The rapid acceleration of global warming and intensifying human activities have exacerbated the fragility and climate sensitivity of ecosystems worldwide,particularly in arid regions.Vegetation,a key component of ecos...The rapid acceleration of global warming and intensifying human activities have exacerbated the fragility and climate sensitivity of ecosystems worldwide,particularly in arid regions.Vegetation,a key component of ecosystems,is critical in enhancing the ecological environment.The Ertix River Basin(ERB)is a transboundary watershed that spans multiple countries,mostly in arid regions.However,research on the fractional vegetation coverage(FVC)and its driving factors in the ERB remains limited.Investigating the spatiotemporal changes in the FVC and its relationship with various factors in the ERB can offer scientific support for optimizing regional vegetation restoration policies and promoting the coordinated development of human-environment interactions.The Moderate-resolution Imaging Spectroradiometer(MODIS)MYD13Q1 V6 data were obtained via the Google Earth Engine platform,and methods including the pixel dichotomy method,Theil-Sen median trend analysis,and Mann‒Kendall test were employed to examine the spatiotemporal dynamics of the FVC in the ERB from 2003 to 2023,with future trend forecast using the Hurst index.The impacts of natural and socioeconomic factors on the FVC were evaluated through the partial least squares-structural equation model(PLS-SEM).The results indicated that the FVC in the ERB showed a slight degradation trend with an average annual decrease of 0.046%during 2003-2023,with significant changes occurring in 2004,2010,and 2019.Spatially,53.380%of the study area was degraded,and the change in the FVC increased gradually from southeast to northwest.The FVC in 63.000%of the study area was highly stable and displayed long-term persistence;and the direct impact of natural factors(path coefficient of 0.617)on the FVC was significantly higher than that of socioeconomic factors(0.167).Among the natural factors,precipitation(0.999)was the most significant.This study reveals the significant impacts of natural and socioeconomic factors on vegetation dynamics in arid regions,and provides a scientific basis for transnational ecological conservation.展开更多
This study aimed to assess sand and dust storm(SDS)risks in arid Central Asia during 2001–2021 from a multisectoral(environment,society,and agriculture)and comprehensive perspective on the Google Earth Engine(GEE)pla...This study aimed to assess sand and dust storm(SDS)risks in arid Central Asia during 2001–2021 from a multisectoral(environment,society,and agriculture)and comprehensive perspective on the Google Earth Engine(GEE)platform.The results show that the areas with moderate or greater SDS risk accounted for 18.75%of the total area of arid Central Asia.The high SDS risk areas are mainly concentrated in the oases around the desert and are most widely distributed in spring and summer.The SDS risk in the oasis area of southern Xinjiang increased significantly,while the SDS risk in the northeastern Aral Sea region and the Kazakh hilly region decreased significantly over the 21 years.Khwarazm of Uzbekistan,located in the Amu Darya River Delta,is the administrative district with the highest comprehensive risk of sandstorms,and the Balkan State of Turkmenistan and Kashi City and Zepu County in China are the administrative districts with the highest multisectoral risk of sandstorms.The results of this study provide a complete picture of SDS risks in the arid Central Asia region and will provide some guidance to policymakers and local authorities in SDS risk mitigation.展开更多
With the emergence of multisource data and the development of cloud computing platforms,accurate prediction of event-scale dust source regions based on machine learning(ML)methods should be considered,especially accou...With the emergence of multisource data and the development of cloud computing platforms,accurate prediction of event-scale dust source regions based on machine learning(ML)methods should be considered,especially accounting for the temporal variability in sample and predictor variables.Arid Central Asia(ACA)is recognized as one of the world’s primary potential sand and dust storm(SDS)sources.In this study,based on the Google Earth Engine(GEE)platform,four ML methods were used for SDS source prediction in ACA.Fourteen meteorological and terrestrial factors were selected as influencing factors controlling SDS source susceptibility and applied in the modeling process.Generally,the results revealed that the random forest(RF)algorithm performed best,followed by the gradient boosting tree(GBT),maximum entropy(MaxEnt)model and support vector machine(SVM).The Gini impurity index results of the RF model indicated that the wind speed played the most important role in SDS source prediction,followed by the normalized difference vegetation index(NDVI).This study could facilitate the development of programs to reduce SDS risks in arid and semiarid regions,particularly in ACA.展开更多
基金funded by the Third Xinjiang Comprehensive Scientific Investigation Project,China(2022xjkk0702)the Western Young Scholars Project of the Chinese Academy of Sciences(2022-XBQNXZ-001)the Tianshan Talent Development Program,China(2022TSYCCX0006).
文摘The rapid acceleration of global warming and intensifying human activities have exacerbated the fragility and climate sensitivity of ecosystems worldwide,particularly in arid regions.Vegetation,a key component of ecosystems,is critical in enhancing the ecological environment.The Ertix River Basin(ERB)is a transboundary watershed that spans multiple countries,mostly in arid regions.However,research on the fractional vegetation coverage(FVC)and its driving factors in the ERB remains limited.Investigating the spatiotemporal changes in the FVC and its relationship with various factors in the ERB can offer scientific support for optimizing regional vegetation restoration policies and promoting the coordinated development of human-environment interactions.The Moderate-resolution Imaging Spectroradiometer(MODIS)MYD13Q1 V6 data were obtained via the Google Earth Engine platform,and methods including the pixel dichotomy method,Theil-Sen median trend analysis,and Mann‒Kendall test were employed to examine the spatiotemporal dynamics of the FVC in the ERB from 2003 to 2023,with future trend forecast using the Hurst index.The impacts of natural and socioeconomic factors on the FVC were evaluated through the partial least squares-structural equation model(PLS-SEM).The results indicated that the FVC in the ERB showed a slight degradation trend with an average annual decrease of 0.046%during 2003-2023,with significant changes occurring in 2004,2010,and 2019.Spatially,53.380%of the study area was degraded,and the change in the FVC increased gradually from southeast to northwest.The FVC in 63.000%of the study area was highly stable and displayed long-term persistence;and the direct impact of natural factors(path coefficient of 0.617)on the FVC was significantly higher than that of socioeconomic factors(0.167).Among the natural factors,precipitation(0.999)was the most significant.This study reveals the significant impacts of natural and socioeconomic factors on vegetation dynamics in arid regions,and provides a scientific basis for transnational ecological conservation.
基金partially supported by the National Natural Science Foundation of China(Grant Nos.42171014,42071424)the UNEP-NSFC International Cooperation Project(42161144004)+2 种基金Natural Science Foundation of Shandong Province(ZR2024QD290)the Youth Innovation Teams in Colleges and Universities of Shandong Province(2022KJ178)the Young Taishan Scholars Program of Shandong Province(tsqn202103065)。
文摘This study aimed to assess sand and dust storm(SDS)risks in arid Central Asia during 2001–2021 from a multisectoral(environment,society,and agriculture)and comprehensive perspective on the Google Earth Engine(GEE)platform.The results show that the areas with moderate or greater SDS risk accounted for 18.75%of the total area of arid Central Asia.The high SDS risk areas are mainly concentrated in the oases around the desert and are most widely distributed in spring and summer.The SDS risk in the oasis area of southern Xinjiang increased significantly,while the SDS risk in the northeastern Aral Sea region and the Kazakh hilly region decreased significantly over the 21 years.Khwarazm of Uzbekistan,located in the Amu Darya River Delta,is the administrative district with the highest comprehensive risk of sandstorms,and the Balkan State of Turkmenistan and Kashi City and Zepu County in China are the administrative districts with the highest multisectoral risk of sandstorms.The results of this study provide a complete picture of SDS risks in the arid Central Asia region and will provide some guidance to policymakers and local authorities in SDS risk mitigation.
基金supported by the National Natural Science Foundation of China(42171014)the UNEPNSFC International Cooperation Project(42161144004)+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA20060301)National Natural Science Foundation of China(42071424)the China Scholarship Council(202104910412).
文摘With the emergence of multisource data and the development of cloud computing platforms,accurate prediction of event-scale dust source regions based on machine learning(ML)methods should be considered,especially accounting for the temporal variability in sample and predictor variables.Arid Central Asia(ACA)is recognized as one of the world’s primary potential sand and dust storm(SDS)sources.In this study,based on the Google Earth Engine(GEE)platform,four ML methods were used for SDS source prediction in ACA.Fourteen meteorological and terrestrial factors were selected as influencing factors controlling SDS source susceptibility and applied in the modeling process.Generally,the results revealed that the random forest(RF)algorithm performed best,followed by the gradient boosting tree(GBT),maximum entropy(MaxEnt)model and support vector machine(SVM).The Gini impurity index results of the RF model indicated that the wind speed played the most important role in SDS source prediction,followed by the normalized difference vegetation index(NDVI).This study could facilitate the development of programs to reduce SDS risks in arid and semiarid regions,particularly in ACA.
