions, irrigation water, atmospheric depositions, etc.) and achieve a more precise quantification. The dataset of soil heavy metals was collected from a typical area in the Chang-Zhu-Tan region, Hunan, China in 2021. The identification of the contribution of soil parent material was accomplished through enrichment factors and crustal reference elements. Meanwhile, the anthropogenic emission was identified with principal component analysis and GeoDetector. GeoDetector was used to accurately point to the pollution source from a spatial differentiation perspective. Subsequently, the pollution pathways linked to the identified sources were determined. Non-metal manufacturing factories were found to be significant anthropogenic sources of local soil contamination, mainly through rivers and atmospheric deposition. Furthermore, the influence of irrigation water on heavy metals showed a more pronounced effect within a distance of 1000 m, became weaker after that, and then gradually disappeared. This model may offer improved technical guidance for practical production and the management of soil heavy metal contamination.

Evapotranspiration is a key driver of water and energy exchanges between terrestrial surfaces and the atmosphere, significantly influencing ecosystem balances. This study focuses on the Yellow River Basin (YRB), where evapotranspiration impacts both ecological dynamics and human activities. By analyzing actual evapotranspiration data from 1982 to 2017, this research provides insights into its spatial and temporal patterns within the YRB. Furthermore, a comprehensive assessment and comparative analysis were performed on four distinct evapotranspiration product datasets: GLDAS-Noah, ERA5-Land, GLEAM v3.8a, and MOD16A2. Employing the Geodetector model, the research identified seven key influencing factors—the digital elevation model (DEM), slope, aspect, precipitation, temperature, soil moisture, and normalized difference vegetation index (NDVI)—and analyzed their impact on evapotranspiration variations, yielding the following insights: (1) Based on the monthly-scale actual evapotranspiration dataset from 1982 to 2017, the annual average evapotranspiration in the YRB fluctuated between 375 and 473 mm, with an average value of 425 mm. A declining trend in the region’s overall evapotranspiration was discerned using the Theil–Sen median slope estimator and Mann–Kendall trend test. (2) The datasets from GLDAS-Noah, ERA5-Land, and GLEAM exhibited the highest correlation with the observed datasets, all exceeding a correlation coefficient of 0.96. In contrast, the MOD16A2 dataset showed the least favorable performance. The ERA5-Land dataset was particularly noteworthy for its close alignment with observational benchmarks, as evidenced by the lowest recorded root mean square error (RMSE) of 5.09 mm, indicative of its outstanding precision. (3) Employing the Geodetector model, a thorough analysis was conducted of the interactions between evapotranspiration and seven critical determinants. The findings revealed that precipitation and the NDVI were the most significant factors influencing evapotranspiration, with q-values of 0.59 and 0.42 in 2010, and 0.71 and 0.59 in 2015, respectively. These results underscore their pivotal role as the main drivers of evapotranspiration variability within the YRB. Conversely, the q-values for slope in 2010 and 2015 were only 0.01 and nearly zero, respectively, indicating their minimal impact on the dynamics of evapotranspiration in the YRB.

Global warming and the urban heat island effect has aroused the attention of research on the outdoor thermal environment. As outdoor spaces often used by citizens, streets play an important role in improving the thermal environment. In this study, six factors relating to street geometries and tree configurations in Busan are measured and quantified to form 32 typical scenarios. The degree of importance of these six factors is evaluated based on ENVI-met simulation results, and GeoDetector is introduced to evaluate the interactions between the factors and their impacts on the outdoor thermal environment. This study confirms the significantly higher impact of street geometry factors on the air temperature and physiological equivalent temperature compared to tree configuration factors. Particularly, Hb/Ws shows the most significant impact during the research period. The impact of interactions between any two factors of street geometry is much higher than that of interactions between the geometry and tree configuration factors and that of interactions between the tree configuration factors. We recommend dynamically adjusting the relationship between street geometry and tree configurations in different situations to improve the outdoor thermal environment, especially at noon and in the afternoon.

Selenium (Se) is a micronutrient that has attracted significant attention, because the threshold for human health is low. During soil surveys in China, large areas of low-Se soil were found, and this condition may increase the probability of people suffering from Se deficiency. A multi-purpose regional geochemical survey conducted in the Lou Shao basin of Hunan Province found abundant Se-rich soils in Lianyuan City. However, as the primary grain-producing area in Hunan Province, the key factors affecting the spatial distribution of soil Se in the cultivated land of Lianyuan City remain to be elucidated. Therefore, based on the data of 5516 topsoil samples (0–20 cm) of cultivated land in Lianyuan City, we used geostatistics, correlation analysis, and a Geodetector to explore the effects of geological conditions (strata), soil types, soil properties, and topography on the distribution of Se in soil. The results showed that (1) in comparison to cultivated land in the Chinese mainland, Japan, Belgium, and Sweden, the cultivated land in Lianyuan City exhibits higher Se contents, with Se-sufficient and Se-rich areas accounting for 9.74% and 88.96% of the total area, respectively; (2) the distribution of high-Se soil was consistent with that in the Longtan Formation, Dalong Formation, and Daye Formation; (3) organic matter (OM) showed a positive correlation with Se, while both the elevation and slope were negatively correlated with Se; (4) stratum had the most significant effect on the spatial variation in soil Se, followed by OM. Lianyuan City is a typical Se-rich area, and the high level of Se in soil reduces the risk of local residents suffering with diseases caused by Se deficiency. The synergistic effect of stratum and OM is the key factor influencing Se enrichment in soils. Moreover, low-lying flat areas are more conducive to the accumulation of Se. This study will help farmers to identify suitable Se-rich cultivation areas in order to increase the Se content in crops, thereby providing a valuable basis for improvements in human health and the optimization of agricultural strategies.

Studying driving factors of the urban heat island phenomenon is vital for enhancing urban ecological environments. Urban functional zones (UFZs), key for planning and management, have a substantial impact on the urban thermal environment through their two-dimensional (2D)/three-dimensional (3D) morphology. Despite prior research on land use and landscape patterns, understanding the effects of 2D/3D urban morphology in different UFZs is lacking. This study employs Landsat-8 remote sensing data to retrieve the land surface temperature (LST). A method combining supervised and unsupervised classification is proposed for UFZ mapping, utilizing multi-source geospatial data. Subsequently, parameters defining the 2D/3D urban morphology of UFZs are established. Finally, the Pearson correlation analysis and GeoDetector are used to analyze the driving factors. The results indicate the following: (1) In the Fifth Ring Road area of Beijing, the residential zones exhibit the highest LST, followed by the industrial zones. (2) In 2D urban morphology, the percentage of built-up landscape (built-PLAND) and Shannon’s diversity index (SHDI) are the main factors influencing LST. In 3D urban morphology, building density, the sky view factor (SVF), and the area-weighted mean shape index (shape index) are the main factors influencing LST. Therefore, low-density buildings with simple and dispersed shapes contribute to mitigating LST, while fragmented distributions of trees, grasslands, and water bodies also play important roles in alleviating LST. (3) In the interactive detection results, all UFZs show the highest interaction detection results with the built-PLAND. (4) Spatial variations are observed in the impact of different UFZs on LST. For instance, in the residential zones, industrial zones, green space zones, and public service zones, the SVF is negatively correlated with LST, while in the commercial zones, the SVF exhibits a positive correlation with LST.

The surface water area (SWA) and terrestrial water storage (TWS) are both essential metrics for assessing regional water resources. However, the combined effects of climate change and human activities on the dynamics of the SWA and TWS have not been extensively researched within the context of the CPEC. To fill this gap, we first analyzed the annual changes in the SWA and TWS in the China–Pakistan Economic Corridor (CPEC) region in recent decades using the methods of correlation analysis and Geodetector. Our findings indicate that Sindh exhibited the highest increase in the SWA at 8.68 ha/km2, whereas FATA showed the least increase at 0.2 ha/km2 from 2002 to 2018. Punjab exhibited a significant decrease in TWS, with a slope of −0.48 cm/year. Azad Kashmir followed with a decrease in TWS at a rate of −0.36 cm/year. Khyber Pakhtunkhwa and FATA exhibited an insignificant increase in TWS, with values of 0.02 cm/year and 0.11 cm/year, respectively. TWS was significantly positively correlated with the SWA in Balochistan and Khyber Pakhtunkhwa. However, other regions showed inconsistent changes; in particular, a decline was observed in Gilgit–Baltistan. The changes in TWS in Balochistan were primarily influenced by the SWA and climate change, while TWS changes in FATA were mainly affected by climate change. In addition, human activities had a primary impact on the TWS changes in Azad Kashmir, Punjab, and Sindh. The influencing factors of TWS changes in different regions of the CPEC mainly involved a dual-factor enhancement and the nonlinear weakening of single factors. These results highlight that under the effect of climate change and human activities, TWS may not increase as surface water area increases. This study contributes to a better understanding of water resource dynamics and can aid in the development of strategies for the efficient and sustainable use of water resources in the CPEC.

In order to determine the distribution area and amount of Artemisia annua Linn. (A. annua) in China, this study estimated the current amount of A. annua specimens based on the field survey sample data obtained from the Fourth National Census of Chinese Medicinal Resources. The amount was calculated using the maximum entropy model (MaxEnt model) and spatio-temporal kriging interpolation. The influencing factors affecting spatial variations in the amount were studied using geographic probes. The results indicated that the amount of A. annua in China was about 700 billion in 2019. A. annua was mainly distributed in the circular coastal belt of Shandong Peninsula, central Hebei, Tianjin, western Liaoning, and along the Yangtze River and in the middle and lower reaches of Jiangsu, Anhui, and the northern Chongqing provinces. The main factors affecting the amount are the precipitation in the wettest and the warmest seasons, the average annual precipitation, and the average temperature in the coldest and the driest seasons. The results show that the amount of A. annua is strongly influenced by precipitation and temperature.

Particulate organic carbon (POC) is an essential component of the carbon pump within marine organisms. Exploring estimation methods for POC holds substantial significance for understanding the marine carbon cycle. In this study, we investigated the spatial heterogeneity of 30 factors and POC concentrations using geodetector to account for nonlinearity, diversity, and complexity. Ultimately, 20 factors including sea surface temperature, sea surface salinity, and chlorophyll-a were selected as modeling variables. Six machine learning models—backpropagation neural network, convolutional neural network, attention-based neural network, random forest (RF), adaptive boosting, and extreme gradient boosting were used to compare their performance. The results indicate that among the six machine learning algorithms, RF exhibits the strongest performance, with a root mean square error of 0.11 [log(mg/m3)] and an average percentage deviation of 2.73%. Global annual average sea surface POC concentrations were estimated for 2007 and compared to NASA’s POC product. The outcomes indicate that the RF model-based estimation method displays enhanced accuracy in estimating POC concentrations within intricate coastal environments, while the backpropagation neural network performed better in estimating POC concentrations in open ocean areas. Leveraging the RF model, global sea surface POC concentrations were estimated for the years 2007 through 2016, enabling a spatiotemporal analysis. The analysis unveils heightened POC concentrations in coastal regions and lower levels in open ocean areas. Furthermore, POC concentrations were greater in high-latitude regions compared to mid and low latitude counterparts. In conclusion, the global sea surface POC product in this study exhibits heightened spatial resolution and improved data completeness in contrast to other products. It enhances the accuracy of conventional POC estimation methods, particularly within coastal regions.

The study of the spatiotemporal evolution of landscape ecological hazard and human and natural influences is essential for conservative management and regional sustainable development. This study applied a landscape pattern analysis method and geodetector to multi-source data for 2000, 2010, and 2020 to analyze changes in and drivers of landscape ecological hazard in Laos. The results indicated that: (1) There were more prominent changes in landscape types in Laos. Forest area decreased, whereas the areas of other landscape types increased. There was an overall steady change in the landscape patterns of Laos. Besides for significant changes in the artificial surface landscape index, landscape indices remained stable; (2) The cumulative high and extreme ecological hazard areas increased by 1,947.81 km2, whereas the cumulative areas of low and minimal ecological hazard decreased by 8,461.8 km2. Areas of low and moderate ecological hazard accounted for > 85% of the total area. Areas of low ecological hazard were mainly in the northwest and southeast. The area of high ecological hazard was concentrated in the central and northeastern regions. The distributions of different landscape ecological hazards in Laos during the study period were similar, with general patterns of decreasing hazard from north to south; (3) A positive Moran’s I of landscape ecological hazard in Laos was obtained. While the agglomeration effect was pronounced, it decreased over time, resulting in a weakening in spatial autocorrelation. A significant positive autocorrelation was observed in the spatial distribution of landscape ecological hazard in the study area. Agglomerated areas of high and low ecological hazard were mainly concentrated in the northeast and southeast, respectively; (4) The spatiotemporal evolution of landscape ecological hazard in Laos over the last 20 years could be attributed to interactions between natural and anthropogenic influences. Natural influences were a significant driver of changes to landscape ecological hazard in Laos, with annual precipitation and average temperature being the most significant. Anthropogenic influences, including socioeconomic factors and regional accessibility, significantly impacted local ecological deterioration in Laos.

As earth surface human activities become more frequent, global ecosystem service functions and especially biodiversity maintenance functions are challenged. This study aimed to analyze spatiotemporal changes in Xi‘an section of the northern foothills of the Qinling Mountains from 1990 to 2020. Temporal and spatial changes in habitat quality in the study area were visualized using InVEST model and land use data, and factors affecting habitat quality were analyzed using Geodetector. The results showed that during the study period, the cultivated land, grassland, and water decreased by 16.40%, 74.37%, and 35.39%, respectively, while the area of forest land and construction land increased, among which the construction land increased by 117.70%, the largest increase, and the forest land increased by 8.47%. The main changes in land use are the conversion of cultivated land into forest land and construction land, and the conversion of grassland into forest land and cultivated land. During the period 1990–2020, the average habitat quality index in the study area changed from 0.8617 to 0.8585, showing a slow decreasing trend. The spatial distribution of habitat quality showed a trend of “high in the south, moderate in the north, and low in the northwest”. The high habitat quality was mainly concentrated in the southern forest land, the middle habitat quality was mainly distributed in the northern cultivated land, and the low habitat quality was mainly distributed in the northwest construction land. The land use type has a great influence on habitat quality, and the interaction between any two factors is stronger than that of a single factor. The temporal and spatial variation of habitat quality is influenced by both natural and human factors. This study provides a theoretical basis for ecological protection and nature reserve planning in the Qinling Mountains region.

As an important ecological-economic development area in China, scientific understanding of the spatial and temporal changes in eco-environment quality (EEQ) and its drivers in the Yangtze River Basin (YRB) is crucial for the effective implementation of ecological protection projects in the YRB. To address the lack of large-scale EEQ assessment in the YRB, this paper uses the Google Earth Engine (GEE) platform and the Remote Sensing Ecological Index (RSEI) to investigate the spatial and temporal characteristics of EEQ in the YRB from 2000 to 2020, and to analyze the impact of various factors on the EEQ of the YRB. This study showed that: (1) The overall EEQ of YRB was at the ‘good’ grade over the past 20 years, showing an increasing trend, with the value changing from 0.70 to 0.77. (2) The YRB's EEQ has positive spatial aggregation characteristics, with the northern part of the Jialing River basin and the Han River basin exhibiting a high-high aggregation type and the upper reaches exhibiting a low-low aggregation type. (3) In the past 20 years, the human activities had a greater impact on the EEQ of the YRB; moreover, all factors had a greater impact on the EEQ than a single factor. The interaction between the biological abundance index and population density had the most effect, with a q-value of 0.737 in 2020.

AbstractNatural hazards always have significant impacts on the regional ecological environment, causing huge damages to biodiversity, soil erosion, and impoverishment. This study aimed to analyze the variation of ecological environment quality in Nepal from 2014 to 2018, with a specific focus on the impact and recovery of the 2015 Ms8.1 Nepal earthquake. We utilized Google Earth Engine (GEE) and remote sensing based ecological index (RSEI) to evaluate the ecological environment quality. Moreover, Moran’s I and Geodetector model were employed to explore spatial clustering and driving forces of post-earthquake recovery. The results indicate that after the 2015 Nepal earthquake, there was a decrease of 0.0225 in the mean RSEI in Nepal. The monitoring of the severe-affected areas showed that the regional environmental quality gradually improved within three years after the earthquake. After a span of three years, the ecological environment managed to recover to its pre-earthquake level. In terms of spatial distribution, there is a notable correlation in Nepal, with the ecological environment exhibiting higher quality in the southern regions and lower quality in the northern regions. By examining the driving forces behind the changes in ecological environment quality during the post-earthquake recovery period, it has been determined that altitude, precipitation, and their interaction serve as influential factors. These findings provide valuable insights into the state of ecological environment recovery after the 2015 Nepal earthquake. Furthermore, this study establishes a robust theoretical foundation for local decision-makers to develop ecological protection measures and enhance local disaster risk management.

AbstractWildfires not only directly affect ecology but also have a huge impact on social and economic stability. Enhancing the understanding of wildfire influences can guide the design of targeted strategies in mitigation plans and adaptation strategies. Here, we explored the spatial-temporal characteristics of wildfires in the Beijing-Tianjin-Hebei region of China and the mechanisms of the effect of potential influencing factors on wildfire occurrence based on 2014–2020 wildfire data. The results show that wildfires mainly occur in spring and winter and decrease yearly. Among all the considered influencing factors, the occurrence of wildfire is most significantly affected by the temperature vegetation dry index (TVDI) and is also greatly affected by population and economy. Through interaction detection, it was found that the normalized difference vegetation index (NDVI) greatly interfered with wildfires. The high occurrence risk of wildfire was distributed in a blocky pattern in the middle and east of the study area. In addition, we also found a positive spatial correlation between the high wildland-urban interface (WUI) presence and high wildfire occurrence. These findings have improved our understanding of the impact of multiple influencing factors on wildfires and are supposed to facilitate the development of wildfire management strategies.

An analysis of the spatiotemporal evolution of ecological quality and its driving factors in the Lanxi urban agglomeration is important for ensuring environmental protection and high-quality, sustainable development in the region. We used moderate resolution imaging spectroradiometer (MODIS) remote sensing images to construct a remote sensing ecological index (RSEI) using principal component analysis (PCA) to reflect the ecological quality of the Lanxi urban agglomeration. The spatial and temporal characteristics and future changes in the RSEI of the study area from 2000 to 2020 were explored using the Sen and Mann–Kendall test and Hurst index, and the effects of natural and human factors on spatial variation in the RSEI of the study area were analyzed using Geodetector and geographically weighted regression (GWR) models. There were three main results. (1) In the past 20 years, the annual average RSEI value in the Lanxi urban agglomeration has been increasing at a rate of 0.0057/a, and areas of unsatisfactory ecological environmental quality have been reduced. (2) The Hurst index indicates that the majority of the area (47.54 % of the study area) will probably experience a degradation trend in the future, with 19.98 % of the area improving and random changes occurring in 9.45 % of the study area. (3) Vegetation type, soil type, and precipitation were the main reasons for the spatial differentiation in RSEI, land use type was the main human influence, and the influence of socioeconomic factors such as population density and gross domestic product (GDP) increased significantly. Vegetation, soil, and land use types were positively correlated with RSEI. The research results are of great significance for promoting the construction of an ecological civilization and coordinating a balance between social development and ecological environmental protection.

Vegetation in China has experienced significant improvements under the combined effects of human activities and climate change. However, when exploring the effects of climate change on vegetation dynamics, existing studies mainly focus on two meteorological factors (temperature and precipitation) and rarely consider aerosol optical depth (AOD) and photosynthetically active radiation (PAR). Therefore, in this study, we further explored the potential dependence of leaf area index (LAI) on temperature (Tem), precipitation (Pre), AOD, and PAR, based on the analysis of the spatiotemporal variations of LAI in different vegetation types in China. Results showed that the LAI increased significantly from 2001 to 2020, with an overall growth rate of 0.0202/yr; the response of LAI for different vegetation types to each influencing factor varied greatly, and the deciduous broadleaf forests were more sensitive to climate change. Quantitative analysis based on the GeoDetector method showed that the most important driving force of LAI change is Pre (0.596); while Tem (0.394) and AOD (0.325) showed similar effects; by contrast, PAR (0.279) had the lowest driving force on LAI. Among them, the change of Pre is the dominant factor affecting LAI for most vegetation types. From the perspective of regional research, we found that the increase of LAI in the South was greater than that in North China, while human activities (urbanization) were unfavorable to the increase of LAI, such as the Yangtze River Delta where the LAI decreased significantly. This study contributes to a better understanding of the response of different vegetation types to climate and atmospheric changes in China over the past 20 years and provides a scientific reference for developing and implementing ecological restoration measures and promoting sustainable development. Overall, China is 'turning green', and air quality is improving.

Rapid urbanization poses significant challenges to ecological preservation in karst ecologically fragile regions. Systematically monitor and evaluate of its urban ecological pattern change and driving factors are the basis for achieving regional sustainable development. Taking Gui’an New Area (GNA) of China as the object, using the Google Earth Engine (GEE) cloud platform, the Remote Sensing Ecological Index (RSEI) method and Geodetector to study ecological quality (EQ) changes between 2010 and 2020. The results show that: (1) An overall increase in RSEI (0.12), with changes concentrated in the +1 to 0 range, revealing spatial autocorrelation. (2) Comparing LU/LC types, forest showed the highest RSEI, followed by shrub, cropland, impervious, grassland, and barren areas. (3) Among factors considered, interaction with greenness had the most significant influence, and LU/LC was the primary external factor affecting EQ. The result provides a reference for decision makers to formulate ecological protection policies and implement regional coordinated development strategies.

Aiming to provide a scientific basis for the prevention and control of soil erosion in the Huaihe region, this study uses the InVEST-SDR model to estimate the temporal and spatial changes of soil erosion and soil conservation in the Huaihe region in 2010, 2015 and 2020, and the driving factors are evaluated and quantified using Random Forest and Geodetector, respectively. The major findings are as follows: (1) From 2010 to 2020, soil loss and soil retention in the Huaihe region gradually intensified, while the spatial distribution trend was consistent from year to year. (2) The results from Random Forest and Geodetector show that changes in soil loss and soil retention are mainly influenced by slope and rainfall. (3) The steepness and length of slopes are the most influencing factors for soil erosion. Soil loss and soil retention vary widely between different land uses. Forests play an important role in soil conservation.

AbstractLandslide susceptibility analysis is an essential tool for landslide hazard management. Correlation analysis of the driving factors before landslide susceptibility analysis is crucial to obtain more accurate results and higher computational efficiency. This article presents an FR-weighted GeoDetector, which can, at different gridding scales, stably screen out the driving factors most relevant to historical landslides in the study area compared to the performance of the original GeoDetector. The correlation analysis result shows that the most relevant seven conditioning factors to historical landslides in the study area are: lithology, distance to road, elevation, slope, STI, SPI, and distance to faults. Four machine learning models (logistic regression [LR], random forest [RF], artificial neural network [ANN], and Xgboost) are implemented for landslide susceptibility analysis, demonstrating that such models can achieve higher accuracy with features filtered by the FR-weighted GeoDetector than with all features. The Xgboost models trained on seven and 12 features were used to generate landslide susceptibility maps. The overlay with historical landslides showed that the models trained on seven features generated a more reasonable landslide susceptibility map, proving that selecting crucial landslide conditioning factors is a better solution than using a full range of landslide conditioning factors.

AbstractLandslides have differential characteristics in different regions. This study explores landslide susceptibility mapping (LSM) based on different evaluation units and proposes a strategy for landslides’ differential characteristics in different sub-regions. Based on data of lithology, elevation, and historical landslides, terrain units (TUs) and slope units (SUs) were obtained. LSM was developed using the Random Forest (RF) model and Light Gradient Boosting Machine (LGBM) model. The LGBM-TUs showed the highest performance and were therefore, selected to obtain LSM. The study area was divided into four sub-regions using the geographically weighted regression (GWR) model, along with spatial differential characteristics of topography conditions. The distribution and characteristics of landslides within each sub-region were assessed using GeoDetector. The results illustrated the reliability of the LGBM-TUs model. Lithology, elevation, and average annual rainfall were the dominant factors, while the influence of other factors on the occurrence of landslides was strengthened only when these factors interacted. This study proposed a new method for LSM research to insight the spatial differential characteristics of landslides in various sub-regions. Our results provide novel insights into landslide mitigation.

The Huaihai Economic Zone (HEZ) has excellent topographic and climatic conditions and is recognized as one of China’s major grain production areas. Identifying the spatiotemporal evolution patterns of arable land and the driving factors can offer valuable insights for protecting arable land, optimizing land use layout, and developing ecological economics in HEZ. In this paper, we analyze the spatiotemporal evolution patterns using spatial autocorrelation, land use transfer matrix, and kernel density calculations and investigate the driving factors of arable land evolution with Geodetector. The results show the following trends in the distribution of arable land within the HEZ: 1) The kernel density distribution of the arable land in HEZ remained relatively consistent from 2005 to 2020, but the density showed a downward trend over time. 2) The distribution of arable land in HEZ exhibited lower density in the Northeast and higher density in the Southwest, showing growth in the initial stage and subsequent declines in the intermediate and final phases. 3) A significant positive spatial correlation was observed in the distribution of arable land in HEZ. The main local cluster types were the “high-high” and “low-low” clusters, and their distribution characteristics were similar to the kernel density of arable land. 4) The change of arable land in HEZ between 2005 and 2020 was primarily driven by the conversion of land types, with a notable shift towards grassland and construction land 5) The main driving factors affecting the spatial distribution of arable land in HEZ included traffic accessibility, air temperature, precipitation, elevation, and slope. The secondary driving factors were land use and soil type. Over time, population and GDP have also evolved into significant driving factors. 6) The factors were bi-enhance after the interaction. In the future, all cities within HEZ should implement rigorous control measures to limit the expansion of arable land usage. Their primary focus should be on revitalizing existing construction land while strictly upholding the “compensation determines occupation” principle. They should also work to optimize the arrangement of arable land and give increased consideration to the interactive effects of traffic accessibility, population, and GDP to protect arable land in a more focused and effective manner.

Rapid and accurate agricultural land evaluation provides essential guidance for the supervision and allocation of agricultural land resources; it also helps to ensure food security. Previous work has mainly evaluated the land quality at the county level by using field sampling data and based on a factor approach. However, it is difficult to achieve uniform, large-scale agricultural land evaluation via conventional approaches because of its spatial heterogeneity, as well as the large temporal and economic costs associated with data acquisition. In this study, we integrated publicly available multimodal data (i.e., satellite remote sensing, environmental, and socioeconomic data) into the Google Earth Engine (GEE) platform, selected the best indicators from each modality using the geodetector, on the basis of which different combinations of input models were designed. And then we developed machine learning (random forest, RF) and deep learning (deep neural network, DNN) models to evaluate the land quality in paddy field and dry land systems in 2013 throughout Guangdong Province, China. The results showed that the performance of our combination of variables decreased in the following order: multimodal > bimodal > unimodal. With the best input combination, the RF model (R2 = 0.91, RMSE = 97.56, and CCC = 0.95) outperformed the DNN model (R2 = 0.89, RMSE = 108.72, and CCC = 0.94) in terms of predicting the quality of paddy field. The RF model (R2 = 0.90, RMSE = 104.27, and CCC = 0.95) also outperformed the DNN model (R2 = 0.86, RMSE = 124.38, and CCC = 0.93) in terms of predicting the quality of dry land. The agricultural land quality estimates obtained using the RF and DNN models were more accurate for paddy field than for dry land systems because of greater land quality homogeneity in paddy fields. This research proposed a simple, low-cost for rapid and accurate agricultural land evaluation at the provincial scale using publicly available multimodal data, which can help to achieve control of the agricultural land grade at multiple spatial and temporal scales.

The distribution of terrestrial soil organic carbon changes with climate and substrate conditions. This study investigated the spatial and temporal distribution of soil organic carbon and its influencing elements in the Yarlung Tsangpo River Basin using meteorological, subsurface, and remotely sensed data over the past 39 years, combined with random forests and geodetector. The results of the study showed that soil organic carbon has been increasing in the study area over the past 39 years, and this change has been concentrated in the central part of the watershed. Among the environmental factors, precipitation, temperature, and leaf area index were the main contributors to the soil organic carbon changes, and the effects of the three factors were more significant at higher altitudes, with a 33 % increase in the significance level. Notably, the interactions among the factors exceeded their individual effects, and the combination of climate and leaf area index showed a significant two-factor enhancement effect, with q-value enhancements generally ranging from 0.15 to 0.3. The two-factor enhancement effect was more pronounced below 3000 m above sea level, where the topography of the study area concentrates soil organic carbon in the gently sloping areas of the catchment.

Urbanization, driven by rapid economic growth, has resulted in dramatic land use/cover change (LUCC), posing substantial risks to urban ecological security. While constructing ecological networks is a productive strategy for achieving a balance between ecological protection and urbanization, the limited research conducted on the impacts of LUCC on the spatial patterns of ecological networks during urbanization poses a hindrance to the scientific construction of ecological networks. Therefore, this study focuses on Jinan City, an important city situated in the downstream region of the Yellow River Basin, China. The study utilized simulation approaches, including MSPA, MCR, InVEST model and gravity model, to investigate and analyze the ecological networks of Jinan City from 2000 to 2020. A set of patterns capturing the changes in ecological network elements was proposed to explore the evolutionary characteristics of the spatial pattern of ecological networks. Finally, Geodetector was utilized to investigate the impact mechanism of LUCC on the spatial pattern of ecological networks. The study findings indicate that: (1) During the last two decades, the urban development in Jinan City has witnessed an expansion of approximately 588.83 km2, and the proportion of ecological land has also increased, but the overall structure and spatial distribution of construction and ecological land are not balanced. (2) Despite the expansion of ecological areas, the escalation of ecological resistance has resulted in significant degradation of the functional capacity of ecological corridors, with a decrease of 8.37 % in the number of primary corridors. The reduced spatial connectivity of the ecological networks has impeded the efficient cycling of ecological processes between ecosystems. (3) The complex interactions and cumulative effects of LUCC during urbanization led to the evolution of the spatial pattern of ecological networks. The transformation of cropland and construction into ecological land has improved the composition elements of the ecological network, while the encroachment on ecological land and disorderly changes in internal types are the principal factors causing the degradation of ecological network composition elements. (4) The spatial pattern of ecological networks in Jinan City is influenced by the coupling of urban expansion and ecological protection policies. However, there is a need for the government to strengthen policy coordination and optimize spatial layout to better manage and conserve ecological networks. This study holds the potential to offer valuable scientific insights and references for guiding spatial planning, ecological protection, and the formulation of sustainable development policies throughout the urbanization process.

Both net primary productivity (NPP) and vegetation phenology play essential roles in influencing the carbon sequestration of terrestrial ecosystems. However, the relationship between vegetation phenology and NPP remains unclear under the effects of global warming. This study used Geodetector to analyze the interaction mechanisms between climate, phenology, elevation, and NPP in Yunnan, China. The results are as follows. (1) NPP is positively correlated with NDVI, LOS, EOS, TEMP, PREC and SRAD, and negatively correlated with ELEV, NDBI and SOS. However, the main influencing factors leading to the variation of NPP differ with the region. (2) The spatial distribution of NPP and LOS in Yunnan Province is mainly influenced by the monsoon, showing a pattern of high in the southwest and low in the northeast, and the interaction between PREC and LOS is the main reason for the variation of NPP. (3) In the high-altitude region of northwest Yunnan, the interaction between altitude and PREC is the primary factor affecting NPP variation. (4) In the tropical monsoon forest region of southern Yunnan, the interaction between SRAD and PREC is the main cause of NPP changes. (5) Despite having a small effect on NPP, SOS was the only phenological factor that showed a significant linear relationship with NPP. This study reveals the complex and diverse interactions between climate, phenology, elevation, and NPP in different regions of Yunnan, and provides a new perspective for understanding the carbon cycle and ecological processes of terrestrial ecosystems under global warming.

As homestays have developed rapidly, it is necessary to understand the factors affecting their density and type distribution at the micro scale. Based on the Internet data of home accommodation, the spatial distribution characteristics of homestays in Shenzhen are studied by the nearest neighbour analysis, and type distribution characteristics are analysed by NVivo. The impacts of factors are discussed through geographical detectors. We conclude that Shenzhen's homestays are multi-centre agglomeration and development in space with the flat floor as the main house structure and the modern style as the main decoration style. The spatial distribution is mainly affected by social and economic factors such as the density of bus stations and subways. The density and diversity of the homestays is high with the nearest and farthest distance to the railway station and the popular tourist destination beach while low with the mediate distance, featured with dumbbell shape.

The urban area of Yunfu City is a typical mountain environment with the thin surface soil layer, broken terrain, and the fragile geological environment, which is a landslide hazard prone region, seriously threatening the safety of residents' lives and property. It is crucial to accurately reveal the landslide risk and its causative factors to develop regional geological disaster prediction and mitigation strategies. However, the risk assessment and cause analysis of landslides in Yunfu City are rarely studied at present. Therefore, this study selects 11 causative factors (elevation, slope, slope aspect, bedding structure, distance from fault, lithology, precipitation, distance from water, normalized vegetation index (NDVI), distance from road, and land use), and integrates the modified frequency ratio model and geographic detector method to analyze the spatial distribution pattern of landslide risk and its causative factors. The results indicate that the maximum predicted area under the receiver operating characteristic curve (AUC) and the average predicted AUC of the modified frequency ratio model perform best at the precision of 5, with the values of 0.83921 and 0.81872, respectively, which can accurately reveal the landslide risk evaluation in the study area. According to the experimental results, the landslide risk in the study area generally shows a trend of high in the north and low in the south. Besides, the overall risk of landslide hazards is low, with the area of very low grade and low grade accounting for 75.20% of the total area. Among the selected 11 potential factors, precipitation, NDVI, slope aspect, distance from water and slope are the main causative factors to induce landslide hazards. Furthermore, this study also describes in detail the combination of factors that are conducive to landslide hazards, which can provide a decision basis for relevant departments to carry out regional landslide hazards prevention and mitigation strategies, and can also provide a reference for geological hazard risk evaluation study in other mountain environment.

The southern red soil region (SRSR) of China, a region that is environmentally sensitive, suffers severe soil erosion (SE) as a result of climate change and human activity. The factors driving SE changes in the SRSR of China under changing settings have, however, only been the subject of a small number of research. Changting County is a typical SE area in the SRSR of China. The spatiotemporal variation characteristics of SE in Changting County from 2000 to 2020 were investigated using the revised universal soil loss equation (RUSLE), while the dominant factors influencing the spatiotemporal dynamic change of SE were analyzed using the optimal parametric geographical detector (OPGD) and logarithmic mean Divisia Index (LMDI). The results showed that the average annual soil erosion modulus in Changting County shows a decreasing trend, with the SE modulus decreased from 10.00 t·hm−2·a−1 in 2000 to 3.38 t·hm−2·a−1 in 2020. Slight SE intensity was the dominant SE class, accounting for more than 75% of the total area, and the proportion is trending upwards. Natural and anthropogenic factors showed significant effects on the spatial variation of SE, of which land use and vegetation cover were the main factors influencing the spatial distribution of SE. Furthermore, the interaction between these two dominant factors explained greatly the spatial variation of SE. The mitigation of SE was mainly attributed to the increase in vegetation cover following the implementation of various ecological projects, contributing to the reduction of SE by 68.7%. In addition, the changes in land use types and rainfall amounts also played positive roles in reducing SE, showing contributions of 20.2% and 11.1%, respectively.

Yellow River Basin of China is an important ecological reserve. However, the spatiotemporal patterns of Gross Primary Production (GPP) in the Yellow River Basin of China are unclear from interannual, seasonal and vegetation-type perspectives. The interactive effects of temperature, precipitation, leaf area index and evapotranspiration on GPP and the climate-driven and human-activity contributions to GPP remain unclear. This study uses solar-induced chlorophyll fluorescence GPP data as indicator. Spatiotemporal characteristics of GPP are explored in the Yellow River Basin of China during 2001–2020 from interannual, seasonal and vegetation-type perspectives. Geodetector and Terrestrial ecosystem model are introduced to dissect the driving mechanisms of GPP under the interaction of multiple factors and to clarify the contributions of climate-driven and human-activity to GPP. The results show a general upward trend of GPP in the Yellow River Basin of China during 2001–2020. However, GPP in local areas where climate change and human activity are intense show a rapid decline, especially in large and medium-sized cities with human settlements. The ranking of single factors driving GPP is leaf area index> precipitation> evapotranspiration> temperature, and the explanatory power for GPP spatial heterogeneity after multi-factor interaction shows nonlinear enhancement. The positive climate-driven effect on GPP is concentrated in the grassland of the Qinghai Tibet Plateau and Qilian Mountains in the west of the Yellow River Basin. The positive human-activity effect on GPP is mainly manifested through crops, concentrated in the southeastern farming region, showing significant seasonal variations. This study provides a theoretical basis for the ecological protection and sustainable development of the Yellow River Basin in China.

Water-related ecosystem services in the Yangtze River Basin are very important. Understanding water yield services and the driving factors behind water yield is extremely critical to the effective protection and management of water resources in the basin. Therefore, this study aims to explore the spatial distribution characteristics and driving factors of water yield in the Yangtze River Basin. Based on the InVEST model, simulate the water yield in the Yangtze River Basin in 2000 and 2020 and the changes over 20 years, and combine with Geodetector to explore the impact of various driving factors on the spatial heterogeneity of water yield in the basin. The results showed that the water yield in the Yangtze River Basin has obvious spatial heterogeneity, which was high in the east and low in the west. There was a significant decrease in water yield depth in the area of high-value water yield in 2000 over a period of 20 years. The influence of precipitation and temperature on the spatial distribution of water yield in the Yangtze River Basin was significantly higher than that of other factors. The results of the interaction detector showed that the interaction between the precipitation factor and other factors had a high explanatory power on the spatial differentiation of water yield. And in the past 20 years, the interaction between precipitation and land use factors had strengthened the explanatory power of the spatial distribution of water yield in the basin. This study reveals the temporal and spatial evolution characteristics of water yield in the Yangtze River Basin and its influencing factors, which will provide scientific data support for ecological environmental protection and water resources management in the basin. [ABSTRACT FROM AUTHOR]

This study takes 1597 key villages and towns part of rural tourism in China as research objects and uses the ArcGIS spatial analysis method, combined with the geodetector and the multiscale geographically weighted regression model, to analyze the intensity and spatial differentiation of factors influencing the spatial distribution of these villages and towns. (1) The key villages and towns of rural tourism exhibit clustering distribution patterns with more locations in the east than in the west. The center of gravity of this distribution shifts to the northwest, displaying a "belt–point" trend with scattered hot spots in Beijing, Tianjin, Hebei, and the Yangtze River Delta, while cold spots are primarily concentrated in the northwest. (2) Spatial variation in the key villages and towns of rural tourism is due to multiple factors, of which population density, resident consumption expenditure, and per capita GDP display the highest explanatory powers for the spatial distribution of tourism. (3) The primary influencing factors are spatial differences in their roles and substantial local imbalances. Positive and negative correlation analysis units exhibit the aggregation characteristics of being blocked and banded. These results can provide valuable guidance for the development of rural tourism, promoting its sustainable development and contributing to the revitalization of rural areas. [ABSTRACT FROM AUTHOR]

Globally, but especially in Sichuan Province (Southwest China), landslides are considered to be one of the most common geological hazards. The purpose of the current study is to develop a novel model based on fractal theory, GeoDetector, and random forest, namely, the "GD-FFR-RF" model, for landslide susceptibility modeling and identifying areas prone to landslides with a case study of Sichuan Province, China. On this basis, fourteen landslide conditioning factors, including slope angle, slope aspect, relief amplitude, surface roughness, cutting depth, elevation, river density, rainfall, stream power index, road density, plan curvature, profile curvature, peak ground acceleration, and lithology, are selected. The information gain ratio is used to determine the significance of different conditioning factors. The overall performance of the three resulting models is assessed using the area under the receiver operating characteristic (ROC) curve (AUC) and several statistical indices. The GD-FFR-RF model yields better overall performance and more accurate results than the FFR-RF and FR-RF models. Therefore, in conclusion, the GD-FFR-RF model can be applied as a promising comprehensive model for the spatial prediction of landslide occurrence in subsequent studies. The generated landslide susceptibility maps will be useful for developing strategies for landslide hazard prevention and mitigation. [ABSTRACT FROM AUTHOR]

Studying the temporal and spatial evolution characteristics of land ecosystem service value and the driving factors of spatial differentiation in the rocky desertification area of Yunnan, Guangxi and Guizhou, to provide theoretical reference for scientific evaluation of ecological governance effect, optimization of land use pattern, and improvement of ecosystem service function. The land use transition matrix was used to reveal the law of land use change in the rocky desertification area of Yunnan, Guangxi and Guizhou from 1990 to 2018, and the modified value equivalent method and geographic detectors was used to explore the temporal and spatial evolution characteristics and driving factors of the service value of land ecosystems. The results show as follows. During the study period, urban construction in the rocky desertification area of Yunnan, Guangxi and Guizhou developed vigorously, and the effect of rocky desertification control was good. The area of construction land and water area increased by 116. 38% and 76. 40% respectively, while the unused land, which is mainly rocky desertification, decreased by 47. 45% and was mainly transformed into grassland and woodland. However, the decrease in the area of arable land and forest land should be alarmed. The value of ecosystem services in the rocky desertification area of Yunnan, Guangxi and Guizhou has been increasing year by year, and the value of ecosystem services has increased from 10 423. 82 billion in 1990 to RMB 10 631. 62 billion in 2018. However, the growth rate is relatively small and the internal differences are obvious, so there is still a long way to go for ecosystem maintenance and improvement. With 25°30′N latitude as the dividing line, the overall spatial variation characteristics of higher in the southwest, followed by the south and lower in the north are maintained, corresponding to the Yunnan, Guangxi and Guizhou regions in order. The differences in the spatial distribution of ecosystem service values in the rocky desertification area of Yunnan, Guangxi and Guizhou are influenced by both physical geography and socio-economic factors, but the internal interaction of socio-economic factors is greater than the interaction between natural factors and socio-economic factors and the internal interaction of natural factors, and the intensity of human activities always dominates. [ABSTRACT FROM AUTHOR]

The Yan River Basin of the Loess Plateau is a key region for ensuring the environmental protection and sustainable development of the Yellow River Basin. Therefore, it is essential to identify how vegetation cover has changed and determine the factors that have driven these changes. In this study, we applied a three-dimensional vegetation cover model to examine the spatiotemporal variation characteristics of vegetation cover at the watershed scale in the Yan River Basin from 2001 to 2020 and forecast future trends. Subsequently, the driving forces of fractional vegetation cover (FVC) change were quantified based on meteorological, surface, and anthropogenic factors to explore the common driving relationships among these factors. (1) The accuracy of 3DFVC is better than that of FVC in the Yanhe River Basin, where the terrain is complex. (2) The temporal change trends indicated that the vegetation cover in the Yan River Basin significantly recovered and the basin FVC increased rapidly from 2001 to 2013 (S = 0.0152/a, p < 0.01) and increased gradually from 2013 to 2020 (S = 0.0015/a). The main reason for the increase in vegetation cover was the enhanced growth of medium FVC. (3) The vegetation spatial distribution showed that the FVC values varied substantially from north to south, indicating spatial heterogeneity, and 83.9% of the area presented a trend of increasing vegetation. Furthermore, vegetation cover was predicted to improve in the future. (4) The spatial heterogeneity of FVC was mainly influenced by relative humidity and rainfall, and the spatial variations in FVC were mainly determined by climate factors. Land use and cover change variations, which are influenced by human activities, represent major factors underlying the observed spatial heterogeneity. Most interactions between driving factors showed two-way enhancement or non-linear enhancement, with relative humidity and land use patterns presenting the strongest explanatory power. This study provides a scientific basis for vegetation conservation in the Yan River Basin and contributes theoretical support for decision-making regarding ecological environmental protection in the Loess Plateau and sustainable development in the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Urban construction has accelerated the deterioration of the urban sound environment, which has constrained urban development and harmed people's health. This study aims to explore the spatiotemporal patterns of environmental sound and determine the influencing factors on the spatial differentiation of sound, thus supporting sustainable urban planning and decision-making. Fine-grained sound data are used in most urban sound-related research, but such data are difficult to obtain. For this problem, this study analyzed sound trends using Array of Things (AoT) sensing data. Additionally, this study explored the influences on the spatial differentiation of sound using GeoDetector (version number: 1.0-4), thus addressing the limitation of previous studies that neglected to explore the influences on spatial heterogeneity. Our experimental results showed that sound levels in different areas of Chicago fluctuated irregularly over time. During the morning peak on weekdays: the four southern areas of Chicago have a high–high sound gathering mode, and the remaining areas are mostly randomly distributed; the sound level of a certain area has a significant negative correlation with population density, park area, and density of bike route; park area and population density are the main factors affecting the spatial heterogeneity of Chicago's sound; and population density and park area play an essential role in factor interaction. This study has some theoretical significance and practical value. Residents can choose areas with lower noise for leisure activities according to the noise map of this study. While planning urban development, urban planners should pay attention to the single and interactive effects of factors in the city, such as parks, road network structures, and points of interest, on the urban sound environment. Researchers can build on this study to conduct studies on larger time scales. [ABSTRACT FROM AUTHOR]

Green buildings are characterized by energy saving and emissions reduction. To achieve the goals of peak emissions and carbon neutrality, large-scale development of green buildings contributes to relieving the pressure of resource shortage and environmental degradation. The extant literature on China's green buildings has mainly focused on standards and rating systems. With regard to the rapid growth of green buildings, research has increasingly focused on the development patterns and mainly concentrated on tlie nationwide or regional scales. It is essential to study tlie spatial and temporal patterns of green buildings and explore tlieir dynamics in urban China witliin a particular city. Taking Guangzhou as a case study, this study comprehensively explored the spatial and temporal patterns of green buildings which were awarded the Chinese Green Building Evaluation Standard or LEED certification from 2008 to 2021, and furtlier identified main factors in tenns of market, economy, environment, and resources driving such patterns witli the aid of ArcGIS spatial analysis and GeoDector. It was found that: (1) Temporally, the growtli of green buildings demonstrates a policy-driven trend of rising first and then descending. (2) Spatially, there are significant differences in green buildings project, construction area, and types in tenns of green office, residential, and otlier public buildings. The green buildings in tlie city center of Guangzhou have considerable differentiation and complex levels. The high-level green buildings are primarily concentrated in Tianlie, Zengcheng, and Huangpu Districts, while tlie low-level green buildings are mostly located in Congliua, Panyu, and Nansha Districts. (3) The spatiotemporal patterns of green buildings exliibited tlie following features: The gravity center of green buildings moved nortliward and then southward;. The range of distribution lias continuously expanded. The developing trend in Guangzhou present northwest-southeast direction witli gradually enlianced directionality. (4) With regard to tlie driving forces, resource factors are the primary driving force in the development of green buildings, followed by economic, market, and environmental factors. Specifically, among the three types of green buildings commercial, residential, and office, resource factors are the most important driving force in developing green commercial and residential buildings, while market factors deeply affect the spatial variation of green office buildings. Notably, environmental factors also play a key role in green residential buildings. From the perspective of geography in the spatiotemporal dimension, tliis study systematically reveals the evolution and detenninants of green buildings in China. The findings provide scientific evidence for governmental decision-makers and urban planners in green buildings, new insights into green buildings development and enrich the theoretical framework of green buildings. [ABSTRACT FROM AUTHOR]

Traditional villages are repositories for preserving human artifacts and cultural heritage. An investigation of the spatial distribution characteristics and factors influencing traditional villages in provincial administrative regions can provide new insights regarding the protection of traditional villages and rural development. This study focused on 275 traditional villages in Henan Province. Using ArcGIS and GeoDa software, we analysed the spatial autocorrelation and heterogeneity of the nearest neighbour index, Gini coefficient, Moran's I, and kernel density of the villages. Additionally, in conjunction with the Python sklearn library and GeoDetector, 15 indicators were selected to construct a decision tree model, spatial lag regression model, and geographic detector. Then the influence and interaction mechanisms of each indicator were analysed. The results revealed that (1) the spatial distribution of traditional villages in Henan Province was clustered and uneven, with a spatial layout comprising "3 high-density areas + 1 medium-density belt"; (2) overall, the number of traditional villages was negatively correlated with altitude, slope, rainfall, population density, proportion of the minority population, and historical-cultural intensity; and (3) the decision tree model results demonstrated that the selected 15 indicators had good predictive ability and that population density was particularly important. The spatial lag regression model results showed that the spatial distribution of traditional villages was positively correlated with distance from rivers, urbanization rate, and tourism resources, and negatively correlated with population density, per capita GRP, historical-cultural intensity, and NDVI. (4) The GeoDetector results indicated that historical-cultural intensity and population density were the two factors with the most significant explanatory power for the spatial differentiation of traditional villages in Henan Province. In terms of interactive factors, population density ∩ population was the strongest interactive driving force, followed by population ∩ historical-cultural intensity. [ABSTRACT FROM AUTHOR]

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China has experienced rapid urbanization, causing dramatic changes to the patterns of urban and rural land use relationships. This paper analyzes the spatiotemporal characteristics of rural settlements in the middle reaches of the Yangtze River region (MRYRR) using remote sensing monitoring data from 1990 to 2020 and reveals the influencing factors of spatiotemporal changes. The research found that (1) the overall increase in the quantity of rural settlements in the MRYRR between 1990 and 2020 was 504.70 km2. The area of transfer of other land use types to rural settlement was 1382.57 km2, of which 78.55% came from arable land and 16.62% came from forests; the area of rural settlement transfer to other land use types was 867.24 km2, of which 66.27% was reclaimed as arable land and 8.48% was transferred into urban built-ups. Changes in different stages and regions are diversified. (2) The spatial layout of rural settlements showed no significant differences in the MRYRR between 1990 and 2020, with a cluster distribution of "five main nuclei and many secondary nuclei" and a spatial distribution of "gathering in the north and east, while scattered in the south and west". (3) The level of fragmentation of rural settlements in the MRYRR has greatly decreased since 1990, with the average patch size expanding significantly. The patch complexity of rural settlements fluctuated moderately, but the connectivity between patches continued to increase, and their distribution became more clustered. (4) Social and economic factors, including rural electricity consumption, the output value of the primary industry, net income per rural resident, urbanization rate, and the effective irrigated area, most strongly impacted the changes in rural settlements in the MRYRR. The research can be utilized as a guide for reconstructing the pattern of rural land use to promote sustainable rural revitalization. [ABSTRACT FROM AUTHOR]

As an important part of soil and water conservation, ecological stability, and climate regulation, vegetation is sensitive to climate change and human disturbance. At present, there is a lack of research on the dynamic changes to vegetation in river basins and sub-basins from a holistic and partial perspective, which limits our ability to understand the spatial heterogeneity of vegetation changes and their influencing factors. In this study, the spatial and temporal variations of vegetation and their influencing factors in the Songliao River Basin (SLB) from 2000 to 2020 were analyzed using Sen's trend method, the Mann–Kendall test, the coefficient of variation method, and the Geodetector method. The results showed that the NDVI (normalized difference vegetation index) in the SLB exhibited an increasing trend of 0.003 yr−1, indicating that the vegetation was greening. In general, climatic factors and soil type were the dominant factors affecting the spatial differentiation of the NDVI in the SLB and sub-basin units. The interactions between the influencing factors were all enhanced, and the population density highlighted its influence on reflected vegetation changes. We also focused on analyzing the spatial differentiation of vegetation changes and influencing factors in the sub-basins. The research results provide a basis for the ecological restoration and stability of the basin. [ABSTRACT FROM AUTHOR]

The Pearl River Delta (PRD) is one of the most dynamic economic regions in the Asia-Pacific region. At present, it still faces many problems, such as the over-exploitation of urban land and unbalanced development. Through the study of the spatial–temporal characteristics of the development intensity of the PRD region and its driving factors, the key points and difficulties of urban development can be intuitively found. In previous studies, geodetector was widely used to determine the impact of driving factors. This paper uses several different research methods, including the Moran index, the semi-variability index, hot and cold spots, etc., based on the land use data of the PRD region in 1990, 2000, 2010, and 2020 to analyze the spatial–temporal characteristics of the development intensity. Combined with the socio-economic data of the statistical yearbook, factor detection and interaction detection of the 10 driving factors of development intensity are carried out based on geodetector, and reasonable optimization suggestions are put forward for the current situation of the region. The main conclusions are as follows: (1) The overall development intensity of the PRD region shows an upward trend, showing a "core periphery" spatial pattern of high center and low periphery centered around the Pearl River estuary. (2) The spatial distribution of cold and hot spots shows agglomeration, mainly in the form of high aggregation and low aggregation. (3) The driving factors for the development intensity for the PRD region in the past 30 years mainly include population agglomeration level, industrial structure level, economic strength level, terrain slope, etc. Among them, any two factors have a stronger interaction than a single factor, and all are enhanced by two factors. The dominant factors of interaction in different periods are different. [ABSTRACT FROM AUTHOR]

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

This study of vegetation coverage in Jiamusi City provides theoretical support for local urban development, land use, and ecological environmental protection. Based on the land cover data and Landsat remote sensing image data from 2000 to 2020, the vegetation cover and land use data of Jiamusi City were extracted. The study includes the following aspects: (1) an analysis of the spatio-temporal changes in vegetation coverage; (2) analysis of the land use situation in Jiamusi City; and (3) investigation of the impact of natural and human factors on vegetation coverage in Jiamusi City using the Geodetector model. The results show that (1) over the past 20 years, the vegetation coverage of Jiamusi has shown a decreasing trend, declining from 25.22% in 2000 to 17.13% in 2020, representing a decrease of 32%. In terms of spatial distribution, the areas of Fuyuan City and Tongjiang City have experienced more significant decreases in vegetation coverage, decreasing by 73.6% and 54.0%, respectively. (2) The land use pattern of Jiamusi City has undergone significant changes during the study period; except for paddy fields (PF), unused land (UL), and construction land (CL), the areas of all the land categories have decreased. The ranking of the single land use dynamic degree in terms of magnitude is as follows: PF, UL, CL, dry farmland (DF), vegetation coverage land (VCL), and wetland (WET). (3) The changes in vegetation coverage were influenced by both natural and human activities and, according to the Geodetector results, the main influencing factors were CL and DF. The key findings of this study emphasize the need for comprehensive land use planning and ecological environmental protection that focus on sustainable development and conservation practices, and lay the groundwork for future ecosystem management and urban planning efforts in Jiamusi City. [ABSTRACT FROM AUTHOR]

Water conservation is a crucial functional aspect of ecosystem service. Revealing the temporal and spatial changes in water conservation and exploring the factors influencing those changes are of great significance for the utilization of water resources and the construction of ecological civilization. In this study, we estimated the water conservation changes in Jiangxi over the 20 years from 2000 to 2020 by the rainfall storage method. Thereafter, the GeoDetector was applied to analyze the contributions from various factors, including climatic variations and ecosystem transformations, to the changes in the water conservation. The results showed three important trends. (1) From 2000 to 2020, farmland, grassland, water and wetland dominated the northern part of the Poyang Lake Basin except for the forests. The transformations of ecosystem types in the study area mainly occurred between forests, farmland and towns in the 20 years. During this period, the urban area showed a significant increase of 92.63%, while the other ecosystem types showed decreasing trends. (2) The province's water conservation function declined from 2000 to 2020, with a total loss of 97.11×104 m3 km–2, and in the study area it is characterized as high in the east and west and low in the middle. (3) Factorial analysis showed that the changes in the water conservation were not caused by any one factor alone. The q values of ecosystem type change interactions with the changes in annual precipitation, annual temperature and sunshine hours calculated by the GeoDetector were 0.555, 0.541 and 0.501, respectively. Therefore, the interactions between factors contributed 50% more than the single factors in affecting the changes of water conservation. [ABSTRACT FROM AUTHOR]