Benthos are aquatic organisms living at the bottom of water bodies for all or most of their life history.Apart from the settled and moving living,their forms of habitat are mostly attached to the hard substrate like r...Benthos are aquatic organisms living at the bottom of water bodies for all or most of their life history.Apart from the settled and moving living,their forms of habitat are mostly attached to the hard substrate like rocks or soft bases such as mud and sand.In addition,there are benthic species that attach to the plant surfaces or other benthic animals,and inhabit the intertidal zone.In terms of feeding methods,most benthic animals feed on suspended matter and sediment.Most of them are invertebrates and are a complex ecological group.According to the size,they can be divided into macrobenthos and meio-benthos[1].Benthos are an important part of the water ecosystem.Using a typical river(Gaya River)in eastern Jilin Province,this paper analyzed the water quality,the components of benthic organisms and their biological density through sampling surveys of three typical locations in the upper,middle and lower reaches of the Gaya River.In addition,it made a biological evaluation of the water quality of the Gaya River.展开更多
Based on low-altitude remote sensing images,this paper established sample set of typical river vegetation elements and proposed river vegetation extraction technical solution to adaptively extract typical vegetation e...Based on low-altitude remote sensing images,this paper established sample set of typical river vegetation elements and proposed river vegetation extraction technical solution to adaptively extract typical vegetation elements of river basins.The main research of this paper were as follows:(1)a typical vegetation extraction sample set based on low-altitude remote sensing images was established.(2)A low-altitude remote sensing image vegetation extraction model based on the focus perception module was designed to realize the end-to-end automatic extraction of different types of vegetation areas of low-altitude remote sensing images to fully learn the spectral spatial texture information and deep semantic information of the images.(3)By comparison with the baseline method,baseline method with embedded focus perception module showed an improvement in the precision by 7.37%and mIoU by 49.49%.Through visual interpretation and quantitative calculation analysis,the typical river vegetation adaptive extraction network has effectiveness and generalization ability,consistent with the needs of practical applications of vegetation extraction.展开更多
文摘Benthos are aquatic organisms living at the bottom of water bodies for all or most of their life history.Apart from the settled and moving living,their forms of habitat are mostly attached to the hard substrate like rocks or soft bases such as mud and sand.In addition,there are benthic species that attach to the plant surfaces or other benthic animals,and inhabit the intertidal zone.In terms of feeding methods,most benthic animals feed on suspended matter and sediment.Most of them are invertebrates and are a complex ecological group.According to the size,they can be divided into macrobenthos and meio-benthos[1].Benthos are an important part of the water ecosystem.Using a typical river(Gaya River)in eastern Jilin Province,this paper analyzed the water quality,the components of benthic organisms and their biological density through sampling surveys of three typical locations in the upper,middle and lower reaches of the Gaya River.In addition,it made a biological evaluation of the water quality of the Gaya River.
文摘Based on low-altitude remote sensing images,this paper established sample set of typical river vegetation elements and proposed river vegetation extraction technical solution to adaptively extract typical vegetation elements of river basins.The main research of this paper were as follows:(1)a typical vegetation extraction sample set based on low-altitude remote sensing images was established.(2)A low-altitude remote sensing image vegetation extraction model based on the focus perception module was designed to realize the end-to-end automatic extraction of different types of vegetation areas of low-altitude remote sensing images to fully learn the spectral spatial texture information and deep semantic information of the images.(3)By comparison with the baseline method,baseline method with embedded focus perception module showed an improvement in the precision by 7.37%and mIoU by 49.49%.Through visual interpretation and quantitative calculation analysis,the typical river vegetation adaptive extraction network has effectiveness and generalization ability,consistent with the needs of practical applications of vegetation extraction.
