Technical Note: Semi-automated effective width extraction from time-lapse RGB imagery of a remote, braided Greenlandic river

C J Gleason,A L Lewinter,V W Chu,L C Smith,L H Pitcher,D C Finnegan

doi:10.5194/hess-19-2963-2015

Abstract

Abstract. River systems in remote environments are often challenging to monitor and understand where traditional gauging apparatus are difficult to install or where safety concerns prohibit field measurements. In such cases, remote sensing, especially terrestrial time-lapse imaging platforms, offer a means to better understand these fluvial systems. One such environment is found at the proglacial Isortoq River in southwestern Greenland, a river with a constantly shifting floodplain and remote Arctic location that make gauging and in situ measurements all but impossible. In order to derive relevant hydraulic parameters for this river, two true color (RGB) cameras were installed in July 2011, and these cameras collected over 10 000 half hourly time-lapse images of the river by September of 2012. Existing approaches for extracting hydraulic parameters from RGB imagery require manual or supervised classification of images into water and non-water areas, a task that was impractical for the volume of data in this study. As such, automated image filters were developed that removed images with environmental obstacles (e.g., shadows, sun glint, snow) from the processing stream. Further image filtering was accomplished via a novel automated histogram similarity filtering process. This similarity filtering allowed successful (mean accuracy 79.6 %) supervised classification of filtered images from training data collected from just 10 % of those images. Effective width, a hydraulic parameter highly correlated with discharge in braided rivers, was extracted from these classified images, producing a hydrograph proxy for the Isortoq River between 2011 and 2012. This hydrograph proxy shows agreement with historic flooding observed in other parts of Greenland in July 2012 and offers promise that the imaging platform and processing methodology presented here will be useful for future monitoring studies of remote rivers.

Highlights

Proglacial streams and rivers along land-terminating edges of the Greenland Ice Sheet are among the world’s most difficult fluvial systems to study in the field, owing to their remoteness, harsh climate, and braided morphology
Discharge variations in large proglacial rivers are of particular scientific interest, as these systems typically derive water from the interior ablation surface of the Greenland Ice Sheet and are useful for inferring runoff mass losses from the ice sheet (Rennermalm et al, 2013; Smith et al, 2015)
This paper proposes a semi-automated processing stream designed to classify and extract hydraulic parameters of interest from large volumes of RGB image data collected from a fixed terrestrial platform, and demonstrates its efficacy in a remote Greenlandic river

Summary

Introduction

Proglacial streams and rivers along land-terminating edges of the Greenland Ice Sheet are among the world’s most difficult fluvial systems to study in the field, owing to their remoteness, harsh climate, and braided morphology. Discharge variations in large proglacial rivers are of particular scientific interest, as these systems typically derive water from the interior ablation surface of the Greenland Ice Sheet and are useful for inferring runoff mass losses from the ice sheet (Rennermalm et al, 2013; Smith et al, 2015). Their high sediment loads, unstable banks, and dynamic braided channels present challenges to traditional in situ river gauging techniques, and long term hydrographs for these rivers are rare. Braided rivers in particular typically display a power-law relationship between floodplain inundation area (which can be remotely sensed) and discharge, which has been exploited using satellites, aerial imagery, and terrestrial time-

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