Most existing studies provide coarse spatial resolution mappings(typically 1 km or more),which fail to capture local-scale heterogeneity of permafrost distribution in the permafrost boundary region.This study employed...Most existing studies provide coarse spatial resolution mappings(typically 1 km or more),which fail to capture local-scale heterogeneity of permafrost distribution in the permafrost boundary region.This study employed 298 ground-truth samples to evaluate six machine learning(ML)algorithms for simulating permafrost distribution in the Genhe River Basin(GRB)of the Greater Khingan Mountains(GKM)based on our detailed investigation(e.g.,16 boreholes)in this region conducted in 2023-2024,while identifying key environmental drivers through Shapley Additive Explanations(SHAP)analysis.Results show that the random forest(RF)model achieved the best performance,with a classification accuracy of 0.83 and a Kappa coefficient of 0.66.The RF-based permafrost map at a 30 m resolution reveals a total permafrost area of approximately 8248.5 km2,accounting for 52.0%of the GRB.The most influential predictors of permafrost distribution are slope(SLO),topographic wetness index(TWI),and degree of topographic relief(DTR),contributing 13.6%,11.1%,and 9.4%,respectively.Other important factors include normalized difference water index(NDWI,6.8%)and land surface temperature(LST,6.1%).Permafrost is mainly distributed in valley bottoms,toe slopes,and gently sloping areas in the upper and middle reaches of the basin.These zones are closely associated with vegetation types such as wetlands,shrubs,and larch forests.Conversely,permafrost is rarely found in croplands or on steep slopes.These findings improve the understanding of permafrost distribution patterns in the transitional zone of Northeast China,and offer critical data and methodological support for high-resolution permafrost mapping across the region.展开更多
Landslides pose a significant threat in the mountainous regions of Nepal.Landslide susceptibility maps are commonly used to identify potential landslide zones by statistically analyzing geological,topographical,and hy...Landslides pose a significant threat in the mountainous regions of Nepal.Landslide susceptibility maps are commonly used to identify potential landslide zones by statistically analyzing geological,topographical,and hydrological factors,assuming that similar conditions may trigger future failures.While such maps provide valuable insights into landslide-triggering conditions,they are limited in assessing risk to settlements and infrastructure located downslope or in valley bottoms.This study integrates machine learning based landslide susceptibility with numerical runout modeling to provide a comprehensive landslide hazard assessment in the Bhotekoshi watershed,overcoming the limitations of traditional models that focus solely on statistical susceptibility.To conduct the susceptibility analysis,a total of 439 landslides were mapped from 2012 to 2021 using satellite images.Of these,70%were used for training two machine learning(ML)models:random forest and Xtreme Gradient Boosting(XGBoost),and the remaining 30%were used for validation.Among the two ML models,Random Forest model demonstrated slightly superior performance,achieving higher predictive accuracy.After the machine learning susceptibility analysis,the study transitions into a regional-scale landslide runout analysis.First,a back analysis of the past landslide event was conducted to fine-tune the model parameters(internal angle of friction and basal friction angle)and validate performance of the runout model.Following the back analysis,the regional-scale numerical modeling of landslide runout was conducted by designating areas classified as the highest susceptibility class in the Random Forest susceptibility map as potential release zones.This approach allows for a detailed examination of landslide propagation and potential impacts along the downslope settlements and infrastructures.The analysis clearly demonstrates that integrating both machine learning and numerical runout methods significantly increases the estimated exposure of population,buildings,and roads within the very high hazard class compared to relying solely on susceptibility methods.Specifically,population exposure rises from 360 to 7743,buildings increase from 97 to 2771,and road exposure expands from 41 to 251 km.This result highlights the significant risk of underestimating exposure in the analyses that solely rely on landslide susceptibility models.Integration of susceptibility and runout analysis improves landslide risk assessment,aiding in land-use planning and disaster mitigation strategies.展开更多
“我不懂中文,也不了解壮族文化,但你们这幅作品真是太美了!请允许我收藏它。”2025年12月,在广西地图院举办的线上创意交流会上,知名国际数据可视化设计师RJ Andrews这样说道。当天,这位设计师用自己团队的《Info We Trust》等4部图集...“我不懂中文,也不了解壮族文化,但你们这幅作品真是太美了!请允许我收藏它。”2025年12月,在广西地图院举办的线上创意交流会上,知名国际数据可视化设计师RJ Andrews这样说道。当天,这位设计师用自己团队的《Info We Trust》等4部图集作品,交换了广西地图院的手绘地图作品《稻那—右江,稻香那韵·文化溯源》。展开更多
“深时数字地球”(Deep-time Digital Earth,简称DDE)是由中国科学家发起和主导,并由国际最大的地学组织——国际地质科学联合会批准的第一个大科学计划。深时数字地球旨在为地球的发展演变创建一个前所未有的互联互通的数字档案,利用...“深时数字地球”(Deep-time Digital Earth,简称DDE)是由中国科学家发起和主导,并由国际最大的地学组织——国际地质科学联合会批准的第一个大科学计划。深时数字地球旨在为地球的发展演变创建一个前所未有的互联互通的数字档案,利用先进的信息技术和数据科学方法,将地质历史的时间尺度与现代地球观测数据相结合,构建一个全面、动态、多维的地球系统模型。古地理图是揭示地表演变过程、板块运动、物种分布变迁等地质和环境资源问题,构建深时数字地球的重要时空可视化工具。从20世纪70年代开始,国外学者开始通过收集的大量以古地磁为主的地球物理数据、地质年代学数据、古生物化石数据等地学数据构建古地理重建模型。经过20年的努力,在EarthByte、Gplates Web Portal等网站发布了叠加地貌图、地质图、高程信息、磁异常、岩性等要素信息的近30种古地理图。当前,国内很多在线地质信息应用系统包含了样品、产状、化石、矿点等要素在现代地图的叠加展示,但是大多数系统缺少在线古地理图可视化功能,因此,无法从时间维度表达地质数据的年代信息。本文作者力求全部采用基于免费开源框架的技术路线构建一个能够快速部署的古地理图可视化Web应用(single page application, SPA)系统,在一个页面内可以切换不同古地理重建模型,展示岩石、古生物化石等兼具空间属性和地质年代学属性的地质要素。采用Vue组件实现前端模块组件与数据的分离,易于与Web GIS系统前端进行数据传输和功能模块的整合,从而可以快速集成进基于B\S架构的地质信息系统中。展开更多
基金financially supported by the Science and Technology Fundamental Resources Investigation Program of China(2022FY100704)the National Natural Science Foundation of China(42376254,42322608)+1 种基金the program of the Key Laboratory of Cryospheric Science and Frozen Soil Engineering,CAS(CSFSE-ZZ-2408)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(2022430).
文摘Most existing studies provide coarse spatial resolution mappings(typically 1 km or more),which fail to capture local-scale heterogeneity of permafrost distribution in the permafrost boundary region.This study employed 298 ground-truth samples to evaluate six machine learning(ML)algorithms for simulating permafrost distribution in the Genhe River Basin(GRB)of the Greater Khingan Mountains(GKM)based on our detailed investigation(e.g.,16 boreholes)in this region conducted in 2023-2024,while identifying key environmental drivers through Shapley Additive Explanations(SHAP)analysis.Results show that the random forest(RF)model achieved the best performance,with a classification accuracy of 0.83 and a Kappa coefficient of 0.66.The RF-based permafrost map at a 30 m resolution reveals a total permafrost area of approximately 8248.5 km2,accounting for 52.0%of the GRB.The most influential predictors of permafrost distribution are slope(SLO),topographic wetness index(TWI),and degree of topographic relief(DTR),contributing 13.6%,11.1%,and 9.4%,respectively.Other important factors include normalized difference water index(NDWI,6.8%)and land surface temperature(LST,6.1%).Permafrost is mainly distributed in valley bottoms,toe slopes,and gently sloping areas in the upper and middle reaches of the basin.These zones are closely associated with vegetation types such as wetlands,shrubs,and larch forests.Conversely,permafrost is rarely found in croplands or on steep slopes.These findings improve the understanding of permafrost distribution patterns in the transitional zone of Northeast China,and offer critical data and methodological support for high-resolution permafrost mapping across the region.
基金China Scholarship Council(CSC)for providing a fully funded post-graduate study in institute of mountain hazards and environment UCASsupported by the National Natural Science Foundation of China(Grant Nos.42361144880)+3 种基金the Science and Technology Program of Xizang(Grant No.XZ202402ZD0001)the Basic Research Program of Qinghai Province(2024-ZJ-904)the Postdoctoral Fellowship Programs of CPSF(Grant Nos.GZC20232571,2024M753153)the International Cooperation Overseas Platform Project,CAS(Grant No.131C11KYSB20200033).
文摘Landslides pose a significant threat in the mountainous regions of Nepal.Landslide susceptibility maps are commonly used to identify potential landslide zones by statistically analyzing geological,topographical,and hydrological factors,assuming that similar conditions may trigger future failures.While such maps provide valuable insights into landslide-triggering conditions,they are limited in assessing risk to settlements and infrastructure located downslope or in valley bottoms.This study integrates machine learning based landslide susceptibility with numerical runout modeling to provide a comprehensive landslide hazard assessment in the Bhotekoshi watershed,overcoming the limitations of traditional models that focus solely on statistical susceptibility.To conduct the susceptibility analysis,a total of 439 landslides were mapped from 2012 to 2021 using satellite images.Of these,70%were used for training two machine learning(ML)models:random forest and Xtreme Gradient Boosting(XGBoost),and the remaining 30%were used for validation.Among the two ML models,Random Forest model demonstrated slightly superior performance,achieving higher predictive accuracy.After the machine learning susceptibility analysis,the study transitions into a regional-scale landslide runout analysis.First,a back analysis of the past landslide event was conducted to fine-tune the model parameters(internal angle of friction and basal friction angle)and validate performance of the runout model.Following the back analysis,the regional-scale numerical modeling of landslide runout was conducted by designating areas classified as the highest susceptibility class in the Random Forest susceptibility map as potential release zones.This approach allows for a detailed examination of landslide propagation and potential impacts along the downslope settlements and infrastructures.The analysis clearly demonstrates that integrating both machine learning and numerical runout methods significantly increases the estimated exposure of population,buildings,and roads within the very high hazard class compared to relying solely on susceptibility methods.Specifically,population exposure rises from 360 to 7743,buildings increase from 97 to 2771,and road exposure expands from 41 to 251 km.This result highlights the significant risk of underestimating exposure in the analyses that solely rely on landslide susceptibility models.Integration of susceptibility and runout analysis improves landslide risk assessment,aiding in land-use planning and disaster mitigation strategies.
文摘“我不懂中文,也不了解壮族文化,但你们这幅作品真是太美了!请允许我收藏它。”2025年12月,在广西地图院举办的线上创意交流会上,知名国际数据可视化设计师RJ Andrews这样说道。当天,这位设计师用自己团队的《Info We Trust》等4部图集作品,交换了广西地图院的手绘地图作品《稻那—右江,稻香那韵·文化溯源》。
文摘“深时数字地球”(Deep-time Digital Earth,简称DDE)是由中国科学家发起和主导,并由国际最大的地学组织——国际地质科学联合会批准的第一个大科学计划。深时数字地球旨在为地球的发展演变创建一个前所未有的互联互通的数字档案,利用先进的信息技术和数据科学方法,将地质历史的时间尺度与现代地球观测数据相结合,构建一个全面、动态、多维的地球系统模型。古地理图是揭示地表演变过程、板块运动、物种分布变迁等地质和环境资源问题,构建深时数字地球的重要时空可视化工具。从20世纪70年代开始,国外学者开始通过收集的大量以古地磁为主的地球物理数据、地质年代学数据、古生物化石数据等地学数据构建古地理重建模型。经过20年的努力,在EarthByte、Gplates Web Portal等网站发布了叠加地貌图、地质图、高程信息、磁异常、岩性等要素信息的近30种古地理图。当前,国内很多在线地质信息应用系统包含了样品、产状、化石、矿点等要素在现代地图的叠加展示,但是大多数系统缺少在线古地理图可视化功能,因此,无法从时间维度表达地质数据的年代信息。本文作者力求全部采用基于免费开源框架的技术路线构建一个能够快速部署的古地理图可视化Web应用(single page application, SPA)系统,在一个页面内可以切换不同古地理重建模型,展示岩石、古生物化石等兼具空间属性和地质年代学属性的地质要素。采用Vue组件实现前端模块组件与数据的分离,易于与Web GIS系统前端进行数据传输和功能模块的整合,从而可以快速集成进基于B\S架构的地质信息系统中。
