A new monitoring index for ecological vulnerability and its application in the Yellow River Basin,China from 2000 to 2022

The ecological environment of the Yellow River Basin has become more fragile under the combined action of natural and manmade activities.However,the change mechanisms of ecological vulnerability in different sub-regions and periods vary,and the reasons for this variability are yet to be explained.Thus,in this study,we proposed a new remote sensing ecological vulnerability index by considering moisture,heat,greenness,dryness,land degradation,and social economy indicators and then analyzed and disclosed the spatial and temporal change patterns of ecological vulnerability of the Yellow River Basin,China from 2000 to 2022 and its driving mechanisms.The results showed that the newly proposed remote sensing ecological vulnerability index had a high accuracy,at 86.36%,which indicated a higher applicability in the Yellow River Basin.From 2000 to 2022,the average remote sensing ecological vulnerability index of the Yellow River Basin was 1.03,denoting moderate vulnerability level.The intensive vulnerability area was the most widely distributed,which was mostly located in the northern part of Shaanxi Province and the eastern part of Shanxi Province.From 2000 to 2022,the ecological vulnerability in the Yellow showed an overall stable trend,while that of the central and eastern regions showed an obvious trend of improvement.The gravity center of ecological vulnerability migrated southwest,indicating that the aggravation of ecological vulnerability in the southwestern regions was more severe than in the northeastern regions of the basin.The dominant single factor of changes in ecological vulnerability shifted from normalized difference vegetation index(NDVI)to temperature from 2000 to 2022,and the interaction factors shifted from temperature∩NDVI to temperature∩precipitation,which indicated that the global climate change exerted a more significant impact on regional ecosystems.The above results could provide decision support for the ecological protection and restoration of the Yellow River Basin.

Spatiotemporal changes in sunshine duration and its influential factors in Chongqing,China from 1961 to 2020

The surface solar radiation in most parts of the world has undergone a phenomenon known as global dimming and brightening,characterized by an initial decrease followed by an increase.As a result,the sunshine duration(SD)has decreased in the past 60 years.Against the backdrop of global dimming and brightening,SD has decreased to varying degrees in many regions of China.Using the observed data of SD,cloud amount(total cloud amount and low cloud amount,abbreviated as TCA and LCA),precipitation,and relative humidity(RH)from 34 meteorological stations in Chongqing during the period of 1961-2020,along with a digital elevation model(DEM)with a resolution of 90 m,this study analyzed the spatiotemporal variations and influencing factors of SD.The analysis employed methods such as linear regression,Mann-Kendall test,wavelet transformation,and DEM-based possible SD distributed model.The results showed that the annual SD in Chongqing has significantly decreased over the last 60 years,with a decreasing interannual trend rate(ITR)of 40.4 h/10a.Except for no obvious trend in spring,SD decreased significantly in summer,autumn and winter at the ITR of 21.1 h/10a,8.5 h/10a and 7.5 h/10a,respectively.An abrupt decrease in the annual SD was found in 1979.The difference before and after the abrupt decrease was 177.7 h.The difference before and after the abrupt decrease was 177.7 h.The annual SD possessed the oscillation period of 11a.The spatial heterogeneity of the mean annual SD during the last 60 years was obvious.The distribution of SD in Chongqing is high in the northeast and low in the southeast.In addition,about 73%of the total area in Chongqing showed a significant and very significant decreasing trend.The regions with significant changes are mainly concentrated in the regions with altitudes of 200~1000 m.The increasing LCA was the main cause of the decrease of the annual SD in the regions with 200-400 m altitude decreased the most and changed the most.Increasing LCA is the primary cause of the reduction in annual SD,showing a strong negative correlation coefficient of-0.7292.In Chongqing,PM2.5 concentration showed a significant decrease trend in annual,spring,autumn and winter during 2000-2020,but the significant correlation between PM2.5 concentration and SD was only in autumn and reached an extremely significant level.

Quantitative contributions of climate change and human activities to vegetation dynamics in the Zoige Plateau from 2001 to 2020

Climate change and human activities such as overgrazing and rapid development of tourism simultaneously affected the vegetation of the Zoige Plateau.However,the spatiotemporal variations of vegetation and the relative contributions of climate change and human activities to these vegetation dynamics remain unclear.Therefore,clarifying how and why the vegetation on the Zoige Plateau changed can provide a scientific basis for the sustainable development of the region.Here,we investigate NDVI trends using the Normalized Difference Vegetation Index(NDVI)as an indicator of vegetation greenness and distinguish the relative effects of climate changes and human activities on vegetation changes by utilizing residual trend analysis and the Geodetector.We find a tendency of vegetation greening from 2001 to 2020,with significant greening accounting for 21.44%of the entire region.However,browning area expanded rapidly after 2011.Warmer temperatures are the primary driver of vegetation changes in the Zoige Plateau.Climatic variations and human activities were responsible for 65.57%and 34.43%of vegetation greening,and 39.14%and 60.86%of vegetation browning,respectively,with browning concentrated along the Yellow,Black and White Rivers.Compared to 2001-2010,the inhibitory effect of human activity and climate fluctuations on vegetation grew dramatically between 2011 and 2020.

Temporal and spatial variation and prediction of water yield and water conservation in the Bosten Lake Basin based on the PLUS-InVEST model

To comprehensively evaluate the alterations in water ecosystem service functions within arid watersheds,this study focused on the Bosten Lake Basin,which is situated in the arid region of Northwest China.The research was based on land use/land cover(LULC),natural,socioeconomic,and accessibility data,utilizing the Patch-level Land Use Simulation(PLUS)and Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST)models to dynamically assess LULC change and associated variations in water yield and water conservation.The analyses included the evaluation of contribution indices of various land use types and the investigation of driving factors that influence water yield and water conservation.The results showed that the change of LULC in the Bosten Lake Basin from 2000 to 2020 showed a trend of increasing in cultivated land and construction land,and decreasing in grassland,forest,and unused land.The unused land of all the three predicted scenarios of 2030(S1,a natural development scenario;S2,an ecological protection scenario;and S3,a cultivated land protection scenario)showed a decreasing trend.The scenarios S1 and S3 showed a trend of decreasing in grassland and increasing in cultivated land;while the scenario S2 showed a trend of decreasing in cultivated land and increasing in grassland.The water yield of the Bosten Lake Basin exhibited an initial decline followed by a slight increase from 2000 to 2020.The areas with higher water yield values were primarily located in the northern section of the basin,which is characterized by higher altitude.Water conservation demonstrated a pattern of initial decrease followed by stabilization,with the northeastern region demonstrating higher water conservation values.In the projected LULC scenarios of 2030,the estimated water yield under scenarios S1 and S3 was marginally greater than that under scenario S2;while the level of water conservation across all three scenarios remained rather consistent.The results showed that Hejing County is an important water conservation function zone,and the eastern part of the Xiaoyouledusi Basin is particularly important and should be protected.The findings of this study offer a scientific foundation for advancing sustainable development in arid watersheds and facilitating efficient water resource management.

Analysis of spatial distribution characteristics and driving factors of ethnicminority villages in China using geospatial technology and statistical models

This study aims to reveal the spatial structural characteristics of 1,652 Ethnic-Minority Villages(EMV)in China and to analyze the mechanisms driving their spatial heterogeneity.EMV are a special type of settlement space that preserve a large number of historical traces of the ethnic culture of ancient China.They are important carriers of China’s excellent traditional culture and are key to the implementation of rural revitalization strategies.In this study,1652 EMV in China were selected as the research subjects.The Nearest Neighbor Index,kernel density,and spatial autocorrelation index were employed to reveal the spatial structural characteristics of minority villages.Neural network models,spatial lag models,and geographical detectors were used to analyze the formation mechanism of spatial heterogeneity in EMV.The results indicate that:(1)EMV exhibit significant spatial differentiation characterized by“single-core with multiple surrounding sub-centers,”“polarization between east and west,”“decreasing quantity from southwest to east coast to northeast to northwest,”and“large dispersion with small agglomeration.”(2)EMV are mainly distributed in areas rich in intangible cultural heritage,with high vegetation coverage and low altitude,far from central cities,and having limited arable land and an underdeveloped economy and transportation,particularly in shaded or riverbank areas.(3)Distance from the nearest river(X3),distance from central cities(X8),national intangible cultural heritage(X9),and NDVI(X10)were the main driving factors affecting the spatial distribution of EMV,whereas elevation(X1)and GDP(X5)had the weakest influence.As EMV are a relatively unique territorial spatial unit,the identification of their spatial heterogeneity characteristics not only deepens the research content of settlement geography,but also involves the assessment,protection,and development of Minority Villages,which is of great significance for the inheritance and utilization of excellent ethnic cultures in the era.

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.

Spatio-temporal Evolution Characteristics and Driving Forces of Winter Urban Heat Island:A Case Study of Rapid Urbanization Area of Fuzhou City,China

Under the influence of anthropogenic and climate change,the problems caused by urban heat island(UHI)has become increasingly prominent.In order to promote urban sustainable development and improve the quality of human settlements,it is significant for exploring the evolution characteristics of urban thermal environment and analyzing its driving forces.Taking the Landsat series images as the basic data sources,the winter land surface temperature(LST)of the rapid urbanization area of Fuzhou City in China was quantitatively retrieved from 2001 to 2021.Combing comprehensively the standard deviation ellipse model,profile analysis and GeoDetector model,the spatio-temporal evolution characteristics and influencing factors of the winter urban thermal environment were systematically analyzed.The results showed that the winter LST presented an increasing trend in the study area during 2001–2021,and the winter LST of the central urban regions was significantly higher than the suburbs.There was a strong UHI effect from 2001 to 2021with an expansion trend from the central urban regions to the suburbs and coastal areas in space scale.The LST of green lands and wetlands are significantly lower than croplands,artificial surface and unvegetated lands.Vegetation and water bodies had a significant mitigation effect on UHI,especially in the micro-scale.The winter UHI had been jointly driven by the underlying surface and socio-economic factors in a nonlinear or two-factor interactive enhancement mode,and socio-economic factors had played a leading role.This research could provide data support and decision-making references for rationally planning urban layout and promoting sustainable urban development.

Spatiotemporal characteristics and driving mechanisms of land use/land cover(LULC)changes in the Jinghe River Basin,China

Understanding the trajectories and driving mechanisms behind land use/land cover(LULC)changes is essential for effective watershed planning and management.This study quantified the net change,exchange,total change,and transfer rate of LULC in the Jinghe River Basin(JRB),China using LULC data from 2000 to 2020.Through trajectory analysis,knowledge maps,chord diagrams,and standard deviation ellipse method,we examined the spatiotemporal characteristics of LULC changes.We further established an index system encompassing natural factors(digital elevation model(DEM),slope,aspect,and curvature),socio-economic factors(gross domestic product(GDP)and population),and accessibility factors(distance from railways,distance from highways,distance from water,and distance from residents)to investigate the driving mechanisms of LULC changes using factor detector and interaction detector in the geographical detector(Geodetector).The key findings indicate that from 2000 to 2020,the JRB experienced significant LULC changes,particularly for farmland,forest,and grassland.During the study period,LULC change trajectories were categorized into stable,early-stage,late-stage,repeated,and continuous change types.Besides the stable change type,the late-stage change type predominated the LULC change trajectories,comprising 83.31% of the total change area.The period 2010-2020 witnessed more active LULC changes compared to the period 2000-2010.The LULC changes exhibited a discrete spatial expansion trend during 2000-2020,predominantly extending from southeast to northwest of the JRB.Influential driving factors on LULC changes included slope,GDP,and distance from highways.The interaction detection results imply either bilinear or nonlinear enhancement for any two driving factors impacting the LULC changes from 2000 to 2020.This comprehensive understanding of the spatiotemporal characteristics and driving mechanisms of LULC changes offers valuable insights for the planning and sustainable management of LULC in the JRB.

Response of ecosystem carbon storage to land use change from 1985 to 2050 in the Ningxia Section of Yellow River Basin,China

Regional sustainable development necessitates a holistic understanding of spatiotemporal variations in ecosystem carbon storage(ECS),particularly in ecologically sensitive areas with arid and semi-arid climate.In this study,we calculated the ECS in the Ningxia Section of Yellow River Basin,China from 1985 to 2020 using the Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST)model based on land use data.We further predicted the spatial distribution of ECS in 2050 under four land use scenarios:natural development scenario(NDS),ecological protection scenario(EPS),cultivated land protection scenario(CPS),and urban development scenario(UDS)using the patch-generating land use simulation(PLUS)model,and quantified the influences of natural and human factors on the spatial differentiation of ECS using the geographical detector(Geodetector).Results showed that the total ECS of the study area initially increased from 1985 until reaching a peak at 402.36×10^(6) t in 2010,followed by a decreasing trend to 2050.The spatial distribution of ECS was characterized by high values in the eastern and southern parts of the study area,and low values in the western and northern parts.Between 1985 and 2020,land use changes occurred mainly through the expansion of cultivated land,woodland,and construction land at the expense of unused land.The total ECS in 2050 under different land use scenarios(ranked as EPS>CPS>NDS>UDS)would be lower than that in 2020.Nighttime light was the largest contributor to the spatial differentiation of ECS,with soil type and annual mean temperature being the major natural driving factors.Findings of this study could provide guidance on the ecological construction and high-quality development in arid and semi-arid areas.

Quantitative distinction of the relative actions of climate change and human activities on vegetation evolution in the Yellow River Basin of China during 1981-2019

Under the combined influence of climate change and human activities,vegetation ecosystem has undergone profound changes.It can be seen that there are obvious differences in the evolution patterns and driving mechanisms of vegetation ecosystem in different historical periods.Therefore,it is urgent to identify and reveal the dominant factors and their contribution rates in the vegetation change cycle.Based on the data of climate elements(sunshine hours,precipitation and temperature),human activities(population intensity and GDP intensity)and other natural factors(altitude,slope and aspect),this study explored the spatial and temporal evolution patterns of vegetation NDVI in the Yellow River Basin of China from 1989 to 2019 through a residual method,a trend analysis,and a gravity center model,and quantitatively distinguished the relative actions of climate change and human activities on vegetation evolution based on Geodetector model.The results showed that the spatial distribution of vegetation NDVI in the Yellow River Basin showed a decreasing trend from southeast to northwest.During 1981-2019,the temporal variation of vegetation NDVI showed an overall increasing trend.The gravity centers of average vegetation NDVI during the study period was distributed in Zhenyuan County,Gansu Province,and the center moved northeastwards from 1981 to 2019.During 1981-2000 and 2001-2019,the proportion of vegetation restoration areas promoted by the combined action of climate change and human activities was the largest.During the study period(1981-2019),the dominant factors influencing vegetation NDVI shifted from natural factors to human activities.These results could provide decision support for the protection and restoration of vegetation ecosystem in the Yellow River Basin.

Spatiotemporal Changes in NDVI and Its Driving Factors in the Kherlen River Basin

Vegetation is an important factor linking the atmosphere,water,soil,and biological functions,and it plays a specific role in the climate change response and sustainable development of regional economies.However,little information is available on vegetation vulnerability and its driving mechanism.Therefore,studying temporal and spatial change characteristics of vegetation and their corresponding mechanisms is important for assessing ecosystem stability and formulating ecological policies in the Kherlen River Basin.We used Moderate-resolution Imaging Spectroradiometer(MODIS)normalized difference vegetation index(NDVI)remote sensing images from 2000 to 2020 to analyse temporal changes in NDVI with the autoregressive moving average model(ARMA)and the breaks for additive season trend(BFAST)in the basin and to assess natural,anthropogenic and topographic factors with the Geodetector model.The results show that:1)the long NDVI time series remained stable in the Kherlen River Basin from 2000 to 2020,with a certain significant mutation period from 2013 to 2017;2)the coefficient of variation(CV)in the analysis of the spatial NDVI was generally constant,mainly at the level of 0.01–0.07,and the spatial NDVI change was minimally impacted by external interference;and 3)temperature and precipitation are the key factors affecting the NDVI in the basin,and changes in local hydrothermal conditions directly affect the local NDVI.The results of this study could provide a scientific basis for the effective protection of the ecological environment and will aid in understanding the influence of vegetation change mechanisms and the corresponding factors.

Untangling the increasing elevation of cropland in China from 1980 to 2020

The redistribution of cropland to areas of higher elevation in China has long affected agricultural development and could seriously threaten national food security.However,there is currently little research reported on this phenomenon,which may limit the improvement of cropland protection policies.To fill this gap,we analyzed the spatiotemporal characteristics and driving mechanisms of increased cropland elevation in China during the period 1980-2020.The average cropland elevation in China increased by 17.38 m from 1980 to 2020.The gravity center of the cropland area and average cropland elevation in China moved to the northwest by 81.00 km and 51.47 km,respectively.The amount of newly added cropland in eastern China was less than that in occupied regions;however,the average elevation of newly added cropland was greater than that of occupied cropland,though the opposite phenomenon was observed in western China.Slope,temperature,land-use intensity,population,economic density,and distance to main roads were the main factors affecting the redistribution of cropland to areas of higher elevation.The effects of these major driving factors exhibited significant spatial and temporal variations in China.This study has important implications for improving existing cropland protection policies and developing more effective cropland management systems in China.

Driving forces behind the spatiotemporal heterogeneity of land-use and land-cover change:A case study of the Weihe River Basin,China

The impact of socioeconomic development on land-use and land-cover change(LUCC)in river basins varies spatially and temporally.Exploring the spatiotemporal evolutionary trends and drivers of LUCC under regional disparities is the basis for the sustainable development and management of basins.In this study,the Weihe River Basin(WRB)in China was selected as a typical basin,and the WRB was divided into the upstream of the Weihe River Basin(UWRB),the midstream of the Weihe River Basin(MWRB),the downstream of the Weihe River Basin(DWRB),the Jinghe River Basin(JRB),and the Luohe River Basin(LRB).Based on land-use data(cultivated land,forestland,grassland,built-up land,bare land,and water body)from 1985 to 2020,we analyzed the spatiotemporal heterogeneity of LUCC in the WRB using a land-use transfer matrix and a dynamic change model.The driving forces of LUCC in the WRB in different periods were detected using the GeoDetector,and the selected influencing factors included meteorological factors(precipitation and temperature),natural factors(elevation,slope,soil,and distance to rivers),social factors(distance to national highway,distance to railway,distance to provincial highway,and distance to expressway),and human activity factors(population density and gross domestic product(GDP)).The results indicated that the types and intensities of LUCC conversions showed considerable disparities across different sub-basins,where complex conversions among cultivated land,forestland,and grassland occurred in the LRB,JRB,and UWRB,with higher dynamic change before 2000.The conversion of other land-use types to built-up land was concentrated in the UWRB,MWRB,and DWRB,with substantial increases after 2000.Additionally,the driving effects of the influencing factors on LUCC in each sub-basin also exhibited distinct diversity,with the LRB and JRB being influenced by the meteorological and social factors,and the UWRB,MWRB,and DWRB being driven by human activity factors.Moreover,the interaction of these influencing factors indicated an enhanced effect on LUCC.This study confirmed the spatiotemporal heterogeneity effects of socioeconomic status on LUCC in the WRB under regional differences,contributing to the sustainable development of the whole basin by managing sub-basins according to local conditions.

Spatio-temporal Changes in Water Conservation Ecosystem Service During 1990–2019 in the Tumen River Basin, Northeast China

The water conservation(WC) function of ecosystems is related to regional ecological security and the sustainable development of water resources, and the assessment of WC and its influencing factors is crucial for ecological and water resource management.The Tumen River Basin(TRB) is located in the core of the Northeast Asian ecological network and has been experiencing severe ecological crises and water shortages in recent years due to climate change and human activities. However, these crises have not been fully revealed to the extent that corresponding scientific measures are lacking. This study analyzed the spatial and temporal evolution characteristics and drivers of WC in the TRB from 1990 to 2019 based on the water yield module of the Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST) model. The results showed that: 1) under the combined effect of nature and socioeconomics, the WC depth of the TRB has slowly increased at a rate of 0.11 mm/yr in the past 30 years, with an average WC depth of 36.14 mm. 2) The main driving factor of the spatial variation in WC is precipitation, there is a significant interaction between precipitation and velocity, the interaction between each factor is higher than the contribution of a single factor, and the interactions between factors all have nonlinear enhancement and two-factor enhancement. 3) Among the seven counties and municipalities in the study area, the southern part of Helong City and the southeastern part of Longjing City are extremely important areas for WC(> 75 mm), and they should be regarded as regional water resources and ecological priority protection areas. It is foreseen that under extreme climate conditions in the future, the WC of the watershed is under great potential threat, and protection measures such as afforestation and forestation should begin immediately. Furthermore, the great interannual fluctuations in WC depth may place more stringent requirements on the choice of time scales in the ecosystem service assessment process.

Detection of Multi-dimensional Driving Forces of Public Environmental Concern in China:Based on Spatial Heterogeneity Perspectives

Public environmental concern(PEC)is an important bottom-up force in building an environmentally sustainable society.Guided by attitude theory,this paper innovatively constructed a PEC evaluation index system,while introducing entropy weighted-TOPSIS(Technique for Order Preference by Similarity to Ideal Solution)to realize the assessment of PEC.Exploratory spatial data analysis was used to portray the spatio-temporal evolution patterns of PEC in 362 Chinese cities at prefecture-level and above from 2011 to 2018.Furthermore,the Geodetector model was performed to identify the multi-dimensional determinants of PEC from the perspective of spatial heterogeneity.The results indicated that:1)PEC in China exhibited a fluctuating upward trend,consistent with the spatial distribution law of‘Heihe-Tengchong Line’and‘Bole-Taipei Line’;2)the driving effect of each factor varied dynamically,but in general,economic development level,population size,industrial wastewater,and education level were the dominant driving factors explaining the spatial variation of PEC;3)risk detection revealed that four factors,government environmental regulations,PM_(2.5),vegetation coverage,and natural resource endowment,had nonlinear effects on PEC;4)the interactions between factors all demonstrated an enhancement in explaining the spatial differentiation of PEC.PEC was driven by the comprehensive interaction of four-dimensional factors of economy,society,pollutant emissions,and ecology.Among them,population agglomeration accompanied by a high level of regional economy and information technology can explain the increase in PEC to the greatest extent.

Spatiotemporal variation of surface albedo and its influencing factors in northern Xinjiang, China

Surface albedo is a quantitative indicator for land surface processes and climate modeling,and plays an important role in surface radiation balance and climate change.In this study,by means of the MCD43A3 surface albedo product developed on the basis of Moderate Resolution Imaging Spectroradiometer(MODIS),we analyzed the spatiotemporal variation,persistence status,land cover type differences,and annual and seasonal differences of surface albedo,as well as the relationship between surface albedo and various influencing factors(including Normalized Difference Snow Index(NDSI),precipitation,Normalized Difference Vegetation Index(NDVI),land surface temperature,soil moisture,air temperature,and digital elevation model(DEM))in the north of Xinjiang Uygur Autonomous Region(northern Xinjiang)of Northwest China from 2010 to 2020 based on the unary linear regression,Hurst index,and Pearson's correlation coefficient analyses.Combined with the random forest(RF)model and geographical detector(Geodetector),the importance of the above-mentioned influencing factors as well as their interactions on surface albedo were quantitatively evaluated.The results showed that the seasonal average surface albedo in northern Xinjiang was the highest in winter and the lowest in summer.The annual average surface albedo from 2010 to 2020 was high in the west and north and low in the east and south,showing a weak decreasing trend and a small and stable overall variation.Land cover types had a significant impact on the variation of surface albedo.The annual average surface albedo in most regions of northern Xinjiang was positively correlated with NDSI and precipitation,and negatively correlated with NDVI,land surface temperature,soil moisture,and air temperature.In addition,the correlations between surface albedo and various influencing factors showed significant differences for different land cover types and in different seasons.To be specific,NDSI had the largest influence on surface albedo,followed by precipitation,land surface temperature,and soil moisture;whereas NDVI,air temperature,and DEM showed relatively weak influences.However,the interactions of any two influencing factors on surface albedo were enhanced,especially the interaction of air temperature and DEM.NDVI showed a nonlinear enhancement of influence on surface albedo when interacted with land surface temperature or precipitation,with an explanatory power greater than 92.00%.This study has a guiding significance in correctly understanding the land-atmosphere interactions in northern Xinjiang and improving the regional land-surface process simulation and climate prediction.

Spatiotemporal dynamics and geo-environmental factors influencing mangrove gross primary productivity during 2000–2020 in Gaoqiao Mangrove Reserve, China

Background:Mangrove forests are a significant contributor to the global carbon cycle,and the accurate estimation of their gross primary productivity(GPP)is essential for understanding the carbon budget within blue carbon ecosystems.Little attention has been given to the investigation of spatiotemporal patterns and ecological variations within mangrove ecosystems,as well as the quantitative analysis of the influence of geo-environmental factors on time-series estimations of mangrove GPP.Methods:This study explored the spatiotemporal dynamics of mangrove GPP from 2000 to 2020 in Gaoqiao Mangrove Reserve,China.A leaf area index(LAI)-based light-use efficiency(LUE)model was combined with Landsat data on Google Earth Engine(GEE)to reveal the variations in mangrove GPP using the Mann-Kendall(MK)test and Theil-Sen median trend.Moreover,the spatiotemporal patterns and ecological variations in mangrove ecosystems across regions were explored using four landscape indicators.Furthermore,the effects of six geo-environmental factors(species distribution,offshore distance,elevation,slope,planar curvature and profile curvature)on GPP were investigated using Geodetector and multi-scale geo-weighted regression(MGWR).Results:The results showed that the mangrove forest in the study area experienced an area loss from 766.26 ha in 2000 to 718.29 ha in 2020,mainly due to the conversion to farming,terrestrial forest and aquaculture zones.Landscape patterns indicated high levels of vegetation aggregation near water bodies and aquaculture zones,and low levels of aggregation but high species diversity and distribution density near building zone.The mean value of mangrove GPP continuously increased from 6.35 g C⋅m^(-2)⋅d^(-1) in 2000 to 8.33 g C⋅m^(-2)⋅d^(-1) in 2020,with 23.21%of areas showing a highly and significantly increasing trend(trend value>0.50).The Geodetector and MGWR analyses showed that species distribution,offshore distance and elevation contributed most to the GPP variations.Conclusions:These results provide guidelines for selecting GPP products,and the combination of Geodetector and MGWR based on multiple geo-environmental factors could quantitatively capture the mode,direction,pathway and intensity of the influencing factors on mangrove GPP variation.The findings provide a foundation for understanding the spatiotemporal dynamics of mangrove GPP at the landscape or regional scale.

Spatial Distribution of Different Types of Villages for Rural Revitalization Strategy and Their Influencing Factors:A Case of Jilin Province,China

Village classification is the first step to implementing China’s rural revitalization(RR)strategy,and understanding the geographic differences in the distribution of village types helps to grasp the pathway of their unique development.This study spatialized9250 villages in Jilin Province(divided into six types)of China,and their distribution characteristics and influencing factors were examined using methods such as kernel density estimation,Ripley’s K function,the co-location quotient,and Geodetector.The results indicate that the spatial distribution balance and density of village types are different.All types of villages show an agglomeration distribution pattern,but the scale and intensity vary.There is a strong spatial association between agglomerative promotion(AP)and stable improvement(SIm)villages,as well as between characteristic protection(CP)and prospering frontier and enriching people(PE)villages.The factors affecting their distribution include terrain undulation,the percentage of arable land,the distance to the county town,road network density,population density,gross domestic product(GDP),and industrial enterprise density.The influencing factors for the distribution of village types are closely related to the function of each village.Based on the differences in the spatial distribution and influencing factors of different village types,policy suggestions are given for classified development.

Characteristics and driving factors of abandoned cultivated land in the hilly regions of southern China:A case study in Longnan,Jiangxi Province

The abandonment of cultivated land in southern China was gradually obvious.This research aims to provide a reference for solving the abandonment of cultivated land in hilly regions and promote rural development in China.We examined Longnan county located in the hilly regions of southern China as an example,where abandoned cultivated land is very common.We analyzed its land use data with a field survey to identify the abandoned cultivated land and geospatial characteristics.From the two aspects of social and natural factors,we analyzed the factors driving cultivated land abandonment with the help of Geodetector.The results showed that in 2019,the total area of the abandoned cultivated land in Longnan county was 4,962.35 hm^(2),covering 39.51% of this region.Among the topographic factors,the abandonment rate is positively correlated with elevation and slope gradient,but not with slope direction.Among the land parcel conditions,the abandonment rate is positively correlated with the access to road network and cultivation distance from settlement.At the county level,the abandonment of cultivated land in study area was affected by multiple factors,among which,the direct factor was the reduction in the labor force,such as the decrease of farming laborers and the increase of female population,which made farming unsustainable.Changes in production factors also promoted transformations in farmers’motivation to engage in production,such as the decrease of grain crops and the increase of cash crops,which was the indirect factor affecting cultivated land abandonment.The development of the rural nonagricultural industry affected farmers’enthusiasm,such as the decrease of farming households,which was the fundamental factor leading to cultivated land abandonment in this area.

Evaluation and Quantitative Attribution Analysis of Water Yield Services in the Peak-cluster Depression Basins in Southwest of Guangxi,China

Karst environmental issues have become one of the hot spots in contemporary international geological research. The same problem of water shortage is one of the hot spots of global concern. The peak-cluster depression basins in southwest of Guangxi is an important water connotation and ecological barrier areas in the Pearl River Basin of China. Thus, studying the spatial and temporal variations and the influencing factors of its water yield services is critical to achieve the sustainable development of water resources and ecological environmental protection in this region. As such, this paper uses the Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST) model to assess the spatial and temporal variabilities of water yield services and its trends in the peak-cluster depression basins in southwest of Guangxi from 2000 to 2020. This work also integrates precipitation(Pre), reference evapotranspiration(ET), temperature(Tem), digital elevation model(DEM), slope, normalized difference vegetation index(NDVI), land use/land cover(LULC) and soil type to reveal the main factors that influence water yield services with the help of Geodetector. Results show that: 1) in time scale,the total annual water yield in the study area show a fluctuating and increasing trend from 2000 to 2020, with a growth rate of 7.3753 × 10^(8)m^(3)/yr, and its multi-year average water yield was 538.07 mm;2) in spatial pattern, with high yield areas mainly distributed in the south of the study area(mainly including Shangsi County, Pingxiang City, Ningming County, Longzhou County and Jingxi County), and low yield areas mainly distributed in Baise City and Nanning City;3) the dominant factor of water yield within karst and non-karst landforms is not necessarily controlled by precipitation, and the explanation degree of DEM factors in karst areas is significantly higher than that in non-karst areas;4) amongst the climatic factors, Pre, ET and Tem are dominant in the spatial pattern of region water yield capacity. among which Pre has the highest explanatory power for the spatial heterogeneity of annual water production, with q values above0.8, and each driver showed a significant interaction on the spatial distribution of water yield, with Pre exhibiting the strongest interaction with LULC.