Progress and prospects in river cross section extraction based on multi-source multisource remote sensing
Natural rivers carry water and materials within a certain boundary geometry.Research on rivers oftenfrequently involves extracting the geometric information of river surfaces and boundaries or hydraulic characteristics,such as flow velocity and discharge.Among these hydraulic attributes,geometric data that pertaining to river cross sections and other river features,which are easier to observe than the dynamic flow characteristics,are indispensable for conducting research on hydrological processes and material fluxes within a river system.Traditionally,the extraction of such data has relied heavily relied on field measurements,posing challenges in obtaining data for from inaccessible areas,such as mountainous regions,canyons,disaster-prone regionsareas,or expansive river basins.With the continuous advancement of multi-source remote sensing technology,which encompassing includes underwater remote sensing,near-earth Earthremote sensing,and satellite remote sensing,it has become possible to addressing the data scarcity in mountainous regions,canyons,and other areas has become possible by integrating multi-source remote sensing observations with limited ground measurements and establishing automatic extraction methods.Building upon the advancements made in the extraction of river cross section morphology over the past two decades,this paper study examines the strengths and limitations of current methods.This studylt presented presents an integrated"air-space-ground"remote sensing data observation scheme,amalgamatedcombined with the corresponding automatic extraction methodologies,such as river surfaces extraction method,river width,extraction method and river water level extraction methods,to extract river information,particularly cross section morphology,in data-scarce or large-scale river basins.Furthermore,this study offered offers valuable insights into the future development trends by considering the existing technical progress in the field.
万方数据
维普
