The 2005 World Conference of Chinese Geographers Held in Beijing

The 2005 World Conference of Chinese Geographers was held on August 16-17, 2005 at Peking University, Beijing, China. A total of more than 1000 Chinese geographical researchers and educators from USA, the United Kingdom, Austria, Canada, France, Ireland, Japan and China （including the mainland, Hong Kong, Macao and Taiwan） attended the conference. More than 700 papers were submitted and discussed during this conference with a wide range of topics spanning scientific and applied research in geography.

THE BRILLIANT GEOGRAPHER OF MING DYNASTY， XU HONGZU AND HIS CONTRIBUTIONS TO SCIENCE

Xu Hongzu (1587-1641) initiated a new orientation on the study of geography, i.e., systematically observing natural phenomena and describing geographical conditions. Basing his analyses upon simple yet precise measurements and comprehensive descriptions of geographical conditions, he investigated the relationships between the various geographical phenomena and made a comparative study of the different geographical features of various different regions. Thus, he broke through the limitations of classical Geography and promoted the development towards modern Geography.

Industrial Carbon Emission Distribution and Regional Joint Emission Reduction:A Case Study of Cities in the Pearl River Basin,China

China’s low-carbon development path will make significant contributions to achieving global sustainable development goals.Due to the diverse natural and economic conditions across different regions in China,there exists an imbalance in the distribution of car-bon emissions.Therefore,regional cooperation serves as an effective means to attain low-carbon development.This study examined the pattern of carbon emissions and proposed a potential joint emission reduction strategy by utilizing the industrial carbon emission intens-ity(ICEI)as a crucial factor.We utilized social network analysis and Local Indicators of Spatial Association(LISA)space-time trans-ition matrix to investigate the spatiotemporal connections and discrepancies of ICEI in the cities of the Pearl River Basin(PRB),China from 2010 to 2020.The primary drivers of the ICEI were determined through geographical detectors and multi-scale geographically weighted regression.The results were as follows:1)the overall ICEI in the Pearl River Basin is showing a downward trend,and there is a significant spatial imbalance.2)There are numerous network connections between cities regarding the ICEI,but the network structure is relatively fragile and unstable.3)Economically developed cities such as Guangzhou,Foshan,and Dongguan are in the center of the network while playing an intermediary role.4)Energy consumption,industrialization,per capita GDP,urbanization,science and techno-logy,and productivity are found to be the most influential variables in the spatial differentiation of ICEI,and their combination in-creased the explanatory power of the geographic variation of ICEI.Finally,through the analysis of differences and connections in urban carbon emissions under different economic levels and ICEI,the study suggests joint carbon reduction strategies,which are centered on carbon transfer,financial support,and technological assistance among cities.

Life forms affect beta-diversity patterns of larch forests in China

Beta-diversity reflects the spatial changes in community species composition which helps to understand how communities are assembled and biodiversity is formed and maintained. Larch(Larix) forests, which are coniferous forests widely distributed in the mountainous and plateau areas in North and Southwest China, are critical for maintaining the environmental conditions and species diversity. Few studies of larch forests have examined the beta-diversity and its constituent components(species turnover and nestedness-resultant components). Here, we used 483 larch forest plots to determine the total betadiversity and its components in different life forms(i.e., tree, shrub, and herb) of larch forests in China and to evaluate the main drivers that underlie this beta-diversity. We found that total betadiversity of larch forests was mainly dependent on the species turnover component. In all life forms,total beta-diversity and the species turnover component increased with increasing geographic, elevational, current climatic, and paleoclimatic distances. In contrast, the nestedness-resultant component decreased across these same distances. Geographic and environmental factors explained 20%-25% of total beta-diversity, 18%-27% of species turnover component, and 4%-16% of nestedness-resultant component. Larch forest types significantly affected total beta-diversity and species turnover component. Taken together, our results indicate that life forms affect beta-diversity patterns of larch forests in China, and that beta-diversity is driven by both niche differentiation and dispersal limitation. Our findings help to greatly understand the mechanisms of community assemblies of larch forests in China.

Spatial-temporal differentiation and influencing factors of rural settlements in mountainous areas: an example of Liangshan Yi Autonomous Prefecture, Southwestern China

Rural settlement is the basic spatial unit for compact communities in rural area. Scientific exploration of spatial-temporal differentiation and its influencing factors is the premise of spatial layout rationalization. Based on land use data of Liangshan Yi Autonomous Prefecture(hereinafter referred to as Liangshan Prefecture) in Sichuan Province, China from 1980 to 2020, compactness index, fractal dimension, imbalance index, location entropy and the optimal parameters-based geographical detector(OPGD) model are used to analyze the spatial-temporal evolution of the morphological characteristics of rural settlements, and to explore the influence of natural geographical factors, socioeconomic factors, and policy factors on the spatial differentiation of rural settlements. The results show that:(1) From 1980 to 2020, the rural settlements area in Liangshan Prefecture increased by 15.96 km^(2). In space, the rural settlements are generally distributed in a local aggregation, dense in the middle and sparse around the periphery. In 2015, the spatial density and expansion index of rural settlements reached the peak.(2) From 1980 to 2020, the compactness index decreased from 0.7636 to 0.7496, the fractal dimension increased from 1.0283 to 1.0314, and the fragmentation index decreased from 0.1183 to 0.1047. The spatial morphological structure of rural settlements tended to be loose, the shape contour tended to be complex, the degree of fragmentation decreased, and the spatial distribution was significantly imbalanced.(3) The results of OPGD detection in 2015 show that the influence of each factor is slope(0.2371) > traffic accessibility(0.2098) > population(0.1403) > regional GDP(0.1325) > elevation(0.0987) > poverty alleviation(0). The results of OPGD detection in 2020 show that the influence of each factor is slope(0.2339) > traffic accessibility(0.2198) > population(0.1432) > regional GDP(0.1219) > poverty alleviation(0.0992) > elevation(0.093). Natural geographical factors(slope and elevation) are the basic factors affecting the spatial distribution of rural settlements, and rural settlements are widely distributed in the river valley plain and the second half mountain area. Socioeconomic factors(traffic accessibility, population, and regional GDP) have a greater impact on the spatial distribution of rural settlements, which is an important factor affecting the spatial distribution of rural settlements. Policy factors such as poverty alleviation relocation have an indispensable impact on the spatial distribution of rural settlements. The research results can provide decisionmaking basis for the spatial arrangement of rural settlements in Liangshan Prefecture, and optimize the implementation of rural revitalization policies.

Modifications in aroma characteristics of'Merlot'dry red wines aged in American,French and Slovakian oak barrels with different toasting degrees

Aging in oak barrels is widely used in enology which could bring flavor changes and aromatic complexity to wines.In the present study,the aroma compounds were analyzed from the‘Merlot’dry red wines,which were fermented in two types of fermenters(stainless steel tank and rotated oak barrel)and aged in six types of oak barrels(three geographic origins×two toasting degrees)for different time(0,3,6 and 9 months,respectively).Results showed that 30 volatiles were associated with barrels and increased during oak aging.The fermenters could influence the intensities of the toast,leathery,smoky,fruity,floral and caramel aromas.The concentration of whisky lactone,eugenol,cis-isoeugenol,and the intensities of the toast and spicy aromas were highest in the wines aged in American oak and were lowest in the wines aged in French oak barrels.The concentrations of guaiacol,syringol,trans-isoeugenol,furfural alcohol,vanilla,cis-whisky lactone enabled the medium toasting barrels to be distinguished from the light toasting ones.The compounds originating from the barrels could be used to distinguish the types of different barrels,but the other general grape-derived and fermentation-derived volatiles could not.The fermenters,oak species and toasting degrees of the barrels all had significant effects on the aroma profiles of the aged‘Merlot’dry red wines,but the influence of the geographic origin was not obvious.

Flood Velocity Prediction Using Deep Learning Approach

Floods are one of the most serious natural disasters that can cause huge societal and economic losses.Extensive research has been conducted on topics like flood monitoring,prediction,and loss estimation.In these research fields,flood velocity plays a crucial role and is an important factor that influences the reliability of the outcomes.Traditional methods rely on physical models for flood simulation and prediction and could generate accurate results but often take a long time.Deep learning technology has recently shown significant potential in the same field,especially in terms of efficiency,helping to overcome the time-consuming associated with traditional methods.This study explores the potential of deep learning models in predicting flood velocity.More specifically,we use a Multi-Layer Perceptron(MLP)model,a specific type of Artificial Neural Networks(ANNs),to predict the velocity in the test area of the Lundesokna River in Norway with diverse terrain conditions.Geographic data and flood velocity simulated based on the physical hydraulic model are used in the study for the pre-training,optimization,and testing of the MLP model.Our experiment indicates that the MLP model has the potential to predict flood velocity in diverse terrain conditions of the river with acceptable accuracy against simulated velocity results but with a significant decrease in training time and testing time.Meanwhile,we discuss the limitations for the improvement in future work.

Spatiotemporal variation of land surface temperature and its driving factors in Xinjiang,China

Land surface temperature(LST) directly affects the energy balance of terrestrial surface systems and impacts regional resources, ecosystem evolution, and ecosystem structures. Xinjiang Uygur Autonomous Region is located at the arid Northwest China and is extremely sensitive to climate change. There is an urgent need to understand the distribution patterns of LST in this area and quantitatively measure the nature and intensity of the impacts of the major driving factors from a spatial perspective, as well as elucidate the formation mechanisms. In this study, we used the MOD11C3 LST product developed on the basis of Moderate Resolution Imaging Spectroradiometer(MODIS) to conduct regression analysis and determine the spatiotemporal variation and differentiation pattern of LST in Xinjiang from 2000 to 2020. We analyzed the driving mechanisms of spatial heterogeneity of LST in Xinjiang and the six geomorphic zones(the Altay Mountains, Junggar Basin, Tianshan Mountains, Tarim Basin, Turpan-Hami(Tuha) Basin, and Pakakuna Mountain Group) using geographical detector(Geodetector) and geographically weighted regression(GWR) models. The warming rate of LST in Xinjiang during the study period was 0.24℃/10a, and the spatial distribution pattern of LST had obvious topographic imprints, with 87.20% of the warming zone located in the Gobi desert and areas with frequent human activities, and the cooling zone mainly located in the mountainous areas. The seasonal LST in Xinjiang was at a cooling rate of 0.09℃/10a in autumn, and showed a warming trend in other seasons. Digital elevation model(DEM), latitude, wind speed, precipitation, normalized difference vegetation index(NDVI), and sunshine duration in the single-factor and interactive detections were the key factors driving the LST changes. The direction and intensity of each major driving factor on the spatial variations of LST in the study area were heterogeneous. The negative feedback effect of DEM on the spatial differentiation of LST was the strongest. Lower latitudes, lower vegetation coverage, lower levels of precipitation, and longer sunshine duration increased LST. Unused land was the main heat source landscape, water body was the most important heat sink landscape, grassland and forest land were the land use and land cover(LULC) types with the most prominent heat sink effect, and there were significant differences in different geomorphic zones due to the influences of their vegetation types, climatic conditions, soil types, and human activities. The findings will help to facilitate sustainable climate change management, analyze local climate and environmental patterns, and improve land management strategies in Xinjiang and other arid areas.

Land-population-industry based village evolution and its influencing factors in the upper Tuojiang River

With economic development and urbanization in China,the rural settlements have experienced great change.To explore the evolution process of rural settlements in terms of land,population and industry can reveal the development law of rural spatial distribution,population structure and industrial economy in different stages and regions.Studying the development status and evolution characteristics of villages in the upper Tuojiang River basin in Southwest China in the past 20 years are of significant value.The upper Tuojiang River basin includes the main types of terrain found in the Southwest region:mountainous,plains,and hills,exhibiting a certain typicality of geographical characteristics.This study took towns and townships at the town-level scale as the basic unit of research,and constructed an evaluation system for village evolution based on'land,population,and industry'.It employed Criteria Importance Through Inter-Criteria Correlation(CRITIC)analysis to examine the characteristics of village evolution in the area from 2000 to 2020,and used geographic detector analysis to identify the leading factors affecting village evolution.The results show that:(1)From 2000 to 2010,villages in the upper Tuojiang River basin experienced significant changes,and the pace of these transformations slowed from 2010 to 2020.(2)From a comprehensive perspective,from 2000 to 2020,villages in hilly areas show a decline,while villages in plain areas near the city center show a positive urbanization development.(3)Road accessibility and distance from the city center are the main factors that explain the spatial differentiation of village evolution degree in the study area.This study elucidates the spatiotemporal evolution characteristics of villages in the upper Tuojiang River basin and identifies the primary factors contributing to their changes,which will provide a reference for investigating the development of rural areas in different terrains of Southwest China.

GIS-based prediction method of shallow landslides induced by heavy rainfall in large mountainous areas

Rainwater runoff that does not infiltrate the soil during heavy rainfall may increase slope instability. The effect of runoff is usually neglected in conventional rainfall-induced slope failure analysis to simplify the model. To analyze the effect of runoff on slope stability, this study simultaneously simulated the effects of surface runoff and rainfall infiltration on bank slopes in the Three Gorges Reservoir Area. A shallow slope failure method that can be used to analyze runoff was proposed based on the modified Green-Ampt model, the simplified Saint-Venant model, and the infinite slope model. In this model, the modified Green–Ampt model was used to estimate the rainfall infiltration capacity and the wetting front depth. The eight-flow(D8) method and the simplified Saint-Venant model were selected to estimate the distribution of runoff. By considering the wetting front depth as the slip surface depth, the factor of safety of the slope could be determined using the infinite slope stability model. A comparison of the different models reveals that runoff can escalate the instability of certain slopes, causing stable slopes to become unstable. Comparison of the unstable areas obtained from the simulation with the actual landslide sites shows that the model proposed in this study can successfully predict landslides at these sites. The slope instability assessment model proposed in this study offers an alternative approach for estimating high-risk areas in large mountainous regions.

Land use change and its driving factors in the ecological function area:A case study in the Hedong Region of the Gansu Province,China

Land use and cover change(LUCC)is important for the provision of ecosystem services.An increasing number of recent studies link LUCC processes to ecosystem services and human well-being at different scales recently.However,the dynamic of land use and its drivers receive insufficient attention within ecological function areas,particularly in quantifying the dynamic roles of climate change and human activities on land use based on a long time series.This study utilizes geospatial analysis and geographical detectors to examine the temporal dynamics of land use patterns and their underlying drivers in the Hedong Region of the Gansu Province from 1990 to 2020.Results indicated that grassland,cropland,and forestland collectively accounted for approximately 99% of the total land area.Cropland initially increased and then decreased after 2000,while grassland decreased with fluctuations.In contrast,forestland and construction land were continuously expanded,with net growth areas of 6235.2 and 455.9 km^(2),respectively.From 1990 to 2020,cropland was converted to grassland,and both of them were converted to forestland as a whole.The expansion of construction land primarily originated from cropland.From 2000 to 2005,land use experienced intensified temporal dynamics and a shift of relatively active zones from the central to the southeastern region.Grain yield,economic factors,and precipitation were the major factors accounting for most land use changes.Climatic impacts on land use changes were stronger before 1995,succeeded by the impact of animal husbandry during 1995-2000,followed by the impacts of grain production and gross domestic product(GDP)after 2000.Moreover,agricultural and pastoral activities,coupled with climate change,exhibited stronger enhancement effects after 2000 through their interaction with population and economic factors.These patterns closely correlated with ecological restoration projects in China since 1999.This study implies the importance of synergy between human activity and climate change for optimizing land use via ecological patterns in the ecological function area.

Examining the Spatiotemporal Dynamics and Determinants of Land Urbanization in Prefecture-level Cities,China

Examining the spatiotemporal dynamics and determinants of land urbanization is critical for promoting healthy urban development and the rational use of land resources.Based on the dataset consisting of land use change data and selected factors in 2010 and2020,this study used visual analysis to reveal the spatiotemporal dynamics of land urbanization across prefecture-level cities in China.Meanwhile,the driving forces underlying land urbanization were examined by using geographical detector technique.Following are the findings:1)we find that there exist notable spatial variances in land urbanization across prefecture-level cities.Currently,the differentiation in land urbanization between the northern and southern cities is more pronounced than that between the coastal and inland cities,or between the eastern and western cities.Prefecture-level cities located in central and western China have experienced the most rapid growth in land urbanization.Conversely,the growth rate in northeastern China is the lowest,while the velocity in eastern China remains relatively stable.By using spatial autocorrelation analysis,this study reveals that the land urbanization level in prefecture-level cities has significant spatial agglomeration.2)We further find that land urbanization in China is influenced by factors related to urban land supply and demand,and urban population growth,economic growth,land financial and political incentive have greater impact on land urbanization than other factors.3)We also find that the impacts of determinants on China’s land urbanization vary over time,the explanatory power of economic development increased,while the explanatory power of state forces declined.We argue that integrating the supply and demand factors of land urbanization can provide a more comprehensive understanding of the driving mechanisms underlying land urbanization in China and other transitional countries,and help decision-makers in these countries formulate more detailed and specific land urbanization policies.

Landscape ecological risk assessment and its driving factors in the Weihe River basin,China

Weihe River basin is of great significance to analyze the changes of land use pattern and landscape ecological risk and to improve the ecological basis of regional development.Based on land use data of the Weihe River basin in 2000,2010,and 2020,with the support of Aeronautical Reconnaissance Coverage Geographic Information System(ArcGIS),GeoDa,and other technologies,this study analyzed the spatial-temporal characteristics and driving factors of land use pattern and landscape ecological risk.Results showed that land use structure of the Weihe River basin has changed significantly,with the decrease of cropland and the increase of forest land and construction land.In the past 20 a,cropland has decreased by 7347.70 km2,and cropland was mainly converted into forest land,grassland,and construction land.The fragmentation and dispersion of ecological landscape pattern in the Weihe River basin were improved,and land use pattern became more concentrated.Meanwhile,landscape ecological risk of the Weihe River basin has been improved.Severe landscape ecological risk area decreased by 19,177.87 km2,high landscape ecological risk area decreased by 3904.35 km2,and moderate and low landscape ecological risk areas continued to increase.It is worth noting that landscape ecological risks in the upper reaches of the Weihe River basin are still relatively serious,especially in the contiguous areas of high ecological risk,such as Tianshui,Pingliang,Dingxi areas and some areas of Ningxia Hui Autonomous Region.Landscape ecological risk showed obvious spatial dependence,and high ecological risk area was concentrated.Among the driving factors,population density,precipitation,normalized difference vegetation index(NDVI),and their interactions are the most important factors affecting the landscape ecological risk of the Weihe River basin.The findings significantly contribute to our understanding of the ecological dynamics in the Weihe River basin,providing crucial insights for sustainable management in the region.

Summary Analysis of National Surveillance on Kashin-Beck Disease from 1990 to 2023

Objective To analyze the epidemiological characteristics and epidemic situation of children with Kashin-Beck disease(KBD)in China,and provide the basis for formulating prevention and control measures.Methods Fixed-point monitoring,moving-point monitoring,and full coverage of monitoring were promoted successively from 1990 to 2023.Some children(7-12 years old)underwent clinical and right-hand X-ray examinations every year.According to the KBD diagnosis criteria,clinical and X-ray assessments were used to confirm the diagnosis.Results In 1990,the national KBD detectable rate was 21.01%.X-ray detection decreased to below 10%in 2003 and below 5%in 2007.Between 2010 and 2018,the prevalence of KBD in children was less than 0.4%,which fluctuated at a low level,and has decreased to 0%since 2019.Spatial epidemiological analysis indicated a spatial clustering of adult patients prevalence rate in the KBD areas.Conclusion The evaluation results of the elimination of KBD in China over the last 5 years showed that all villages in the monitored areas have reached the elimination standard.While the adult KBD patients still need for policy consideration and care.

Factors affecting land dissection density in geomorphological regions of China

Land dissection density(LDD)provides morphological evidence regarding prior intense soil erosion and quantifies the distribution of land dissections.A comprehensive understanding of the potential factors influencing the spatial pattern and value of the LDD is vital in geological disasters,soil erosion,and other related domains.Land dissection phenomena in China affects large areas with different morphological,pedological,and climatic characteristics.Prior studies have focused on the potential factors influencing the LDD at a watershed scale.However,these results are insufficient to reflect the status quo of dissection development and its primary influencing factors on a national scale.LDD’s spatial patterns and the dominant factors at a regional scale in millions of square kilometers remain to be ascertained.This study used the geomorphon-based method and the geographical detector model to quantify the spatial pattern of LDD over China and identify the dominant factors affecting this pattern in China’s six first-order geomorphological regions(GR1~GR6).The results yield the following findings:(1)LDD in China ranges from 0~4.55 km/km^(2),which is larger in central and eastern regions than in other regions of China;(2)dominant factors and their dominant risk subcategories vary with each geomorphological region’s primary internal and external forces;(3)the influence of natural factors is more significant on the large regional scale in millions of square kilometers compared to anthropogenic factors;relief degree of land surface(RDLS)is dominant in GR1,GR2,and GR5;the slope is dominant in GR6,soil type is dominant in GR3 and GR4,and lithology plays a critical role in the dominant interactions of GR3,GR4,and GR6;(4)the interactions between factors on LDD’s spatial pattern have a more significant effect than individual factors.

Exploring impacts of COVID-19 on spatial and temporal patterns of visitors to Canadian Rocky Mountain National Parks from social media big data

COVID-19 posed challenges for global tourism management.Changes in visitor temporal and spatial patterns and their associated determinants pre-and peri-pandemic in Canadian Rocky Mountain National Parks are analyzed.Data was collected through social media programming and analyzed using spatiotemporal analysis and a geographically weighted regression(GWR)model.Results highlight that COVID-19 significantly changed park visitation patterns.Visitors tended to explore more remote areas peri-pandemic.The GWR model also indicated distance to nearby trails was a significant influence on visitor density.Our results indicate that the pandemic influenced tourism temporal and spatial imbalance.This research presents a novel approach using combined social media big data which can be extended to the field of tourism management,and has important implications to manage visitor patterns and to allocate resources efficiently to satisfy multiple objectives of park management.

Species’ geographical range, environmental range and traits lead to specimen collection preference of dominant plant species of grasslands in Northern China

Many different factors,such as species traits,socio-economic factors,geographical and environmental factors,can lead to specimen collection preference.This study aims to determine whether grassland specimen collection in China is preferred by species traits(i.e.,plant height,flowering and fruiting period),environmental range(i.e.,the temperature and precipitation range)and geographical range(i.e.,distribution range and altitudinal range).Ordinary least squares models and phylogenetic generalized linear mixed models were used to analyze the relationships between specimen number and the explanatory variables.Random Forest models were then used to find the most parsimonious multivariate model.The results showed that interannual variation in specimen number between 1900 and 2020 was considerable.Specimen number of these species in southeast China was notably lower than that in northwest China.Environmental range and geographical range of species had significant positive correlations with specimen number.In addition,there were relatively weak but significant associations between specimen number and species trait(i.e.,plant height and flowering and fruiting period).Random Forest models indicated that distribution range was the most important variable,followed by flowering and fruiting period,and altitudinal range.These findings suggest that future floristic surveys should pay more attention to species with small geographical range,narrow environmental range,short plant height,and short flowering and fruiting period.The correction of specimen collection preference will also make the results of species distribution model,species evolution and other works based on specimen data more accurate.

Geographical Variation in the Advertisement Calls of Leptobrachella ventripunctata(Anura:Megophryidae)in Southwestern China

In anurans,advertisement calls(ACs)are an essential form of intraspecific communication.This study evaluates geographical variation in the ACs of Leptobrachella ventripunctata in the Guizhou Plateau,southwestern China,and explores correlations between call characteristics,body size,and environmental factors.ACs are simple calls of L.ventripunctata,and apparent differences were observed in the ACs among different geographical populations of L.ventripunctata.The Call duration(CD)revealed a significant positive correlation with altitude and a significant negative correlation with temperature and humidity.Moreover,the Dominant frequency(DF)exhibited a significant negative correlation with altitude and the habitat closure degree and a significant positive correlation with temperature.These variations in ACs between different geographical populations of L.ventripunctata may critically impact the adaptive evolution of species,and the calls may also be relevant for environmental selection.

Spatial patterns and driving forces of urban vegetation greenness in China:A case study comprising 289 cities

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

Spatiotemporal patterns and drivers of cultivated land conversion in Inner Mongolia Autonomous Region,northern China

Protection and optimization of cultivated land resources are of great significance to national food security.Cultivated land conversion in northern China has increased in recent years due to the industrialization and urbanization of society.However,the assessment of cultivated land conversion in this area is insufficient,posing a potential risk to cultivated land resources.This study evaluated the evolution and spatiotemporal patterns of cultivated land conversion in Inner Mongolia Autonomous Region,China,and the driving factors to improve rational utilization and to protect cultivated land resources.The spatiotemporal patterns of cultivated land conversion in Inner Mongolia were analyzed using the cultivated land conversion index,kernel density analysis,a standard deviation ellipse model,and a geographic detector.Results showed that from 2000 to 2020,the trends in cultivated land conversion area and rate in Inner Mongolia exhibited fluctuating growth,with the total area of cultivated land conversion reaching 7307.59 km^(2) at a rate of 6.69%.Spatial distribution of cultivated land conversion was primarily concentrated in the Hetao Plain,Nengjiang Plain,Liaohe Plain,and the Hohhot-Baotou-Ordos urban agglomeration.Moreover,the standard deviational ellipse of cultivated land conversion in Inner Mongolia exhibited a directional southwest-northeast-southwest-northeast distribution,with the northeast-southwest direction identified as the main driving force of spatial change in cultivated land conversion.Meanwhile,cultivated land conversion exhibited an increase-decrease-increase change process,indicating that spatial distribution of cultivated land conversion in Inner Mongolia became gradually apparent within the study period.The geographic detector results further revealed that the main driving factors of cultivated land conversion in Inner Mongolia were the share of secondary and tertiary industries and per-unit area yield of grain,with explanatory rates of 57.00%,55.00%,and 51.00%,respectively.Additionally,improved agricultural production efficiency and the coordinated development of population urbanization and industry resulted in cultivated land conversion.Collectively,the findings of this study indicated that,from 2000 to 2020,the cultivated land conversion in Inner Mongolia was significant and fluctuated in time,and had strong spatial heterogeneity.The primary drivers of these events included the effects of agriculture,population,and social economy.