Automated monitoring of river ice processes using shore-based imagery

被引：31

作者：

Ansari, S. ^{[1
]}

Rennie, C. D. ^{[1
]}

Seidou, O. ^{[1
,2
]}

Malenchak, J. ^{[3
]}

Zare, S. G. ^{[4
]}

机构：

[1] Univ Ottawa, Dept Civil Engn, Ottawa, ON K1N 6N5, Canada

[2] United Nations Univ, Inst Water Environm & Hlth UNU INWEH, 204-175 Longwood Rd South, Hamilton, ON L8P 0A1, Canada

[3] Manitoba Hydro, Dept Water Resources Engn, Sect Head Ice & Environm Engn, Winnipeg, MB, Canada

[4] Hatch, Dept Hydrotech Engn, Calgary, AB, Canada

来源：

COLD REGIONS SCIENCE AND TECHNOLOGY | 2017年 / 142卷

基金：

加拿大自然科学与工程研究理事会;

关键词：

River ice; Terrestrial photogrammetry; Shore-based imagery; TERRESTRIAL PHOTOGRAMMETRY; SNOW; APPLICABILITY; MODEL;

D O I：

10.1016/j.coldregions.2017.06.011

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

River hydraulics is drastically influenced by the presence of river ice, which inevitably occurs in cold regions. Terrestrial monitoring of river ice, using a time-lapse camera system on the Lower Nelson River, northern Manitoba, Canada, was conducted for a comprehensive study of the effects of river ice cover on hydraulic characteristics. An automated image processing algorithm was developed to analyze the time series of terrestrial images. The presented image processing algorithm consists of five main steps: preprocessing, image registration, geo-rectification, target detection and final quantitative river ice cover calculations. The developed algorithm was able to detect and quantify important river ice cover characteristics such as the percentage of area covered by ice, the location of the leading edge, and the speed of border ice growth and recession. Potentially, these observations may be used to improve the ice formation and break-up algorithms in river ice models.

引用

页码：1 / 16

页数：16

共 48 条

[1]

Acres KGS, 2011, CONAWAPA GENERATING

[2]

[Anonymous], 1990, Computer Graphics: Principles and Practice

[3]

[Anonymous], 1985, INTRO DIGITAL IMAGE

[4] Graph-Driven Diffusion and Random Walk Schemes for Image Segmentation [J].