Flood disasters can have a serious impact on people's production and lives, and can cause hugelosses in lives and property security. Based on multi-source remote sensing data, this study establisheddecision tree c...Flood disasters can have a serious impact on people's production and lives, and can cause hugelosses in lives and property security. Based on multi-source remote sensing data, this study establisheddecision tree classification rules through multi-source and multi-temporal feature fusion, classified groundobjects before the disaster and extracted flood information in the disaster area based on optical imagesduring the disaster, so as to achieve rapid acquisition of the disaster situation of each disaster bearing object.In the case of Qianliang Lake, which suffered from flooding in 2020, the results show that decision treeclassification algorithms based on multi-temporal features can effectively integrate multi-temporal and multispectralinformation to overcome the shortcomings of single-temporal image classification and achieveground-truth object classification.展开更多
Landslide susceptibility map(LSM)is a crucial tool for managing landslide hazards and identifying potential landslide areas.However,current LSMs rely primarily on static landslide-related factors with little variation...Landslide susceptibility map(LSM)is a crucial tool for managing landslide hazards and identifying potential landslide areas.However,current LSMs rely primarily on static landslide-related factors with little variation over several decades,thereby overlooking the movement of slopes and failing to capture landslide dynamics.The long-term ground deformation map(GDM)derived from multi-temporal interferometric synthetic aperture radar(MT-InSAR)can effectively address the shortcomings.Fengjie County is an important area for geohazard management in the Three Gorges Reservoir Area(TGRA),China.Landslides in this area,however,cause significant socio-economic loss due to geological,tectonic,climatic,and anthropological factors.This research aims to integrate random forest(RF)with MT-InSAR to generate a landslide dynamic susceptibility map(LDSM)for Fengjie County,enhancing the reliability of landslide risk management.First,the RF model was employed to generate a static LSM,whereas MT-InSAR was utilized to obtain the GDM of the study area from January 2020 to June 2023.The static LSM and the GDM were subsequently integrated using a dynamic weight matrix to derive the LDSM.Our analysis covered a temporal framework spanning three years,focusing on spatiotemporal changes in landslide susceptibility levels and the influence of climate factors.Compared with the static LSM,the LDSM can promptly identify moving landslide areas,reduce high landslide susceptibility areas,and achieve greater accuracy.Moreover,the spatiotemporal changes in landslide susceptibility are regulated by the total annual rainfall,with wet years being more conducive to landslides than dry years.The proposed LDSM offers useful insights for the dynamic prevention and refined management of landslide hazards in the TGRA,significantly enhancing the resilience in this region.展开更多
文摘Flood disasters can have a serious impact on people's production and lives, and can cause hugelosses in lives and property security. Based on multi-source remote sensing data, this study establisheddecision tree classification rules through multi-source and multi-temporal feature fusion, classified groundobjects before the disaster and extracted flood information in the disaster area based on optical imagesduring the disaster, so as to achieve rapid acquisition of the disaster situation of each disaster bearing object.In the case of Qianliang Lake, which suffered from flooding in 2020, the results show that decision treeclassification algorithms based on multi-temporal features can effectively integrate multi-temporal and multispectralinformation to overcome the shortcomings of single-temporal image classification and achieveground-truth object classification.
基金supported by the National Science Fund for Distinguished Young Scholars(Grant No.42225702)the Maria Skłodowska-Curie Action(MSCA)-UPGRADE(mUltiscale IoT equipPed lonG linear infRastructure resilience built and sustAinable DevelopmEnt)project-HORIZON-MSCA-2022-SE-01(Grant No.101131146)。
文摘Landslide susceptibility map(LSM)is a crucial tool for managing landslide hazards and identifying potential landslide areas.However,current LSMs rely primarily on static landslide-related factors with little variation over several decades,thereby overlooking the movement of slopes and failing to capture landslide dynamics.The long-term ground deformation map(GDM)derived from multi-temporal interferometric synthetic aperture radar(MT-InSAR)can effectively address the shortcomings.Fengjie County is an important area for geohazard management in the Three Gorges Reservoir Area(TGRA),China.Landslides in this area,however,cause significant socio-economic loss due to geological,tectonic,climatic,and anthropological factors.This research aims to integrate random forest(RF)with MT-InSAR to generate a landslide dynamic susceptibility map(LDSM)for Fengjie County,enhancing the reliability of landslide risk management.First,the RF model was employed to generate a static LSM,whereas MT-InSAR was utilized to obtain the GDM of the study area from January 2020 to June 2023.The static LSM and the GDM were subsequently integrated using a dynamic weight matrix to derive the LDSM.Our analysis covered a temporal framework spanning three years,focusing on spatiotemporal changes in landslide susceptibility levels and the influence of climate factors.Compared with the static LSM,the LDSM can promptly identify moving landslide areas,reduce high landslide susceptibility areas,and achieve greater accuracy.Moreover,the spatiotemporal changes in landslide susceptibility are regulated by the total annual rainfall,with wet years being more conducive to landslides than dry years.The proposed LDSM offers useful insights for the dynamic prevention and refined management of landslide hazards in the TGRA,significantly enhancing the resilience in this region.
