Image processing methods for in situ estimation of cohesive sediment floc size, settling velocity, and density

被引:33
|
作者
Smith, S. Jarrell [1 ]
Friedrichs, Carl T. [2 ]
机构
[1] US Army Engineer Res & Dev Ctr, Coastal & Hydraul Lab, Vicksburg, MS 39180 USA
[2] Virginia Inst Marine Sci, Coll William & Mary, Gloucester Point, VA 23062 USA
来源
LIMNOLOGY AND OCEANOGRAPHY-METHODS | 2015年 / 13卷 / 05期
基金
美国国家科学基金会;
关键词
SUSPENDED PARTICLES; ESTUARY; PLUME; PIV; FLOCCULATION; INSTRUMENT; TURBULENCE; TRANSPORT; BEHAVIOR; INSSEV;
D O I
10.1002/lom3.10022
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Recent advances in development of in situ video settling columns have significantly contributed toward fine-sediment dynamics research through concurrent measurement of suspended sediment floc size distributions and settling velocities, which together also allow inference of floc density. Along with image resolution and sizing, two additional challenges in video analysis from these devices are the automated tracking of settling particles and accounting for fluid motions within the settling column. A combination of particle tracking velocimetry (PTV) and particle image velocimetry (PIV) image analysis techniques is described, which permits general automation of image analysis collected from video settling columns. In the fixed image plane, large-particle velocities are determined by PTV and small-particle velocities are tracked by PIV and treated as surrogates for fluid velocities. The large-particle settling velocity (relative to the suspending fluid) is determined by the vector difference of the large and small-particle settling velocities. The combined PTV/PIV image analysis approach is demonstrated for video settling column data collected within a dredge plume in Boston Harbor. The automated PTV/PIV approach significantly reduces uncertainties in measured settling velocity and inferred floc density.
引用
收藏
页码:250 / 264
页数:15
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